Medicine, Faculty of
DSpace
UBCV
Izadkhah, Mojan
Master of Science - MSc
University of British Columbia
We investigated the spatiotemporal patterns of neural activity underlying prosaccade and antisaccade planning using deep learning combined with explainable AI techniques applied to scalp electroencephalography (EEG). EEG was recorded from 20 participants using a 64-channel BioSemi system while they completed one block of randomized left- or rightward prosaccades, followed by a block of randomized left- or rightward antisaccades. Saccade onset timing was extracted using electrooculography (EOG). For prosaccades, the number of accepted trials per participant was M = 436.45 (SD = 61.21), range = 273–492; for antisaccades, M = 436.15 (SD = 75.25), range = 194–519. Mean reaction times were 172.51 ms (SD = 21.37) for prosaccades and 229.50 ms (SD = 18.19) for antisaccades. A 3D convolutional neural network was trained on approximately 100 ms of pre-saccadic EEG data. To address the relatively limited number of trials per participant, we developed a novel data augmentation pipeline. Classifiers were trained separately for each participant, and performance was evaluated on held-out data from the same individual. For prosaccade (rightward vs. leftward) classification, mean accuracy across participants was M = .84 (SD = .14, n = 20); for antisaccade (leftward vs. rightward) classification, M = .85 (SD = .14, n = 19). Both results were substantially and statistically significantly above chance, p < .001. To interpret the model’s predictions, we applied a saliency-based post-hoc interpretability method, which revealed contralateral saliency patterns in both prosaccade and antisaccade tasks, as well as an additional ipsilateral frontal contribution unique to antisaccades within the analyzed window preceding saccade onset. Overall, our findings demonstrate that explainable deep learning can predict saccade direction from brief EEG windows and uncover interpretable neural dynamics associated with motor planning and inhibition.
Predicting Eye Movement Directionfrom Pre-Saccadic EEG: A DeepLearning and Explainable AIApproach to Investigating the NeuralBasis of Motor PlanningbyMojan IzadkhahB.Eng., Sharif University of Technology, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Neuroscience)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2025© Mojan Izadkhah 2025The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Predicting Eye Movement Direction from Pre-Saccadic EEG: ADeep Learning and Explainable AI Approach to Investigating theNeural Basis of Motor Planningsubmitted by Mojan Izadkhah in partial fulfillment of the requirementsfor the degree of Master of Science in Neuroscience.Examining Committee:Ipek Oruc, Associate Professor, Ophthalmology and Visual Sciences, UBCSupervisorJason Barton, Professor, Ophthalmology and Visual Sciences, UBCSupervisory Committee MemberTodd Handy, Professor, Psychology, UBCSupervisory Committee MemberJames W. Tanaka, Professor, Psychology, University of VictoriaAdditional ExamineriiAbstractWe investigated the spatiotemporal patterns of neural activity underlyingprosaccade and antisaccade planning using deep learning combined withexplainable AI techniques applied to scalp electroencephalography (EEG).EEG was recorded from 20 participants using a 64-channel BioSemi systemwhile they completed one block of randomized left- or rightward prosac-cades, followed by a block of randomized left- or rightward antisaccades.Saccade onset timing was extracted using electrooculography (EOG). Forprosaccades, the number of accepted trials per participant was M = 436.45(SD = 61.21), range = 273–492; for antisaccades,M = 436.15 (SD = 75.25),range = 194–519. Mean reaction times were 172.51 ms (SD = 21.37) forprosaccades and 229.50 ms (SD = 18.19) for antisaccades. A 3D convolu-tional neural network was trained on approximately 100 ms of pre-saccadicEEG data. To address the relatively limited number of trials per participant,we developed a novel data augmentation pipeline. Classifiers were trainedseparately for each participant, and performance was evaluated on held-outdata from the same individual. For prosaccade (rightward vs. leftward)classification, mean accuracy across participants was M = .84 (SD = .14,n = 20); for antisaccade (leftward vs. rightward) classification, M = .85(SD = .14, n = 19). Both results were substantially and statistically sig-nificantly above chance, p < .001. To interpret the model’s predictions, weapplied a saliency-based post-hoc interpretability method, which revealedcontralateral saliency patterns in both prosaccade and antisaccade tasks, aswell as an additional ipsilateral frontal contribution unique to antisaccadeswithin the analyzed window preceding saccade onset. Overall, our findingsdemonstrate that explainable deep learning can predict saccade directionfrom brief EEG windows and uncover interpretable neural dynamics associ-ated with motor planning and inhibition.iiiLay SummaryThis study explored how the brain prepares for eye movements—specifically,fast gaze shifts called saccades—by analyzing brain activity recorded usingelectroencephalography (EEG). Participants performed two tasks: lookingtoward (prosaccade) or away from (antisaccade) a visual target. We useddeep learning, specifically convolutional neural networks (CNNs), to predictthe direction of planned eye movements from pre-saccadic EEG signals. Ourmodels accurately predicted saccade direction based on less than 100 mil-liseconds of brain activity before the onset of the movement. Using saliencymaps, we identified when and where on the scalp the most informative sig-nals occurred. Both prosaccades and antisaccades showed strong contralat-eral saliency of parietal, and occipital regions involved in eye movementplanning, with mirror-image patterns for leftward versus rightward saccadesand an additional frontal contribution unique to antisaccades. These find-ings demonstrate that deep learning can reveal meaningful neural patternsunderlying rapid eye movement decisions and highlight the distinct brainnetworks involved in pro- versus antisaccades. This research bridges neuro-science and artificial intelligence to advance brain–computer interface devel-opment.ivPrefaceThis thesis is the original intellectual product of the author, Mojan Izad-khah. It was written and edited with the assistance of generative artificialintelligence tools, specifically ChatGPT (OpenAI). These tools were usedsolely to refine the language, improve clarity, and rephrase content origi-nally drafted by the author. All ideas, analyses, and interpretations are thesole intellectual contribution of the author.All experimental procedures and data collection were conducted in ac-cordance with the ethical standards approved by the University of BritishColumbia Behavioural Research Ethics Board (BREB; Ethics CertificateNumber H21-03360) and the Vancouver Coastal Health Research Institute.This research was supported in part by computational resources and ser-vices provided by Advanced Research Computing (ARC) at the Universityof British Columbia.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . xv1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Neural and Cognitive Foundations of Saccadic Eye Move-ments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Visual Perception and the Role of the Fovea . . . . . 11.1.2 Saccadic Eye Movements . . . . . . . . . . . . . . . . 21.1.3 Neural Control of Saccades (Cortical and SubcorticalMechanisms) . . . . . . . . . . . . . . . . . . . . . . . 31.1.4 Prosaccades vs. Antisaccades . . . . . . . . . . . . . . 41.1.5 Cognitive and Neural Differences in Anti vs. Pro Sac-cades . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.1.6 Hemispheric Lateralization in Saccade Control . . . . 61.1.7 Antisaccades as a Window into Executive Function . 71.2 Why EEG is a Good Modality for Investigating Saccades . . 81.3 From Feature Engineering to End-to-End Learning . . . . . 91.4 Deep Learning in Vision and Neuroscience . . . . . . . . . . 111.5 Deep Learning Models for EEG Decoding . . . . . . . . . . . 121.5.1 Convolutional Neural Networks for EEG Decoding . . 141.5.2 Recurrent and Long Short-Term Memory Networks . 15viTable of Contents1.5.3 Transformer-Based Architectures . . . . . . . . . . . 161.6 Interpreting CNN Decisions in EEG Research . . . . . . . . 171.6.1 Gradient-Based Saliency Maps . . . . . . . . . . . . . 181.6.2 Integrated Gradients and Related Methods . . . . . . 191.6.3 Gradient-Class Activation Maps (Grad-CAM) . . . . 201.7 Objectives of This Study . . . . . . . . . . . . . . . . . . . . 222 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.2 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.3 EEG Recording . . . . . . . . . . . . . . . . . . . . . . . . . 252.4 EEG Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 272.5 Saccade Detection . . . . . . . . . . . . . . . . . . . . . . . . 272.6 Dataset Curation . . . . . . . . . . . . . . . . . . . . . . . . 282.6.1 Synchronization . . . . . . . . . . . . . . . . . . . . . 282.6.2 Classification Tasks . . . . . . . . . . . . . . . . . . . 292.7 Model Development . . . . . . . . . . . . . . . . . . . . . . . 302.8 Training Procedure, Data Augmentation, and Model Evalu-ation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.9 Model Interpretation . . . . . . . . . . . . . . . . . . . . . . 342.9.1 Single-Subject Saliency Maps . . . . . . . . . . . . . . 342.9.2 Group Average . . . . . . . . . . . . . . . . . . . . . . 352.9.3 Time Analysis . . . . . . . . . . . . . . . . . . . . . . 352.9.4 Electrode Analysis . . . . . . . . . . . . . . . . . . . . 352.9.5 Region Correspondence Check . . . . . . . . . . . . . 352.9.6 Feature Analysis . . . . . . . . . . . . . . . . . . . . . 383 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.1 Saccade Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 403.2 Classification Performance . . . . . . . . . . . . . . . . . . . 403.3 Model Interpretation: Saliency Maps . . . . . . . . . . . . . 483.3.1 Saliency Maps . . . . . . . . . . . . . . . . . . . . . . 483.3.2 Time analysis . . . . . . . . . . . . . . . . . . . . . . 483.3.3 Electrode Analysis . . . . . . . . . . . . . . . . . . . . 513.3.4 Region Correspondence Check . . . . . . . . . . . . . 513.3.5 Feature Analysis . . . . . . . . . . . . . . . . . . . . . 574 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . 654.1 Saccade Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 654.2 Model Performance . . . . . . . . . . . . . . . . . . . . . . . 67viiTable of Contents4.3 Model Interpretation . . . . . . . . . . . . . . . . . . . . . . 694.3.1 Saliency Maps . . . . . . . . . . . . . . . . . . . . . . 694.3.2 Time Analysis . . . . . . . . . . . . . . . . . . . . . . 704.3.3 Electrode Analysis . . . . . . . . . . . . . . . . . . . . 714.3.4 Region Correspondence Check . . . . . . . . . . . . . 724.3.5 Saliency-Based Feature Space Reveals Underlying Struc-ture in ERP Dynamics . . . . . . . . . . . . . . . . . 734.4 Broader Implications . . . . . . . . . . . . . . . . . . . . . . 734.5 Limitations and Future Directions . . . . . . . . . . . . . . . 74Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77viiiList of Tables2.1 Topographical layout of the 64 EEG channels mapped onto a9× 9 spatial grid for use in the 3D EEGNet model . . . . . . 312.2 Layer-wise count of learnable parameters in the 3D EEGNetarchitecture. Parentheses indicate the number of input andoutput channels for each layer. The model was configuredwith fc features = 1200. . . . . . . . . . . . . . . . . . . . 312.3 Detailed architecture of the 3D EEGNet model used in thisstudy. The table specifies kernel sizes, output channels, paddingvalues, and other implementation details for each layer. . . . 323.1 Latency (ms) and count of accepted prosaccade responsesacross participants, reported separately for rightward andleftward trials. . . . . . . . . . . . . . . . . . . . . . . . . . . 413.2 Latency (ms) and count of accepted antisaccade responsesacross participants, reported separately for rightward andleftward trials. . . . . . . . . . . . . . . . . . . . . . . . . . . 423.3 Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftwardprosaccade classification task, time-locked to saccade onset. . 443.4 Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftwardantisaccade classification task, time-locked to saccade onset. . 453.5 Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftwardprosaccade classification task, time-locked to stimulus onset. . 463.6 Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftwardantisaccade classification task, time-locked to stimulus onset. 473.7 Test performance for classification tasks: right vs. left sac-cades within prosaccade or antisaccade tasks. . . . . . . . . . 47ixList of Tables3.8 Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas,for each well-performing participant, based on the top fiveelectrodes from the rightward saliency map in the prosaccadeclassification task. . . . . . . . . . . . . . . . . . . . . . . . . 553.9 Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas,for each well-performing participant, based on the top fiveelectrodes from the leftward saliency map in the prosaccadeclassification task. . . . . . . . . . . . . . . . . . . . . . . . . 553.10 Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas,for each well-performing participant, based on the top fiveelectrodes from the rightward saliency map in the antisac-cade classification task. . . . . . . . . . . . . . . . . . . . . . 563.11 Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas,for each well-performing participant, based on the top fiveelectrodes from the leftward saliency map in the antisaccadeclassification task. . . . . . . . . . . . . . . . . . . . . . . . . 563.12 Electrodes with maximum saliency for rightward and leftwardprosaccade trials across 25 time points of interest. Each rowindicates the electrode with the highest saliency at a spe-cific time point, shown separately for rightward and leftwardsaliency maps. . . . . . . . . . . . . . . . . . . . . . . . . . . 583.13 Electrodes with maximum saliency for rightward and leftwardantisaccade trials across 25 time points of interest. Each rowindicates the electrode with the highest saliency at a spe-cific time point, shown separately for rightward and leftwardsaliency maps. . . . . . . . . . . . . . . . . . . . . . . . . . . 61xList of Figures2.1 Schematic of a single trial in the experimental task. Each trialbegan with a central fixation cross (0–1000 ms), followed bya lateralized target stimulus (1000–2000 ms). Depending onthe task condition, participants made a prosaccade (towardthe target) or antisaccade (in the opposite direction). Aftertarget offset, a blank screen was shown for 500 ms before thenext trial began. . . . . . . . . . . . . . . . . . . . . . . . . . 252.2 Electrode layout of the 64-channel BioSemi EEG system, il-lustrating standard 10–20 placement across the scalp. . . . . 262.3 Examples of saccade onset detection for three representa-tive rightward trials. The y-axis shows the derivative of theEX3–EX4 signal (µV/ms), and the x-axis represents time rel-ative to target onset at 0 ms. Dots indicate the time pointsidentified by the algorithm as saccade onsets. . . . . . . . . . 292.4 Diagram of the 3D EEGNet architecture used in this study.The model includes three main convolutional blocks, eachimplemented using 3D convolutions to jointly model spatialand temporal EEG features. These stages consist of tempo-ral, depthwise, and separable convolutions, followed by batchnormalization, dropout, and max pooling. A fully connectedlayer with sigmoid activation produces the final binary clas-sification output. . . . . . . . . . . . . . . . . . . . . . . . . . 322.5 Scalp topographic maps of regions of interest, shown in red:(A) FEF Left, (B) FEF Right, (C) PEF Left, (D) PEF Right,(E) DLPFC Left, (F) DLPFC Right, (G) SEF, and (H) P100related region. Image orientation follows EEG convention,with the left side of the image corresponding to the left hemi-sphere and the right side to the right hemisphere. . . . . . . . 37xiList of Figures3.1 Group-level average of normalized scalp topographic maps ofsaliency values for leftward (A) and rightward (B) prosac-cade trials. Saliency maps from trials with 100% accuracywere normalized and averaged per subject (n = 16; test ac-curacy ≥ 75%), then averaged across subjects to produce thegroup-level maps. The displayed time window spans from101.66 ms to 7.82 ms before saccade onset. Black arrowsmark time points with high discriminative saliency. Imageorientation follows EEG convention, with the left side of theimage corresponding to the left hemisphere and the right sideto the right hemisphere. . . . . . . . . . . . . . . . . . . . . . 493.2 Group-level average of normalized scalp topographic maps ofsaliency values for leftward (A) and rightward (B) antisac-cade trials. Saliency maps from trials with 100% accuracywere normalized and averaged per subject (n = 15; test ac-curacy ≥ 75%), then averaged across subjects to produce thegroup-level maps. The displayed time window spans from101.66 ms to 7.82 ms before saccade onset. Black arrowsmark time points with high discriminative saliency. Imageorientation follows EEG convention, with the left side of theimage corresponding to the left hemisphere and the right sideto the right hemisphere. . . . . . . . . . . . . . . . . . . . . . 503.3 Group-level average saliency time courses for leftward (blue)and rightward (red) saccades in the prosaccade (A) and an-tisaccade (B) tasks. Each curve reflects the mean saliencyacross 64 electrodes and 16 (prosaccade) or 15 (antisaccade)high-performing participants (accuracy ≥ 75%). The x-axisrepresents time relative to saccade onset, ranging from –101.66 msto –7.82 ms. The black boxes indicate the areas of interest:–46.92 to –43.01 ms for the prosaccade task and –27.37 to–19.55 ms for the antisaccade task. . . . . . . . . . . . . . . 52xiiList of Figures3.4 Time-averaged group-level normalized scalp topographic mapsof saliency values, averaged over T = 25 time points, for (A)leftward prosaccade, (B) rightward prosaccade, (C) leftwardantisaccade, and (D) rightward antisaccade trials. Saliencymaps from trials with 100% accuracy were normalized andaveraged within each well-performing subject, then averagedacross subjects, and finally averaged across all time pointsto produce each map. Subjects were selected based on thecorresponding task. Image orientation follows EEG conven-tion, with the left side of the image corresponding to the lefthemisphere and the right side to the right hemisphere. . . . . 533.5 Scatterplot comparing prosaccade group-level saliency values,time-locked to saccade onset, at key electrode–time points.Red markers represent saliency values from the rightwardprosaccade map, plotted with the right-saliency map on theX-axis and the left-saliency map on the Y-axis. Blue markersrepresent analogous values from the leftward prosaccade map.Each point is labeled with the corresponding electrode andtime point. The dashed line indicates the decision boundarylearned by a linear SVM classifier. . . . . . . . . . . . . . . . 593.6 Scatterplot comparing group-averaged ERP values, time-lockedto saccade onset, for prosaccades at key electrode–time points.Red markers represent ERP values from rightward prosac-cades, plotted with the right-ERP on the X-axis and the left-ERP on the Y-axis. Blue markers represent analogous valuesfrom leftward prosaccades. Each point is labeled with thecorresponding electrode and time point. The dashed line in-dicates the decision boundary learned by a linear SVM classifier. 603.7 Scatterplot comparing group-level saliency values, time-lockedto saccade onset, for antisaccades at key electrode–time points.Red markers represent saliency values from the rightward an-tisaccade map, plotted with the right-saliency map on theX-axis and the left-saliency map on the Y-axis. Blue mark-ers represent analogous values from the leftward antisaccademap. Each point is labeled with the corresponding electrodeand time point. The dashed line indicates the decision bound-ary learned by a linear SVM classifier. . . . . . . . . . . . . . 62xiiiList of Figures3.8 Scatterplot comparing group-averaged ERP values, time-lockedto saccade onset, for antisaccades at key electrode–time points.Red markers represent ERP values from rightward antisac-cades, plotted with the right-ERP on the X-axis and the left-ERP on the Y-axis. Blue markers represent analogous valuesfrom leftward antisaccades. Each point is labeled with thecorresponding electrode and time point. The dashed line in-dicates the decision boundary learned by a linear SVM classifier. 64xivAcknowledgementsI would like to express my sincere gratitude to my supervisor, Dr. Ipek Oruc,for her exceptional guidance, patience, and encouragement throughout thisresearch. I am also thankful to Dr. Cathy Zhong and Dr. Jason Barton,whose expertise and constructive feedback helped shape the direction andclarity of this work.Special thanks go to my parents, Narges Nasr Esfahani and Hamid Izad-khah, my partner, Mahbod Taherian, and my friends, Yasamin Medghalchiand Pegah Tekieh whose unwavering support, understanding, and encour-agement sustained me throughout this journey.xvChapter 1Introduction1.1 Neural and Cognitive Foundations ofSaccadic Eye Movements1.1.1 Visual Perception and the Role of the FoveaVisual perception in primates begins with an image projected onto the eye,where it is captured by the retina—a thin, layered sheet of neural tissueat the back of the eye that transduces light into electrical signals for fur-ther processing in the brain [Dowling, 1987]. However, this image is notprocessed uniformly across the retinal surface. Because analyzing the entirevisual field at high resolution would be energetically and anatomically costly,the primate visual system has evolved to allocate its limited computationalresources efficiently [Rosenholtz, 2016]. As a result, high-acuity vision is con-centrated in a small central region of the retina called the fovea [Dowling,1987], while the surrounding periphery supports lower-resolution process-ing optimized for motion detection, spatial awareness, and light sensitiv-ity [Rosenholtz, 2016].Within the central foveal pit, multiple retinal layers are displaced, al-lowing light to impinge directly on a densely packed mosaic of photorecep-tors—specialized light-sensitive cells that initiate visual processing by con-verting photons into neural signals. Anatomical and psychophysical dataindicate that the entire fovea spans approximately 5.5° of visual angle [Hen-drickson, 2005, Kroell and Rolfs, 2022]. Despite its small extent, the foveahas an outsized influence on visual processing: it accounts for more than40% of cortical area V1—the primary visual cortex, which is the first corti-cal region to receive and process visual input from the retina— [Curcio et al.,1990, Tootell et al., 1988, Hendrickson, 2005], and is essential for tasks thatrequire fine spatial resolution such as reading, object recognition, and faceperception. In contrast, peripheral vision—though critical for guiding at-tention and detecting motion—has lower spatial resolution due to reducedcone density and greater convergence of photoreceptor signals.To exploit the high-resolution capabilities of the fovea, the visual system11.1. Neural and Cognitive Foundations of Saccadic Eye Movementsrelies on an active sampling strategy. Rather than maintaining a fixed gaze,primates continually update their view of the environment by shifting theireyes to different parts of the scene. These gaze shifts allow for detailed visualanalysis through a series of fixations, ensuring that behaviorally relevantobjects are brought into high-acuity central vision [Findlay and Gilchrist,2003, Rayner, 2009].1.1.2 Saccadic Eye MovementsHumans and other primates frequently redirect their gaze to align relevantstimuli with the fovea. Since high-acuity vision is limited to a small centralregion, saccadic eye movements are required to bring objects of interest intofocal view. These rapid movements occur between periods of visual fixa-tion—moments when the eyes are relatively still and visual information isacquired and processed. Saccades can be broadly classified into two types:reflexive saccades, which are automatic eye movements triggered by suddenor salient stimuli in the periphery; and voluntary saccades, which are inten-tionally generated based on internal goals, such as when deciding to look ata specific object or location.Saccades are among the fastest movements produced by the human body,typically reaching peak velocities of up to 700°/s for larger amplitudes, withsome primate studies reporting up to 1,000°/s in monkeys [Gooding andBasso, 2008, De Renzi, 2013, Gouras, 1985]. They typically occur severaltimes per second during tasks like reading or scene exploration [Crevecoeurand Kording, 2017], with humans making approximately 3–4 saccades persecond in natural viewing conditions [Henderson et al., 1998, Henderson,2003, Koevoet et al., 2025]. The primary function of saccades is to optimizevisual input by directing the high-resolution part of the retina (fovea) towardbehaviorally important objects [Findlay and Gilchrist, 2003, Rayner, 2009].In typical circumstances, saccades are directed toward salient stimuli(prosaccades). However, under certain experimental conditions, individualsare instructed to look away from a stimulus. This is called the antisaccadetask, first introduced by Peter Hallett in 1978 as an artificial paradigm totest cognitive control, where participants must inhibit the reflexive glanceto a cue and instead make a voluntary eye movement in the opposite di-rection [Hallett, 1978, Munoz and Everling, 2004]. Antisaccades requiresuppression of the automatic response and vector inversion to generate amirror saccade. They are slower and more error-prone than prosaccades,reflecting the increased cognitive control needed to execute them [Hutton,2002]. This task is a powerful probe of executive function and top-down21.1. Neural and Cognitive Foundations of Saccadic Eye Movementscontrol processes.1.1.3 Neural Control of Saccades (Cortical and SubcorticalMechanisms)The generation of saccades is controlled by a widely distributed network ofcortical and subcortical structures [Everling and Fischer, 1998]. In the hu-man cerebral cortex, multiple “eye fields” contribute to voluntary eye move-ment control. Key regions include the Frontal Eye Fields (FEF), locatedin the prefrontal cortex, which are crucial for initiating voluntary saccades;the Supplementary Eye Field (SEF), on the medial frontal surface, whichplays a central role in internally guided saccades—those generated withoutan immediate external cue, such as when choosing to look at a rememberedlocation—and in sequential saccades, which require the planning and execu-tion of a series of eye movements in a specific order. The Parietal Eye Field(PEF), typically localized to the intraparietal sulcus, supports spatial at-tention and saccade target selection. In humans, the posterior intraparietalsulcus contains regions proposed as the functional homologs of the Lat-eral Intraparietal Area (LIP) described in non-human primates [Schluppecket al., 2005, Sereno et al., 2001], and the human PEF is often considered thefunctional equivalent of macaque LIP [Koyama et al., 2004]. These regionsare important for spatial attention and visuomotor integration [Bells et al.,2020, Munoz and Everling, 2004].Another important region is the Dorsolateral Prefrontal Cortex (DLPFC),which is associated with higher executive functions and has been implicatedin suppressing automatic or reflexive saccades [Funahashi et al., 1993, DeS-ouza et al., 2003]. The Anterior Cingulate Cortex (ACC) has also been im-plicated in the antisaccade task, particularly in monitoring conflict, detect-ing errors, and supporting cognitive control during response inhibition [Polliet al., 2005]. These cortical areas are densely interconnected and contributedistinct roles in processing sensory input, selecting saccade targets, andpreparing motor commands to initiate eye movements [Lynch and Tian,2006].Their outputs project to the Superior Colliculus (SC) as well as toother brainstem premotor circuits involved in saccade control [Lynch andTian, 2006]. The SC is a pivotal structure that integrates cortical commandswith sensory information and orchestrates the brainstem saccade generatorcircuitry [Schiller et al., 1980]. Subcortical loops involving the basal gan-glia (especially the caudate nucleus and substantia nigra reticulata) furthermodulate saccade initiation: these loops provide inhibitory control, gatingsignals to the SC to either permit or prevent a saccade [Munoz and Ev-31.1. Neural and Cognitive Foundations of Saccadic Eye Movementserling, 2004]. Finally, the premotor neurons in the brainstem (within thereticular formation) execute the saccade by sending commands to the ocu-lar motor nuclei, which in turn project via cranial nerves to the extraocularmuscles [Shook et al., 1990]. This cortico-subcortical network ensures thatsaccades can be either triggered or withheld as needed, based on currentgoals and visual inputs [Shook et al., 1990, Gandhi and Keller, 1997, Seg-raves, 1992].While these cortical and subcortical oculomotor regions are central tothe initiation and control of saccades, the processing of incoming visualstimuli also plays a crucial role. In particular, a well-established markerof early visual processing is the P100 component, a positive deflection oc-curring around 100 ms after stimulus onset, most prominent over occipitalelectrodes. The P100 reflects early sensory activity in the visual cortex andprovides an important baseline for understanding how visual information isprocessed [Di Russo et al., 2002]. Another related component is the N170,a negative deflection peaking around 140–170 ms over posterior temporalsites, which is associated with higher-level visual categorization processessuch as face and object recognition [Bentin et al., 1996].1.1.4 Prosaccades vs. AntisaccadesNot all saccades are of the same type. Prosaccades generally refer to thenormal, stimulus-driven eye movements toward a target. For example, whena peripheral stimulus suddenly appears, the reflexive response is a prosac-cade to look at that stimulus. Prosaccades represent the simplest case: thegoal of the eye movement (to fixate the stimulus) is spatially congruentwith the stimulus location. The neural circuitry can directly transform thevisual input into a motor command – a mostly automatic sensorimotor map-ping [Munoz and Everling, 2004]. In contrast, an antisaccade is a voluntaryeye movement made away from a stimulus. In the laboratory antisaccadetask, a subject fixates a central point and, when a peripheral cue flashes, isinstructed not to look at it but instead to make a saccade to the oppositeside (the mirror location of the cue) [Munoz and Everling, 2004]. This tasktherefore imposes an incompatible stimulus–response mapping: the locationof the target and the required saccade are in opposite directions. Correctperformance of an antisaccade requires two additional cognitive operationsbeyond those of a prosaccade: first, the suppression of the automatic impulseto glance at the sudden stimulus, and second, the vector inversion to vol-untarily generate a saccade of equal magnitude to the opposite side [Munozand Everling, 2004]. Because of these extra steps, antisaccades are more41.1. Neural and Cognitive Foundations of Saccadic Eye Movementschallenging and typically have longer reaction times than prosaccades – adifference known as the antisaccade cost [Hallett, 1978, Tseng et al., 2018].In healthy adults, prosaccades can be initiated very rapidly, whereas antisac-cades are consistently delayed by tens of milliseconds due to the time neededfor inhibition and cognitive transformation [Tseng et al., 2018, Moon et al.,2007]. Antisaccades are also prone to errors: the most common mistake isa direction error – i.e. a reflexive prosaccade toward the stimulus when anantisaccade was required. Such errors occur when the subject fails to sup-press the automatic saccade, revealing the dominance of the prepotent “lookat it” response in those moments [Munoz and Everling, 2004]. Even whenno outright error is made, the additional cognitive demands of antisaccades(inhibition and vector inversion) contribute to greater variability in responsetiming and accuracy compared to prosaccades [Tseng et al., 2018].1.1.5 Cognitive and Neural Differences in Anti vs. ProSaccadesThe prosaccade and antisaccade paradigms engage overlapping oculomo-tor circuits, but the antisaccade specifically recruits higher-order controlprocesses. In prosaccade trials, the visual stimulus is directly driving thesaccade through well-practiced pathways (from retina to superior collicu-lus and cortical eye fields) with minimal need for executive oversight. Inantisaccade trials, however, the brain must impose a “stop signal” on thereflexive route and re-route the command intentionally. This is achievedby increased activity in frontal cortical regions that exert top-down inhibi-tion. Neuroimaging and electrophysiology show that, during antisaccades,there is enhanced activation in the DLPFC, FEF, SEF and other frontalareas compared to prosaccades, even before the eyes move [Munoz and Ev-erling, 2004]. These frontal areas are thought to send inhibitory signals tothe saccade-generating neurons (in FEF and SC) that would otherwise trig-ger a glance toward the stimulus [Pierrot-Deseilligny et al., 2002, Gaymardet al., 1998]. Consistently, single-neuron recordings in monkeys performingantisaccades have found “suppression” neurons in the FEF and SC that re-flect this active braking of the automatic saccade, as well as preparatoryset activity that represents the upcoming antisaccade goal (rather than thestimulus) in advance [Munoz and Everling, 2004]. In essence, the antisac-cade task forces the oculomotor system to decouple stimulus perception fromresponse preparation, engaging the brain’s executive control mechanisms toa much greater degree than a prosaccade task. Importantly, the antisaccadeperformance (latency and error rate) provides a window into an individual’s51.1. Neural and Cognitive Foundations of Saccadic Eye Movementsexecutive control. Success on antisaccade trials requires the rapid suppres-sion of a reflex and the coordination of a voluntary plan, functions thatdepend on the integrity of frontal lobe circuits. Accordingly, antisaccadelatencies are often used as an index of cognitive control efficiency [Everlingand Fischer, 1998], and error rates indicate failures of inhibitory control.Stochastic accumulator models of saccade generation have offered hypothe-ses for the variability in antisaccade performance [Cutsuridis, 2017b,a]. Inthese models, each saccade (pro or anti) is triggered when neural activityin movement-related neurons (in FEF, SC, etc.) builds up to a threshold.The model explains that if the baseline level of activity is too high or thegrowth rate too fast in the reflexive (prosaccade) pathway, a threshold cross-ing can occur before inhibitory control has time to act [Cutsuridis, 2017a].This would produce a fast error (a premature prosaccade toward the target).Conversely, strong top-down inhibition can keep the baseline low and delaythe buildup, giving the voluntary plan time to reach threshold first. Suchmodels account for the finding that antisaccade errors tend to cluster atshort reaction times—reflecting a failure of inhibition due to initial neuralstate, rather than a mere lapse in understanding the task [Aponte et al.,2017]. In summary, the additional neural processing required for antisac-cades (inhibition and vector inversion) underlies their longer latency andgreater susceptibility to errors and trial-to-trial variability, distinguishingthem from prosaccades on both behavioral and neural levels.1.1.6 Hemispheric Lateralization in Saccade ControlAn interesting aspect of the oculomotor control network is its hemisphericorganization. Each cerebral hemisphere primarily controls saccades towardthe opposite visual hemifield: for instance, the right FEF and right SC arechiefly responsible for leftward eye movements, and vice versa [Munoz andEverling, 2004]. However, the two hemispheres do not contribute equallyto oculomotor control. There is substantial evidence for right-hemispheredominance in the control of gaze, particularly in antisaccadic inhibition.Clinical and experimental studies have demonstrated that disruption of theright DLPFC or right FEF leads to marked impairments in antisaccade per-formance [Nyffeler et al., 2007, Gaymard et al., 1999]. For instance, transcra-nial magnetic stimulation (TMS) over the right DLPFC increases reflexiveerror rates bilaterally in antisaccade tasks, indicating a critical role in sup-pressing automatic saccades [Nyffeler et al., 2007]. Similarly, lesions to rightFEF have been associated with bilateral impairments in voluntary saccadegeneration, whereas comparable left-sided lesions tend to produce more lim-61.1. Neural and Cognitive Foundations of Saccadic Eye Movementsited, contralateral deficits [Gaymard et al., 1999, Rivaud et al., 1994]. Thisasymmetry is consistent with broader models of attentional control, whichpropose that the right hemisphere supports monitoring and control of bothvisual fields, while the left hemisphere is more strongly lateralized to con-tralateral space[Corbetta and Shulman, 2011, Mesulam, 1981]. The righthemisphere’s broader role in spatial attention and inhibitory control mayalso underlie the greater frequency and severity of visuospatial neglect fol-lowing right-hemisphere strokes[Corbetta and Shulman, 2002]. Functionalneuroimaging supports a degree of rightward lateralization in the control ofantisaccades as well. Many PET and fMRI studies have reported greateractivation in the right prefrontal cortex, including the right DLPFC andright inferior frontal gyrus, compared to the left during antisaccade perfor-mance [DeSouza et al., 2003, Ford et al., 2005, Kaufman et al., 2010].1.1.7 Antisaccades as a Window into Executive FunctionBecause successful antisaccade performance hinges on inhibitory controland the flexible redirection of behavior, the antisaccade task has becomea prominent tool for probing executive functions of the frontal lobe [Hut-ton and Ettinger, 2006]. Early lesion studies demonstrated that patientswith prefrontal damage—particularly affecting the dorsolateral and frontaleye field regions—exhibit markedly elevated antisaccade error rates and in-creased latency when initiating correct antisaccades, even though they fullyunderstand the instructions. For example, [Guitton et al., 1985] reportedthat unilateral frontal eye field lesions cause failures to suppress reflexiveglances and impair the generation of correct goal-directed eye movements,with bilateral deficits often observed despite the lesion being restricted toone hemisphere.These deficits underscore the critical role of frontal regions in volitionaloculomotor control. Indeed, antisaccade error rate is often taken as a clinicalmeasure of frontal lobe integrity [Hutton and Ettinger, 2006], and studieshave shown that the task engages dorsolateral prefrontal systems and revealsmarkedly elevated error rates in clinical populations such as schizophre-nia [Barton et al., 2002]. Neurological disorders that affect fronto-striatalcircuits—such as Parkinson’s disease [Waldthaler et al., 2021], schizophre-nia [Broerse et al., 2001], and ADHD [Munoz et al., 2003]—frequently man-ifest antisaccade abnormalities.Altogether, the antisaccade paradigm elegantly demonstrates the contri-bution of the prefrontal executive system in regulating behavior: it requiresoverriding a potent reflex (looking at a new stimulus) in favor of an arbi-71.2. Why EEG is a Good Modality for Investigating Saccadestrary, goal-driven action (looking the other way), a hallmark of executivefunction [Munoz and Everling, 2004].Researchers have leveraged the antisaccade task not only to study behav-ior but also to investigate the neural signals underlying executive control.For instance, EEG and Event Related Potential (ERP) studies have identi-fied characteristic brain potentials associated with the preparation and ex-ecution of antisaccades. A frontocentral negative wave—sometimes referredto as the antisaccade contingent negative variation (CNV)—is often observedin the period after the cue but before the antisaccade is made, reflecting thesubject’s effort to internally maintain the task rule and inhibit the impend-ing reflex. This wave typically begins shortly after cue onset and builds upover the cue-target interval, peaking just before saccade initiation, in linewith its role in temporal expectation and motor preparation [Kononowiczand Penney, 2016]. Comparisons of ERPs on antisaccade versus prosaccadetrials show enhanced negative-going activity over prefrontal scalp regionswhen an antisaccade is required [Duschek et al., 2025, Richards, 2013].This line of research holds practical relevance: decoding such signalscould lead to biomarkers of executive dysfunction, or even enable brain–computer interface systems that monitor a user’s attention and control state.In the context of this thesis, understanding the neural dynamics of saccadesand antisaccades—and being able to detect them in real time with EEG—isa crucial step in linking the cognitive control of eye movements with mea-surable brain activity.1.2 Why EEG is a Good Modality forInvestigating SaccadesElectroencephalography (EEG) is uniquely suited for studying the neuralprocesses underlying saccadic eye movements due to its high temporal res-olution, non-invasiveness, and sensitivity to rapid cortical dynamics. Sac-cades are generated through a cascade of tightly time-locked neural eventsacross frontal and parietal brain regions, often unfolding within 200 millisec-onds. EEG’s millisecond-scale temporal precision enables direct observationof these transient neural signatures, including both early preparatory activ-ity and motor execution signals [Luck, 2014, Kulke et al., 2016].Compared to other neuroimaging modalities, EEG offers distinct ad-vantages. Functional MRI provides excellent spatial resolution but suffersfrom sluggish hemodynamic response delays, making it poorly suited fortracking rapid sensorimotor processes. Magnetoencephalography (MEG)81.3. From Feature Engineering to End-to-End Learningalso achieves millisecond temporal resolution and superior source localiza-tion relative to EEG, since magnetic fields are less distorted by skull andscalp conductivity [Ha¨ma¨la¨inen et al., 1993, Baillet, 2017]. However, MEGsystems are expensive, require specialized facilities such as magneticallyshielded rooms, and are far less portable [Supek and Aine, 2016]. WhileEEG is somewhat lower in spatial precision, it remains more practical andaccessible.This makes EEG ideal for tracking fast cognitive and sensorimotor pro-cesses such as the presaccadic cortical negativity (PSN). The PSN is a slow,negative-going potential over frontal and vertex scalp regions (e.g., Fz, Cz)that typically begins around 950–1200 ms before saccade onset and becomesmore pronounced contralateral to the saccade direction [Yılmaz et al., 2006].Other key components include lateralized readiness potentials and attention-related negativities that precede saccade execution [Everling et al., 1997,Haggard and Eimer, 1999]. These EEG components are thought to reflectoculomotor planning and motor suppression—processes central to both re-flexive and volitional eye movements.Importantly, EEG is particularly informative for investigating corticalsources implicated in saccade control, especially the FEF, SEF, and DLPFC,all of which contribute to attention, target selection, and voluntary gazeinitiation [McDowell et al., 2008]. Although EEG cannot directly resolvesubcortical structures like the superior colliculus or basal ganglia, and maybe less sensitive to deeper cortical regions such as the ACC, it still capturesthe upstream cortical dynamics that govern the decision to move the eyesand the timing of that decision.Recent advances—particularly deep learning-based decoding—have sig-nificantly enhanced EEG’s signal sensitivity. These approaches enable theextraction of meaningful patterns directly from raw or minimally processeddata, reducing reliance on traditional signal processing techniques [Makeiget al., 2004].1.3 From Feature Engineering to End-to-EndLearningTraditional EEG classification pipelines rely on hand-crafted preprocess-ing and feature extraction steps—such as band-specific filtering, waveletdecomposition, dimensionality reduction (e.g., Principal Component Anal-ysis (PCA) Buzzell et al. [2022], Blankertz et al. [2007]), and selection ofinformative time intervals or electrode channels, often based on prior knowl-91.3. From Feature Engineering to End-to-End Learningedge Amini et al. [2010], Uktveris and Jusas [2015], Vega-Escobar et al.[2015]. While effective in many cases, these approaches are hypothesis-driven and demand substantial domain expertise, which may overlook subtleor non-obvious patterns in the data.Typical pipelines incorporate tools like Independent Component Analy-sis (ICA) for artifact removal Delorme et al. [2007], Common Spatial Pat-terns (CSP) for spatial filtering Ang et al. [2008], and classifiers such as Lin-ear Discriminant Analysis (LDA) Mika et al. [1999], Blankertz et al. [2011],Support Vector Machines (SVM), or k-Nearest Neighbors (kNN) Lotte et al.[2018], Hossain et al. [2023]. Although these methods have demonstratedsolid performance in tasks like motor imagery, attention monitoring, andemotion recognition, they often struggle with with generalization across sub-jects and recording sessions, due to the non-stationary and high-dimensionalnature of EEG signals Hossain et al. [2023].In contrast, deep learning enables end-to-end learning, a paradigm inwhich the entire processing pipeline—from raw input to final decision—ismodeled and optimized jointly within a single architecture LeCun et al.[2015]. Rather than depending on manually engineered features, end-to-end models automatically learn representations that are most predictivefor a given task. This approach removes the need for task-specific featureselection and allows for the discovery of abstract, multi-level features thatmay be inaccessible to traditional pipelines.End-to-end learning has gained success in diverse domains beyond EEG.For example, in computer vision, convolutional neural networks (CNNs)trained on raw pixel data have surpassed traditional pipelines in image clas-sification and object detection tasks Krizhevsky et al. [2012]. In naturallanguage processing, transformer-based models such as BERT and GPTlearn directly from raw text without explicit syntactic parsing or manualfeature engineering Devlin et al. [2019], Brown et al. [2020]. These successeshave motivated similar approaches in bio-signals like EEG, where deep ar-chitectures (e.g., CNNs, RNNs) can capture complex temporal and spatialdynamics.However, the end-to-end approach is not without limitations. One promi-nent concern is the black-box nature of deep learning models. While thesearchitectures can learn powerful representations, their internal decision pro-cesses are often opaque and difficult to interpret Rudin [2019]. This lackof transparency poses challenges for clinical applications and neuroscienceresearch, where interpretability is crucial. Furthermore, end-to-end mod-els typically require large labeled datasets and significant computationalresources, which may not be feasible in all EEG studies.101.4. Deep Learning in Vision and Neuroscience1.4 Deep Learning in Vision and NeuroscienceOver the past two decades, deep learning—particularly convolutional neu-ral networks (CNNs)—has profoundly transformed the fields of computervision and neuroscience. Early biologically inspired models like the neocog-nitron [Fukushima, 1980] and LeNet-5 [LeCun et al., 1998] introduced theidea of hierarchical feature extraction but were constrained by limited dataand computational resources. As a result, traditional vision pipelines reliedheavily on handcrafted features [Dalal and Triggs, 2005, Lowe, 2004].The resurgence of deep learning was driven by the availability of largeannotated datasets, advances in GPU computing, and improved trainingalgorithms. A pivotal moment came with AlexNet’s performance in the2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), whereit dramatically reduced classification error compared to traditional meth-ods [Krizhevsky et al., 2012]. This success catalyzed widespread adoptionof deep CNNs, which demonstrated that high-capacity models could learnrich visual representations directly from raw data.Subsequent architectures such as VGGNet [Simonyan and Zisserman,2014], GoogLeNet [Szegedy et al., 2015], and ResNet [He et al., 2016] pushedperformance even further, enabling breakthroughs in face recognition [Taig-man et al., 2014], object detection [Ren et al., 2015, He et al., 2017], sceneclassification [Zhou et al., 2016], and generative modeling [Gatys et al., 2016,Goodfellow et al., 2014]. Today, CNNs underpin real-world applications in-cluding autonomous driving, medical diagnostics, and augmented realitysystems.In medicine, CNNs have been adopted for tasks such as skin lesion classi-fication [Esteva et al., 2017], diabetic retinopathy screening [Gulshan et al.,2016], and lung cancer detection in CT scans [Ardila et al., 2019]. Thesesystems often match or exceed expert-level performance and offer scalable,consistent decision-making pipelines [Malik et al., 2019].Beyond applied vision, deep learning has increasingly contributed to neu-roscience by offering data-driven models that mirror biological visual sys-tems. CNNs trained on natural images exhibit hierarchical representationsthat resemble the receptive field structure of early and intermediate visualcortices [Yamins et al., 2014]. This alignment has enabled researchers touse CNNs to simulate cortical responses, decode neural activity, and evenpredict behavior from brain data.For instance, [Delavari et al., 2025] showed that CNN-extracted featurescan generate synthetic fMRI responses to novel images. These syntheticactivations not only align with biological data but also enhance decoding111.5. Deep Learning Models for EEG Decodingaccuracy under noisy conditions, suggesting that CNNs can augment tradi-tional neuroimaging pipelines. Similarly, CNNs have been used to decodefMRI responses to visual object categories [Khademi et al.], interpret retinalfundus images [Berk et al., 2022, Delavari et al., 2023], and helps charac-terize the naturalistic visual exposure [Kamensek et al., 2024, Stolzenberget al., 2022].Other studies have employed CNNs to predict visual attention and saliencyfrom neural signals [Ku¨mmerer et al., 2016], or to model how visual repre-sentations evolve across cortical hierarchies during dynamic visual stimu-lation [Gu¨c¸lu¨ and Van Gerven, 2015, Wen et al., 2018]. These findingsunderscore the potential of deep networks not only as predictive models,but also as scientific tools that offer testable hypotheses about the brain.In this context, CNNs are increasingly regarded not just as classifiers,but as interpretable instruments that bridge empirical neuroscience andcomputational modeling. When trained on neurophysiological or behav-ioral data, CNNs can help identify the brain regions and time windows thatcontribute most to perceptual or motor outcomes. This bidirectional inter-action—where artificial models inform neuroscience, and neural data refinemodel design—has deepened our understanding of sensory processing andcognitive function. CNNs thus occupy a central role in advancing both ar-tificial intelligence and systems neuroscience.1.5 Deep Learning Models for EEG DecodingThe growing integration of deep learning into EEG decoding can be tracedback to the evolution of brain–computer interface (BCI) research in the early2000s. A major catalyst in this development was the BCI Competition se-ries [Blankertz et al., 2004], which standardized benchmark datasets andevaluation protocols and highlighted the challenges of decoding noisy, non-stationary neural signals from scalp recordings. Early pipelines dependedon handcrafted spatial and spectral features, followed by traditional classi-fiers such as LDA or SVM. However, the growing success of deep learningin computer vision and natural language processing motivated researchersto explore its potential in EEG-based classification tasks. Around the mid-2010s, studies demonstrated that CNNs and other deep models could matchor surpass conventional approaches in tasks like motor imagery decoding(i.e., classifying imagined movements such as left- versus right-hand move-ment from EEG signals), even when trained on raw or minimally prepro-cessed EEG [Schirrmeister et al., 2017, Lawhern et al., 2018]. These findings121.5. Deep Learning Models for EEG Decodingmarked a turning point, initiating a broader paradigm shift toward end-to-end learning in neural signal decoding.Deep learning has since become an essential paradigm in EEG decod-ing by enabling data-driven, end-to-end models that eliminate the need forhandcrafted features. This is especially advantageous in EEG, where dataare high-dimensional, noisy, and highly variable across subjects and sessions.By learning directly from raw or minimally processed signals, deep learn-ing models can uncover subtle patterns that traditional approaches mightmiss [Lotte et al., 2018, Hossain et al., 2023].In the context of neuroimaging, decoding refers to the process of predict-ing experimental conditions, cognitive states, or behavioral variables frombrain activity [Haynes and Rees, 2006, Naselaris et al., 2011]. In EEG specif-ically, decoding models aim to translate patterns of electrical activity intomeaningful information about underlying neural processes [Blankertz et al.,2011]. This is considered important because successful decoding impliesthat the relevant cognitive or motor variables are reliably represented in themeasured neural signals. As such, decoding is not only a practical tool forclassification (e.g., in brain–computer interfaces) but also a scientific methodfor probing the neural encoding of information [Grootswagers et al., 2017]. Itprovides empirical insight into where, when, and how specific mental statesare expressed in brain activity, and helps test hypotheses about distributedversus localized representations, temporal dynamics, and task-relevant mod-ulations of neural responses [King and Dehaene, 2014].This paradigm shift has enabled the decoding of a wide range of cogni-tive and motor processes, including covert attention [Farahat et al., 2019],motor imagery [Tabar and Halici, 2016], and preparatory activity before eyemovements [Kastrati et al., 2021]. In the case of saccades, where timingand spatial dynamics are crucial, deep learning models can isolate relevantneural patterns even in short pre-saccadic windows.Integrating EEG with deep neural networks supports the modeling of dis-tributed cortical activity, often at the level of individual trials. Many modelsare now trained with minimal preprocessing—commonly limited to bandpassfiltering and epoching—allowing feature learning to be entirely data-driven.This reduces reliance on expert-defined spatial filters or frequency bands,enabling broader generalization and cross-subject applicability [Craik et al.,2019].In the following subsections, we review three major classes of deep learn-ing architectures that have been applied to EEG decoding: CNNs, recurrentand long short-term memory networks (RNNs/LSTMs), and transformer-based models—highlighting their respective strengths, limitations, and rel-131.5. Deep Learning Models for EEG Decodingevance to this thesis.1.5.1 Convolutional Neural Networks for EEG DecodingConvolutional Neural Network (CNN)s are feed-forward neural networksthat use convolutional filters to automatically learn local patterns in struc-tured input data. In EEG decoding, CNNs can learn both temporal fea-tures and spatial features (e.g., electrode interactions), often with minimalpreprocessing. Their modular design allows them to scale from compactarchitectures for mobile BCI systems to deeper variants for more complextasks [Lawhern et al., 2018, Schirrmeister et al., 2017].CNNs offer several advantages for EEG analysis:(1) Spatio-Temporal Feature Learning: CNNs can be structuredto explicitly capture time-based features per channel using 1D convolutions,and cross-channel patterns using spatial filters. For instance, shallow CNNsemulate bandpass filtering followed by spatial projection, which aligns withclassical EEG analysis while remaining fully trainable [Schirrmeister et al.,2017]. Researchers have shown that CNN-learned filters often resemble phys-iologically meaningful topographies [Yamins and DiCarlo, 2016].(2) Small Dataset Efficacy: EEG datasets are often small and vari-able. CNNs—especially compact architectures like EEGNet, are designedto minimize overfitting through architectural constraints such as depthwise-separable convolutions [Lawhern et al., 2018]. These models generalize wellacross tasks despite having relatively few parameters. One reason for thisefficiency is that CNNs embed strong inductive biases through their use ofconvolutional filters, which assume that nearby inputs are correlated. Thisassumption holds to some extent in EEG data, where adjacent electrodesoften capture related neural activity. Moreover, neighboring time points aretypically treated as adjacent in the input to CNNs, and the assumption oftemporal correlation is often valid as well. Together, these spatial and tem-poral biases allow CNNs to learn effectively from fewer parameters, reducingthe amount of training data needed to achieve robust performance.(3) EEG-Specific Designs: CNN architectures have been customizedfor EEG input in several innovative ways. One approach maps electrodepositions onto a 2D grid to allow image-based CNNs to capture spatialfeatures [Yang et al., 2018]. Another uses time-frequency spectrograms ar-ranged into 3D volumes, allowing 3D CNNs to jointly learn spatial andspectral information [Li et al., 2024]. Additionally, sinc-convolutions havebeen employed to enforce interpretable bandpass-like behavior in learnedfilters [Borra et al., 2020].141.5. Deep Learning Models for EEG Decoding(4) Efficiency and Practicality: CNNs are efficient at training andinference, making them well-suited for real-time BCI applications.(5) Interpretability: Techniques like saliency mapping, class activa-tion mapping, and filter visualization have improved the interpretabilityof CNNs. This is crucial in neuroscience applications where understandingmodel decisions might be as important as achieving high performance [Sturmet al., 2016].CNNs remain a practical and robust foundation for EEG decoding inboth research and real-world applications.1.5.2 Recurrent and Long Short-Term Memory NetworksRecurrent Neural Networks (RNNs), first introduced by Elman [1990], are aclass of neural networks designed to process sequential data. They maintaina hidden state that gets updated at each time step, allowing the model to re-tain temporal context and model dependencies over time. In EEG analysis,RNNs are used to capture temporal dynamics such as oscillatory patternsor the structure of ERPs. However, standard RNNs suffer from the vanish-ing gradient problem, where gradients used during backpropagation shrinkexponentially over time steps. This makes it difficult for the network tolearn long-range dependencies, especially in lengthy sequences such as EEGrecordings [Pascanu et al., 2013]. As a result, RNNs can converge slowlyor fail to capture critical long-duration patterns. Furthermore, they pro-cess each time step sequentially, which limits training efficiency. RNNs alsodo not inherently model spatial relationships across EEG channels: inputsfrom multiple electrodes are usually concatenated into a flat vector, causingthe model to ignore electrode topography unless additional structures areadded. Due to these limitations and their relatively opaque internal rep-resentations, only about 10–15% of EEG deep learning studies have usedpure RNNs [Hossain et al., 2023]. Nonetheless, some successful applicationsexist—for instance, in motor imagery classification [Leo´n et al., 2020] andmental stress detection [Sundaresan et al., 2021].Long Short-Term Memory (LSTM) networks, introduced by Hochre-iter and Schmidhuber [1997], are a specialized form of RNNs designed toovercome the vanishing gradient issue. LSTMs introduce a gating mecha-nism—comprising input, forget, and output gates—which regulate the flowof information into and out of an internal cell state. This allows LSTMs toretain and update information over longer time spans more effectively thanstandard RNNs.In the context of EEG, LSTMs are particularly useful for decoding tem-151.5. Deep Learning Models for EEG Decodingporal patterns that span several hundred milliseconds or involve variabletiming—such as detecting slow-developing event-related activity or track-ing oscillatory phase relationships over time. LSTM-based models havebeen successfully applied to challenging tasks like epileptic seizure predic-tion [Hussein et al., 2019] and classification of cognitive or emotional statesfrom continuous EEG [Zhao and Chen, 2021]. They also perform well onbenchmarks like the BCI Competition motor imagery dataset [Hossain et al.,2023].Despite these strengths, LSTMs share several drawbacks with traditionalRNNs. They contain a large number of trainable parameters—mainly dueto their multiple gates—which increases the risk of overfitting and requiresmore data and regularization. They also train more slowly than CNNsand, like RNNs, do not inherently model spatial relationships between EEGelectrodes. Interpretability remains limited: while gate activations can beinspected or input perturbation methods applied, the internal states ofLSTMs do not easily correspond to neurophysiological features. Never-theless, LSTMs remain valuable tools for EEG decoding, especially whencapturing temporal sequences is central to the task.1.5.3 Transformer-Based ArchitecturesTransformers, introduced by Vaswani et al. [2017] in 2017 for natural lan-guage processing, are a class of deep learning models based on self-attention.They replaced recurrence with a self-attention mechanism, allowing themodel to weigh the importance of different input elements regardless of theirposition in the sequence. Self-attention works by computing pairwise simi-larity scores between all elements in the input sequence, enabling the modelto dynamically focus on the most relevant parts of the input when generatingrepresentations. This architecture proved highly effective in modeling long-range dependencies and quickly became foundational for tasks in NaturalLanguage Processing, vision, and time-series analysis due to its scalability,parallelizability, and flexibility [Dosovitskiy et al., 2020].In EEG analysis, transformers have recently gained traction owing tothese same strengths [Vafaei and Hosseini, 2025]. Unlike CNNs or RNNs,which rely on local receptive fields or sequential processing, transformersuse self-attention to allow each time point—or EEG fragment—to attendto all others directly. This capability enables them to capture long-rangetemporal dependencies and cross-channel interactions more effectively.Transformers encode EEG input either as a sequence of channel-wiseembeddings or as patches of signal data, processing them in parallel. Atten-161.6. Interpreting CNN Decisions in EEG Researchtion weights are learned to identify the most informative time windows orchannels for a given task. This makes transformers particularly well-suitedfor decoding tasks involving global context—for instance, identifying con-nections between early and late brain responses or distant electrodes [Vafaeiand Hosseini, 2025].Empirical results in EEG research are encouraging. For instance, [Sharmaet al., 2023] used a transformer for motor imagery classification and achievedcompetitive performance by leveraging attention to highlight relevant temporal-frequency features. Similarly, [Du et al., 2022] developed a temporal–spatialtransformer for person identification, effectively modeling both temporal andcross-channel patterns using multi-head attention .However, transformer models also come with significant challenges. Aprimary concern is their high data requirement: transformers typically con-tain millions of parameters and lack inductive biases tailored to EEG data,making them prone to overfitting on small datasets. Effective training of-ten requires extensive data augmentation, transfer learning, or pre-trainingstrategies [Vafaei and Hosseini, 2025]. Additionally, transformers scale quadrat-ically in complexity with sequence length, making them computationallyexpensive for long EEG recordings or high-density electrode arrays.To mitigate these issues, researchers are exploring more efficient andEEG-specific variants, such as hybrid models combining CNN front-endswith transformer back-ends, or using graph-based attention mechanisms tai-lored to electrode topology. Despite their complexity, transformers can alsoenhance interpretability through their attention maps. Some EEG studieshave used these maps to highlight which time periods or frequency bandsthe model focuses on. Still, interpreting attention is nontrivial due to themulti-layer and multi-head structure of the model.In summary, transformer-based architectures represent a powerful andincreasingly popular approach in EEG deep learning. Their ability to modellong-range and global patterns gives them an edge over traditional architec-tures in some applications [Vafaei and Hosseini, 2025], but their practicaluse remains constrained by data availability and computational cost.1.6 Interpreting CNN Decisions in EEGResearchDeep neural networks like CNNs can achieve remarkable classification per-formance on EEG data, but their “black-box” nature raises concerns indomains like neuroscience where interpretability is crucial. Understanding171.6. Interpreting CNN Decisions in EEG Researchwhat patterns a trained CNN has learned can shed light on the neurophys-iological phenomena underlying its decisions, thereby increasing trust andenabling scientific insights. To this end, a variety of interpretability methodshave been developed. Broadly, these include feature attribution approachesthat highlight important input features for a given prediction, and othertechniques like feature visualization that aim to reveal what internal repre-sentations the model has learned. In this section, we review several of themost commonly used interpretability methods for CNNs, with an emphasison their application to EEG analysis, and discuss the pros and cons of eachin this context.1.6.1 Gradient-Based Saliency MapsOne of the earliest and simplest attribution methods is the saliency map,introduced by [Simonyan et al., 2013]. This approach computes the gradientof the model’s output (e.g., the score for the predicted class) with respectto each input feature—such as each time point or electrode in an EEG trial.The magnitude of this gradient reflects how sensitively the prediction wouldchange if that input feature were perturbed, thereby serving as a measureof feature importance. Additionally, the sign of the gradient indicates thedirection of the feature’s contribution to the output. The result is an im-portance map of the same dimensions as the input (i.e. an EEG time-seriesor time × electrodes matrix) highlighting regions that most strongly influ-ence the model’s output. Saliency maps are appealing for EEG becausethey are model-agnostic to architecture (they require only the ability tobackpropagate gradients, not any specific layer types) and they offer fine-grained resolution in both time and space, directly pinpointing importanttime points and channels. Indeed, saliency-based visualizations have beensuccessfully applied in a number of EEG studies. For example, [Farahatet al., 2019] used gradient-based saliency to decode covert attention fromEEG, revealing which temporal EEG features were utilized by their CNNdecoder. Similarly, saliency maps helped identify the spatiotemporal regionsrelevant to P300 event-related potential detection in a CNN, correspondingwell to known P300 scalp topographies and latency [Borra et al., 2021]. In aclinical application, [Lopes et al., 2023] employed saliency maps to uncoverEEG biomarkers of Alzheimer’s disease, demonstrating that the CNN’s deci-sions were based on neurophysiologically meaningful features in EEG modu-lation spectra. These examples highlight that even the basic gradient-basedmap can yield human-interpretable insights from EEG models.However, saliency maps also come with important limitations. Gradients181.6. Interpreting CNN Decisions in EEG Researchin deep networks can be extremely noisy and unstable: small perturbationsto the input or parameters might cause large changes in the saliency map.Moreover, if the model’s neurons are saturated (i.e. in a flat region of theiractivation function), the gradient can be near-zero even for features thatwere critical to the prediction. This means a saliency map might sometimesmisleadingly suggest that no features were important, simply because thenetwork has already confidently activated for those features.Another common issue is that researchers often take the absolute valueof the gradient to estimate feature importance. However, in EEG studies likeours, the direction of contribution also matters—specifically, whether a giveninput feature has a positive or negative influence on the model’s decision tofavor one class over another. This signed approach is more informative, asit aligns with methods like Integrated Gradients and Layer-wise RelevancePropagation that produce signed relevance scores [Sundararajan et al., 2017,Binder et al., 2016].Overall, saliency maps offer high resolution and direct interpretability.However, they also have downsides, including potential noise, lack of robust-ness, and difficulty in capturing non-linear interactions or saturated neuronsin deep EEG networks. In this work, we specifically attempted to addressthe issue of noise.1.6.2 Integrated Gradients and Related MethodsTo address some of the limitations of simple gradient-based attribution,more advanced techniques have been developed. One widely used method isIntegrated Gradients (IG) [Sundararajan et al., 2017]. IG mitigates issuessuch as saturation and thresholding by computing the accumulated gradientsalong a straight-line path from a predefined baseline input to the actualinput. Typically, a baseline is chosen to represent the absence of meaningfulfeatures—such as a zero signal or an average EEG signal. The gradient isthen averaged as the input transitions from this baseline to its full value.However, this approach introduces a new challenge: selecting an appropriatebaseline is non-trivial in the context of EEG, and the choice can heavilyinfluence the resulting attribution map. A poorly chosen baseline may leadto misleading or uninformative interpretations.The integral of these gradients yields an attribution score for each inputfeature. Intuitively, IG asks: as we gradually activate features from thebaseline to the actual input, which components cause the largest increase inthe model’s output? The resulting attributions tend to be more stable andtheoretically grounded, satisfying desirable axioms such as sensitivity and191.6. Interpreting CNN Decisions in EEG Researchimplementation invariance. IG also has practical advantages in EEG analy-sis. It can detect non-linear effects—assigning importance to features whoseinstantaneous gradient may be zero due to saturation but which nonethelessaffect the output when introduced gradually.IG produces attributions on the same scale as the input (like saliencymaps), but generally yields less noisy and more faithful explanations ofthe model’s decision-making process. A related, simpler method is input× gradient [Ancona et al., 2017], which multiplies each input feature byits corresponding gradient. This method can be viewed as a first-orderapproximation of IG: when the model behaves approximately linearly aroundthe input, the two methods yield similar results. Input × gradient has thebenefit of down-weighting high-gradient regions with negligible input values(which may reflect noise) while up-weighting salient features with substantialamplitude.Empirical studies on EEG interpretation have shown that methods suchas IG, input× gradient, and other reference-based approaches like DeepLIFT[Shrikumar et al., 2017] generally outperform plain saliency maps in identify-ing meaningful features. These methods tend to generate more robust expla-nations and better pass sanity checks, such as sensitivity to label or weightrandomization. Nonetheless, integrated-gradient methods have drawbacks:they require selecting a suitable baseline—an ill-defined task in EEG—andincur a higher computational cost due to the need for multiple gradientevaluations along the input path. Care must also be taken to ensure thatthe interpolation path lies within the data manifold to avoid generatingimplausible intermediate inputs.1.6.3 Gradient-Class Activation Maps (Grad-CAM)Saliency and IG operate directly on input features, which is model-agnosticbut might not leverage the hierarchical feature structure of deep networks.Grad-CAM is a different approach that works at the level of a CNN’s con-volutional feature map to produce more general localization of importantregions [Selvaraju et al., 2020]. Originally developed for interpreting imageclassifiers, Grad-CAM computes the gradient of the target class score withrespect to the feature maps of a given convolutional layer (typically the lastconvolutional layer). By averaging these gradients over channels of thatlayer, one obtains a weight for each feature map (indicating how importantthat feature map is for the class). Then a weighted combination of thosefeature maps (followed by a ReLU to keep only positive contributions) formsa coarse heatmap, which is finally upsampled to the input size for visual-201.6. Interpreting CNN Decisions in EEG Researchization. The result is a class-specific activation map indicating which partsof the input region were most influential. In the context of EEG, if thenetwork uses convolutional layers (for example, temporal convolutions andspatial filters across channels), Grad-CAM can highlight the time segmentsand sensor locations that were most relevant for a particular class decision.A major advantage of Grad-CAM for natural images is its ability to high-light higher-level semantic features learned by the model—such as objectsor regions that carry clear visual meaning. This makes the outputs intuitiveand interpretable in the context of vision tasks. However, in EEG, suchhigh-level semantic features do not exist. Instead, each pixel in the inputcorresponds to a specific electrode and time point. Grad-CAM does notpreserve this precise mapping, making it difficult to interpret the resultingsaliency maps in terms of specific spatiotemporal features. As a result, fur-ther analysis in EEG applications becomes considerably more challenging.Consequently, the limitations of Grad-CAM in EEG applications are no-table. First, Grad-CAM requires access to intermediate convolutional layeroutputs, so it can only be applied to CNN architectures (and not, say, purefully-connected networks or transformers, at least not without analogousCAM techniques for those architectures). Second, the resolution of a Grad-CAM map is inherently constrained by the spatial/temporal downsamplingof the CNN. EEG CNNs often include pooling or strides that reduce thetemporal resolution of feature maps; thus, a Grad-CAM can only locateimportant intervals coarsely. Important fine-grained details might be lost,which is problematic if one needs to pinpoint a specific EEG componentor a brief event. In contrast, methods like saliency or IG can indicate im-portance at the level of individual time samples or electrodes. Anotherissue is that the standard Grad-CAM formulation uses a ReLU on the com-bined map, effectively showing only positively contributing regions (featuresthat increase the score for that class). Features whose absence or negativephase might support the class (negative relevance) are not highlighted. Thismeans Grad-CAM provides a one-sided explanation. There are extensionssuch as Grad-CAM++ (which refines the weighting using higher-order gra-dients) and Guided Grad-CAM [Selvaraju et al., 2020] (which multipliesthe Grad-CAM heatmap by a guided backpropagation map to regain higherresolution), but these add complexity and in some cases can inherit the un-reliability of guided backpropagation. In summary, Grad-CAM is a powerfultool for getting a high-level picture of “where and when” the network is look-ing in the EEG, especially for spatially localized phenomena, but it trades offtemporal precision and can miss sign information. And interpreting higherlevel features might be harder in EEG context.211.7. Objectives of This StudyIn summary, interpretability methods are essential for bridging the gapbetween deep learning EEG decoding and neurophysiological understand-ing. Gradient-based saliency maps and their refined versions (IntegratedGradients, guided gradients, etc.) provide fine-resolution insight into modeldecisions but must be used with awareness of their potential noise and pit-falls. Grad-CAM offers a more coarse but class-specific view that can beadvantageous for spatially and temporally extended patterns, albeit at thecost of precision, and interpretation of those higher order features.In this thesis, we employed signed gradient-based saliency maps to inter-pret CNN decisions. Retaining signed attributions allowed us to distinguishexcitatory contributions (features that increased the model’s output) frominhibitory contributions (features that decreased it), providing richer in-sights than absolute-value attributions. To address the potential noisinessof raw gradients, we restricted analyses to high-performing models and av-eraged saliency maps across inputs and participants, yielding more stableand generalizable patterns. Other attribution methods, such as IntegratedGradients or Grad-CAM, were not adopted here due to challenges in se-lecting an appropriate baseline for EEG data and the loss of fine temporalresolution inherent to Grad-CAM.1.7 Objectives of This StudyThis study investigates whether the direction of a saccadic eye movement,specifically, leftward or rightward prosaccades and antisaccades—can be de-coded from non-invasive EEG signals prior to movement onset. The goal isto determine if pre-saccadic neural activity contains sufficient informationto predict a person’s intended eye movement direction.To explore this, we recorded high-temporal-resolution EEG during a cuedsaccade task and trained spatially aware CNNs to classify saccade directionbased solely on brain activity preceding the eye movement. By restrictingthe analysis to the preparatory window, we aimed to isolate the neural cor-relates of motor planning and response selection, minimizing contaminationfrom ocular artifacts.Beyond classification performance, this thesis seeks to interpret the decision-making process of the CNN. Using saliency maps, we analyzed which spa-tiotemporal EEG features the model relied on most and how these featuresaligned with known neural mechanisms of saccade preparation. This ap-proach reflects a form of model-driven dimensionality reduction, whereinthe network identifies a compact set of discriminative features that would221.7. Objectives of This Studybe difficult to isolate using traditional ERP analysis.We hypothesize that the salient features identified by the model willcorrespond to activity over regions known to be involved in saccade planningand execution—namely, the SEF, FEF, PEF, and DLPFC.In summary, this work aims not only to achieve accurate decoding ofsaccade direction from pre-movement EEG but also to provide interpretableinsights into the cortical dynamics underlying the preparation and controlof eye movements, examined through deep learning-based analysis.23Chapter 2MethodsTo evaluate the feasibility of decoding pre-saccadic intentions from EEGsignals, we conducted a within-subject experiment involving cued prosaccadeand antisaccade tasks. The following sections describe the participants,experimental procedure, data acquisition, neural decoding framework, andinterpretability analysis used to address the study’s objectives.2.1 ParticipantsA total of 20 participants (11 females), aged between 21 and 40 (M = 28.75,SD = 5.26), were included in the study. The study was approved by theBehavioural Research Ethics Board (BREB) of the University of BritishColumbia and the Vancouver Coastal Health Research Institute, and allparticipants provided informed consent.2.2 ProcedureThe task involved 500 prosaccade trials followed by a 10-minute break andthen 550 antisaccade trials. To account for an anticipated error rate ofapproximately 10% in antisaccades, the number of antisaccade trials wasincreased to ensure the collection of at least 500 valid trials per condition.Participants were seated 85 cm from a CRT monitor. The horizontaldistance between the central fixation point and each peripheral target was15 cm, corresponding to a visual angle of 10◦.Each trial began with a yellow fixation cross (radius: 1.5 cm) presentedat the center of the screen for 1000 ms. A white circular target (radius:1 cm) then appeared either to the left or right of fixation and remained onscreen for 1000 ms. In the prosaccade task, participants were instructed tolook directly at the target; in the antisaccade task, they were instructed tolook in the opposite direction at an equivalent distance.All trials were automatically presented without requiring participant in-put, and the direction of the target was pseudorandomized and balanced242.3. EEG Recordingto ensure an equal number of leftward and rightward saccades within eachblock. The sequence of events for each trial is illustrated in Figure 2.1.Figure 2.1: Schematic of a single trial in the experimental task. Each trialbegan with a central fixation cross (0–1000 ms), followed by a lateralizedtarget stimulus (1000–2000 ms). Depending on the task condition, partici-pants made a prosaccade (toward the target) or antisaccade (in the oppositedirection). After target offset, a blank screen was shown for 500 ms beforethe next trial began.2.3 EEG RecordingEEG was recorded from each participant in a wakeful state over a period ofapproximately forty-five minutes using a 64-channel BioSemi system, withelectrodes placed according to the international 10–20 system, as shownin Figure 2.2. Additionally, five extra electrodes were positioned as fol-lows: EX1 on the left mastoid, EX2 on the right mastoid, EX3 next to theleft eye, EX4 next to the right eye, and EX5 below the right eye. Elec-trode impedances were kept below 5 kΩ by applying conductive gel to thecap electrodes. Gel was applied at the beginning of the experiment, andimpedance was checked both during the recording and after the break toensure acceptable levels were maintained; gel was reapplied if necessary.The reference electrodes—Common Mode Sense (CMS) and Driven RightLeg (DRL)—were placed on the back of the head in accordance with theBioSemi protocol. EEG signals were sampled at 256 Hz.Participants performed the experiment in a room with consistent ambi-ent lighting. They were instructed to minimize body and head movementsduring each trial and to move only their eyes when performing the task.Blinking was permitted only after completing the saccade and during theblank screen interval; participants were specifically asked not to blink whilefixating or executing the saccade, to reduce contamination of EEG data with252.3. EEG RecordingFigure 2.2: Electrode layout of the 64-channel BioSemi EEG system, illus-trating standard 10–20 placement across the scalp.262.4. EEG Preprocessingmuscle artifacts and to preserve the integrity of baseline correction.During the rest period between the prosaccade and antisaccade blocks,participants were allowed to move more freely, but were advised to avoidvigorous movement to maintain stable electrode contact. The full EEG ses-sion lasted between 2 and 2.5 hours per participant, including approximately15 minutes for task instruction, 45 minutes for EEG setup, 20 minutes forthe prosaccade task, a 10-minute break, 25 minutes for the antisaccade task,and an additional 20–30 minutes for cap removal and cleanup.2.4 EEG PreprocessingFirst, we extracted all the trials from the recorded signal based on the onsetof the targets, capturing data from 100 ms before to 500 ms after eachtarget onset. Although the target remained visible for a total of 1 second,we limited each trial to this timeframe since we were primarily interested inthe signal before the start of the eye movement. We then performed baselinecorrection electrode-wise, using the 100 ms period before the onset of thetarget, adjusting each trial by subtracting the mean activity recorded duringthis baseline period for each electrode individually. This process was appliedto the 64 channels but not to the 5 EX electrodes, as the EX electrodes weresolely used for detecting the onset of the saccade and were not included inany other analyses.2.5 Saccade DetectionWe developed an algorithm to detect the onset of prosaccades and anti-saccades using EOG channels EX3 and EX4, which recorded EMG signalsfrom the left and right eye muscles, respectively. In each epoch, we firstsubtracted EX4 from EX3. This approach enhances the signal-to-noise ra-tio: a rightward eye movement results in decreased EX3 and increased EX4,while a leftward movement results in increased EX3 and decreased EX4.By subtracting EX4 from EX3, the resulting signal becomes more distinct,aiding in the accurate detection of movement onset.Subsequently, we calculated the derivative of this signal by subtractingeach data point from the next, further refining the movement detection.The search for saccade onset began 80 ms after the target appeared, asthis is the minimum time required to initiate a saccade. The search windowextended to 500 ms after target onset.272.6. Dataset CurationWithin this window, we identified the onset of a saccade by detecting aconsistent directional change in the derivative signal that lasted at least 20ms. The expected direction was determined based on the task: a rightwardmovement for a prosaccade with a right target or an antisaccade with aleft target, and a leftward movement for a prosaccade with a left target oran antisaccade with a right target. If such a movement was detected, theepoch was accepted as correct and included in the dataset; otherwise, it wasrejected.We then evaluated the speed of the eye movement. If the speed—measuredas the mean of the derivative signal—was less than 1.28 µV/ms (indicatinginsufficient movement), the epoch was excluded. In addition, if a blink oc-curred during the same time window, large EMG signals were observed, andthe trial was automatically excluded by the algorithm.Using this approach, we reliably detected the onset of eye movements(prosaccades and antisaccades) and excluded invalid trials due to blinks,misdirected responses, or insufficient movement. Three representative trialsshowing detected saccade onsets based on the EX3–EX4 derivative signalare shown in Figure 2.3.Finally, we examined individual differences by computing correlationsbetween prosaccade and antisaccade latencies across participants, as wellas between the number of accepted trials for each task. These correlationswere computed using the Pearson correlation coefficient, which quantifies thestrength and direction of the linear relationship between two variables. Thisanalysis was motivated by the trial-to-trial variability in saccadic latencies.2.6 Dataset Curation2.6.1 SynchronizationTo examine the temporal dynamics of neural activity, we organized thedata using two synchronization schemes. The primary analysis focused onsaccade-synchronized epochs, in which trials were aligned to the onset ofeye movement (prosaccade or antisaccade). Saccade onset times were de-termined using the detection procedure described above, and the identifiedonset point as well as the immediately preceding time point were excludedto avoid contamination from movement-related artifacts. In addition, weconstructed a stimulus-synchronized dataset, in which trials were aligned totarget onset. This complementary analysis let us assess potential contribu-tions of stimulus-related activity in addition to saccade-related activity.282.6. Dataset CurationFigure 2.3: Examples of saccade onset detection for three representativerightward trials. The y-axis shows the derivative of the EX3–EX4 signal(µV/ms), and the x-axis represents time relative to target onset at 0 ms.Dots indicate the time points identified by the algorithm as saccade onsets.2.6.2 Classification TasksWe performed two classification tasks, each focused on distinguishing sac-cade direction within a single task type:• Prosaccade: Rightward vs. Leftward prosaccades• Antisaccade: Rightward vs. Leftward antisaccadesMore classification tasks could be defined, such as distinguishing betweensubsets of prosaccades and antisaccades, but the focus of this thesis is onclassification within these two conditions, since the block-wise design of theexperiment meant that right vs. left saccades was the only variable withineach of the prosaccade and antisaccade conditions.The dataset size for each classification task was determined separatelyfor each participant, based on the minimum number of accepted trials avail-able across the two conditions being compared (e.g., leftward vs. rightwardsaccades).While each participant initially completed up to 250 prosaccade trialsand 275 antisaccade trials per direction, the number of accepted trials percondition (prosaccade-left, prosaccade-right, antisaccade-left, antisaccade-right) varied across individuals. To ensure balanced training for each binary292.7. Model Developmentclassification task, the number of samples was set to the smaller of the twoclass sizes.All available trials from the class with fewer samples were retained, andan equal number of trials were randomly selected from the class with moresamples. This procedure was applied independently for each subject, ensur-ing that each subject-specific classifier was trained on a balanced datasetwhile maximizing trial inclusion.2.7 Model DevelopmentIn this study, we used EEGNet [Lawhern et al., 2018] as the basis for ourmodel architecture and adapted it into a 3D spatially aware design to over-come the limitations of a 1D electrode arrangement. In this design, time istreated as one dimension, while electrodes are arranged on a 2D plane toreflect their actual spatial distribution on the scalp. Specifically, the 64 EEGchannels were reorganized into a 9 × 9 grid, with 17 empty positions filledwith zeros to complete the structure. The electrode placement in the 9× 9grid is shown in Table 2.1. This adaptation preserves the spatial relation-ships between electrodes, enabling the model to capture complex patterns inthe data more effectively. To maintain model efficiency, we limited the num-ber of parameters to 3,194 for inputs with 25 time points and implementeddropout to prevent overfitting and enhance generalizability. The parametercounts for each layer are shown in Table 2.2, and a detailed breakdown ofthe implementation for all layers is provided in Table 2.3.The main structure of the model consists of a temporal convolutionfollowed by depthwise convolution layers and separable convolution layers.These general blocks are illustrated in Figure 2.4.The input to the model consists of EEG data with dimensions (1, 9, 9, T ),where T represents the temporal length of the signal. For all analyses, weused T = 25, corresponding to a 97.66 ms window.The model outputs a two-element vector corresponding to the two tar-get classes, and the final prediction is determined by selecting the neu-ron with the higher value. All network weights were randomly initializedat the start of training. The model was implemented in PyTorch, usingits core modules for network definition, optimization, and data handling,with training performed on a GPU. For optimization, we used binary cross-entropy loss together with the Adam optimizer. A learning rate sched-uler was applied using the ReduceLROnPlateau function with the followingparameters: ReduceLROnPlateau(optimizer, mode=’max’, factor=0.1,302.8. Training Procedure, Data Augmentation, and Model Evaluationpatience=5, verbose=True).Table 2.1: Topographical layout of the 64 EEG channels mapped onto a9× 9 spatial grid for use in the 3D EEGNet modelFP1 FPz FP2AF7 AF3 AFz AF4 AF8F7 F5 F3 F1 Fz F2 F4 F6 F8FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8T7 C5 C3 C1 Cz C2 C4 C6 T8TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8P7 P5 P3 P1 Pz P2 P4 P6 P8P9 PO7 PO3 POz PO4 PO8 P10O1 Oz Iz O2Table 2.2: Layer-wise count of learnable parameters in the 3D EEGNetarchitecture. Parentheses indicate the number of input and output channelsfor each layer. The model was configured with fc features = 1200.Layer Parameter CountTemporal Conv3D (1 → 24) 72Depthwise Conv3D (24 → 24, groups = 24) 216Separable Depthwise Conv3D (24 → 24, groups = 24) 216Separable Pointwise Conv3D (24 → 12) 288Fully Connected Layer (1200 → 2) 2402Total Learnable Parameters 31942.8 Training Procedure, Data Augmentation, andModel EvaluationWe implemented a novel data augmentation strategy, developed for thisstudy, to expand our training dataset and improve model generalizability.For each participant, we trained an individual classification model. Specif-ically, we randomly designated 100 leftward trials and 100 rightward trialsas an independent test set (i.e., 200 trials held out for testing), while theremaining trials were used as a training pool.During each training epoch, we generated 500 independent random train-ing batches for each class, with each batch a fresh instantiation obtained by312.8. Training Procedure, Data Augmentation, and Model EvaluationTable 2.3: Detailed architecture of the 3D EEGNet model used in this study.The table specifies kernel sizes, output channels, padding values, and otherimplementation details for each layer.LayerKernelSizeOutputChannelsPaddingOtherDetailsInput - 1 - EEG input (1, 9, 9, T)Layer 1 (3, 1, 1) 24 (1, 0, 0) Temporal convolutionBatchNorm3D - 24 - -Dropout - - - Dropout rate = 0.15Layer 2 (1, 3, 3) 24 (0, 1, 1) Groups = 24BatchNorm3D - 24 - -Dropout - - - Dropout rate = 0.15MaxPool3D (1, 2, 2) - - 3D max poolingLayer 3 (1, 3, 3) 24 (0, 1, 1) Groups = 24Conv3D (Pointwise) (1, 1, 1) 12 - Pointwise convolutionBatchNorm3D - 12 - -Dropout - - - Dropout rate = 0.15MaxPool3D (1, 2, 2) - - 3D max poolingFully ConnectedLayer- 1 - Input size = 1200Figure 2.4: Diagram of the 3D EEGNet architecture used in this study. Themodel includes three main convolutional blocks, each implemented using 3Dconvolutions to jointly model spatial and temporal EEG features. Thesestages consist of temporal, depthwise, and separable convolutions, followedby batch normalization, dropout, and max pooling. A fully connected layerwith sigmoid activation produces the final binary classification output.322.8. Training Procedure, Data Augmentation, and Model Evaluationsampling with replacement from the training pool. Each batch consisted of100 trials (either all leftward or all rightward), which were averaged togetherto form a single input to the model. Thus, each model input—whether rep-resenting the leftward or rightward class—was the result of averaging 100individual trials. This approach effectively augmented the data, reducedtrial-to-trial variability, and enhanced the signal-to-noise ratio of both train-ing and testing inputs.The model was trained for 10 epochs using this augmented data. We op-timized model parameters using gradient descent, a first-order optimizationalgorithm commonly employed to minimize training loss in neural networks.After each epoch, the model parameters were updated based on the com-puted training loss.Following the final (tenth) epoch, we evaluated model performance onthe held-out test set. For testing, we applied the same averaging procedure:the 100 leftward and 100 rightward trials were each averaged to produce onerepresentative input per class.To ensure a robust evaluation, we repeated the entire training and testingprocedure 100 times. In each repetition, a new random subset of 100 leftwardand 100 rightward trials was selected for testing, and a new training set wasdrawn from the remaining trials. This procedure was applied separately foreach participant.Participants with fewer than 100 valid trials for either class in a givenclassification task were excluded from that specific analysis due to insuffi-cient data.This repeated random sub-sampling validation strategy provided a reli-able estimate of the model’s performance by accounting for variability dueto train-test partitioning. We report the average classification accuracy andarea under the curve (AUC) across the 100 runs to demonstrate the consis-tency of results.To assess whether classification performance exceeded chance, we per-formed a one-sample t-test comparing individual accuracies across 20 partic-ipants against the 50% chance level for each classification task. Additionally,we computed Pearson correlation coefficients to examine whether individualclassification performance was associated with the number of accepted trialsand the mean saccadic latencies across participants in each task condition.332.9. Model Interpretation2.9 Model InterpretationSigned saliency maps were used to interpret the model’s predictions, fol-lowing the general gradient-based attribution framework introduced by [Si-monyan et al., 2013]. These maps were computed by taking the gradientof the predicted class score with respect to the input features, without ap-plying an absolute operation, thereby preserving both positive and nega-tive contributions. The implementation was written directly in PyTorch,using automatic differentiation to compute the gradients, rather than rely-ing on external saliency libraries. The resulting attribution maps had thesame shape as the model input (T × 9 × 9), providing one relevance scoreper time–electrode point. The 17 non-informative (NaN) electrode posi-tions—those not corresponding to actual EEG channels—were set to zeroafter generation of each map, as they do not represent real electrodes.Algorithm 1 Signed Saliency Map Generation1: Input: trained model M , input tensor X, target class y2: Set M to evaluation mode3: Enable gradient tracking for X4: Compute output: O ←M(X)5: Select target score: s← O[y]6: Compute loss: L←∑ s7: Zero gradients in M8: Backpropagate: L.backward()9: Extract saliency: S ← ∇XL10: Replace NaN electrode positions in S with zeros11: Return: saliency map S2.9.1 Single-Subject Saliency MapsFor each participant, saliency maps were generated from all models thatachieved 100% test accuracy on both classes of a given classification task.Each test input was passed through the corresponding high-performing model,producing two maps: one for each class in the binary classification. Eachresulting saliency map was then normalized by dividing all values in themap by the maximum absolute value found within it, with a small constant(1 × 10−8) added to the denominator to prevent division by zero. This en-sured that all relevance scores were scaled to fall within the range [−1, 1],preserving their sign while making them comparable across inputs and par-ticipants.342.9. Model InterpretationAll saliency maps for each class were aggregated to create participant-level average maps—one for leftward and one for rightward saccades in boththe prosaccade and antisaccade classifications.2.9.2 Group AverageTo compute group-level saliency maps, only participants with an averageclassification accuracy above 75% were included, ensuring that only high-performing models contributed to the final results. The saliency maps fromthese participants were then averaged across subjects, resulting in two grand-average maps—one for each classification class.2.9.3 Time AnalysisWe conducted a time-resolved analysis using the group-level saliency mapsobtained from the procedure above, specifically for the main task that metthe inclusion criterion of group-level classification accuracy exceeding 75%.For each of the resulting maps, we computed the mean saliency activationacross electrodes by averaging only over the 64 valid EEG channels, explicitlyexcluding the 17 zero-padded positions in the 9×9 grid layout.2.9.4 Electrode AnalysisUsing the same group-level saliency maps described in Subsection 2.9.2, wecomputed the average saliency across the temporal dimension for each classand for each task that met the inclusion criteria specified in Subsection 2.9.2.Because the 64 electrodes were arranged on a 9× 9 grid (with some emptypositions), averaging across all the time points yielded a 9×9 spatial grid foreach condition within each task. From these averaged maps, we identifiedthe top 8 electrode positions (i.e., the 8 most positively salient electrodes outof the 64 EEG channels) for each classification condition at the group level,providing a focused subset ( 12.5% of channels) while avoiding excessivespatial dispersion.2.9.5 Region Correspondence CheckFor each region of interest (SEF, the P100-related region, PEF, FEF, andDLPFC), we identified the corresponding electrode positions based on thespatial definitions provided in Scrivener and Reader [2022]. The boldedelectrodes represent the main electrodes associated with each region, whilethe others are nearby supporting electrodes. Due to variability in head size,352.9. Model Interpretationcap placement, and individual anatomy, these surrounding electrodes mayalso reflect activation from the intended area.• SEF (Right and Left): Fz, Cz, FCz, FC1, FC2• P100 (Right and Left): P1, P2, P3, P4• PEF (Right): CP2, CP4, CPz, C2• PEF (Left): CP1, CP3, CPz, C1• FEF (Right): FC6, FT8, F6, FC4, C6• FEF (Left): FC5, FT7, F5, FC3, C5• DLPFC (Right): F4, F6, AF4• DLPFC (Left): F3, F5, AF3The regions corresponding to each Region of Interest (ROI) are depictedin Figure 2.5, using the same scalp topographic map setup as for the saliencymaps, to aid their interpretation.Using the subject-level saliency maps described in Subsection 2.9.1, weseparately analyzed classification tasks for leftward and rightward saccadesin well-performing participants, specifically in tasks where the overall clas-sification accuracy exceeded 75%. For each condition, we identified the top5 electrodes with the highest positive saliency values at each of the T timepoints. We limited the selection to five electrodes in order to highlightthe most strongly contributing sensors while avoiding excessive dispersionacross the scalp, thereby focusing on a compact and interpretable subset ofelectrodes at each time point; this choice was made empirically to balanceclarity and interpretability. This process resulted in a T × 5 table per par-ticipant, per class (leftward or rightward), listing the names of the 5 mostsalient electrodes at each time point.We then quantified the overlap between these top-ranked electrodes andthe predefined ROIs listed above. Specifically, for each participant and eachdirection (leftward and rightward) in both the prosaccade and antisaccadeclassification tasks, we counted how many of the top electrodes at eachtime point belonged to each ROI. When combining across multiple ROIs,we ensured that each electrode was only counted once, even if it appearedin more than one region definition. To provide a reference point, we alsocomputed chance-level expectations by multiplying the number of electrodesin each ROI by the probability of random selection (1/64) and scaling this362.9. Model InterpretationFigure 2.5: Scalp topographic maps of regions of interest, shown in red: (A)FEF Left, (B) FEF Right, (C) PEF Left, (D) PEF Right, (E) DLPFC Left,(F) DLPFC Right, (G) SEF, and (H) P100 related region. Image orientationfollows EEG convention, with the left side of the image corresponding to theleft hemisphere and the right side to the right hemisphere.372.9. Model Interpretationvalue by the total number of selections (T ×5 = 125). This yielded expectedcounts of 9.77 for SEF and FEF (5 electrodes each), 7.81 for P100 and PEF(4 electrodes each), and 5.86 for DLPFC (3 electrodes), calculated separatelyfor left and right hemispheres. In addition, we computed the maximumpossible counts, corresponding to the case in which the same ROI electrodeswere consistently selected at every time point: 125 for SEF and FEF, 100for P100 and PEF, and 75 for DLPFC. The resulting overlap counts wereaggregated and reported for each well-performing participant in each task.2.9.6 Feature AnalysisProsaccade DirectionWe analyzed the Prosaccade Direction task using the group level averagesaliency maps described in Subsection 2.9.2 to identify the most informativespatiotemporal features distinguishing leftward from rightward prosaccades.At each of the T time points used for input of the model, we selected twokey electrodes: the one with the maximum value in the average rightwardsaliency map, and the one with the maximum value in the average leftwardsaliency map. This yielded a table with T rows, each listing the leftward-max and rightward-max electrodes at that time point.Using these electrode–time pairs, we extracted saliency values from boththe rightward and leftward saliency maps. For each time point, we obtainedtwo data points:• A point from the rightward saliency map (visualized as a red point):its x-coordinate is the value at the right-max electrode, and its y-coordinate is the value at the left-max electrode.• A point from the leftward saliency map (visualized as a blue point):its x-coordinate is the value at the same right-max electrode, and itsy-coordinate is the value at the same left-max electrode.This procedure produced a total of 50 data points (25 red and 25 blue),each representing a saliency-based spatiotemporal feature pair at a specifictime point. We trained a linear SVM to classify these points and assesswhether saliency-derived features could effectively separate leftward andrightward prosaccades.To compare with classical ERP-based features, we repeated the sameanalysis using ERP values instead of saliency. For each of the T elec-trode–time point pairs identified earlier, we extracted the average ERP am-plitude across all trials from well-performing participants. These ERP-based382.9. Model Interpretationfeature pairs were similarly visualized and classified using a linear SVM,enabling a direct comparison of saliency-based versus ERP-based discrimi-native power.Antisaccade DirectionWe applied the same analysis procedure described above to the Antisac-cade Direction task. Specifically, we used the averaged antisaccade saliencymaps (T × 9 × 9) to identify the most informative spatiotemporal featuresdistinguishing leftward from rightward antisaccades.At each of the T time points, we selected two electrodes: the one withthe maximum value in the average rightward saliency map and the onewith the maximum value in the average leftward saliency map. From theseelectrode–time point pairs, we extracted saliency values from both saliencymaps to form coordinate pairs. This resulted in 50 data points (25 fromeach saliency map), which were then used as input features to train a linearSVM classifier.We also repeated the analysis using ERP data. Using the same elec-trode–time point pairs identified above, we extracted the average ERP am-plitudes across trials from high-performing participants for each condition.These ERP-based feature pairs were then used as input to a linear SVMclassifier to assess whether the underlying neural signals could be similarlydistinguished between leftward and rightward antisaccades.The code used for data augmentation, model development, and saliencymap generation is publicly available at https://github.com/mojanizadkhah/3dEEGNet.39Chapter 3Results3.1 Saccade MetricsAcross 20 participants, only saccades accepted by our detection algorithm(as specified in the Methods section) were included in the analysis. In total,there were on average 436.45 (SD = 61.21) accepted prosaccade trials and436.15 (SD = 75.25) accepted antisaccade trials. On average, participantsproduced 217.65 (SD = 33.78) accepted rightward and 218.80 (SD = 28.60)accepted leftward prosaccades, compared to 216.95 (SD = 41.20) rightwardand 219.20 (SD = 36.24) leftward accepted antisaccades. Among theseaccepted trials, prosaccades had an overall mean reaction time of 172.51 ms(SD = 21.37), whereas antisaccades were substantially longer at 229.50 ms(SD = 18.19). Specifically, prosaccades averaged 171.38 ms (SD = 23.53)for rightward and 173.65 ms (SD = 22.23) for leftward movements, whileantisaccades averaged 232.22 ms (SD = 19.21) for rightward and 226.78 ms(SD = 18.28) for leftward movements. Detailed latency and trial count datafor rightward and leftward prosaccades and antisaccades, reported separatelyfor each participant, are presented in Tables 3.1 and 3.2.A Pearson correlation analysis revealed a moderate positive relationshipbetween the number of accepted prosaccade and antisaccade trials acrossparticipants (r = 0.52, p = 0.018), suggesting that participants who con-tributed more accepted prosaccade trials also tended to contribute moreaccepted antisaccade trials.A Pearson correlation between prosaccade and antisaccade latencies acrossparticipants revealed a weak, non-significant positive relationship (r = 0.32,p = 0.166), suggesting limited association between response speeds in thetwo tasks.3.2 Classification PerformanceSaccade-synchronized – Test set performance: For prosaccade clas-sification, based on 100 models per participant across 20 participants and403.2. Classification PerformanceTable 3.1: Latency (ms) and count of accepted prosaccade responses acrossparticipants, reported separately for rightward and leftward trials.ProsaccadesParticipant Right Latency (ms) Left Latency (ms) Right Count Left CountP1 200.27 191.90 230 239P2 136.68 159.47 199 181P3 182.46 174.55 169 165P4 192.80 226.72 236 245P5 210.75 205.18 245 239P6 152.25 147.10 243 236P7 165.46 160.57 240 248P8 165.54 156.96 240 238P9 163.67 178.61 238 235P10 175.92 142.09 220 211P11 150.17 161.22 115 158P12 136.34 165.13 227 224P13 220.28 203.87 232 235P14 178.64 190.91 246 246P15 180.10 176.12 226 232P16 177.21 164.21 213 206P17 144.34 145.81 205 190P18 168.19 172.20 161 180P19 144.14 158.61 238 237P20 182.30 191.78 230 231Average 171.38 173.65 217.65 218.80413.2. Classification PerformanceTable 3.2: Latency (ms) and count of accepted antisaccade responses acrossparticipants, reported separately for rightward and leftward trials.AntisaccadesParticipant Right Latency (ms) Left Latency (ms) Right Count Left CountP1 255.06 245.61 238 243P2 233.87 236.58 209 202P3 251.04 257.15 222 201P4 239.29 254.17 236 238P5 227.46 216.52 262 250P6 211.38 204.83 202 215P7 221.26 222.32 247 245P8 248.17 234.57 233 240P9 222.29 215.49 212 222P10 251.84 238.32 180 226P11 221.93 223.44 216 184P12 235.67 226.90 239 244P13 271.59 252.53 146 159P14 209.53 198.77 258 237P15 216.54 205.75 233 247P16 212.99 206.27 213 227P17 209.47 221.90 226 207P18 265.67 249.68 84 110P19 223.04 215.38 234 217P20 216.38 209.42 249 270Average 232.22 226.78 216.95 219.20423.2. Classification Performanceusing the time window from –101.66 to –7.82 ms prior to saccade onset, themean test AUC was M = 0.91 (SD = 0.13), and the mean test accuracywas M = 0.84 (SD = 0.14). One-sample t-tests confirmed that both met-rics were significantly above chance level (50%): accuracy, t(19) = 10.95,p < .0001; AUC, t(19) = 13.76, p < .0001. Test accuracy, AUC, and theirstandard deviations across 100 models are reported individually for eachparticipant in Table 3.3. For antisaccade classification, participant 18 hadonly 84 accepted rightward trials (Table 3.2), which was below the minimumof 100 models per class required for the augmentation method. Therefore,this participant was excluded from the antisaccade classification analysis.Across the remaining 19 participants, the mean test AUC was M = 0.93(SD = 0.09), and the mean test accuracy was M = 0.85 (SD = 0.14). One-sample t-tests confirmed that both metrics were significantly above chance:accuracy, t(18) = 11.07, p < .0001; AUC, t(18) = 19.70, p < .0001. Test ac-curacy, AUC, and their standard deviations across 100 models are reportedindividually for each participant in Table 3.4.Stimulus-synchronized – Test set performance:For prosaccade classification, averaged over 100 models per participant(20 participants) and using the time window from 0.00 to 93.84 ms, themean test accuracy wasM = 0.51 (SD = 0.10), and the mean test AUC wasM = 0.53 (SD = 0.16). One-sample t-tests showed that neither accuracynor AUC were significantly above chance: accuracy, t(19) = 0.56, p = .582;AUC, t(19) = 0.84, p = .411. For antisaccade classification (19 participants,excluding participant 18 as noted above), the mean test accuracy was M =0.56 (SD = 0.10), and the mean test AUC wasM = 0.60 (SD = 0.18), withperformance significantly above chance: accuracy, t(18) = 2.52, p = .022;AUC, t(18) = 2.38, p = .029. Test accuracy, AUC, and their standarddeviations across 100 models are reported individually for each participantin Table 3.5 and Table 3.6, respectively.We examined whether individual classification performance was associ-ated with the size of the development set across all four tasks (saccade- andstimulus-synchronized decoding for prosaccade and antisaccade right-vs-leftclassification). For the prosaccade saccade-synchronized task, there was astrong positive correlation between decoding accuracy and the number ofaccepted trials (r = 0.76, p < .001), but no significant association withmean saccadic latency (r = 0.16, p = .509). For the prosaccade stimulus-synchronized task, neither the number of accepted trials (r = 0.24, p = .318)nor mean latency (r = 0.02, p = .933) showed a significant correlation withdecoding accuracy.In the antisaccade saccade-synchronized task, decoding performance was433.2. Classification PerformanceTable 3.3: Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftward prosaccade classi-fication task, time-locked to saccade onset.Participant Test ACC Test AUC Test ACC SD Test AUC SDP1 0.99 1.00 0.07 0.00P2 0.68 0.83 0.28 0.36P3 0.58 0.60 0.32 0.48P4 0.78 0.90 0.27 0.30P5 0.98 1.00 0.11 0.00P6 1.00 1.00 0.00 0.00P7 0.86 0.94 0.26 0.24P8 0.92 0.99 0.18 0.10P9 0.99 1.00 0.07 0.00P10 0.66 0.74 0.28 0.41P11 0.50 0.54 0.10 0.49P12 0.88 0.95 0.21 0.22P13 0.80 0.94 0.26 0.24P14 0.92 0.99 0.19 0.10P15 0.96 1.00 0.14 0.00P16 0.88 0.96 0.22 0.20P17 0.88 0.96 0.23 0.19P18 0.85 0.98 0.25 0.14P19 0.89 0.98 0.21 0.11P20 0.85 0.96 0.24 0.19Average 0.84 0.91 0.14 0.13443.2. Classification PerformanceTable 3.4: Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftward antisaccade clas-sification task, time-locked to saccade onset.Participant Test ACC Test AUC Test ACC SD Test AUC SDP1 0.94 1.00 0.16 0.05P2 0.87 0.96 0.22 0.20P3 0.55 0.75 0.29 0.44P4 0.99 1.00 0.07 0.00P5 0.82 0.80 0.25 0.34P6 1.00 1.00 0.05 0.00P7 0.93 1.00 0.17 0.05P8 0.96 1.00 0.14 0.00P9 0.99 1.00 0.07 0.00P10 0.76 0.92 0.26 0.23P11 1.00 1.00 0.00 0.00P12 0.91 0.98 0.19 0.11P13 0.74 0.92 0.25 0.24P14 0.74 0.85 0.28 0.34P15 0.76 0.92 0.25 0.23P16 0.94 0.99 0.16 0.07P17 0.80 0.90 0.27 0.30P19 0.82 0.94 0.25 0.24P20 0.57 0.68 0.30 0.46Average 0.85 0.93 0.14 0.09453.2. Classification Performancenot significantly associated with either the number of accepted trials (r =−0.02, p = .937) or mean saccadic latency (r = −0.10, p = .672). Similarly,for the antisaccade stimulus-synchronized task, decoding accuracy showedno significant relationship with trial count (r = 0.29, p = .235) or latency(r = −0.06, p = .802).Table 3.5: Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftward prosaccade classi-fication task, time-locked to stimulus onset.Participant Test ACC Test AUC Test ACC SD Test AUC SDP1 0.44 0.38 0.29 0.48P2 0.54 0.59 0.27 0.48P3 0.36 0.23 0.27 0.40P4 0.59 0.66 0.30 0.47P5 0.65 0.76 0.31 0.42P6 0.71 0.80 0.29 0.40P7 0.56 0.59 0.34 0.48P8 0.57 0.59 0.33 0.49P9 0.52 0.55 0.28 0.49P10 0.46 0.52 0.28 0.49P11 0.50 0.60 0.25 0.47P12 0.66 0.66 0.32 0.47P13 0.55 0.62 0.31 0.47P14 0.42 0.40 0.31 0.48P15 0.30 0.23 0.28 0.42P16 0.52 0.46 0.29 0.49P17 0.38 0.34 0.28 0.47P18 0.57 0.68 0.27 0.46P19 0.44 0.40 0.31 0.49P20 0.52 0.54 0.32 0.49Average 0.51 0.53 0.10 0.16Group-level test accuracy, AUC, and standard deviations for the classi-fication tasks are reported in Table 3.7.Taken together, these stimulus-synchronized analyses served primarilyas a control to confirm that successful classification was not driven by visualstimulation. Accordingly, all subsequent analyses are restricted to saccade-synchronized data, which more directly reflect processes of saccadic plan-ning.463.2. Classification PerformanceTable 3.6: Test accuracy, AUC, and their standard deviations across 100models for each participant in the rightward vs. leftward antisaccade clas-sification task, time-locked to stimulus onset.Participant Test ACC Test AUC Test ACC SD Test AUC SDP1 0.54 0.55 0.32 0.50P2 0.48 0.49 0.26 0.49P3 0.57 0.65 0.29 0.47P4 0.78 0.86 0.26 0.35P5 0.40 0.34 0.29 0.47P6 0.56 0.62 0.30 0.48P7 0.56 0.64 0.31 0.48P8 0.62 0.70 0.30 0.45P9 0.55 0.58 0.30 0.49P10 0.60 0.69 0.28 0.46P11 0.64 0.74 0.32 0.42P12 0.69 0.78 0.29 0.40P13 0.32 0.10 0.25 0.29P14 0.64 0.72 0.32 0.43P15 0.45 0.40 0.33 0.49P16 0.55 0.54 0.27 0.49P17 0.62 0.71 0.28 0.42P19 0.58 0.70 0.25 0.45P20 0.49 0.52 0.31 0.49Average 0.56 0.60 0.10 0.18Table 3.7: Test performance for classification tasks: right vs. left saccadeswithin prosaccade or antisaccade tasks.Saccade-SynchronizedTask Description Test ACC Test AUC Test ACC SD Test AUC SDRight vs. Left (Prosaccade) 0.84 0.91 0.14 0.13Right vs. Left (Antisaccade) 0.85 0.93 0.14 0.09Stimulus-SynchronizedTask Description Test ACC Test AUC Test ACC SD Test AUC SDRight vs. Left (Prosaccade) 0.51 0.53 0.10 0.16Right vs. Left (Antisaccade) 0.56 0.60 0.10 0.18473.3. Model Interpretation: Saliency Maps3.3 Model Interpretation: Saliency Maps3.3.1 Saliency MapsFigures 3.1 and 3.2 show the group-level average saliency maps for left-ward and rightward prosaccade and antisaccade trials, respectively, time-locked to saccade onset. In both conditions, there is a clear mirroring ofsaliency patterns between rightward and leftward saccades. In prosaccadetrials, saliency was initially strongest at contralateral occipital sites around90–100 ms before saccade onset (e.g., PO3), later shifting to contralateralparietal regions in the 20–50 ms window before movement (e.g., P4, CP2).Less pronounced frontal and central saliency was also observed closer tosaccade onset, approximately 27–10 ms before movement (e.g., FC4, FC6,C2). In antisaccade trials, early saliency emerged over contralateral occipito-parietal sites (-80 to -50 ms) (e.g., P3, CP3, P4, CP4, PO8), then shifted tocentral regions closer to saccade onset (e.g., C3, CP3, CP2), and finally toipsilateral frontal sites (e.g., AF4, F4, FC3). Overall, prosaccades showed aposterior-to-anterior progression of discriminatory features, whereas antisac-cades exhibited a biphasic pattern with broader and stronger saliency signal,including frontal contributions less pronounced in prosaccades compared toantisaccades.3.3.2 Time analysisUsing the group-level saliency maps, we aimed to identify the most informa-tive temporal features for distinguishing saccade directions. The rationalefor this analysis was to determine whether the saliency values exhibited sys-tematic shifts in sign across time, thereby highlighting substantial periodsof discriminative activity. Because positive saliency for one class typicallycorresponds to negative saliency for the other, mirror-symmetric patternsare expected; however, abrupt transitions from predominantly positive topredominantly negative values suggest distinct time windows in which class-specific information emerges. By averaging saliency across all electrodes ateach time point, we obtained a simplified overview of the temporal dynam-ics, without needing to focus on individual electrodes. Figure 3.3 showsthese time courses for leftward and rightward prosaccade and antisaccadetrials.In the prosaccade task, the average saliency values exhibited a signchange, with the shift beginning at approximately −46.92 ms and endingaround −43.01 ms before saccade onset. A similar change was observed in483.3. Model Interpretation: Saliency MapsFigure 3.1: Group-level average of normalized scalp topographic maps ofsaliency values for leftward (A) and rightward (B) prosaccade trials. Saliencymaps from trials with 100% accuracy were normalized and averaged per sub-ject (n = 16; test accuracy ≥ 75%), then averaged across subjects to producethe group-level maps. The displayed time window spans from 101.66 ms to7.82 ms before saccade onset. Black arrows mark time points with high dis-criminative saliency. Image orientation follows EEG convention, with theleft side of the image corresponding to the left hemisphere and the right sideto the right hemisphere.493.3. Model Interpretation: Saliency MapsFigure 3.2: Group-level average of normalized scalp topographic mapsof saliency values for leftward (A) and rightward (B) antisaccade trials.Saliency maps from trials with 100% accuracy were normalized and averagedper subject (n = 15; test accuracy ≥ 75%), then averaged across subjectsto produce the group-level maps. The displayed time window spans from101.66 ms to 7.82 ms before saccade onset. Black arrows mark time pointswith high discriminative saliency. Image orientation follows EEG conven-tion, with the left side of the image corresponding to the left hemisphereand the right side to the right hemisphere.503.3. Model Interpretation: Saliency Mapsthe antisaccade task, with the shift starting at −27.37 ms and ending around−19.55 ms.3.3.3 Electrode AnalysisTo identify the most spatially informative regions, we created time-averagedgroup-level saliency maps by averaging the saliency values across T = 25pre-saccadic time points. The resulting maps (Figure 3.4) provide a sin-gle topographic representation of the overall contribution of each electrode,collapsing across time to emphasize where the most reliable directional infor-mation was located. This approach allows us to visualize the electrodes thatcarried the strongest discriminative signal independent of temporal fluctua-tions.These maps showed mirrored patterns between leftward and rightwardsaccades in each classification task, with contralateral positive saliency rel-ative to movement direction in both tasks; antisaccades additionally exhib-ited an ipsilateral frontal contribution not observed in prosaccades. Fromthese maps, we extracted the eight electrodes with the highest positivesaliency values for each class of each condition. The choice of eight elec-trodes—representing one quarter of the 64-channel montage—provided afocused subset of the most strongly activated sites. The selected electrodesare listed below.• Prosaccade Rightward: FC3, F3, PO3, AF7, P1, C3, AF3, PO7• Prosaccade Leftward: P6, CP2, P4, CP6, FC2, C2, PO8, P2• Antisaccade Rightward: CP3, C3, FC3, P3, Pz, AF4, F2, PO3• Antisaccade Leftward: PO8, P4, P2, CP4, PO4, CP2, P6, P103.3.4 Region Correspondence CheckTo investigate spatial patterns of activation, we examined the overlap be-tween saliency maps and anatomically defined regions of interest (ROIs),including the supplementary eye field (SEF), P100-related areas, frontal eyefields (FEF), parietal eye fields (PEF), and dorsolateral prefrontal cortex(DLPFC). This analysis was based on the top five most salient electrodesfor each well-performing participant, identified separately for rightward andleftward saliency maps, across both prosaccade and antisaccade tasks.513.3. Model Interpretation: Saliency MapsFigure 3.3: Group-level average saliency time courses for leftward (blue)and rightward (red) saccades in the prosaccade (A) and antisaccade (B)tasks. Each curve reflects the mean saliency across 64 electrodes and 16(prosaccade) or 15 (antisaccade) high-performing participants (accuracy ≥75%). The x-axis represents time relative to saccade onset, ranging from–101.66 ms to –7.82 ms. The black boxes indicate the areas of interest:–46.92 to –43.01 ms for the prosaccade task and –27.37 to –19.55 ms for theantisaccade task. 523.3. Model Interpretation: Saliency MapsFigure 3.4: Time-averaged group-level normalized scalp topographic mapsof saliency values, averaged over T = 25 time points, for (A) leftward prosac-cade, (B) rightward prosaccade, (C) leftward antisaccade, and (D) rightwardantisaccade trials. Saliency maps from trials with 100% accuracy were nor-malized and averaged within each well-performing subject, then averagedacross subjects, and finally averaged across all time points to produce eachmap. Subjects were selected based on the corresponding task. Image orien-tation follows EEG convention, with the left side of the image correspondingto the left hemisphere and the right side to the right hemisphere.533.3. Model Interpretation: Saliency MapsThe overlap counts between these top electrodes and each ROI are pre-sented in Tables 3.8 and 3.9 for the prosaccade task, and in Tables 3.10and 3.11 for the antisaccade task.For the rightward saliency map in the prosaccade task:• Visual cortex (P100) and SEF combined: 26.88• Right hemisphere ROIs (FEF, PEF, DLPFC): 23.31• Left hemisphere ROIs (FEF, PEF, DLPFC): 30.50For the leftward saliency map in the prosaccade task:• Visual cortex (P100) and SEF combined: 29.56• Right hemisphere ROIs (FEF, PEF, DLPFC): 34.75• Left hemisphere ROIs (FEF, PEF, DLPFC): 14.88For the rightward saliency map in the antisaccade task:• Visual cortex (P100) and SEF combined: 26.60• Right hemisphere ROIs (FEF, PEF, DLPFC): 22.67• Left hemisphere ROIs (FEF, PEF, DLPFC): 30.33For the leftward saliency map in the antisaccade task:• Visual cortex (P100) and SEF combined: 30.33• Right hemisphere ROIs (FEF, PEF, DLPFC): 26.47• Left hemisphere ROIs (FEF, PEF, DLPFC): 20.00Full breakdowns of individual participant overlaps for each ROI can be foundin the referenced tables.543.3. Model Interpretation: Saliency MapsTable 3.8: Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas, for each well-performing participant, based on the top five electrodes from the rightwardsaliency map in the prosaccade classification task.Participant P100 SEF FEF(R) PEF(R) DLPFC(R) FEF(L) PEF(L) DLPFC(L)P1 13 11 2 1 6 11 27 8P4 10 18 11 13 10 11 7 7P5 30 1 3 33 0 19 1 15P6 14 16 15 7 15 6 15 2P7 9 6 11 17 19 2 10 2P8 4 26 9 10 9 11 11 4P9 10 25 3 9 0 18 4 28P12 12 12 2 19 2 16 9 7P13 2 15 3 5 13 11 9 9P14 14 18 11 11 16 8 4 8P15 20 7 8 9 2 11 25 11P16 10 19 11 14 8 5 12 8P17 8 34 2 3 4 23 14 3P18 9 10 2 2 3 27 16 17P19 6 23 7 3 1 31 3 23P20 10 8 3 1 26 2 4 2Average 11.31 15.56 6.44 9.81 8.38 13.25 10.69 9.63Table 3.9: Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas, for each well-performing participant, based on the top five electrodes from the leftwardsaliency map in the prosaccade classification task.Participant P100 SEF FEF(R) PEF(R) DLPFC(R) FEF(L) PEF(L) DLPFC(L)P1 6 25 37 22 4 0 1 0P4 10 15 5 13 7 12 6 9P5 3 6 4 2 42 2 5 2P6 19 8 6 23 0 5 4 7P7 16 18 5 15 1 16 6 5P8 19 8 3 20 2 7 12 6P9 9 16 17 16 13 2 2 0P12 11 17 9 17 5 6 13 9P13 12 13 6 25 5 2 18 6P14 12 24 14 18 3 10 6 2P15 8 35 25 26 7 0 0 0P16 17 20 4 9 8 11 7 7P17 28 0 4 11 1 0 0 1P18 20 8 16 33 5 5 2 5P19 23 10 4 19 2 1 7 1P20 19 18 4 32 0 16 11 11Average 14.50 15.06 10.19 18.81 6.56 5.94 6.25 4.44553.3. Model Interpretation: Saliency MapsTable 3.10: Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas, for each well-performing participant, based on the top five electrodes from the rightwardsaliency map in the antisaccade classification task.Participant P100 SEF FEF(R) PEF(R) DLPFC(R) FEF(L) PEF(L) DLPFC(L)P1 10 7 3 5 3 20 24 12P2 20 13 0 0 3 17 21 13P4 10 9 0 1 2 8 15 37P5 10 20 8 11 11 6 14 6P6 8 16 16 8 16 2 11 0P7 10 22 11 6 8 11 12 0P8 18 12 6 9 4 8 12 7P9 13 12 11 7 17 5 13 4P10 10 11 4 10 4 11 13 15P11 16 27 8 5 15 1 15 0P12 7 24 10 7 8 15 10 7P15 13 17 4 9 11 11 16 7P16 13 11 11 20 3 6 9 10P17 6 13 28 7 31 2 2 0P19 9 12 5 6 3 14 17 10Average 11.53 15.07 8.33 7.40 9.27 9.13 13.60 8.53Table 3.11: Activation counts across right- and left-hemisphere ROIs (FEF,PEF, DLPFC), as well as SEF and P100-related visual areas, for each well-performing participant, based on the top five electrodes from the leftwardsaliency map in the antisaccade classification task.Participant P100 SEF FEF(R) PEF(R) DLPFC(R) FEF(L) PEF(L) DLPFC(L)P1 22 21 10 27 4 1 2 2P2 33 3 7 33 1 4 0 0P4 28 2 5 33 0 5 1 1P5 28 17 2 21 1 4 9 2P6 7 22 7 7 6 25 10 10P7 14 7 0 6 2 8 16 6P8 16 14 12 20 2 8 7 4P9 10 12 11 18 9 5 11 7P10 7 23 7 15 8 11 12 5P11 28 4 2 9 0 4 2 9P12 13 13 6 20 5 13 7 11P15 7 14 8 6 4 13 11 12P16 19 8 3 16 5 4 12 1P17 21 15 2 17 1 10 5 15P19 12 15 8 11 7 9 6 11Average 17.67 12.67 6.00 17.27 3.67 8.27 7.40 6.40563.3. Model Interpretation: Saliency Maps3.3.5 Feature AnalysisTo evaluate whether the electrode–time point features highlighted by thesaliency maps truly captured discriminative neural information, we testedif a highly simplified feature space would still allow reliable separation ofsaccade directions. By reducing the full set of 1,600 features (25 time points× 64 electrodes) to pairs of electrode–time point features, we could assesswhether the most salient dimensions alone were sufficient for classification.ProsaccadeTo implement this analysis, we extracted saliency values at two electrodelocations per time point, as described in the Methods section (Table 3.12),resulting in a two-dimensional feature space. A linear SVM classifier wasthen trained on these features, yielding the following decision boundary:0.70x− 1.90 y − 0.02 = 0This boundary perfectly separated the two groups, with no misclassifiedsamples, as illustrated in Figure 3.5To evaluate the extracted features derived from model-generated saliency,we repeated the analysis using ERP signal values. For each of the sameelectrode–timepoint pairs, we extracted the mean ERP amplitude across alltrials from the 16 high-performing participants in prosacade task. A linearSVM classifier was then trained on these ERP-based features, yielding thefollowing decision boundary:−1.26x− 1.48 y + 0.85 = 0The boundary misclassified 4 out of 50 samples, indicating that the twogroups were largely linearly separable, as illustrated in Figure 3.6AntisaccadeTo quantitatively assess the separability of saccade directions based on an-tisaccade saliency features, we extracted saliency values at two electrodelocations per time point, as described in the Methods section shown inTable 3.13, resulting in a two-dimensional feature space. A linear SVMclassifier was then trained on these features, yielding the following decisionboundary:2.81x− 2.41 y + 0.041 = 0573.3. Model Interpretation: Saliency MapsTable 3.12: Electrodes with maximum saliency for rightward and leftwardprosaccade trials across 25 time points of interest. Each row indicates theelectrode with the highest saliency at a specific time point, shown separatelyfor rightward and leftward saliency maps.Time (ms) Right Saliency Map Left Saliency Map-101.66 FC2 C2-97.75 AF4 P2-93.84 F2 C2-89.93 Fz P2-86.02 FP2 P2-82.11 CPz C2-78.20 P3 FC2-74.29 F2 P2-70.38 FC3 C4-66.47 Cz CP2-62.56 F4 P6-58.65 CPz C2-54.74 AF8 PO8-50.83 F2 C6-46.92 FC3 FC2-43.01 FC4 PO8-39.10 Pz F4-35.19 FC3 PO8-31.28 F4 P4-27.37 AF4 P2-23.46 PO7 P6-19.55 FC4 P4-15.64 C2 C4-11.73 FC3 P2-7.82 FC6 PO4583.3. Model Interpretation: Saliency MapsFigure 3.5: Scatterplot comparing prosaccade group-level saliency values,time-locked to saccade onset, at key electrode–time points. Red markersrepresent saliency values from the rightward prosaccade map, plotted withthe right-saliency map on the X-axis and the left-saliency map on the Y-axis. Blue markers represent analogous values from the leftward prosaccademap. Each point is labeled with the corresponding electrode and time point.The dashed line indicates the decision boundary learned by a linear SVMclassifier.593.3. Model Interpretation: Saliency MapsFigure 3.6: Scatterplot comparing group-averaged ERP values, time-lockedto saccade onset, for prosaccades at key electrode–time points. Red markersrepresent ERP values from rightward prosaccades, plotted with the right-ERP on the X-axis and the left-ERP on the Y-axis. Blue markers representanalogous values from leftward prosaccades. Each point is labeled withthe corresponding electrode and time point. The dashed line indicates thedecision boundary learned by a linear SVM classifier.603.3. Model Interpretation: Saliency MapsThis boundary perfectly separated the two groups, with no misclassifiedsamples, as illustrated in Figure 3.7Table 3.13: Electrodes with maximum saliency for rightward and leftwardantisaccade trials across 25 time points of interest. Each row indicates theelectrode with the highest saliency at a specific time point, shown separatelyfor rightward and leftward saliency maps.Time (ms) Right Saliency Map Left Saliency Map-101.66 C3 PO4-97.75 F2 PO4-93.84 P3 CP4-89.93 CP3 P4-86.02 FC1 FC5-82.11 C3 P2-78.20 P3 P4-74.29 P3 CP4-70.38 Pz PO8-66.47 Pz P2-62.56 P3 P4-58.65 Pz PO8-54.74 Pz PO8-50.83 CP3 P4-46.92 C3 P2-43.01 AF4 P2-39.10 CP3 PO8-35.19 CP3 PO8-31.28 C3 PO8-27.37 AF4 PO8-23.46 FC2 P4-19.55 C1 P2-15.64 CP3 CP2-11.73 FC2 PO8-7.82 C5 PO8To evaluate the extracted features derived from model-generated saliency,we repeated the analysis using ERP signal values. For each of the sameelectrode–timepoint pairs, we extracted the mean ERP amplitude across alltrials from the 16 high-performing participants in prosacade task. A linearSVM classifier was then trained on these ERP-based features, yielding the613.3. Model Interpretation: Saliency MapsFigure 3.7: Scatterplot comparing group-level saliency values, time-lockedto saccade onset, for antisaccades at key electrode–time points. Red markersrepresent saliency values from the rightward antisaccade map, plotted withthe right-saliency map on the X-axis and the left-saliency map on the Y-axis. Blue markers represent analogous values from the leftward antisaccademap. Each point is labeled with the corresponding electrode and time point.The dashed line indicates the decision boundary learned by a linear SVMclassifier.623.3. Model Interpretation: Saliency Mapsfollowing decision boundary:−1.86x+ 0.46 y − 0.34 = 0The boundary misclassified 1 out of 50 samples, indicating that the twogroups were almost perfectly linearly separable, as illustrated in Figure 3.8633.3. Model Interpretation: Saliency MapsFigure 3.8: Scatterplot comparing group-averaged ERP values, time-lockedto saccade onset, for antisaccades at key electrode–time points. Red markersrepresent ERP values from rightward antisaccades, plotted with the right-ERP on the X-axis and the left-ERP on the Y-axis. Blue markers representanalogous values from leftward antisaccades. Each point is labeled withthe corresponding electrode and time point. The dashed line indicates thedecision boundary learned by a linear SVM classifier.64Chapter 4Discussion and Conclusion4.1 Saccade MetricsIn our dataset of 20 participants, the mean saccadic reaction time was172.51 ms for prosaccades and 229.50 ms for antisaccades, indicating thatantisaccades were on average about 57 ms slower than prosaccades. Thesevalues are consistent with prior reports. For example, prosaccade reactiontimes typically follow a unimodal distribution with a peak around 160–190 ms [Kunita and Fujiwara, 2022], and in one mixed pro/antisaccade taskprosaccade latencies averaged about 167 ms [Wang et al., 2013]. Antisaccadelatencies, in turn, are regularly tens to hundreds of milliseconds longer: inthe same mixed paradigm, antisaccades averaged 284 ms [Wang et al., 2013],and other studies have reported even higher antisaccade latencies depend-ing on task demands. For instance, [Everling et al., 1997] observed a meanantisaccade latency of 504 ms versus 431 ms for prosaccades (a difference of73 ms) in a stimulus-triggered task. Similarly, [Moon et al., 2007], using aninterleaved pro/antisaccade paradigm during MEG recording, found averagelatencies of about 229 ms for prosaccades and 273 ms for antisaccades (a44 ms difference). Notably, the overall response speeds in our study weresubstantially faster than those in both studies—particularly compared to[Everling et al., 1997], whose absolute latencies were considerably longer.This discrepancy likely reflects methodological differences: our blocked de-sign (all prosaccade trials completed before all antisaccade trials) reducedtask-switching demands, whereas [Everling et al., 1997] employed stimulus-triggered saccades and [Moon et al., 2007] used an interleaved paradigm.Running the prosaccade block first and the antisaccade block second mayhave further minimized task switch cost and allowed participants to main-tain a consistent strategy within each block, resulting in shorter reactiontimes for both saccade types. However, as [Barton et al., 2006] emphasize,absolute latencies are highly paradigm-dependent, so the more meaning-ful measure across studies is the antisaccade cost—the additional delay ofantisaccades relative to prosaccades—rather than absolute reaction times.Regardless of these procedural differences, the antisaccade cost in reac-654.1. Saccade Metricstion time observed in our study (∼57 ms) falls squarely within the range re-ported across behavioral and neurophysiological studies of antisaccades [Hal-lett, 1978, Everling et al., 1997, Moon et al., 2007]. This robust patternsupports the view that antisaccades generally require longer processing thanprosaccades. The delay reflects the extra cognitive steps needed to inhibitthe reflexive glance toward the sudden peripheral stimulus and instead gen-erate a voluntary saccade in the opposite direction [Hallett, 1978, Everlinget al., 1997, Moon et al., 2007]. Neurophysiological investigations have im-plicated a frontoparietal network—including the frontal eye fields and intra-parietal sulcus—in mediating this vector inversion and inhibitory control,thereby accounting for the prolonged antisaccade latencies [Everling andFischer, 1998, Zhang and Barash, 2000].In addition to longer reaction times, antisaccades are known to producehigher error rates than prosaccades. In healthy young adults, the antisaccadeerror rate (failure to inhibit the reflexive prosaccade) is often around 10–15%under standard conditions. For instance, [Moon et al., 2007] reported about13% antisaccade errors (versus 4% errors on prosaccades) in their mixedparadigm, a figure consistent with general expectations. Based on suchliterature, we anticipated a 10% error rate, and the final number of acceptedtrials was nearly identical between prosaccades (436.45) and antisaccades(436.15).It is also worth noting that we found only a weak, non-significant cor-relation between individuals’ mean prosaccade and antisaccade latencies(r = 0.32, p = 0.166). In other words, being a “fast responder” on prosac-cade trials did not strongly predict being fast on antisaccade trials. If gen-eral processing speed were a major determinant of saccadic reaction times,a stronger correlation would be expected, but our results suggest that suchgeneral speed contributes little to saccade reaction time. This implies thatthe additional cognitive demands of the antisaccade task introduce inter-individual variability in response speed that is not simply a uniform exten-sion of one’s prosaccade reaction time. Participants who had more acceptedtrials in the prosaccade task tended to also have more accepted trials inthe antisaccade task (r = 0.52, p = 0.018), suggesting that overall taskengagement was a relatively consistent trait across individuals. However,even participants with fewer valid trials still showed the expected latencyprolongation in antisaccades, underscoring that the antisaccade delay is ageneral phenomenon rather than an artifact of trial count or practice effects.664.2. Model Performance4.2 Model PerformanceWe observed a clear distinction in classification performance between mod-els time-locked to saccade onset and those time-locked to stimulus onset.Our classifiers performed very well when aligned to the saccade execution.For the saccade-synchronized models, the average test accuracy was 84%for prosaccades and 85% for antisaccades, with corresponding AUCs of 0.91and 0.93, respectively—indicating that neural activity preceding the saccadeexecution conveyed strong directional information. By contrast, stimulus-synchronized models performed near chance level—approximately 51% accu-racy for prosaccade trials and 56% for antisaccade trials, with the prosaccaderesult not significantly above 50%. This stark drop in performance was ex-pected. When EEG data are aligned to stimulus onset, the motor planningprocesses that carry crucial directional information occur at variable timesacross trials, depending on individual saccadic latencies. As a result, thisactivity becomes misaligned and effectively cancels out, leaving the classifierwith little consistent signal from which to predict saccade direction. More-over, the analysis window for the stimulus-synchronized condition (0–94 ms)likely preceded the reliable emergence of classic stimulus-related componentssuch as the P100 and N170, reducing the amount of stimulus-evoked signalavailable for classification. The use of a small dot stimulus rather than alarger patterned target may also have contributed to weaker early visualresponses and, consequently, to the limited decoding performance. It isworth noting that the hemispheric asymmetries discussed in the Introduc-tion (i.e., consistent right-hemisphere engagement during both leftward andrightward saccades) do not directly contribute to directional decoding. Be-cause saliency maps highlight features that distinguish left from right trials,neural activity that is consistently right-lateralized across both directions isnot discriminative and therefore does not appear as a driver of classificationperformance.In contrast, antisaccades require additional cognitive operations—suchas inhibiting a reflexive saccade and preparing a goal-directed response inthe opposite direction—which may unfold over a longer time course. How-ever, the neural signatures of these processes tend to emerge later in thetrial, and their timing and spatial patterns are more variable across individ-uals. This likely explains why our stimulus-locked antisaccade classifier onlymodestly exceeded chance. In summary, it is primarily the neural activitytime-locked to the saccade execution (i.e. the peri-movement dynamics andmotor preparation) that carries reliable information about movement di-rection, leading to high classification accuracy in the saccade-synchronized674.2. Model Performanceanalyses, whereas the stimulus-locked window does not contain a strong,consistent directional signal for the classifier to learn.To further probe the classification results, we examined whether indi-vidual differences in classifier performance were related to task-level factors.We were particularly interested in whether having more accepted trials (i.e.,more training data) or having faster response times would correspond tohigher classification accuracy for a given participant. In the prosaccadesaccade-synchronized condition, there was a clear relationship: participantswith more accepted trials tended to achieve higher classification accuracy(r = 0.76, p < .001). This makes intuitive sense, as a larger number ofclean trials provides the model with more examples of each class (right vsleft saccades), likely leading to more robust feature learning and better gen-eralization on the test data. By contrast, prosaccade classification accuracyshowed no significant correlation with that participant’s mean saccadic la-tency (r = 0.16, p = .51), suggesting that whether someone was generallyquick or slow to respond did not affect how well the EEG-based classifiercould distinguish the direction of their saccades. In the stimulus-lockedprosaccade condition, we found no significant correlations with either trialcount or latency (|r| < .30, p > .30), which is unsurprising given that clas-sification in that condition was essentially at chance for most participants –any small variations in accuracy there would be dominated by noise.For the antisaccade classification tasks, the situation was somewhat dif-ferent. In both saccade-aligned and stimulus-aligned antisaccade classifica-tion, we did not observe any significant correlation between classificationaccuracy and the number of accepted trials (r = −0.02 and r = 0.29, re-spectively, both p > .2). We also found no reliable correlation with meanantisaccade reaction time (r = −0.10 and r = −0.06 in the saccade-lockedand stimulus-locked cases, p > .6). In other words, unlike the prosaccadecase, having more antisaccade trials did not guarantee better classificationperformance for an individual, and a participant’s antisaccade speed was un-related to how well the classifier decoded their trial directions. There are afew possible reasons for this. First, the antisaccade task is tends to be morevariable – it engages more complex neural circuitry (for inhibition and vec-tor inversion) and tends to show greater between-trial and between-subjectvariability in neural responses. This added complexity could mean that evenwith more data, the classifier doesn’t gain as much consistent signal as itdoes in the simpler prosaccade task. In our dataset, most participants hada fairly high number of antisaccade trials (since we balanced trial counts),so the range of trial counts was narrower and possibly insufficient to revealan effect on accuracy (especially after excluding one participant with un-684.3. Model Interpretationusually few antisaccade trials). It’s also possible that the lack of correlationis partly due to ceiling effects or range restrictions in the classification per-formance – many participants were already classifying antisaccade directionwith relatively high accuracy (0.85–1% for several individuals, see Table3.4),so additional trials might not boost performance much further. Lastly, theabsence of any correlation with antisaccade latency reinforces that the dif-ferences in brain activity driving classification are not simply a function ofhow quickly the saccade is made; rather, they reflect qualitative aspects oftask processing. For example, a participant who takes longer to initiate an-tisaccades is not necessarily using a fundamentally different neural strategy– they may just have a longer decision process or stricter threshold, whichdoesn’t obviously improve or impair the separability of their left vs righttrial EEG patterns.In summary, our classification analyses show that we can reliably pre-dict saccade direction from EEG signals, especially when focusing on peri-movement neural dynamics. Prosaccade tasks, with their more stereotypedand reflexive nature, yielded very high directional classification accuracyand benefited from larger training sets. Antisaccade direction was also de-coded with slightly higher accuracy, showing that there is sufficient direction-specific information in pre-saccadic activity to allow robust prediction beforethe eye movement occurs. One caveat is that this pre-saccadic window, ex-tending up to about 100 ms before saccade onset, may also capture activityrelated to visual stimulation in addition to saccade planning, which couldinflate predictive performance. To address this possibility, we conducted asecond analysis using stimulus-synchronized EEG signals. If decoding reliedon visual responses, performance should have remained high or improvedunder stimulus alignment. Instead, we observed lower accuracy, indicat-ing that the primary source of information enabling successful directionalprediction was motor planning activity rather than visual stimulation.4.3 Model Interpretation4.3.1 Saliency MapsIn prosaccade trials, early occipital saliency around 90–100 ms showed clearlateralization that contributed strongly to distinguishing leftward from right-ward saccades. Later, in the 20–50 ms window before movement, saliencyshifted to contralateral frontal and centro-parietal sites, indicating that fea-tures from these regions contributed strongly to distinguishing leftward fromrightward saccades. This pattern is compatible with known involvement of694.3. Model Interpretationthe FEF and PEF in saccade preparation, though the saliency maps them-selves only indicate discriminative contributions rather than direct neuralactivation. Overall, the prosaccade classifier relied on contralateral occip-ital, parietal, and frontal features in a coherent posterior-to-anterior se-quence. Because the SEF is a midline region, its role would not be expectedto produce lateralized differences and therefore may not be visible in thisanalysis.In antisaccade trials, early saliency between –80 and –50 ms highlightedcontralateral occipito-parietal sites, providing strong discriminatory infor-mation for distinguishing leftward from rightward saccades. Later (–40 to–20 ms), saliency shifted to frontal-central regions contralateral to the re-quired saccade, suggesting that features from these regions were most infor-mative during the preparation of the voluntary eye movement. This two-stepsequence—stimulus hemisphere first, then movement hemisphere—resemblespreviously described antisaccade dynamics [Moon et al., 2007], although hereit should be understood in terms of classifier-relevant features rather thandirect physiological signals. Compared to prosaccades, antisaccade saliencyshowed broader and stronger activations overall, consistent with the classi-fier drawing more information from medial frontal features, including thosealigned with SEF and anterior cingulate cortex, which are often linked toinhibitory control [Richards, 2013, Cieslik et al., 2016].In summary, the saliency maps indicated different spatial-temporal pat-terns of discriminatory information for prosaccades and antisaccades. Prosac-cades were characterized by a streamlined contralateral progression from oc-cipital to parietal to frontal features, whereas antisaccades showed a biphasicpattern with early stimulus-related and later movement-related discrimina-tory contributions. Both tasks involved occipital, parietal, and frontal fea-tures (with possible contributions from SEF), but differed in timing andhemispheric coordination. These findings highlight the distinct ways inwhich the classifier exploited EEG features to separate leftward and right-ward saccades, without implying direct measurement of underlying neuralgenerators.4.3.2 Time AnalysisOur saliency analysis reveals pronounced temporal and spatial features pre-dictive of saccade direction that align with known oculomotor physiology.Electrode averaged saliency map Figure 3.3 showed that, in prosaccades,the most informative time point occurred just before saccade onset (around–46.92 to –43.01 ms) when the saliency traces for leftward versus rightward704.3. Model Interpretationsaccades crossed in polarity. In antisaccades, a similar crossover emergedslightly later (saliency peaks –27.37 ms, maximal by –19.55 ms). This tim-ing matches classical presaccadic ERP findings: previous studies report acontralateral parietal positivity beginning 30–300 ms before saccade on-set [Richards, 2003, Becker et al., 1973, Csibra et al., 1997, Kurtzberg andVaughan Jr, 1980, Moster and Goldberg, 1990] and a sharp positive spike10–20 ms pre-saccade. In our data, the CNN-derived saliency signaturesappear to capture those subtle ERP components: the observed polarity re-versals likely reflect the contralateral buildup of preparatory potentials inopposite hemispheres for opposite saccade directions. Notably, the CNNcould decode movement direction from these patterns, implying a reliable,direction-specific lateralized signal in the final 50 ms of pre-saccadic activity.4.3.3 Electrode AnalysisFigure 3.4 illustrates a clear mirroring between leftward and rightward sac-cades in both tasks, closely matching the symmetry observed in the group-level saliency maps. This pattern is expected, as electrodes that positivelypredict one class in a binary task should contribute inversely to the other.The maps also reveal a strong lateralization: left-hemisphere sites contributemore positively to rightward saccades, while right-hemisphere sites con-tribute more positively to leftward saccades. In the antisaccade condition,this contralateral organization is accompanied by an additional ipsilateralfrontal contribution, a signal that may reflect the greater top-down controland attentional demands required to inhibit a reflexive response and gener-ate a voluntary saccade in the opposite direction. Analysis of the eight mostsalient electrodes further confirms these findings.The top-saliency electrodes for each condition fell predominantly in con-tralateral oculomotor regions. For prosaccades, the eight highest-saliencychannels for rightward saccades (e.g. F3, FC3, PO3, PO7 – all left-hemisphere)contrasted with those for leftward saccades (e.g. P6, P4, CP6, PO8 – allright-hemisphere). Antisaccade maps showed a similar contralateral bias:rightward antisaccades drew most saliency from left-hemisphere sites (e.g.C3, FC3, P3), whereas leftward antisaccades implicated right-hemispheresites (e.g. PO8, P4, CP4). These electrodes align broadly with known eye-movement regions: frontal channels near FC3/F3 (left) or FC2 (right) likelyindex the FEF or DLPFC, while parietal channels (e.g. P3/P4, PO7/PO8)correspond to PEF and extrastriate visual areas. This pattern is consistentwith decades of work showing that FEF, SEF, DLPFC, and parietal areasgenerate prosaccade and antisaccade [Richards, 2003].714.3. Model Interpretation4.3.4 Region Correspondence CheckTo quantify the overlap between salient features and brain regions known tobe associated with saccades, we compared each participant’s top-saliencyelectrodes to predefined ROIs. This analysis revealed systematic hemi-spheric asymmetries. In the prosaccade task, top channels for rightwardsaccades overlapped more with left-hemisphere eye-field ROIs than right-hemisphere ones (overlap units: left 30.50 vs. right 23.31; visual/SEF com-bined 26.88). By contrast, leftward prosaccades engaged the right hemi-sphere more (right 34.75 vs. left 14.88; visual/SEF 29.56). A similar trendappeared for antisaccades (Rightward: left 30.33 vs. right 22.67; Leftward:right 26.47 vs. left 20.00). The full breakdown of overlaps is provided inTables 3.8, 3.9, 3.10, and 3.11.When benchmarked against chance-level expectations, the ROI overlapsrevealed robust above-chance involvement of visual and oculomotor areas.For prosaccades, both P100 and SEF consistently exceeded their chance lev-els, with average counts of ∼11–15 compared to expected values of 7.8–9.8.Rightward prosaccades additionally showed above-chance contributions fromleft-hemisphere FEF, PEF, and DLPFC, consistent with contralateral re-cruitment, whereas leftward prosaccades strongly engaged right-hemispherePEF (18.8 vs. 7.8 at chance) while left-hemisphere ROIs remained near orbelow baseline.A similar pattern emerged for antisaccades. Rightward antisaccadesshowed above-chance overlap in P100, SEF, left PEF, and bilateral DLPFC,while leftward antisaccades produced marked overrepresentation in P100(17.7 vs. 7.8) and right PEF (17.3 vs. 7.8), again pointing to contralateraldominance. Notably, some ROIs—particularly ipsilateral FEFs—remainedat or below chance, reinforcing the lateralized nature of the effect.Taken together, these results demonstrate that the saliency maps high-lighted canonical saccade-related regions at rates two- to three-fold higherthan expected by random selection, with clear contralateral biases for left-versus rightward saccades. This pattern aligns with neurophysiological ev-idence that the left hemisphere preferentially supports rightward orienting,whereas the right hemisphere contributes more broadly to both directions ofattentional control [Shulman et al., 2010]. SEF, in particular, showed con-sistently above-chance involvement across all tasks, underscoring its centralrole in both prosaccade and antisaccade preparation. Although the abso-lute chance levels are approximate—owing to spatial correlations amongelectrodes and the convolutional structure of the models, which in turn af-fect the generation of saliency maps—the strong deviations from baseline724.4. Broader Implicationsindicate that the observed lateralized saliency reflects genuine functionalorganization of the oculomotor network rather than random dispersion.4.3.5 Saliency-Based Feature Space Reveals UnderlyingStructure in ERP DynamicsThe 100% separability of saccade directions in the simplified saliency-basedfeature space for both prosaccade and antisaccade conditions, as shownin Figures 3.5 and 3.7, confirms that the electrode-time points contribut-ing positively to leftward versus rightward classifications are distinct andseperable. Given that these features were directly extracted from saliencymaps—representations of the model’s internal decision-making—it is un-surprising that they enabled perfect discrimination in the prosaccade andantisaccade conditions. What is more revealing, however, is that the sameelectrode–timepoint pairs, when applied to raw ERP amplitudes, still yieldeda high degree of class separability (92% in prosaccade and 98% in antisac-cade). This suggests that the model’s saliency was not arbitrarily focusedbut had, in effect, discovered features within the ERP space that alreadyencode direction-specific information.The fact that ERP values alone, at specific electrode–time points, sup-ported robust classification demonstrates that the saliency analysis reducedthe original 1,600 features (25 time points × 64 electrodes) to a compact sub-set of 50 that perfectly separated leftward from rightward saccades. Thisindicates that the model effectively extract sparse features from a wholebrain recording that were discriminative for the task. The ability to isolatesuch a small set of highly informative features underscores the potential ofdeep learning as a tool for data-driven discovery of latent structure withinnoisy neural signals.Rather than highlighting arbitrary regions of high activation, the saliencymaps appear to surface dimensions within the ERP signal space that effec-tively support class separation. These dimensions, once revealed, provideclear and intuitive separation between conditions. This strengthens the ar-gument that model-derived salience can be used to guide targeted electro-physiological investigation and supports its utility as both an explanatoryand exploratory tool in cognitive neuroscience.4.4 Broader ImplicationsOur study contributes to ongoing developments at the intersection of cog-nitive neuroscience, EEG-based machine learning, and explainable AI. By734.5. Limitations and Future Directionsintegrating deep learning with high-resolution EEG and saliency analysis, weoffer new insights into the dynamic, lateralized neural processes underlyingsaccade and antisaccade planning. The spatiotemporal patterns observedin the final ∼100 ms prior to saccade onset suggest that direction-specificinformation emerges through a coordinated interplay between the occipi-tal, parietal, and frontal regions—an interaction that may be obscured bytraditional averaging methods.The methodological framework we developed—combining data-drivensaliency analysis, spatially aware model design, and tailored data augmenta-tion—enables decoding of fast, distributed neural processes. Our proposed3D convolutional architecture builds on classical filter-bank EEG modelswhile leveraging the 2D topography of the electrode grid to enhance spa-tial feature extraction. Together, these components provide a powerful andflexible system for studying neurocognitive dynamics with high temporalprecision.Importantly, our interpretability pipeline reveals not only which featuresdrive model decisions, but also where and when these features emerge in thebrain. The ability to attribute model predictions to specific spatiotemporalpatterns strengthens confidence in the results and enables alignment withknown neurophysiological mechanisms. This makes the approach well-suitedfor guiding future hypothesis-driven research and for refining experimentaltargeting of specific brain circuits or time windows.In practical terms, the high classification performance of our model,combined with its interpretability and minimal preprocessing requirements,demonstrates its potential for real-world applications. As an end-to-endsystem, our pipeline facilitates EEG-based classification without the needfor extensive domain expertise, making it accessible for use in assistivebrain–computer interfaces, cognitive monitoring, and clinical diagnostics.Ultimately, this work highlights how interpretable deep learning models canbridge the gap between complex neural data and actionable cognitive in-sights.4.5 Limitations and Future DirectionsDespite the contributions of this study, several limitations remain that offervaluable direction for future research.Data and model constraints. Our approach utilized a convolutionalneural network with fixed input dimensions, necessitating strict control overthe temporal window of the input EEG data. To avoid contamination from744.5. Limitations and Future Directionsmotion artifacts and maintain precise alignment with saccade onset, we con-strained the analysis to a narrow pre-saccadic interval in stimulus-lockedprosaccade trials. For consistency, the same pre-saccadic windowing wasapplied to saccade-locked trials, avoiding any backward extension from tar-get onset.Consequently, all epochs were trimmed to a fixed duration, which ledto the exclusion of shorter trials and a reduction in the amount of usabledata. This presented a trade-off between the length of the analysis windowand the number of trials retained. On one hand, longer windows may cap-ture more informative temporal dynamics; on the other hand, reducing thenumber of training samples can degrade model performance. Since post-hocinterpretability techniques—such as saliency map analysis—are only mean-ingful when applied to well-performing models, we selected a window ofapproximately 100 ms as a compromise to preserve both model accuracyand explainability.Limitations of EEG. While EEG provides excellent temporal reso-lution and accessibility for non-invasive brain monitoring, it is inherentlylimited in its ability to detect signals from deep brain structures involvedin saccade generation. For instance, the anterior cingulate cortex (ACC) isknown to play a role in the cognitive control of saccadic eye movements [Polliet al., 2005], but its depth makes it difficult to reliably capture its activitywith EEG. Similarly, critical subcortical structures such as the SC [Lynchand Tian, 2006], which play a crucial role in saccade planning and execu-tion, remain largely inaccessible to surface EEG recordings. Moreover, evenwhen activity is recorded from cortical regions, EEG cannot differentiatebetween excitatory and inhibitory postsynaptic potentials. This ambiguitycomplicates the interpretation of signal origins and limits our understand-ing of the underlying neural mechanisms—particularly in contexts whereinhibitory control (e.g., in antisaccades) is essential.Binary classification and directional symmetry. This study em-ployed binary classification to distinguish between defined classes. In suchsettings, the model may learn decision boundaries that are more influencedby one class than the other. As a result, saliency maps for the two classesoften appear as approximate opposites—a pattern we observed in our re-sults. This symmetry can complicate interpretation: for example, a nega-tive saliency value in the rightward class may either indicate that an increasein that feature reduces the likelihood of a rightward prediction, or that itcontributes positively to a leftward prediction. Although mathematicallyequivalent in a binary classifier, these scenarios may correspond to differentneurophysiological processes. Such ambiguity highlights the limitations of754.5. Limitations and Future Directionsinterpreting a single class’s saliency map in isolation.To address this, we recommend interpreting saliency values within thecontext of both classes: a positive saliency should be understood as sup-porting the current class while detracting from the opposite class, and viceversa. This dual-class framing is essential for meaningful interpretation inbinary classification, particularly in domains like neuroscience where signalpolarity carries functional significance.Ambiguities in saliency interpretation. Although saliency mapsreveal which input features most influence a model’s predictions, their in-terpretation in a neurophysiological context remains limited. A positivesaliency value indicates that a feature contributes positively to the model’soutput, but it does not specify whether the model is responding to an in-crease in a positive-going deflection or a reduction in a negative-going de-flection. While both represent an increase in the raw signal value, these twocases are not equivalent in EEG interpretation—e.g., a larger P300 compo-nent versus a diminished N200 may have very different functional implica-tions. This ambiguity complicates the alignment of model-derived explana-tions with well-established neuroscientific interpretations of EEG waveforms.Model generalizability. Our study was limited to a controlled datasetfrom a specific experimental paradigm. To ensure the robustness and practi-cal applicability of our approach, future work should validate both the modelarchitecture and interpretability framework across diverse EEG datasets,participant populations, and task contexts.Future directions in architecture design. There remains significantroom for improving performance and biological fidelity by exploring alter-native neural architectures. Recurrent models such as RNNs [Elman, 1990]and LSTMs [Hochreiter and Schmidhuber, 1997] are well-suited for modelingtemporal dynamics in EEG, while transformer-based models [Vaswani et al.,2017] may capture long-range spatial dependencies across electrodes andtime points. Additionally, integrating hybrid models that combine CNNs forlocal spatial processing with attention mechanisms for global feature selec-tion may enhance both accuracy and interpretability—though such modelsshould ideally be trained on larger datasets.Overall, the current work demonstrates the promise of explainable deeplearning for uncovering dynamic neural mechanisms underlying saccade con-trol. Addressing these limitations through methodological innovation andarchitectural advances will help further close the gap between complex EEGdata and cognitive-level understanding.76BibliographyZahra Amini, Vahid Abootalebi, and Mohammad T Sadeghi. A comparativestudy of feature extraction methods in p300 detection. In 2010 17th Ira-nian Conference of Biomedical Engineering (ICBME), pages 1–4. IEEE,2010.Marco Ancona, Enea Ceolini, Cengiz O¨ztireli, and Markus Gross. Towardsbetter understanding of gradient-based attribution methods for deep neu-ral networks. arXiv preprint arXiv:1711.06104, 2017.Kai Keng Ang, Zheng Yang Chin, Haihong Zhang, and Cuntai Guan. Filterbank common spatial pattern (fbcsp) in brain-computer interface. In2008 IEEE international joint conference on neural networks (IEEE worldcongress on computational intelligence), pages 2390–2397. IEEE, 2008.Eduardo A Aponte, Dario Scho¨bi, Klaas E Stephan, and Jakob Heinzle.The stochastic early reaction, inhibition, and late action (seria) model forantisaccades. PLoS computational biology, 13(8):e1005692, 2017.Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua JReicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, GregCorrado, et al. End-to-end lung cancer screening with three-dimensionaldeep learning on low-dose chest computed tomography. Nature medicine,25(6):954–961, 2019.Sylvain Baillet. Magnetoencephalography for brain electrophysiology andimaging. Nature neuroscience, 20(3):327–339, 2017.Jason JS Barton, Mariya V Cherkasova, Kristen Lindgren, Donald C Goff,James M Intriligator, and Dara S Manoach. Antisaccades and task switch-ing: studies of control processes in saccadic function in normal subjectsand schizophrenic patients. Annals of the New York Academy of Sciences,956(1):250–263, 2002.77BibliographyJason JS Barton, Cathleen Greenzang, Rebecca Hefter, Jay Edelman, andDara S Manoach. Switching, plasticity, and prediction in a saccadic task-switch paradigm. Experimental Brain Research, 168(1):76–87, 2006.W Becker, O Hoehne, K Iwase, and HH Kornhuber. Cerebral and ocularmuscle potentials preceding voluntary eye movements in man. Electroen-cephalogr. Clin. Neurophysiol. Suppl, 33:99–104, 1973.Sonya Bells, Silvia L Isabella, Donald C Brien, Brian C Coe, Douglas PMunoz, Donald J Mabbott, and Douglas O Cheyne. Mapping neuraldynamics underlying saccade preparation and execution and their relationto reaction time and direction errors. Human brain mapping, 41(7):1934–1949, 2020.Shlomo Bentin, Truett Allison, Aina Puce, Erik Perez, and Gregory Mc-Carthy. Electrophysiological studies of face perception in humans. Journalof cognitive neuroscience, 8(6):551–565, 1996.Aaron Berk, Gulcenur Ozturan, Parsa Delavari, David Maberley, O¨zgu¨rYılmaz, and Ipek Oruc. Learning from few examples: Classifying sexfrom retinal images via deep learning. arXiv preprint arXiv:2207.09624,2022.Alexander Binder, Gre´goire Montavon, Sebastian Lapuschkin, Klaus-RobertMu¨ller, and Wojciech Samek. Layer-wise relevance propagation for neuralnetworks with local renormalization layers. In Artificial Neural Networksand Machine Learning–ICANN 2016: 25th International Conference onArtificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Pro-ceedings, Part II 25, pages 63–71. Springer, 2016.Benjamin Blankertz, K-R Muller, Gabriel Curio, Theresa M Vaughan, Ger-win Schalk, Jonathan R Wolpaw, Alois Schlogl, Christa Neuper, GertPfurtscheller, Thilo Hinterberger, et al. The bci competition 2003:progress and perspectives in detection and discrimination of eeg singletrials. IEEE transactions on biomedical engineering, 51(6):1044–1051,2004.Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe,and Klaus-Robert Muller. Optimizing spatial filters for robust eeg single-trial analysis. IEEE Signal processing magazine, 25(1):41–56, 2007.78BibliographyBenjamin Blankertz, Steven Lemm, Matthias Treder, Stefan Haufe, andKlaus-Robert Mu¨ller. Single-trial analysis and classification of erp com-ponents—a tutorial. NeuroImage, 56(2):814–825, 2011.Davide Borra, Silvia Fantozzi, and Elisa Magosso. Interpretable andlightweight convolutional neural network for eeg decoding: Applicationto movement execution and imagination. Neural Networks, 129:55–74,2020.Davide Borra, Silvia Fantozzi, and Elisa Magosso. A lightweight multi-scale convolutional neural network for p300 decoding: analysis of trainingstrategies and uncovering of network decision. Frontiers in Human Neu-roscience, 15:655840, 2021.Annelies Broerse, Trevor J Crawford, and Johan A den Boer. Parsing cogni-tion in schizophrenia using saccadic eye movements: a selective overview.Neuropsychologia, 39(7):742–756, 2001.Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Ka-plan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sas-try, Amanda Askell, et al. Language models are few-shot learners. Ad-vances in neural information processing systems, 33:1877–1901, 2020.George A Buzzell, Yanbin Niu, Selin Aviyente, and Edward Bernat. A prac-tical introduction to eeg time-frequency principal components analysis(tf-pca). Developmental cognitive neuroscience, 55:101114, 2022.Edna C Cieslik, Isabelle Seidler, Angela R Laird, Peter T Fox, and Simon BEickhoff. Different involvement of subregions within dorsal premotor andmedial frontal cortex for pro-and antisaccades. Neuroscience & biobehav-ioral reviews, 68:256–269, 2016.Maurizio Corbetta and Gordon L Shulman. Control of goal-directed andstimulus-driven attention in the brain. Nature reviews neuroscience, 3(3):201–215, 2002.Maurizio Corbetta and Gordon L Shulman. Spatial neglect and attentionnetworks. Annual review of neuroscience, 34(1):569–599, 2011.Alexander Craik, Yongtian He, and Jose L Contreras-Vidal. Deep learningfor electroencephalogram (eeg) classification tasks: a review. Journal ofneural engineering, 16(3):031001, 2019.79BibliographyFrederic Crevecoeur and Konrad P Kording. Saccadic suppression as a per-ceptual consequence of efficient sensorimotor estimation. Elife, 6:e25073,2017.Gergely Csibra, Mark H Johnson, and Leslie A Tucker. Attention and ocu-lomotor control: A high-density erp study of the gap effect. Neuropsy-chologia, 35(6):855–865, 1997.Christine A Curcio, Kenneth R Sloan, Robert E Kalina, and Anita E Hen-drickson. Human photoreceptor topography. Journal of comparative neu-rology, 292(4):497–523, 1990.Vassilis Cutsuridis. Behavioural and computational varieties of responseinhibition in eye movements. Philosophical Transactions of the RoyalSociety B: Biological Sciences, 372(1718):20160196, 2017a.Vassilis Cutsuridis. A neural accumulator model of antisaccade performanceof healthy controls and obsessive-compulsive disorder patients. 2017b.Navneet Dalal and Bill Triggs. Histograms of oriented gradients for humandetection. In 2005 IEEE computer society conference on computer visionand pattern recognition (CVPR’05), volume 1, pages 886–893. Ieee, 2005.Ennio De Renzi. Oculomotor disturbances in hemispheric disease. In Neu-ropsychology of Eye Movement, pages 177–199. Psychology Press, 2013.Parsa Delavari, Gulcenur Ozturan, Lei Yuan, O¨zgu¨r Yilmaz, and Ipek Oruc.Artificial intelligence, explainability, and the scientific method: A proof-of-concept study on novel retinal biomarker discovery. PNAS Nexus, 2(9):pgad290, 2023. doi: 10.1093/pnasnexus/pgad290.Parsa Delavari, Leonid Sigal, and Ipek Oruc. Cnn-extracted features gen-erate synthetic fmri responses to unseen images. Vision Research, 234:108641, 2025.Arnaud Delorme, Terrence Sejnowski, and Scott Makeig. Enhanced detec-tion of artifacts in eeg data using higher-order statistics and independentcomponent analysis. Neuroimage, 34(4):1443–1449, 2007.Joseph FX DeSouza, Ravi S Menon, and Stefan Everling. Preparatory set as-sociated with pro-saccades and anti-saccades in humans investigated withevent-related fmri. Journal of neurophysiology, 89(2):1016–1023, 2003.80BibliographyJacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert:Pre-training of deep bidirectional transformers for language understand-ing. In Proceedings of the 2019 conference of the North American chapterof the association for computational linguistics: human language technolo-gies, volume 1 (long and short papers), pages 4171–4186, 2019.Francesco Di Russo, Ant´ıgona Mart´ınez, Martin I Sereno, Sabrina Pitzalis,and Steven A Hillyard. Cortical sources of the early components of thevisual evoked potential. Human brain mapping, 15(2):95–111, 2002.Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn,Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min-derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16words: Transformers for image recognition at scale. arXiv preprintarXiv:2010.11929, 2020.John E Dowling. The retina: an approachable part of the brain. HarvardUniversity Press, 1987.Yang Du, Yongling Xu, Xiaoan Wang, Li Liu, and Pengcheng Ma. Eegtemporal–spatial transformer for person identification. Scientific Reports,12(1):14378, 2022.Stefan Duschek, Paulina Piwkowski, Thomas Rainer, Johannes Vorwerk,Laurine Riml, and Ulrich Ettinger. Neural correlates of proactive andreactive control investigated using a novel precued antisaccade paradigm.Psychophysiology, 62(2):e70015, 2025.Jeffrey L Elman. Finding structure in time. Cognitive science, 14(2):179–211, 1990.Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter,Helen M Blau, and Sebastian Thrun. Dermatologist-level classification ofskin cancer with deep neural networks. nature, 542(7639):115–118, 2017.S Everling, P Krappmann, and H Flohr. Cortical potentials preceding pro-and antisaccades in man. Electroencephalography and clinical Neurophys-iology, 102(4):356–362, 1997.Stefan Everling and Burkhart Fischer. The antisaccade: a review of basicresearch and clinical studies. Neuropsychologia, 36(9):885–899, 1998.81BibliographyAmr Farahat, Christoph Reichert, Catherine M Sweeney-Reed, and Her-mann Hinrichs. Convolutional neural networks for decoding of covertattention focus and saliency maps for eeg feature visualization. Journalof neural engineering, 16(6):066010, 2019.John M Findlay and Iain D Gilchrist. Active vision: The psychology oflooking and seeing. Number 37. Oxford University Press, 2003.Kristen A Ford, Herbert C Goltz, Matthew RG Brown, and Stefan Ev-erling. Neural processes associated with antisaccade task performanceinvestigated with event-related fmri. Journal of neurophysiology, 94(1):429–440, 2005.Kunihiko Fukushima. Neocognitron: A self-organizing neural network modelfor a mechanism of pattern recognition unaffected by shift in position.Biological cybernetics, 36(4):193–202, 1980.Shintaro Funahashi, Matthew V Chafee, and Patricia S Goldman-Rakic.Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task. Nature, 365(6448):753–756, 1993.Neeraj J Gandhi and Edward L Keller. Spatial distribution and dischargecharacteristics of superior colliculus neurons antidromically activated fromthe omnipause region in monkey. Journal of neurophysiology, 78(4):2221–2225, 1997.Leon A Gatys, Alexander S Ecker, and Matthias Bethge. Image style transferusing convolutional neural networks. In Proceedings of the IEEE confer-ence on computer vision and pattern recognition, pages 2414–2423, 2016.B Gaymard, CJ Ploner, S Rivaud-Pechoux, and C Pierrot-Deseilligny. Thefrontal eye field is involved in spatial short-term memory but not in re-flexive saccade inhibition. Experimental Brain Research, 129(2):288–301,1999.Bertrand Gaymard, Christoph J Ploner, S1 Rivaud, AI Vermersch, andCharles Pierrot-Deseilligny. Cortical control of saccades. Experimentalbrain research, 123(1):159–163, 1998.Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, DavidWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gener-ative adversarial nets. Advances in neural information processing systems,27, 2014.82BibliographyDiane C Gooding and Michele A Basso. The tell-tale tasks: a review ofsaccadic research in psychiatric patient populations. Brain and cognition,68(3):371–390, 2008.Peter Gouras. Oculomotor system. Princiles of neurol science, pages 571–583, 1985.Tijl Grootswagers, Susan G Wardle, and Thomas A Carlson. Decodingdynamic brain patterns from evoked responses: A tutorial on multivariatepattern analysis applied to time series neuroimaging data. Journal ofcognitive neuroscience, 29(4):677–697, 2017.Umut Gu¨c¸lu¨ and Marcel AJ Van Gerven. Deep neural networks reveal agradient in the complexity of neural representations across the ventralstream. Journal of Neuroscience, 35(27):10005–10014, 2015.D Guitton, Henry A Buchtel, and RM Douglas. Frontal lobe lesions inman cause difficulties in suppressing reflexive glances and in generatinggoal-directed saccades. Experimental brain research, 58(3):455–472, 1985.Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu,Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner,Tom Madams, Jorge Cuadros, et al. Development and validation of a deeplearning algorithm for detection of diabetic retinopathy in retinal fundusphotographs. jama, 316(22):2402–2410, 2016.Patrick Haggard and Martin Eimer. On the relation between brain potentialsand the awareness of voluntary movements. Experimental brain research,126:128–133, 1999.Peter E Hallett. Primary and secondary saccades to goals defined by in-structions. Vision research, 18(10):1279–1296, 1978.Matti Ha¨ma¨la¨inen, Riitta Hari, Risto J Ilmoniemi, Jukka Knuutila, andOlli V Lounasmaa. Magnetoencephalography—theory, instrumentation,and applications to noninvasive studies of the working human brain. Re-views of modern Physics, 65(2):413, 1993.John-Dylan Haynes and Geraint Rees. Decoding mental states from brainactivity in humans. Nature reviews neuroscience, 7(7):523–534, 2006.Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residuallearning for image recognition. In Proceedings of the IEEE conference oncomputer vision and pattern recognition, pages 770–778, 2016.83BibliographyKaiming He, Georgia Gkioxari, Piotr Dolla´r, and Ross Girshick. Mask r-cnn.In Proceedings of the IEEE international conference on computer vision,pages 2961–2969, 2017.JM Henderson, A Hollingworth, and G Underwood. Eye guidance in readingand scene perception. chap. Eye Movements During Scene Viewing: AnOverview). New York: Elsevier, 1998.John M Henderson. Human gaze control during real-world scene perception.Trends in cognitive sciences, 7(11):498–504, 2003.Anita Hendrickson. Organization of the adult primate fovea. In Maculardegeneration, pages 1–23. Springer, 2005.Sepp Hochreiter and Ju¨rgen Schmidhuber. Long short-term memory. Neu-ral Computation, 9(8):1735–1780, 11 1997. ISSN 0899-7667. doi: 10.1162/neco.1997.9.8.1735. URL https://doi.org/10.1162/neco.1997.9.8.1735.Khondoker Murad Hossain, Md Ariful Islam, Shahera Hossain, Anton Ni-jholt, and Md Atiqur Rahman Ahad. Status of deep learning for eeg-basedbrain–computer interface applications. Frontiers in computational neuro-science, 16:1006763, 2023.Ramy Hussein, Hamid Palangi, Rabab K Ward, and Z Jane Wang. Opti-mized deep neural network architecture for robust detection of epilepticseizures using eeg signals. Clinical Neurophysiology, 130(1):25–37, 2019.Samuel B Hutton and Ulrich Ettinger. The antisaccade task as a researchtool in psychopathology: a critical review. Psychophysiology, 43(3):302–313, 2006.SB Hutton. Improved antisaccade performance in schizophrenia with risperi-done. Journal of Neurology, Neurosurgery & Psychiatry, 72(4):429–429,2002.Todd Kamensek, Grace Iarocci, and Ipek Oruc. Atypical daily visual expo-sure to faces in adults with autism spectrum disorder. Current Biology,34(18):4197–4208, 2024.Ard Kastrati, Martyna Beata Plomecka, Roger Wattenhofer, and NicolasLanger. Using deep learning to classify saccade direction from brain ac-tivity. In ACM Symposium on Eye Tracking Research and Applications,pages 1–6, 2021.84BibliographyLiam D Kaufman, Jay Pratt, Brian Levine, and Sandra E Black. Anti-saccades: a probe into the dorsolateral prefrontal cortex in alzheimer’sdisease. a critical review. Journal of Alzheimer’s Disease, 19(3):781–793,2010.M. Khademi, H. Y. Im, and I. Oruc. Decoding neural representations inhigher-order visual cortex: An explainable ai-based fmri analysis.Jean-Re´mi King and Stanislas Dehaene. Characterizing the dynamics ofmental representations: the temporal generalization method. Trends incognitive sciences, 18(4):203–210, 2014.Damian Koevoet, Laura Van Zantwijk, Marnix Naber, Sebastiaan Mathoˆt,Stefan Van der Stigchel, and Christoph Strauch. Effort drives saccadeselection. ELife, 13:RP97760, 2025.Tadeusz W Kononowicz and Trevor B Penney. The contingent negativevariation (cnv): Timing isn’t everything. Current Opinion in BehavioralSciences, 8:231–237, 2016.Minoru Koyama, Isao Hasegawa, Takahiro Osada, Yusuke Adachi, KiyoshiNakahara, and Yasushi Miyashita. Functional magnetic resonance imagingof macaque monkeys performing visually guided saccade tasks: compari-son of cortical eye fields with humans. Neuron, 41(5):795–807, 2004.Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet clas-sification with deep convolutional neural networks. Advances in neuralinformation processing systems, 25, 2012.Lisa M Kroell and Martin Rolfs. Foveal vision anticipates defining featuresof eye movement targets. Elife, 11:e78106, 2022.Louisa V Kulke, Janette Atkinson, and Oliver Braddick. Neural differencesbetween covert and overt attention studied using eeg with simultaneousremote eye tracking. Frontiers in human neuroscience, 10:592, 2016.M Ku¨mmerer, TSA Wallis, and M Bethge. Deepgaze ii: Reading fixationsfrom deep features trained on object recognition. arxiv. arXiv preprintarXiv:1610.01563, 2016.Kenji Kunita and Katsuo Fujiwara. Influence of sports experience on dis-tribution of pro-saccade reaction time under gap condition. Journal ofPhysiological Anthropology, 41(1):4, 2022.85BibliographyDiane Kurtzberg and Herbert G Vaughan Jr. Differential topography ofhuman eye movement potentials preceding visually triggered and self-initiated saccades. Progress in brain research, 54:203–208, 1980.Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gor-don, Chou P Hung, and Brent J Lance. Eegnet: a compact convolutionalneural network for eeg-based brain–computer interfaces. Journal of neuralengineering, 15(5):056013, 2018.Yann LeCun, Le´on Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE,86(11):2278–2324, 1998. doi: 10.1109/5.726791. URL http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf.Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature,521(7553):436–444, 2015.Javier Leo´n, Juan Jose´ Escobar, Andre´s Ortiz, Julio Ortega, Jesu´s Gonza´lez,Pedro Mart´ın-Smith, John Q Gan, and Miguel Damas. Deep learning foreeg-based motor imagery classification: Accuracy-cost trade-off. Plos one,15(6):e0234178, 2020.Xiaoguang Li, Yaqi Chu, and Xuejian Wu. 3d convolutional neural networkbased on spatial-spectral feature pictures learning for decoding motor im-agery eeg signal. Frontiers in Neurorobotics, 18:1485640, 2024.Marilia Lopes, Raymundo Cassani, and Tiago H Falk. Using cnn saliencymaps and eeg modulation spectra for improved and more interpretablemachine learning-based alzheimer’s disease diagnosis. Computational In-telligence and Neuroscience, 2023(1):3198066, 2023.Fabien Lotte, Laurent Bougrain, Andrzej Cichocki, Maureen Clerc, MarcoCongedo, Alain Rakotomamonjy, and Florian Yger. A review of clas-sification algorithms for eeg-based brain–computer interfaces: a 10 yearupdate. Journal of neural engineering, 15(3):031005, 2018.David G Lowe. Distinctive image features from scale-invariant keypoints.International journal of computer vision, 60:91–110, 2004.Steven J Luck. An introduction to the event-related potential technique. MITpress, 2014.86BibliographyJC Lynch and J-R Tian. Cortico-cortical networks and cortico-subcorticalloops for the higher control of eye movements. Progress in brain research,151:461–501, 2006.Scott Makeig, Stefan Debener, Julie Onton, and Arnaud Delorme. Miningevent-related brain dynamics. Trends in cognitive sciences, 8(5):204–210,2004.Paras Malik, Monika Pathania, Vyas Kumar Rathaur, et al. Overview ofartificial intelligence in medicine. Journal of family medicine and primarycare, 8(7):2328–2331, 2019.Jennifer E McDowell, Kara A Dyckman, Benjamin P Austin, and Brett AClementz. Neurophysiology and neuroanatomy of reflexive and volitionalsaccades: evidence from studies of humans. Brain and cognition, 68(3):255–270, 2008.M-Marchsel Mesulam. A cortical network for directed attention and uni-lateral neglect. Annals of Neurology: Official Journal of the AmericanNeurological Association and the Child Neurology Society, 10(4):309–325,1981.Sebastian Mika, Gunnar Ratsch, Jason Weston, Bernhard Scholkopf, andKlaus-Robert Mullers. Fisher discriminant analysis with kernels. In Neuralnetworks for signal processing IX: Proceedings of the 1999 IEEE signalprocessing society workshop (cat. no. 98th8468), pages 41–48. Ieee, 1999.So Young Moon, Jason JS Barton, Szymon Mikulski, Frida E Polli,Matthew S Cain, Mark Vangel, Matti S Ha¨ma¨la¨inen, and Dara SManoach. Where left becomes right: a magnetoencephalographic studyof sensorimotor transformation for antisaccades. Neuroimage, 36(4):1313–1323, 2007.Mark L Moster and Gary Goldberg. Topography of scalp potentials preced-ing self-initiated saccades. Neurology, 40(4):644–644, 1990.Douglas P Munoz and Stefan Everling. Look away: the anti-saccade taskand the voluntary control of eye movement. Nature Reviews Neuroscience,5(3):218–228, 2004.Douglas P Munoz, Irene T Armstrong, Karen A Hampton, and Kimberly DMoore. Altered control of visual fixation and saccadic eye movements inattention-deficit hyperactivity disorder. Journal of neurophysiology, 90(1):503–514, 2003.87BibliographyThomas Naselaris, Kendrick N Kay, Shinji Nishimoto, and Jack L Gallant.Encoding and decoding in fmri. Neuroimage, 56(2):400–410, 2011.Thomas Nyffeler, Rene´ M Mu¨ri, Y Bucher-Ottiger, Charles Pierrot-Deseilligny, Bertrand Gaymard, and Sophie Rivaud-Pechoux. Inhibitorycontrol of the human dorsolateral prefrontal cortex during the anti-saccadeparadigm- a transcranial magnetic stimulation study. European Journalof Neuroscience, 26(5):1381–1385, 2007.Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. On the difficulty oftraining recurrent neural networks. In International conference on ma-chine learning, pages 1310–1318. Pmlr, 2013.CH Pierrot-Deseilligny, CJ Ploner, RM Mu¨ri, B Gaymard, and S Rivaud-Pechoux. Effects of cortical lesions on saccadic: eye movements in humans.Annals of the New York Academy of Sciences, 956(1):216–229, 2002.Frida E Polli, Jason JS Barton, Matthew S Cain, Katharine N Thakkar,Scott L Rauch, and Dara S Manoach. Rostral and dorsal anterior cingu-late cortex make dissociable contributions during antisaccade error com-mission. Proceedings of the National Academy of Sciences, 102(43):15700–15705, 2005.Keith Rayner. Eye movements and attention in reading, scene perception,and visual search. The quarterly journal of experimental psychology, 62(8):1457–1506, 2009.Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: To-wards real-time object detection with region proposal networks. Advancesin neural information processing systems, 28, 2015.John E Richards. Cortical sources of event-related potentials in the prosac-cade and antisaccade task. Psychophysiology, 40(6):878–894, 2003.John E Richards. Cortical sources of erp in prosaccade and antisaccadeeye movements using realistic source models. Frontiers in systems neuro-science, 7:27, 2013.S Rivaud, RM Mu¨ri, B Gaymard, AI Vermersch, and C Pierrot-Deseilligny.Eye movement disorders after frontal eye field lesions in humans. Experi-mental Brain Research, 102(1):110–120, 1994.Ruth Rosenholtz. Capabilities and limitations of peripheral vision. Annualreview of vision science, 2(1):437–457, 2016.88BibliographyCynthia Rudin. Stop explaining black box machine learning models for highstakes decisions and use interpretable models instead. Nature machineintelligence, 1(5):206–215, 2019.Peter H Schiller, Sean D True, and Janet L Conway. Deficits in eye move-ments following frontal eye-field and superior colliculus ablations. Journalof neurophysiology, 44(6):1175–1189, 1980.Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Do-minique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger,Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball.Deep learning with convolutional neural networks for eeg decoding andvisualization. Human brain mapping, 38(11):5391–5420, 2017.Denis Schluppeck, Paul Glimcher, and David J Heeger. Topographic orga-nization for delayed saccades in human posterior parietal cortex. Journalof neurophysiology, 94(2):1372–1384, 2005.Catriona L Scrivener and Arran T Reader. Variability of eeg electrodepositions and their underlying brain regions: visualizing gel artifacts froma simultaneous eeg-fmri dataset. Brain and behavior, 12(2):e2476, 2022.MARK A Segraves. Activity of monkey frontal eye field neurons projectingto oculomotor regions of the pons. Journal of neurophysiology, 68(6):1967–1985, 1992.Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, RamakrishnaVedantam, Devi Parikh, and Dhruv Batra. Grad-cam: visual explanationsfrom deep networks via gradient-based localization. International journalof computer vision, 128:336–359, 2020.MI Sereno, Sabrina Pitzalis, and Antigona Martinez. Mapping of contralat-eral space in retinotopic coordinates by a parietal cortical area in humans.Science, 294(5545):1350–1354, 2001.Neha Sharma, Avinash Upadhyay, Manoj Sharma, and Amit Singhal. Deeptemporal networks for eeg-based motor imagery recognition. ScientificReports, 13(1):18813, 2023.BL Shook, M Schlag-Rey, and J Schlag. Primate supplementary eye field: I.comparative aspects of mesencephalic and pontine connections. Journalof Comparative Neurology, 301(4):618–642, 1990.89BibliographyAvanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning impor-tant features through propagating activation differences. In Internationalconference on machine learning, pages 3145–3153. PMlR, 2017.Gordon L. Shulman, Daniel L. Pope, Serguei V. Astafiev, Michael P.McAvoy, Abraham Z. Snyder, and Maurizio Corbetta. Right hemispheredominance during spatial selective attention and target detection occursoutside the dorsal frontoparietal network. Journal of Neuroscience, 30(10):3640–3651, Mar 2010. doi: 10.1523/JNEUROSCI.4085-09.2010.Karen Simonyan and Andrew Zisserman. Very deep convolutional networksfor large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside con-volutional networks: Visualising image classification models and saliencymaps. arXiv preprint arXiv:1312.6034, 2013.Anastasia Stolzenberg, Mahmoud Khademi, Todd Kamensek, and IpekOruc. How many unique faces do we see in a typical day? Journalof Vision, 22(14):4468–4468, 2022. doi: 10.1167/jov.22.14.4468.Irene Sturm, Sebastian Lapuschkin, Wojciech Samek, and Klaus-RobertMu¨ller. Interpretable deep neural networks for single-trial eeg classifi-cation. Journal of neuroscience methods, 274:141–145, 2016.Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attributionfor deep networks. In International conference on machine learning, pages3319–3328. PMLR, 2017.Avirath Sundaresan, Brian Penchina, Sean Cheong, Victoria Grace, AntoniValero-Cabre´, and Adrien Martel. Evaluating deep learning eeg-basedmental stress classification in adolescents with autism for breathing en-trainment bci. Brain Informatics, 8(1):13, 2021.Selma Supek and Cheryl J Aine. Magnetoencephalography. Springer, 2016.Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and AndrewRabinovich. Going deeper with convolutions. In Proceedings of the IEEEconference on computer vision and pattern recognition, pages 1–9, 2015.Yousef Rezaei Tabar and Ugur Halici. A novel deep learning approach forclassification of eeg motor imagery signals. Journal of neural engineering,14(1):016003, 2016.90BibliographyYaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf. Deep-face: Closing the gap to human-level performance in face verification.In Proceedings of the IEEE conference on computer vision and patternrecognition, pages 1701–1708, 2014.Roger B Tootell, Eugene Switkes, Martin S Silverman, and Susan L Hamil-ton. Functional anatomy of macaque striate cortex. ii. retinotopic orga-nization. Journal of neuroscience, 8(5):1531–1568, 1988.Philip Tseng, Mu-Chen Wang, Yu-Hui Lo, and Chi-Hung Juan. Anodal andcathodal tdcs over the right frontal eye fields impacts spatial probabilityprocessing differently in pro-and anti-saccades. Frontiers in neuroscience,12:421, 2018.Tomas Uktveris and Vacius Jusas. Comparison of feature extraction methodsfor eeg bci classification. In International Conference on Information andSoftware Technologies, pages 81–92. Springer, 2015.Elnaz Vafaei and Mohammad Hosseini. Transformers in eeg analysis: Areview of architectures and applications in motor imagery, seizure, andemotion classification. Sensors, 25(5):1293, 2025.Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all youneed. Advances in neural information processing systems, 30, 2017.L Vega-Escobar, AE Castro-Ospina, and L Duque-Mun˜oz. Feature extrac-tion schemes for bci systems. In 2015 20th symposium on signal processing,images and computer vision (STSIVA), pages 1–6. IEEE, 2015.Josefine Waldthaler, Lena Stock, Justus Student, Johanna Sommerkorn,Stefan Dowiasch, and Lars Timmermann. Antisaccades in parkinson’sdisease: A meta-analysis. Neuropsychology Review, 31(4):628–642, 2021.Jingxin Wang, Jing Tian, Rong Wang, and Valerie Benson. Increased at-tentional focus modulates eye movements in a mixed antisaccade task foryounger and older adults. PloS one, 8(4):e61566, 2013.Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, andZhongming Liu. Neural encoding and decoding with deep learning fordynamic natural vision. Cerebral cortex, 28(12):4136–4160, 2018.91BibliographyDaniel LK Yamins and James J DiCarlo. Using goal-driven deep learningmodels to understand sensory cortex. Nature neuroscience, 19(3):356–365,2016.Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, DarrenSeibert, and James J DiCarlo. Performance-optimized hierarchical mod-els predict neural responses in higher visual cortex. Proceedings of thenational academy of sciences, 111(23):8619–8624, 2014.Yilong Yang, Qingfeng Wu, Yazhen Fu, and Xiaowei Chen. Continuousconvolutional neural network with 3d input for eeg-based emotion recog-nition. In Neural Information Processing: 25th International Conference,ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceed-ings, Part VII 25, pages 433–443. Springer, 2018.Alpaslan Yılmaz, Cem Su¨er, and C¸ig˘dem O¨zesmi. Dependence of presaccadiccorticak potentials on the predictability of stimulus. Journal of ClinicalPractice and Research, 28(3):138, 2006.Mingsha Zhang and Shabtai Barash. Neuronal switching of sensorimotortransformations for antisaccades. Nature, 408(6815):971–975, 2000.Y Zhao and D Chen. Expression eeg multimodal emotion recognition methodbased on the bidirectional lstm and attention mechanism computationaland mathematical methods in medicine 2021, 2021.Bolei Zhou, Aditya Khosla, Agata Lapedriza, Antonio Torralba, and AudeOliva. Places: An image database for deep scene understanding. arXivpreprint arXiv:1610.02055, 2016.92
Thesis/Dissertation
eng
Neuroscience
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Oruc, Ipek
Predicting eye movement direction from pre-saccadic EEG : a deep learning and explainable AI approach to investigating the neural basis of motor planning
Text