Predicting eye movement direction from pre-saccadic EEG : a deep learning and explainable AI approach to investigating the neural basis of motor planning
department or school: Medicine, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Izadkhah, Mojan
issued: 2025-10-20T21:11:31Z
issued: 2025
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: 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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92679
note: 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 convo…
has type: Thesis/Dissertation
date issued: 2025-11
is shown at: 10.14288/1.0450506
language: eng
degree discipline: Neuroscience
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Oruc, Ipek
type: Text
identifier URI: http://hdl.handle.net/2429/92679