Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
identifier citation: Sensors 16 (2): 201 (2016)
creator: Mahrous, Hesham
creator: Ward, Rabab Kreidieh
issued: 2019-05-24T17:01:08Z
issued: 2016-02-05
description: This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/70306
note: sensorsArticleBlock Sparse Compressed Sensing ofElectroencephalogram (EEG) Signals by ExploitingLinear and Non-Linear DependenciesHesham Mahrous and Rabab Ward *ECE, University of British Colombia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada;hesham.m.mahrous@gmail.com* Correspondence: rababw@ece.ubc.ca; Tel.: +1-604-266-4161Academic Editors: Yun Liu, Han-Chieh Chao, Pony Chu and Wendong XiaoReceived: 6 December 2015; Accepted: 29 January 2016; Published: 5 February 2016Abstract: This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram(EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensorsis limited. For the single EEG channel case, known as the single measurement vector (SMV) problem,the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. Thismethod exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within themeasurement vector of a single channel. For the multichannel case, known as the multi-measurementvector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method hasbeen proposed. This method learns the joint correlation structure in the multichannel signals bywhitening the model in the temporal and the spatial domains. Our proposed method represents themulti-channels signal data as a vector that is constructed in a specific way, so that it has a better blocksparsity structure than the conventional representation obtained by stacking the measurement vectorsof the different channels. To reconstruct the multichannel EEG signals, we modify the parameters ofthe BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependencystructures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsitystructure. The proposed method is shown to significantly outperform existing SMV and also MMVmethods. It also shows significant lower compression e…
has type: Article
is shown at: 10.14288/1.0378979
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/s16020201
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
subject: EEG signals
subject: Tele-monitoring
subject: Compressed sensing
subject: BSBL
subject: Multivariate compression
subject: Linear and nonlinear dependency
type: Text
identifier URI: http://hdl.handle.net/2429/70306