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Sensors 2016, 16(2), 201; doi:10.3390/s16020201

Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies

ECE, University of British Colombia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada
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Author to whom correspondence should be addressed.
Academic Editors: Yun Liu, Han-Chieh Chao, Pony Chu and Wendong Xiao
Received: 6 December 2015 / Revised: 24 January 2016 / Accepted: 29 January 2016 / Published: 5 February 2016
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Abstract

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. View Full-Text
Keywords: EEG signals; tele-monitoring; compressed sensing; BSBL; multivariate compression; linear and nonlinear dependency EEG signals; tele-monitoring; compressed sensing; BSBL; multivariate compression; linear and nonlinear dependency
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mahrous, H.; Ward, R. Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies. Sensors 2016, 16, 201.

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