Dictionary selection for compressed sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning

Abstract

Abstract Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using a sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal, is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluate the performance of the framework when using various wavelet bases while considering their key attributes namely number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with the discrete cosine basis, often used in the literature, and the alternative of not using a sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and Discrete Cosine Transform (DCT)) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes, as compared to using no dictionaries.