Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals.

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ID: 78882
2020
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Abstract
Wearable electronics capable of recording and transmitting biosignals can provide convenient and pervasive health monitoring. A typical EEG recording produces large amount of data. Conventional compression methods cannot compress date below Nyquist rate, thus resulting in large amount of data even after compression. This needs large storage and hence long transmission time. Compressed sensing has proposed solution to this problem and given a way to compress data below Nyquist rate. In this paper, double temporal sparsity based reconstruction algorithm has been applied for the recovery of compressively sampled EEG data. The results are further improved by modifying the double temporal sparsity based reconstruction algorithm using schattern-p norm along with decorrelation transformation of EEG data before processing. The proposed modified double temporal sparsity based reconstruction algorithm out-perform block sparse bayesian learning and Rackness based compressed sensing algorithms in terms of SNDR and NMSE. Simulation results further show that the proposed algorithm has better convergence rate and less execution time.
Reference Key
tayyib2020acceleratedplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tayyib, Muhammad;Amir, Muhammad;Javed, Umer;Akram, M Waseem;Yousufi, Mussyab;Qureshi, Ijaz M;Abdullah, Suheel;Ullah, Hayat;
Journal PloS one
Year 2020
DOI
10.1371/journal.pone.0225397
URL
Keywords
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