Open AccessAccelerated sparsity based reconstruction of compressively sensed multichannel EEG signals
Author(s) -
Muhammad Tayyib,
Muhammad Amir,
Umer Javed,
Muhammad Waseem Akram,
Mussyab Yousufi,
I. M. Qureshi,
Suheel Abdullah,
Hayat Ullah
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0225397
Subject(s) - computer science , compressed sensing , decorrelation , nyquist rate , wearable technology , algorithm , rate of convergence , block (permutation group theory) , data compression , artificial intelligence , signal reconstruction , speech recognition , wearable computer , signal processing , computer vision , key (lock) , sampling (signal processing) , mathematics , computer hardware , geometry , computer security , filter (signal processing) , embedded system , digital signal processing
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.