Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

• Published in: PloS one Vol. 15; no. 1; p. e0225397
• Main Authors: Tayyib, Muhammad, Amir, Muhammad, Javed, Umer, Akram, M. Waseem, Yousufi, Mussyab, Qureshi, Ijaz M., Abdullah, Suheel, Ullah, Hayat
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.01.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Compression; Computer and Information Sciences; Computer simulation; Convulsions & seizures; Data compression; Dictionaries; EEG; Electroencephalography; Electroencephalography - methods; Engineering; Engineering and Technology; Field programmable gate arrays; Fourier transforms; Health; Humans; Image Processing, Computer-Assisted; Machine learning; Medicine and Health Sciences; Methods; Monitoring, Physiologic; Multichannel communication; Noise; Physical Sciences; Reconstruction; Recording; Research and Analysis Methods; Signal Processing, Computer-Assisted; Sparsity; Time compression; Wearable Electronic Devices - trends; Islamabad Pakistan; China; Pakistan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0225397

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.