Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

Autoři: Muhammad Tayyib aff001;  Muhammad Amir aff001;  Umer Javed aff001;  M. Waseem Akram aff002;  Mussyab Yousufi aff001;  Ijaz M. Qureshi aff003;  Suheel Abdullah aff001;  Hayat Ullah aff001
Působiště autorů: Faculty of Engineering and Technology, International Islamic University Islamabad, Islamabad, Pakistan aff001;  Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, China aff002;  Department of Electrical Engineering, Air University, Islamabad, Pakistan aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
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.

Klíčová slova:

Algorithms – Compressed sensing – Data compression – Electroencephalography – Fourier analysis – Man-computer interface – Mathematical functions – Signal processing


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2020 Číslo 1