Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals.
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 a...
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Online Access: | https://doi.org/10.1371/journal.pone.0225397 |
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doaj-8071227fc5184f75bfb4ba87f71b71e92021-03-03T21:19:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01151e022539710.1371/journal.pone.0225397Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals.Muhammad TayyibMuhammad AmirUmer JavedM Waseem AkramMussyab YousufiIjaz M QureshiSuheel AbdullahHayat UllahWearable 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.https://doi.org/10.1371/journal.pone.0225397 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Muhammad Tayyib Muhammad Amir Umer Javed M Waseem Akram Mussyab Yousufi Ijaz M Qureshi Suheel Abdullah Hayat Ullah |
spellingShingle |
Muhammad Tayyib Muhammad Amir Umer Javed M Waseem Akram Mussyab Yousufi Ijaz M Qureshi Suheel Abdullah Hayat Ullah Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals. PLoS ONE |
author_facet |
Muhammad Tayyib Muhammad Amir Umer Javed M Waseem Akram Mussyab Yousufi Ijaz M Qureshi Suheel Abdullah Hayat Ullah |
author_sort |
Muhammad Tayyib |
title |
Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals. |
title_short |
Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals. |
title_full |
Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals. |
title_fullStr |
Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals. |
title_full_unstemmed |
Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals. |
title_sort |
accelerated sparsity based reconstruction of compressively sensed multichannel eeg signals. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2020-01-01 |
description |
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. |
url |
https://doi.org/10.1371/journal.pone.0225397 |
work_keys_str_mv |
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