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

Souhrn

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


Zdroje

1. Villena. A, Tardon. Lorenzo. J, Barbancho. I, Barbancho. A. M, B Elvira., and H Niels. T. “Preprocessing for Lessening the Influence of Eye Artifacts in EEG Analysis,” Applied Sciences, vol. 9, no. 9, pp. 1757, 2019. doi: 10.3390/app9091757

2. Zou. X, Feng. L and Sun. H, “Robust compressive sensing of multichannel EEG signals in the presence of impulsive noise,” Information Sciences, vol. 429, pp. 120–129, 2018. doi: 10.1016/j.ins.2017.11.002

3. Zhu. J and Chen. C, Su. S and Chang. Z, “Compressive Sensing of Multichannel EEG Signals via lq Norm and Schatten- p Norm Regularization,” Mathematical Problems in Engineering, pp. 208–216, 2016.

4. Liu. Y, De-Vos. M and Van-Huffel. S, “Compressed sensing of multichannel EEG signals: The simultaneous cosparsity and low-rank optimization,” IEEE Transactions on Biomedical Engineering, vol. 62, pp. 2055–2061, 2015. doi: 10.1109/TBME.2015.2411672

5. Delorme. A and Makeig. S, “EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, pp. 9–21, 2004. doi: 10.1016/j.jneumeth.2003.10.009

6. Zheng. Q, Zhu. F and Heng. P. A, “Robust support matrix machine for single trial EEG classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 3, pp. 551–562, 2018. doi: 10.1109/TNSRE.2018.2794534

7. Nie. F, Huang. H and Ding. C, “Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization,” AAAI Conference on Artificial Intelligence, pp. 655–661, 2012.

8. Ma. T, Li. H, Yang. H, Lv. X, Li. P, Liu. T and Yao. D, “The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing,” Journal of Neuroscience Methods, vol. 275, pp. 80–92, 2017. doi: 10.1016/j.jneumeth.2016.11.002

9. Zhao. W, Sun. B, Wu. T and Yang. Z, “Compressed sensing,EEG,VLSI,data compression,sparse sensing matrix,spike sorting,wireless neural interface,” IEEE Transactions on Biomedical Circuits and Systems, vol. 12, no. 1, pp. 242–254, 2018.

10. Majumdar. A, Shukla. A and Ward. R, “Combining Sparsity with Rank-Deficiency for Energy Efficient EEG Sensing and Transmission over Wireless Body Area Network,” ICASSP 2015, 2015.

11. Fan Y. R, Huang. T. Z, Liu. J and Zhao. X. L, “Compressive sensing via nonlocal smoothed rank function,” PLoS ONE, vol. 11, no. 9, pp. 1–15, 2016. doi: 10.1371/journal.pone.0162041

12. Eftekhari. A, Vandereycken. B, Vilmart. G and Zygalakis. K, “Explicit Stabilised Gradient Descent for Faster Strongly Convex Optimisation,” arXiv preprint arXiv:1805.07199, 2018.

13. Liu. X. J and Xia. S. T, “Constructions of quasi-cyclic measurement matrices based on array codes,” IEEE International Symposium on Information Theory—Proceedings, vol. 34, no. 1, pp. 479–483, 2013.

14. Capurro. I, Lecumberry. F, Martin. A, Ramirez. I, Rovira. E and Seroussi. G, “Efficient Sequential Compression of Multichannel Biomedical Signals,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 4, pp. 904–616, 2017. doi: 10.1109/JBHI.2016.2582683

15. Yuan. G and Hu. W, “A conjugate gradient algorithm for large-scale unconstrained optimization problems and nonlinear equations,” Journal of Inequalties and Applications, vol. 113, no. 4, pp. 1–19, Feb. 2018.

16. Bertoni. N, Senecirathna. B, Pareschi. F, Mangia. M, Rovatti. R, Abshire. P, Simon. J and Setti. G, “Low-power EEG monitor based on Compressed Sensing with Compressed Domain Noise Rejection,” IEEE International Symposium Circuits Systems, pp. 522-525, 2016.

17. Cisotto. G, Guglielmi. A. V, Badia. L and Zanella. A, “Joint compression of EEG and EMG signals for wireless biometrics,” 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1-6, 2018.

18. Xie. Y, Gu. S, Liu. Y, Zuo. W, Zhang. W and Zhang. L, “Low rank,low-level vision,weighted Schatten p-norm,” IEEE Transactions on Image Processing, vol. 25, no. 107, pp. 4842–48575, 2016.

19. Mahrous. H and Ward. R, “A Low Power Dirac Basis Compressed Sensing Framework for EEG using a Meyer Wavelet Function Dictionary” IEEE Canadian Conference on Electrical and Computer Engineering, 2016.

20. Peng G. J, “Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation,” IEEE Transactions on Image Processing, 2019. doi: 10.1109/TIP.2019.2896541

21. Alcarez. G. D, Favaro. F, Lecumberry. F, Martin. A, Oliver. J. P, Oreggioni. J, Ramirez. I, Seroussi. G and Steinfeld. L, “Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless,” IEEE Transactions on Biomedical Circuits and Systems, vol. 12, no. 1, pp. 231–241, 2018.

22. Schetinin. V and Jakaite. L, “Extraction of features from sleep EEG for Bayesian assessment of brain development,” PLoS ONE, vol. 12, no. 3, pp. 1–13, 2017. doi: 10.1371/journal.pone.0174027

23. Zhang. Y, Wabg. Y, Jin. J and Wang. X, “Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification,” International Journal of Neural Systems, vol. 26, 2016.

24. Zhang. J, Li. Y, Gu. Z, and Yu. Z. L, “Recoverability analysis for modified compressive sensing with partially known support,” PLoS ONE, vol. 60, no. 1, pp. 221–224, 2013.

25. Zhang. Z, Jung. T. P, Makeig. S and Rao. B. D, “Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 221–224, 2012. doi: 10.1109/TBME.2012.2217959

26. Minaee. S and Wang. Y, “An ADMM Approach to Masked Signal Decomposition Using Subspace Representation,” IEEE Transactions on Image Processing, 2019. doi: 10.1109/TIP.2019.2894966 30703020

27. Liu. B, Zhang. Z, Xu. G, Fan. H and Fu. Q, “Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation,” Biomedical Signal Processing and Control, vol. 11, pp. 80–88, Feb. 2014. doi: 10.1016/j.bspc.2014.02.010

28. Aggarwal. P and Gupta. A, “Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI,” Computers in Biology and Medicine, vol. 91, pp. 255–266, 2017. doi: 10.1016/j.compbiomed.2017.10.020

29. Christensen. C. B, Harte. J. M, Lunner. T and Kidmose. P, “Ear-EEG-Based Objective Hearing Threshold Estimation Evaluated on Normal Hearing Subjects,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 1026–1034, 2018. doi: 10.1109/TBME.2017.2737700

30. Kessy. A, Lewin. A and Strimmer. K, “Optimal Whitening and Decorrelation,” The American Statistician, vol. 72, no. 4, pp. 309–314, 2018. doi: 10.1080/00031305.2016.1277159

31. Mangia. M, Pareschi. F, Cambareri. V, Rovatti. R and Setti. G, “Rakeness-Based Design of Low-Complexity Compressed Sensing,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 64, no. 5, pp. 1201–1213, 2017. doi: 10.1109/TCSI.2017.2649572

32. Mehmood. R. M, Du. R and Lee. H, “Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors,” IEEE Access, pp. 14797–14806, 2017. doi: 10.1109/ACCESS.2017.2724555

33. Xia. D and Koltchinskii. V, “Estimation of low rank density matrices: Bounds in Schatten norms and other distances,” Electronic Journal of Statistics, vol. 10, no. 2, pp. 2717–2745, 2016. doi: 10.1214/16-EJS1192

34. Zhang. C, Ahmad. M and Wang. Y, “ADMM Based Privacy-Preserving Decentralized Optimization,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 3, pp. 565–580, 1998. doi: 10.1109/TIFS.2018.2855169

35. Shukla. A and Majumdar. A, “Row-sparse Blind Compressed Sensing for Reconstructing Multi-channel EEG Signals,” Biomedical Signal Processing and Control, vol. 18, pp. 174–178, 2015. doi: 10.1016/j.bspc.2014.09.003

36. Sun. B, Chen. Q, Xu. X, He. Y and Jiang. J, “Permuted and Filtered Spectrum Compressive Sensing,” IEEE Signal Processing Letters, vol. 20, no. 7, pp. 685–688, 2013. doi: 10.1109/LSP.2013.2258464

37. Marques. E. C, Miciel. N, Naviner. L, Cai. H. A. O and Yang. J. U. N, “A Review of Sparse Recovery Algorithms,” IEEE Access, 2019.

38. Craven. D, Mcginley. B, Kilmartin. L, Glavin. M and Jones. E, “Compressed Sensing for Bioelectric Signals: A Review,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 2, pp. 529–540, 2015. doi: 10.1109/JBHI.2014.2327194


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