Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals



Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electro Cardio Gram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.

Author Biographies

Vivek Upadhyaya, Malaviya National Institute of Technology

Ph.D. Scholar, Department of Electronics and Communication

Mohammad Salim, Malaviya National Institute of Technology

Professor, Department of Electronics and Communication


Zou, Xiuming, Lei Feng, and Huaijiang Sun. "Compressive Sensing of Multichannel EEG Signals Based on Graph Fourier Transform and Cosparsity." Neural Processing Letters (2019): 1-10.

Tayyib, Muhammad, Muhammad Amir, Umer Javed, M. Waseem Akram, Mussyab Yousufi, Ijaz M. Qureshi, Suheel Abdullah, and Hayat Ullah. "Accelerated sparsity-based reconstruction of compressively sensed multichannel EEG signals." Plus one 15, no. 1 (2020): e0225397.

Şenay, Seda, Luis F. Chaparro, Mingui Sun, and Robert J. Sclabassi. "Compressive sensing and random filtering of EEG signals using Slepian basis." In 2008 16th European signal processing conference, pp. 1-5. IEEE, 2008.

Gurve, Dharmendra, Denis Delisle-Rodriguez, Teodiano Bastos-Filho, and Sridhar Krishnan. "Trends in Compressive Sensing for EEG Signal Processing Applications." Sensors 20, no. 13 (2020): 3703.

Amezquita-Sanchez, Juan P., Nadia Mammone, Francesco C. Morabito, Silvia Marino, and Hojjat Adeli. "A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals." Journal of neuroscience methods 322 (2019): 88-95.

DeVore, R. "Nonlinear approximation Acta Numerica, 51 {150." (1998).

Einstein, A., B. Podolsky, and N. Rosen, 1935, “Can quantum-mechanical description of physical reality be considered complete?”, Phys. Rev. 47, 777-780.

Baraniuk, R. G. "Compressive sensing, IEEE Signal Proc." Mag 24, no. 4 (2007): 118-120.

Upadhyaya, Vivek, and Mohammad Salim. "Basis & Sensing Matrix as key effecting Parameters for Compressive Sensing." In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pp. 1-6. IEEE, 2018.

E. Candes. Compressive sampling. In Proc. Int. Congress of Math., Madrid, Spain, Aug. 2006.

E. Candes and J. Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Found. Compute. Math., 6(2):227-254, 2006.

E. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52(2):489-509, 2006.

E. Candes, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math., 59(8):1207-1223, 2006.

E. Candes and T. Tao. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inform. Theory, 52(12):5406-5425, 2006.

D. Donoho. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289-1306, 2006.

S. Kirolos, J. Laska, M. Wakin, M. Duarte, D. Baron, T. Ragheb, Y. Massoud, and R.G. Baraniuk, “Analog-to-information conversion via random demodulation,” in Proc. IEEE Dallas Circuits Systems Workshop, Oct. 2006, pp. 71-74.

Zhang, Zhilin, Tzyy-Ping Jung, Scott Makeig, and Bhaskar D. Rao. "Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning." IEEE Transactions on Biomedical Engineering 60, no. 2 (2012): 300-309.

Joshi, Amit Mahesh, and Vivek Upadhyaya. "Analysis of compressive sensing for non-stationary music signal." In 2016 International Conference on Advances in Computing, Communications, and Informatics (ICACCI), pp. 1172-1176. IEEE, 2016.

Wang, Zhou, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13, no. 4 (2004): 600-612.

Nibheriya, Khushboo, Vivek Upadhyaya, and Ashok Kumar Kajla. "To Analysis the Effects of Compressive Sensing on Music Signal with variation in Basis & Sensing Matrix." In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1121-1126. IEEE, 2018.

Zhang, Zhilin, Tzyy-Ping Jung, Scott Makeig, and Bhaskar D. Rao. "Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware." IEEE Transactions on Biomedical Engineering 60, no. 1 (2012): 221-224.






Biomedical Engineering