Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

Abstract

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.

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

“…To address the above-mentioned shortcomings, we propose modified BCS algorithm to solve subproblems as single unit. This not only achieve better performance but also results in less computations [19].…”

“…There are various methods used in the literature for the efficient reconstruction of EEG signal from sparse reconstruction using compressed sensing, however, there are limitations to these in terms of high compression ratio, whenever algorithms try to achieve reconstruction from high compression, they face degradation in signal, in [19], accelerated double temporal based sparse reconstruction is done for EEG signal. The use of schattern-p norm made it useful technique in terms of execution time, however, this technique is prone to noise in high compression ratio.…”

Trends in Compressive Sensing for EEG Signal Processing Applications

“…In order to enforce inherent correlation across different channels and cosparsity of multichannel EEG signals, X. Zou et al [81] proposed a graph Fourier transform and nonconvex optimization (GFTN)-based method, which can exploit the accurate adjacent relationship between the real physical channels. Similar to [80], this work also used ADMM for signal reconstruction.…”

“…Furthermore, M. Tayyib et al [ 80 ] proposed an accelerated sparsity-based reconstruction of compressively sensed multichannel EEG signals using ADMM. First, they obtained the norm along with the decorrelation transformation of EEG data, and then double temporal sparsity-based reconstruction algorithm has been applied for the signal reconstruction.…”

An FPGA-based analysis of trade-offs in the presence of ill-conditioning and different precision levels in computations

“…An example in this regard is inverse electroencephalography (inverse EEG), which is presented in the following section. Electrical activity is generated by the bioelectrical activity of a large population of neurons working synchronously [3,4], which is recorded by electrodes located on the scalp using electroencephalography (EEG). EEG is related to the bioelectrical sources using a model that considers the head as a conductive inhomogeneous medium of multiple layers that represent the different regions of the head, i.e., brain, skull, and scalp.…”