Effect of sensing matrices on quality index parameters for block sparse bayesian learning-based EEG compressive sensing

Abstract

Due to the ongoing research in the medical domain, we get lot of data for storage and transmission purposes. Real-time processing and reduction of medical data are tedious. Hence, an approach is required to compress the data and reconstruct it by using a few samples. We proposed a model with a remote Health Care Unit & Patient for EEG signals in this work. In this model, our prime concern is to reduce the number of samples to reconstruct a compressed EEG signal. So, to reduce the number of samples, we opt for compressive sensing approach. As it is a well-known concept, Compressive Sensing is the framework that mainly depends upon the Sensing matrix for compression and the Basis matrix for representation. By considering this fact, we demonstrate a technique, which is a combination of the Compressive Sensing and BSBL by employing different measurement matrices. Since BSBL has already been mentioned in the literature, we compared the results based on this demonstration with the previously mentioned approach and found a significant change in the parameters mentioned in the result and analysis section.