Joint sparsity recovery for compressive sensing based EEG system

Abstract

The last decade has witnessed tremendous efforts to shape the internet of thing (IoT) platforms to be well suited for healthcare applications. These applications involve the deployment of remote monitoring platforms to collect different information about several vital signs such as electroencephalogram (EEG). However, the deployment of these platforms faces several limitations in terms of high power consumption and system complexity. High energy consumption associated with the continuous wireless data transmission can be optimized by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well chosen sensing matrices to take random projections of the data in sub-Nyquist sampling rates. In addition, system complexity can be reduced by using hardware friendly structured sensing matrices. This paper quantifies the performance of CS-based scheme for vital sign acquisition for a connected health application over an IoT platform taking multi-channel EEG signals as a case study. In addition, the paper exploits the joint sparsity of multi-channel EEG signals as well as a designed sparsifying basis to increase the sparsity of the EEG signal to improve the reconstruction quality, hence, increase the efficiency of the system. 2017 IEEE.