Exploiting inter-channel correlation in EEG signal reconstruction

We address the problem of sensing and transmitting EEG signals over wireless body area network (WBAN). Compressed sensing (CS) techniques are used profusely to this end. However, all prior techniques concentrate on piecemeal reconstruction of EEG signals from individual channels. For the first time, we show how inter-channel correlation can be exploited to achieve better reconstruction. We propose to model the multi-channel signal ensemble as (1) a low-rank matrix, (2) a row-sparse matrix (in transform domain) and (3) a combination of the two. Improved signal reconstruction is not the only goal of this work; our approach can also reduce the total energy requirement of the EEG sensor nodes by a significant amount. Prior techniques could only reduce transmission energy, but our method can reduce acquisition energy and eliminate the processing energy required for compression.