Spatio-temporal compressed sensing for real-time wireless EEG monitoring

Abstract

Wearable electronics capable of recording and transmitting biosignals can provide convenient and pervasive health monitoring. The wireless transmission bandwidth limits the number of recording sites that can be monitored at one time. Compressed sensing (CS) is a promising approach that uses computationally efficient encoding to reduce the number of samples that are transmitted wirelessly, allowing more channels to be monitored over a transmission channel. The rakeness CS approach shows improved performance for higher compression rates, but in prior work it has only been evaluated for single channel data. We analyze the fidelity tradeoffs for compressed sensing implemented on a mobile electroencephalography (EEG) system. We propose several methods for spatiotemporal encoding in rakeness CS and evaluate the performance using a spontaneous EEG dataset recorded during moderate movement. Reconstruction performance depends strongly on the compression ratio and weakly on the method of spatiotemporal encoding. This suggests weak spatial correlation between the different channels of EEG data, which were recorded in an experiment involving self-initiated movement.