Reducing Power Consumption in LNA by Utilizing EEG Signals as Basis Matrix in Compressed Sensing

The application of compressed sensing has gained interest for its potential to achieve low power consumption in wireless electroencephalogram (EEG) measurement devices. In this study, we propose a system that utilizes the EEG basis (EEGB) matrix, allowing for the same reconstruction accuracy as a discrete cosine transform (DCT) matrix, which is a well known conventional matrix, while realizing low-power consumption in a low noise amplifier (LNA). Our theoretical analysis reveals that, for a 5x compression aiming at an equivalent normalized mean square error of 0.25, the use of the EEGB matrix can decrease the power consumption of the LNA by approximately 75% compared to using a DCT matrix.