A compressive sampling approach for brain-machine interfaces based on transcranial Doppler sonography: A case study of resting-state maximal cerebral blood velocity signals
Transcranial Doppler sonography was recently proposed as an approach for brain-machine interfaces. However, monitoring maximal cerebral blood flow velocity signals for extensive time periods can generate large volumes of data for processing. In this paper, a compressive sensing (CS) approach is proposed based on a time-frequency dictionary formed by modulated discrete prolate spheroidal sequences (MDPSS). To test the proposed scheme, we examined maximal cerebral blood flow velocity signals acquired from 20 healthy subjects during a resting state. The results of our analysis clearly depicted that these signals can be accurately reconstructed using only 30% and 50% of original samples. Hence, the proposed MDPSS-based CS approach is a valid tool for diminishing the number of acquired samples during brain-machine operations using transcranial Doppler sonography.