A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

Abstract

Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.