Steady-State Somatosensory Evoked Potential based Brain-Computer Interface for Sit-to-Stand Movement Intention

Abstract

The purpose of this study was to develop sit-to-stand movement intention decoding algorithms based on brain responses to the vibrotactile stimulation. Specifically, we studied a simultaneous hybrid brain-computer interface (BCI) by combining steady-state somatosensory evoked potential (SSSEP) and a motor imagery (MI) task. In our BCI system, a user could generate two possible commands by concentrating on one of two vibration stimuli, which were attached to the left and right hand. The statistical method based on the mutual information between the spatial-temporal patterns was used to detect the user's intention of sit or stand from the electroencephalography (EEG) signals. The results of our offline experiments demonstrated the feasibility of hybrid MI-SSSEP based BCI system for decoding sit-to-stand movement intention. It is expected that the proposed method and algorithms can be implanted in the future to control the lower limb exoskeleton robot.