A Multi-Class Tactile Brain-Computer Interface Based on Stimulus-Induced Oscillatory Dynamics

We proposed a multi-class tactile braincomputer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile-induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L); 2) selective sensation on right hand (SS-R); 3) bilateral selective sensation; and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9 +/- 8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3 +/- 4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had an accuracy of 75.2 +/- 8.3%, with the best discrimination reached for the classes SS-L, SS-R, and SS-S. Finally, four classes were classified with an accuracy of 59.4 +/- 7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.