An Online Bimanual EEG-MI-BCI With Shared Control for Bilateral Robotic-Assisted Training.

Bimanual motor tasks are commonplace in daily life and are often integrated into rehabilitation therapies. However, previous brain-computer interface (BCI) for robotic-assisted rehabilitation predominantly focused on motor imagery (MI) of single limb. Moreover, the BCI-driven robotic system has been plagued due to the difficulty of decoding electroencephalography (EEG) accurately and robustly. In this study, we presented a novel EEG-MI-BCI system for online bilateral robot-assisted training, consisting of: 1) a bimanual EEG-MI paradigm involving the imagination of three coordinated movement directions (left, middle, and right) of both hands; and 2) a shared control strategy that relies on prior knowledge-based assistance for correcting direction decoding errors and robot autonomy for managing movement velocity. The experiment included two parts: a one-step bimanual EEG-MI task and a multi-step bimanual reaching task assisted by a robot. First, one-step offline and online decoding experiments were implemented to assess the feasibility of the proposed bimanual EEG-MI paradigm using six common models. The offline results from eight human participants indicated that all models achieved significantly higher average accuracy compared to the chance level (33.33%), with EEGNet yielding the highest accuracy of 52.93%. In addition, the optimal model, EEGNet, achieved an online accuracy of 49.67%. Second, an online multi-step task was implemented using the bimanual MI paradigm and shared control strategy. The average success rate was 48.33% without assistance, which increased to 71.67%, 80.00%, and 90.00% with assistance at levels of low, moderate, and high, respectively. These results demonstrated the online feasibility of decoding coordinated directions based on the developed EEG-MI-BCI system and real-time control of bilateral robot for potential rehabilitation therapies.