Online Adaptive Synchronous BCI System with Attention Variations

Abstract

In real-life scenarios, outside of the laboratory setting, the performance of brain-computer interface (BCI) systems is influenced by the user's mental state such as attentional diversion. Here, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution. Electroencephalography (EEG) signals were recorded from twelve channels in twelve healthy participants and two stroke patients while performing 50 trials of ankle dorsiflexion simultaneously with an auditory oddball task. For each participant , the selected channels, classifiers and features from the offline mode were used in the online mode to predict the attention status. For both healthy controls and subacute stroke patients, feedback to the user on attentional status reduced the amount of attentional diversion created by the oddball task. The findings presented here demonstrate that the users' attention can be monitored in a fully online BCI system, and further, that real-time neurofeedback on the attentional state of the user can be implemented to focus the attention of the user back onto the main task of the BCI for neuromodulation. Monitoring the users' attention status will have a major impact in the BCI for neurorehabilitation area in the future.