UCSF

Attention has come under acute focus within the neuropsychological world in past decades, and the rise of brain-computer interfaces (BCI) during EEG offers a means to personalize attention training therapies. Semi-stable EEG topographies, called “microstates,” have been found to be functionally relevant to attention-oriented tasks and shown to influence awareness in the time period directly before a stimulus. In a BCI designed to train attention, we may expect to see a group difference in microstates. Specifically, it could be that microstate D—functionally relevant to attention and task-switching—increases while microstate C—functionally relevant to task-negative and saliency networks—decreases within the group that successfully learns via neurofeedback. The reversed pattern may be true in groups that either fails to learn through neurofeedback or received sham neurofeedback. We may also expect microstates D and C to relate to a behavioral outcome measure that indexes training performance. Accordingly, we used EEGLAB to process BCI attention-training data, derive microstate topographies for individual participants, cluster grand mean topographies for the entire study group, and extract temporal statistics to measure microstate temporal presence during pre-stimulus training. Overall, microstate D had greater temporal presence in those who successfully self-regulated neural cognition during the BCI task compared to those who could not achieve this; microstate C had greater temporal presence in those who could not self-regulate neural cognition during the BCI task compared to those who did so successfully. This analysis highlights differences in BCI performance but failed to find meaningful changes over training.