Error probability of intracranial brain computer interfaces under non-task elicited brain states

Objective: Intracranial brain computer interfaces (BCIs) can be connected to the user's cortex permanently. The interfaces response when fed with non-task elicited brain activity becomes important as design criterion: ideally intracranial BCIs should remain silent. We study their error probability in the form of false alarms. Methods: Using electrocorticograms recorded during task and non-task brain states, we compute false alarms, investigate their origin and introduce strategies to reduce them, using signal detection theory, classifier cascading and adaptation concepts. Results: We show that the incessant dynamics of the brain is prone to spontaneously produce signals, the spectral and topographical characteristics of which can resemble those associated with common control tasks, generating brain state classification errors. Conclusions: In addition to hit and bit rates, response of BCIs to non-task brain states constitutes an important measure of BCI performance. Static classification cascading reduces considerably false positives during no-task brain states. Significance: False alarms in intracranial BCIs are undesirable and could have dangerous consequences for the users. Designs which effectively incorporate the error correction strategies discussed in this paper, could be more successful when taken from the laboratory or acute care setting and used in the real world.