Brain Machine Interface System Automation Considering User Preferences and Error Perception Feedback

This paper addresses the problem of mental fatigue caused by prolonged use of Brain Machine Interface (BMI) Systems. We propose a system that gradually becomes autonomous by learning user preferences and by considering error perception feedback. As a particular application, we show that our system allows patients to control electronic appliances in a hospital room, and learns the correlation of room sensor data, brain states, and user control commands. Moreover, error perception feedback based on a brain potential called error related negativity (ERN)-that spontaneously occurs when the user perceives an error made by the system-was used to correct system's mistakes and improve its learning performance. Experimental results with volunteers demonstrate that our system reduces the level of mental fatigue, and achieves over 90% overall learning performance when error perception feedback is considered.