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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...
In this research we present a non-invasive Brain-Machine Interface (BMI) system that allows patients with motor paralysis conditions to control electronic appliances in a hospital room. The novelty of our system compared to other BMI applications is that our system gradually becomes autonomous by learning user actions (i.e. turning on/off window, lights, etc.) under certain environment conditions...
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