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This research presents a Non-Invasive Brain- Machine Interface (BMI) that allows persons, who have suffered from motor paralysis conditions, to control appliances in a hospital room by using only electromyogram signals (EMG) generated by muscle contractions such as eyebrow movement. The novelty of our system compared to other BMI applications is that our system gradually learns user actions and preferences under certain room environment conditions (temperature, illumination, etc.) and brain states (i.e. awake, sleepy, etc.). By providing learning capabilities to the system, the system achieves certain degree of automation and patients are relieved from mental fatigue or stress caused by continuously controlling appliances using a BMI. We present a hierarchical architecture that allows the user to select appliances (window, lights, etc.) and operate them with minimum effort. Our system uses an extended version of the Bayes Point Machine approach trained with Expectation Propagation to approximate a posterior probability from previously observed user actions, which leads to a predictive distribution over a new combination of brain states and environmental conditions.
Christian I. Penaloza, Yasushi Mae, Kenichi Ohara, and Tatsuo Arai: "BMI-based Learning System for Appliance Control Automation", IEEE International Conference on Robotics and Automation (ICRA 2013), Karlsruhe, Germany. May 6 - 10, 2013.