Design and Improvement of Brain-Machine Interface Using Portable NIRS Device

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In recent years, robotics and medical sciences have focused on a new input interface, "the brain-machine interface." If a practical BMI system is realized, it could have pioneering applications in various new fields, e.g., human-robot interaction and supportive care for persons with special needs. Therefore, we aimed to design a practical BMI system using a portable Near-Infrared Spectroscopy (NIRS) device. In this research, we developed a LVQ-based cerebral state classifier for a portable NIRS device that enabled to classify cerebral blood flow patterns corresponding to several human mental imagery (e.g, mental commands given to a robot) and human emotions. Then, we discussed problems to utilize the NIRS-based BMI system in the daily environment and designed the improved LVQ-based classifier. In addition to that, we experimentally investigated the response of the cerebral blood flow against external visual stimuli.