| Summary: | 碩士 === 國立交通大學 === 電機工程學系 === 102 === Although the well developed in medical area, rehabilitation is still the main therapy for the patient who suffered from paralysis caused by stroke and other brain diseases. The spontaneous neuro-rehabilitation which combines with electroencephalogram (EEG) is an active and efficient way to rehabilitation. However judging the subject’s brain state of motor imagery precisely is the key factor of this spontaneous neuro-rehabilitation. To judge the brain state more accurately, we need to take multi-channel EEG cap in tradition. But the time consuming and troublesome channel preparation makes the rehabilitation system unpractical. This research enhances the judgment accuracy by a series of machine learning algorithm like frequency decomposition, Common Spatial Pattern (CSP) and classifier. We reduce the channel number to two and obtain the optimal channel position by pre-collecting and analyzing the subjects’ whole scalp EEG. So we can have a high performance only with two channel when practical operating. After establishing the protocol, this research implements the hardware and software and test the system. The average accuracy of the judgments is over 70% no matter in the off-line analysis or the on-line practical operation. Through this rehabilitation system, we hope the patients’ life can be changed heavily.
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