碩士 國立臺北科技大學 機械工程系機電整合碩士班 106 In this study, we aimed to develop a MI-based multi-functional brain-computer interface communication system for patients with ALS. The MI-BCI syetem included three functions, Yes/No immediate-reply function, Patients’ needs selection function and communication board-spelling function. We extract fractal dimension as the feature in the MI-BCI system. Fisher criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. The average accuracy from 12 patients by using the Top 5-Channel of each patient for resting vs MI classification can achieve 89%. Moreover, in four-class MI classification, using 30-Channel of each patient can achieve 82.67%. Those results can prove a efficiency and feasibility way for further MI-based BCI applications.