| Summary: | 碩士 === 國立陽明大學 === 腦科學研究所 === 98 === This thesis aims to develop an algorithm composed of two-level classifiers based on single-trial electroencephalography (EEG) signal for the recognition of movement versus resting state and subsequently left arm movement versus right arm movement. The first step of algorithm is feature extraction. We estimate a subject-specific frequency band related to the arm movement from time frequency map of each subject. The first-level classifier is designed to classify the movement versus resting state in which either the cross mutual information (CMI) or the conventional beta-rebound power feature are employed. The second-level classifier is designed to recognize the left- or right-arm movement where the significant temporal period is defined followed by one of the three filters for the feature extractions: Morlet wavelet transform filters, intrinsic mode function (IMF) filters, Butterworth band pass filters. Once EEG signals on all channels were processed by these filters, the CMIs of filtered signals between any two channels were computed, respectively.
The recognition results of motion versus resting state show that the recognition rates from the CMI and beta-rebound power feature are comparable. In the second-level classification, the averaged classification rates of left- versus right-arm movement obtained from the Butterworth band pass filter with whole-brain CMI maps are 83%±13% which are superior to that from Morlet wavelet transform and IMF. In addition, the recognition rates resulted from subject-specific frequency bands have a significant difference ( p≤0.014, Mann-Whitney U method) compared with other bands. Moreover, the recognition rate obtained from using the significant temporal period is 7% higher than that from using the whole epoch. In conclusion, we suggest using the beta-rebound power as a feature for the detection of motion versus resting state and using the Butterworth band pass filter with whole-brain CMI maps on signal of significant temporal period for the recognition of left- versus right-arm movement.
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