期刊
CLINICAL EEG AND NEUROSCIENCE
卷 43, 期 1, 页码 32-38出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1550059411429528
关键词
amplitude modulation; brain-computer interface; discrete wavelet transform; electroencephalography; fuzzy c-means (FCM)
资金
- Ministry of Education, Taiwan
This study proposed a recognized system for electroencephalogram (EEG) data classification. In addition to the wavelet-based amplitude modulation (AM) features, the fuzzy c-means (FCM) clustering is used for the discriminant of left finger lifting and resting. The features are extracted from discrete wavelet transform (DWT) data with the AM method. The FCM is then applied to recognize extracted features. Compared with band power features, k-means clustering, and linear discriminant analysis (LDA) classifier, the results indicate that the proposed method is satisfactory in applications of brain-computer interface (BCI).
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