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Application of Multiscale Amplitude Modulation Features and Fuzzy C-Means to Brain–Computer Interface
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 discret...
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Published in: | Clinical EEG and Neuroscience 2012-01, Vol.43 (1), p.32-38 |
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container_title | Clinical EEG and Neuroscience |
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creator | Hsu, Wei-Yen Li, Yu-Chuan Hsu, Chien-Yeh Liu, Chien-Tsai Chiu, Hung-Wen |
description | 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). |
doi_str_mv | 10.1177/1550059411429528 |
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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).</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><pmid>22423549</pmid><doi>10.1177/1550059411429528</doi><tpages>7</tpages></addata></record> |
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subjects | Accuracy Algorithms Brain Brain - physiology Brain research Data processing Discriminant analysis Electrodes Electroencephalography - methods Evoked Potentials, Motor - physiology Female Fuzzy Logic Humans Male Methods Noise Older people Pattern Recognition, Automated - methods Sensitivity and Specificity User-Computer Interface |
title | Application of Multiscale Amplitude Modulation Features and Fuzzy C-Means to Brain–Computer Interface |
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