Application of multiscale amplitude modulation features and fuzzy C-means to brain-computer interface

Wei Yen Hsu, Yu Chuan Li, Chien-Yeh Hsu, Chien Tsai Liu, Hung Wen Chiu

研究成果: 雜誌貢獻文章

18 引文 (Scopus)

摘要

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).
原文英語
頁(從 - 到)32-38
頁數7
期刊Clinical EEG and Neuroscience
43
發行號1
DOIs
出版狀態已發佈 - 一月 2012

指紋

Brain-Computer Interfaces
Cluster Analysis
Wavelet Analysis
Discriminant Analysis
Information Systems
Fingers
Electroencephalography

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology

引用此文

Application of multiscale amplitude modulation features and fuzzy C-means to brain-computer interface. / Hsu, Wei Yen; Li, Yu Chuan; Hsu, Chien-Yeh; Liu, Chien Tsai; Chiu, Hung Wen.

於: Clinical EEG and Neuroscience, 卷 43, 編號 1, 01.2012, p. 32-38.

研究成果: 雜誌貢獻文章

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