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

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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).

Original languageEnglish
Pages (from-to)32-38
Number of pages7
JournalClinical EEG and Neuroscience
Volume43
Issue number1
DOIs
Publication statusPublished - Jan 2012

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

Keywords

  • amplitude modulation
  • brain-computer interface
  • discrete wavelet transform
  • electroencephalography
  • fuzzy c-means (FCM)

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology

Cite this

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.

In: Clinical EEG and Neuroscience, Vol. 43, No. 1, 01.2012, p. 32-38.

Research output: Contribution to journalArticle

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