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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Bibliographic Details
Published in:Clinical EEG and Neuroscience 2012-01, Vol.43 (1), p.32-38
Main Authors: Hsu, Wei-Yen, Li, Yu-Chuan, Hsu, Chien-Yeh, Liu, Chien-Tsai, Chiu, Hung-Wen
Format: Article
Language:English
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
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Summary: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).
ISSN:1550-0594
2169-5202
DOI:10.1177/1550059411429528