TY - JOUR
T1 - Application of multiscale amplitude modulation features and fuzzy C-means to brain-computer interface
AU - Hsu, Wei Yen
AU - Li, Yu Chuan
AU - Hsu, Chien-Yeh
AU - Liu, Chien Tsai
AU - Chiu, Hung Wen
PY - 2012/1
Y1 - 2012/1
N2 - 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).
AB - 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).
KW - amplitude modulation
KW - brain-computer interface
KW - discrete wavelet transform
KW - electroencephalography
KW - fuzzy c-means (FCM)
UR - http://www.scopus.com/inward/record.url?scp=84859773750&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84859773750&partnerID=8YFLogxK
U2 - 10.1177/1550059411429528
DO - 10.1177/1550059411429528
M3 - Article
C2 - 22423549
AN - SCOPUS:84859773750
VL - 43
SP - 32
EP - 38
JO - Clinical EEG and Neuroscience
JF - Clinical EEG and Neuroscience
SN - 1550-0594
IS - 1
ER -