Unsupervised Fuzzy C-Means Clustering for Motor Imagery EEG Recognition

勤益科大機構典藏 > 電資學院 > 資訊工程系(所) > 【資訊工程系所】期刊論文 > Item 987654321/6869

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題名:	Unsupervised Fuzzy C-Means Clustering for Motor Imagery EEG Recognition
作者:	林基源
貢獻者:	資訊工程系
日期:	2011-08
上傳時間:	2017-12-17 13:06:01 (UTC+8)
摘要:	In this study, an electroencephalogram (EEG) recognition system is proposed on single-trial motor imagery (MI) data. Fuzzy c-means (FCM) clustering is used for the unsupervised recognition of left and right MI data by combining with selected active segments and multiresolution fractal features. Active segment selection is used to detect active segments situated at most discriminable areas in the time-frequency domain. The multiresolution fractal features are then extracted by using modified fractal dimension from wavelet data. Finally, FCM clustering is used as the discriminant of MI features. The FCM clustering is an adaptive approach suitable for the clustering of non-stationary biomedical signals. Compared with several popular supervised classifiers, FCM clustering provides a potential for BCI application. © 2011 ICIC International. (36 refs)
關聯:	International Journal of Innovative Computing, Information and Control
顯示於類別:	[資訊工程系(所)] 【資訊工程系所】期刊論文

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