Unsupervised fuzzy c-means clustering for motor imagery EEG recognition

Wei Yen Hsu, Chi Yuan Lin, Wen Feng Kuo, Michelle Liou, Yung Nien Sun, Arthur Chih Hsin Tsai, Hsien Jen Hsu, Po Hsun Chen, I. Ru Chen

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4965-4976
Number of pages12
JournalInternational Journal of Innovative Computing, Information and Control
Issue number8
Publication statusPublished - 2011 Aug

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Unsupervised fuzzy c-means clustering for motor imagery EEG recognition'. Together they form a unique fingerprint.

Cite this