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Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm
Author(s) -
Liu Guangquan,
Zhang Dingguo,
Meng Jianjun,
Huang Gan,
Zhu Xiangyang
Publication year - 2012
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1293
Subject(s) - brain–computer interface , computer science , adaptability , artificial intelligence , classifier (uml) , electroencephalography , pattern recognition (psychology) , data set , fuzzy logic , linear discriminant analysis , adaptation (eye) , algorithm , set (abstract data type) , unsupervised learning , data mining , machine learning , psychology , ecology , physics , psychiatry , optics , biology , programming language
SUMMARY This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.