Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs.

Xydeas, Costas and Angelov, Plamen and Chiao, Shih-Yang and Reoullas, Michalis (2006) Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs. Computers in Biology and Medicine, 36 (10). pp. 1064-1083. ISSN 0010-4825

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Two novel approaches to the problem of brain signals (electroencephalogram (EEG)) classification are introduced in the paper. The first method is based on a modular probabilistic network architecture that employs multiple dependant hidden Markov models (DM-HMM-D) on the input features (channels). The second method, eClass, is based on an on-line evolvable fuzzy rule base of EEG signal prototypes that represent each class and take into consideration the spatial proximity between input signals. Both approaches use supervised learning but differ in their mode of operation. eClass is designed recursively, on-line, and has an evolvable structure, while DM-HMM-D is trained off-line, in a block-based mode, and has a fixed architecture. Both methods have been extensively tested on real EEG data that is recorded during several experimental sessions involving a single female subject who is exposed to mild pain induced by a laser beam. Experimental results illustrate the viability of the proposed approaches and their potential in solving similar classification problems. (c) Elsevier

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Journal Article
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Computers in Biology and Medicine
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special issue on Intelligent Technologies in Medicine and Bioinformatics. The final, definitive version of this article has been published in the Journal, Computers in Biology and Medicine 36 (10), 2006, © ELSEVIER.
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?? eeghmm networkson-line evolving clusteringevolving fuzzy rule-based classificationdcs-publications-idart-785dcs-publications-creditsdspdcs-publications-personnel-id248263health informaticscomputer science applicationsqa75 electronic computers. computer sc ??
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30 Sep 2009 13:56
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10 Jul 2024 23:46