TY - JOUR

T1 - Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs.

AU - Xydeas, Costas

AU - Angelov, Plamen

AU - Chiao, Shih-Yang

AU - Reoullas, Michalis

N1 - 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.

PY - 2006/10

Y1 - 2006/10

N2 - 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

AB - 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

KW - EEG

KW - HMM networks

KW - On-line evolving clustering

KW - Evolving fuzzy rule-based classification

KW - DCS-publications-id

KW - art-785

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 24

KW - 82

KW - 63

U2 - 10.1016/j.compbiomed.2005.09.006

DO - 10.1016/j.compbiomed.2005.09.006

M3 - Journal article

VL - 36

SP - 1064

EP - 1083

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

IS - 10

ER -