DocumentCode :
1682731
Title :
HMMs and Coupled HMMs for multi-channel EEG classification
Author :
Zhong, Shi ; Ghosh, Joydeep
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Volume :
2
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
1154
Lastpage :
1159
Abstract :
A variety of Coupled HMMs (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. This paper introduces a novel distance coupled HMM. It then compares the performance of several HMM and CHMM models for a multi-channel EEG classification problem. The results show that, of all approaches examined, the multivariate HMM that has low computational complexity surprisingly outperforms all other models
Keywords :
computational complexity; electroencephalography; hidden Markov models; medical image processing; computational complexity; coupled hidden Markov models; multichannel EEG classification; multiple interdependent sequences; Artificial neural networks; Biological neural networks; Brain computer interfaces; Brain modeling; Data mining; Electroencephalography; Feature extraction; Hidden Markov models; Information analysis; Linear discriminant analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
ISSN :
1098-7576
Print_ISBN :
0-7803-7278-6
Type :
conf
DOI :
10.1109/IJCNN.2002.1007657
Filename :
1007657
Link To Document :
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