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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16Available to Purchase
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006
eBook Chapter
47 A Comparative Study of Time Delay Neural Networks and Hidden Markov Models for Electroencephalographic Signal Classification Available to Purchase
By
Amin Fazel
,
Amin Fazel
Dept. of CS and Electrical Eng. School of Computing and Eng.
University of Missouri Kansas City
Kansas City, Missouri
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Reza Derakhshani
Reza Derakhshani
Dept. of CS and Electrical Eng. School of Computing and Eng.
University of Missouri Kansas City
Kansas City, Missouri
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Page Count:
6
-
Published:2006
Citation
Fazel, A, & Derakhshani, R. "A Comparative Study of Time Delay Neural Networks and Hidden Markov Models for Electroencephalographic Signal Classification." Intelligent Engineering Systems through Artificial Neural Networks, Volume 16. Ed. Dagli, CH, Buczak, AL, Enke, DL, Embrechts, M, & Ersoy, O. ASME Press, 2006.
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In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM) for Electroencephalogram (EEG) signal classification. The specific focus of this study is Brain-Computer Interfacing (BCI), where near-real time detection of mental commands during a multi-channel EEG recording is desired. We argue that HMM and TDNN should be preferred over the rigid, one-size-fits-all methods of the more traditional EEG signal classifiers. To analyze the utility of modern classification methods for BCI, we compare and discuss the performance of our suggested TDNN and HMM EEG classifiers with the reported best results on BCI 2003 EEG benchmark dataset Ia.
Abstract
Introduction
Methods
Experimental Results
Conclusions and Future Work
References
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