Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41024
Title: Kernel based hidden Markov model with applications to eeg signal classification
Authors: Xu, W.
Wu, J.
Huang, Z. 
Guan, C.
Keywords: EEG classification
Hidden Markov Model
Kernel method
Issue Date: 2005
Source: Xu, W.,Wu, J.,Huang, Z.,Guan, C. (2005). Kernel based hidden Markov model with applications to eeg signal classification. Proceedings of the 3rd IASTED International Conference on Biomedical Engineering 2005 : 401-404. ScholarBank@NUS Repository.
Abstract: To enhance the performance of hidden Markov models for EEG signal classification, we present here a new model referred to as kernel based hidden Markov model (KHMM). Due to the embedded HMM structure, this model is capable of capturing well the temporal change of a time-series signal. Furthermore, KHMM has better discrimination and generalization capability inherited from kernel methods. We evaluate the kernel based hidden Markov model by applying it to EEG signal classification when motor imagery is performed, yielding positive experimental results.
Source Title: Proceedings of the 3rd IASTED International Conference on Biomedical Engineering 2005
URI: http://scholarbank.nus.edu.sg/handle/10635/41024
ISBN: 0889864780
Appears in Collections:Staff Publications

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