HMMs and Coupled HMMs for multi-channel EEG classification
Published 25 June 2003
Saishang Zhong, Joydeep Ghosh
Citations125
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TL;DR
A novel distance coupled HMM is introduced and the performance of several HMM and CHMM models for a multi-channel EEG classification problem is compared to show that the multivariate HMM that has low computational complexity surprisingly outperforms all other models.
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
Computer ScienceNeuroscience
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