NDLI: Linear dynamic models for classification of single-trial EEG

Content Provider	IEEE Xplore Digital Library
Author	Samdin, S.B. Chee-Ming Ting Salleh, S.-H. Ariff, A.K. Mohd Noor, A.B.
Copyright Year	2013
Description	Author affiliation: Centre for Biomed. Eng., UTM, Skudai, Malaysia (Samdin, S.B.; Salleh, S.-H.; Ariff, A.K.) \|\| Center for Biomed. Eng. (CBE), UTM, Skudai, Malaysia (Chee-Ming Ting)
Abstract	This paper investigates the use of linear dynamic models (LDMs) to improve classification of single-trial EEG signals. Existing dynamic classification of EEG uses discrete-state hidden Markov models (HMMs) based on piecewise-stationary assumption, which is inadequate for modeling the highly non-stationary dynamics underlying EEG. The continuous hidden states of LDMs could better describe this continuously changing characteristic of EEG, and thus improve the classification performance. We consider two examples of LDM: a simple local level model (LLM) and a time-varying autoregressive (TVAR) state-space model. AR parameters and band power are used as features. Parameter estimation of the LDMs is performed by using expectation-maximization (EM) algorithm. We also investigate different covariance modeling of Gaussian noises in LDMs for EEG classification. The experimental results on two-class motor-imagery classification show that both types of LDMs outperform the HMM baseline, with the best relative accuracy improvement of 14.8% by LLM with full covariance for Gaussian noises. It may due to that LDMs offer more flexibility in fitting the underlying dynamics of EEG.
Starting Page	4827
Ending Page	4830
File Size	555928
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610628
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Brain models Electroencephalography Computational modeling Mathematical model Accuracy brain computer interface (BCI) Linear dynamic model (LDM) hidden Markov model (HMM)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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