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Linear dynamic models for classification of single-trial EEG

Samdin, S. Balqis and Ting, Chee-Ming and Salleh, Sh-Hussain and Ariff, A.K. and Noor, A. B. Mohd (2013) Linear dynamic models for classification of single-trial EEG. In: 2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), JUL 03-07, 2013, Osaka, Japan.

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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. Keywords

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Linear Dynamic Model (LDM)
Subjects:Q Science > QH Natural history
Divisions:Biosciences and Medical Engineering
ID Code:51144
Deposited By: Haliza Zainal
Deposited On:27 Jan 2016 09:53
Last Modified:11 Jul 2017 12:38

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