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An expectation-maximization algorithm based Kalman smoother approach for single-trial estimation of event-related potentials

Ting, Chee Ming and Samdin, Siti Balqis and Shaikh Salleh, Sheikh Hussain and Omar, Mohd. Hafizi and Ismail, Kamarulafizam (2012) An expectation-maximization algorithm based Kalman smoother approach for single-trial estimation of event-related potentials. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS . pp. 6534-6538. ISSN 1557-170X

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Official URL: http://dx.doi.org/10.1109/EMBC.2012.6347491

Abstract

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.

Item Type:Article
Uncontrolled Keywords:Biology society
Subjects:Q Science > QH Natural history
Divisions:Biosciences and Medical Engineering
ID Code:46588
Deposited By: Haliza Zainal
Deposited On:22 Jun 2015 13:56
Last Modified:17 Sep 2017 08:36

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