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Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm

Samdin, Siti Balqis and Ting, Chee-Ming and Shaikh Salleh, Sheikh Hussein and Hamedi, Mahyar and Mohd. Noor, A. B. (2014) Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm. IEEE Workshop on Statistical Signal Processing Proceedings . pp. 181-184.

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Official URL: http://dx.doi.org/10.1109/SSP.2014.6884605


This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We apply two derived frequency domain measures i.e. TV partial directed coherence (TV-PDC) and TV directed transfer function (TV-DTF), to investigate dynamic causal interactions between motor areas in discriminating motor imagery (MI) of left and right hand. Event-related changes of information flows around beta-band, in a unidirectional way between left and right hemispheres are observed during MI. A difference in inter-hemispheric connectivity patterns is found between left and right-hand movements, implying potential usage for BCI.

Item Type:Article
Uncontrolled Keywords:multivariate autoregressive model, state-space model, EM algorithm, dynamic cortical connectivity, EEG
Subjects:Q Science > QH Natural history
Divisions:Biosciences and Medical Engineering
ID Code:52733
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 11:54
Last Modified:30 Jun 2018 08:26

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