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

S. Balqis Samdin; Chee Ming Ting; Sh Hussain Salleh; Mahyar Hamedi; A. B.Mohd Noor

doi:10.1109/SSP.2014.6884605

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

S. Balqis Samdin, Chee Ming Ting, Sh Hussain Salleh, Mahyar Hamedi, A. B.Mohd Noor

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	181-184
Number of pages	4
ISBN (Print)	9781479949755
DOIs	https://doi.org/10.1109/SSP.2014.6884605
Publication status	Published - 2014
Externally published	Yes
Event	IEEE Workshop on Statistical Signal Processing (SSP) 2014 - Gold Coast, Australia Duration: 29 Jun 2014 → 2 Jul 2014 https://ieeexplore.ieee.org/xpl/conhome/6872768/proceeding (Proceedings)

Workshop

Workshop	IEEE Workshop on Statistical Signal Processing (SSP) 2014
Abbreviated title	SSP 2014
Country/Territory	Australia
City	Gold Coast
Period	29/06/14 → 2/07/14
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6872768/proceeding (Proceedings)

Keywords

dynamic cortical connectivity
EEG
EM algorithm
Multivariate autoregressive model
state-space model

Access to Document

10.1109/SSP.2014.6884605

Cite this

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title = "Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm",

abstract = "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.",

keywords = "dynamic cortical connectivity, EEG, EM algorithm, Multivariate autoregressive model, state-space model",

author = "Samdin, {S. Balqis} and Ting, {Chee Ming} and Salleh, {Sh Hussain} and Mahyar Hamedi and Noor, {A. B.Mohd}",

year = "2014",

doi = "10.1109/SSP.2014.6884605",

language = "English",

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pages = "181--184",

booktitle = "2014 IEEE Workshop on Statistical Signal Processing, SSP 2014",

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Samdin, SB, Ting, CM, Salleh, SH, Hamedi, M & Noor, ABM 2014, Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm. in 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. IEEE, Institute of Electrical and Electronics Engineers, pp. 181-184, IEEE Workshop on Statistical Signal Processing (SSP) 2014, Gold Coast, Queensland, Australia, 29/06/14. https://doi.org/10.1109/SSP.2014.6884605

Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm. / Samdin, S. Balqis; Ting, Chee Ming; Salleh, Sh Hussain et al.
2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 181-184.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Noor, A. B.Mohd

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AB - 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.

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ER -

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

Abstract

Workshop

Keywords

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