Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm
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 st...
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| Format: | Article |
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IEEE Xplore Digital Library
2014
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| _version_ | 1796859776869597184 |
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| author | Samdin, Siti Balqis Ting, Chee-Ming Shaikh Salleh, Sheikh Hussein Hamedi, Mahyar Mohd. Noor, A. B. |
| author_facet | Samdin, Siti Balqis Ting, Chee-Ming Shaikh Salleh, Sheikh Hussein Hamedi, Mahyar Mohd. Noor, A. B. |
| author_sort | Samdin, Siti Balqis |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T19:32:14Z |
| format | Article |
| id | utm.eprints-52733 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:32:14Z |
| publishDate | 2014 |
| publisher | IEEE Xplore Digital Library |
| record_format | dspace |
| spelling | utm.eprints-527332018-06-30T00:26:34Z http://eprints.utm.my/52733/ Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm Samdin, Siti Balqis Ting, Chee-Ming Shaikh Salleh, Sheikh Hussein Hamedi, Mahyar Mohd. Noor, A. B. QH Natural history 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. IEEE Xplore Digital Library 2014 Article PeerReviewed 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. http://dx.doi.org/10.1109/SSP.2014.6884605 DOI: 10.1109/SSP.2014.6884605 |
| spellingShingle | QH Natural history Samdin, Siti Balqis Ting, Chee-Ming Shaikh Salleh, Sheikh Hussein Hamedi, Mahyar Mohd. Noor, A. B. Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm |
| title | Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm |
| title_full | Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm |
| title_fullStr | Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm |
| title_full_unstemmed | Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm |
| title_short | Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm |
| title_sort | estimating dynamic cortical connectivity from motor imagery eeg using kalman smoother em algorithm |
| topic | QH Natural history |
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