Paper Info
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.
Year
DOI
Venue
2014
10.1109/SSP.2014.6884605
Statistical Signal Processing
Keywords
Field
DocType
Kalman filters,autoregressive processes,electroencephalography,expectation-maximisation algorithm,frequency-domain analysis,medical signal processing,smoothing methods,transfer functions,BCI,EM algorithm,KF estimation,KS,Kalman filter,Kalman smoother,MI,TV coefficients,TV directed transfer function,TV partial directed coherence,TV-DTF,TV-MAR models,TV-PDC,beta-band,dynamic causal interactions,dynamic cortical connectivity estimation,expectation-maximization algorithm,frequency domain measures,full noise covariate,induced instantaneous correlation modelling,information flows,inter-hemispheric connectivity patterns,left movements,motor imagery EEG,right-hand movements,scalp EEG,state-space framework,time-varying multivariate autoregressive model,volume conduction problem,EEG,EM algorithm,Multivariate autoregressive model,dynamic cortical connectivity,state-space model
Frequency domain,Autoregressive model,Pattern recognition,Expectation–maximization algorithm,Brain–computer interface,State-space representation,Speech recognition,Kalman filter,Artificial intelligence,Mathematics,Electroencephalography,Motor imagery
Conference
Citations 
PageRank 
References 
1
0.38
4
Authors
5
Name
Order
Citations
PageRank
S. Balqis Samdin1204.57
Chee-Ming Ting27213.17
S. Hussain3479.46
Mahyar Hamedi4183.84
A. B. Mohd Noor510.38