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Estimation of effective brain connectivity with dual kalman filter and EEG source localization methods

Rajabioun, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/s13246-017-0578-7
  3. Abstract:
  4. Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions. © 2017, Australasian College of Physical Scientists and Engineers in Medicine
  5. Keywords:
  6. Dual kalman filter ; Effective brain connectivity ; Multivariate autoregressive model ; Source localization methods ; Bandpass filters ; Brain ; Brain mapping ; Kalman filters ; Least squares approximations ; Mean square error ; Signal to noise ratio ; Brain source localization ; EEG source localization ; Effective brain connectivities ; Effective connectivities ; Least mean square error ; Low resolution brain electromagnetic tomographies ; Multivariate autoregressive models ; Source localization method ; Biomedical signal processing ; Algorithm ; Controlled study ; Dual Kalman filter ; electroencephalogram ; Functional connectivity ; Physical parameters ; Signal noise ratio ; Simulation
  7. Source: Australasian Physical and Engineering Sciences in Medicine ; Volume 40, Issue 3 , 2017 , Pages 675-686 ; 01589938 (ISSN)
  8. URL: https://link.springer.com/article/10.1007/s13246-017-0578-7