An expectation-maximization algorithm based Kalman smoother approach for event-related desynchronization (ERD) estimation from EEG.
We consider the problem of event-related desynchronization (ERD) estimation. In existing approaches, model parameters are usually found manually through experimentation, a tedious task that often leads to suboptimal estimates. We propose an expectation-maximization (EM) algorithm for model parameter estimation that is fully automatic and gives optimal estimates. Further, we apply a Kalman smoother to obtain ERD estimates. Results show that the EM algorithm significantly improves the performance of the Kalman smoother. Application of the proposed approach to the motor-imagery EEG data shows that useful ERD patterns can be obtained even without careful selection of frequency bands.
Khan
Mohammad Emtiyaz
ME
Department of Computer Science, University of British Columbia, Vancouver, Canada, BC V6TIZ4, Canada. emtiyaz@gmail.com
Dutt
Deshpande Narayana
DN
D000465:Algorithms; D003198:Computer Simulation; D003344:Cortical Synchronization; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D016013:Likelihood Functions; D008959:Models, Neurological; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted