title: An expectation-maximization algorithm based Kalman smoother approach for event-related desynchronization (ERD) estimation from EEG.
abstract: 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.
authors.affiliation: Department of Computer Science, University of British Columbia, Vancouver, Canada, BC V6TIZ4, Canada. emtiyaz@gmail.com
authors.forename: Mohammad Emtiyaz
authors.initials: ME
authors.lastname: Khan
authors.forename: Deshpande Narayana
authors.initials: DN
authors.lastname: Dutt
mesh_terms: 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