Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS).
An adaptive spectrum estimation method for nonstationary electroencephalogram by means of time-varying autoregressive moving average modeling is presented. The time-varying parameter estimation problem is solved by Kalman filtering along with a fixed-interval smoothing procedure. Kalman filter is an optimal filter in the mean square sense and it is a generalization of other adaptive filters such as recursive least squares or least mean square. Furthermore, by using the smoother the unavoidable tracking lag of adaptive filters can be avoided. Due to the properties of Kalman filter and benefits of the smoothing the time-frequency resolution of the presented Kalman smoother spectra is extremely high. The presented approach is applied to estimation of event-related synchronization/desynchronization (ERS/ERD) dynamics of occipital alpha rhythm measured from three healthy subjects. With the Kalman smoother approach detailed spectral information can be extracted from single ERS/ERD samples.
Department of Applied Physics, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland. Mika.Tarvainen@uku.fi
Mika P
MP
Tarvainen
Jaana K
JK
Hiltunen
Perttu O
PO
Ranta-aho
Pasi A
PA
Karjalainen
D000465:Algorithms; D000513:Alpha Rhythm / Q000379:methods; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009778:Occipital Lobe / Q000502:physiology; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013598:Systems Theory