Tracking of nonstationary EEG with Kalman smoother approach

Author

Tarvainen, M.P. ; Ranta-aho, P.O. ; Karjalainen, P.A.

Author_Institution

Dept. of Appl. Phys., Kuopio Univ., Finland

Volume

2

fYear

2001

fDate

2001

Firstpage

1796

Abstract

An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristics of real EEG during an eyes open/closed test. The observed tracking ability of Kalman smoother, compared to other methods considered, seemed to be better.

Keywords

adaptive Kalman filters; adaptive estimation; autoregressive moving average processes; electroencephalography; least mean squares methods; medical signal processing; smoothing methods; spectral analysis; synchronisation; Kalman smoother; adaptive ARMA modelling; alpha band characteristics; desynchronization/synchronization; eyes open/closed test; fixed-interval smoothing; minimum mean square estimator; nonstationary EEG; state space equations; time-varying power spectral density; tracking lag; Adaptive algorithm; Autoregressive processes; Brain modeling; Electroencephalography; Equations; Eyes; Kalman filters; Least squares approximation; Resonance light scattering; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7211-5

Type

conf

DOI

10.1109/IEMBS.2001.1020569

Filename

1020569