Estimation of event-related synchronization changes by a new TVAR method

Author

Kaipio, Jari P. ; Karjalainen, Pasi A.

Author_Institution

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

Volume

44

Issue

8

fYear

1997

Firstpage

649

Lastpage

656

Abstract

The modeling of nonstationary electroencephalogram (EEG) with time-varying autoregressive (TVAR) models is discussed. The classical least squares TVAR approach is modified so that prior assumptions about the signal can be taken into account in an optimal way. The method is then applied to the estimation of event-related synchronization changes in the EEG. The results show that the new approach enables effective estimation of the parameter evolution of the time-varying EEG with better time resolution compared to previous methods. The new method also allows single-trial analysis of the event-related synchronization.

Keywords

autoregressive processes; electroencephalography; medical signal processing; physiological models; electrodiagnostics; event-related synchronization; event-related synchronization changes estimation; parameter evolution; single-trial analysis; time resolution; time-varying EEG; Associate members; Brain modeling; Electroencephalography; Least squares approximation; Least squares methods; Parameter estimation; Physics; Polynomials; Signal resolution; Stochastic processes; Algorithms; Electroencephalography; Evoked Potentials, Visual; Female; Humans; Least-Squares Analysis; Models, Neurological; Reference Values; Time Factors;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.605421

Filename

605421