Autoregressive and bispectral analysis techniques: EEG applications

Author

Ning, T. ; Bronzino, J.D.

Author_Institution

Dept. of Eng., Trinity Coll., Hartford, CT, USA

Volume

9

Issue

1

fYear

1990

fDate

3/1/1990 12:00:00 AM

Firstpage

47

Lastpage

50

Abstract

Some basic properties of autoregressive (AR) modeling and bispectral analysis are reviewed, and examples of their application in electroencephalography (EEG) research are provided. A second-order AR model was used to score cortical EEGs in order. In tests performed on five adult rats to distinguish between different vigilance states such a quiet-waking (QW), rapid-eye-movement (REM), and slow-wave sleep (SWS), SWS activity was correctly identified over 96% of the time, and a 95% agreement rate was achieved in recognizing the REM sleep stage. In a bispectral analysis of the rat EEG, third-order cumulant (TOC) sequences of 32 epochs belonging to the same vigilance state were estimated and then averaged. Preliminary results have shown that bispectra of hippocampal EEGs during REM Sleep exhibit significant quadratic phase couplings between frequencies in the 6-8-Hz range, associated with the theta rhythm.<>

Keywords

electroencephalography; spectral analysis; 6 to 8 Hz; Fourier transform; adult rats; autoregressive modelling; bispectral analysis techniques; cortical EEG; electroencephalography; hippocampal EEG; quadratic phase couplings; quiet-waking; rapid-eye-movement; slow-wave sleep; theta rhythm; third order cumulant sequences; vigilance states; Autocorrelation; Autoregressive processes; Brain modeling; Electroencephalography; Equations; Frequency; Gaussian distribution; Parametric statistics; Random processes; White noise;

fLanguage

English

Journal_Title

Engineering in Medicine and Biology Magazine, IEEE

Publisher

ieee

ISSN

0739-5175

Type

jour

DOI

10.1109/51.62905

Filename

62905