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. 1990;9(1):47-50.
doi: 10.1109/51.62905.

Autoregressive and bispectral analysis techniques: EEG applications

T Ning  1 , J D Bronzino
Affiliations

Autoregressive and bispectral analysis techniques: EEG applications

T Ning et al. IEEE Eng Med Biol Mag. 1990.

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.

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