[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].
Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.
Schmidt
K
K
Institut für Medizinische Statistik, Informatik und Dokumentation, Klinikum der Friedrich Schiller Universität Jena. iks@imsid.uni-jena.de
Witte
H
H
D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography; D005583:Fourier Analysis; D006801:Humans; D012815:Signal Processing, Computer-Assisted