title: A method for estimating long-range power law correlations from the electroencephalogram.
abstract: Recent research has found long-range electroencephalogram (EEG) power law correlations, indicating time scale invariance. However, the EEG is also rather noisy, displaying short-term decorrelation like white noise--i.e., what is scale invariant at one time period may disappear in the next. The paradoxical combination of short-range divergence, but long-range correlations, suggests that any long-range correlations detected in one sample may be spurious, since they could be related to amplitude fluctuations. To overcome this problem, this paper suggests a new technique for analysing EEG signals segmented by zero-crossings, using detrended fluctuation analysis (DFA), evaluated across two time periods (TIME) and different sites (SITE). A mean scaling exponent across all subjects and sites of alpha = 0.67 was observed. MANOVA analysis indicates no significant main effect for TIME or interaction with SITE, suggesting that the zero-crossing method may be successful in determining the fractal nature of EEG dynamics across relatively long time scales.
authors.affiliation: Mathematical and Information Sciences, Commonwealth Scientific and Industrial Research Organisation, Locked Bag 17, North Ryde, NSW 1670, Australia. paul.watters@csiro.au
authors.forename: Paul A
authors.initials: PA
authors.lastname: Watters
authors.forename: Frances
authors.initials: F
authors.lastname: Martin
mesh_terms: D002540:Cerebral Cortex / Q000502:physiology; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D008962:Models, Theoretical; D010507:Periodicity; D015203:Reproducibility of Results