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A method for estimating long-range power law correlations from the electroencephalogram [An article from: Biological Psychology]
Book Details
Author(s)P.A. Watters, F. Martin
PublisherElsevier
ISBN / ASINB000RR0MYA
ISBN-13978B000RR0MY2
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Biological Psychology, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
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 @a=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.
Description:
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 @a=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.
