Brain signal analysis based on recurrences.
The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.
Schinkel
Stefan
S
Interdisciplinary Centre for Dynamics of Complex Systems, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany. schinkel@agnld.uni-potsdam.de
Marwan
Norbert
N
Kurths
Jürgen
J
D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D010775:Photic Stimulation; D011932:Reading; D012815:Signal Processing, Computer-Assisted; D014796:Visual Perception / Q000502:physiology