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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.

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Author:Stefan Schinkel, Norbert Marwan, Jürgen KurthsORCiDGND
URL:http://www.sciencedirect.com/science/journal/09284257
DOI:https://doi.org/10.1016/j.jphysparis.2009.05.007
ISSN:0928-4257
Document Type:Article
Language:English
Year of first Publication:2009
Year of Completion:2009
Release Date:2017/03/25
Source:Journal of physiology Paris. - ISSN 0928-4257. - 103 (2009), 6, S. 315 - 323
Organizational units:Zentrale und wissenschaftliche Einrichtungen / Interdisziplinäres Zentrum für Dynamik komplexer Systeme
Peer Review:Referiert