Brain signal analysis based on recurrences

被引：32

作者：

Schinkel, Stefan ^{[1
]}

Marwan, Norbert ^{[1
,2
]}

Kurths, Juergen ^{[2
,3
]}

机构：

[1] Univ Potsdam, Interdisciplinary Ctr Dynam Complex Syst, D-14469 Potsdam, Germany

[2] Potsdam Inst Klimafolgenforsch EV, D-14412 Potsdam, Germany

[3] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany

来源：

JOURNAL OF PHYSIOLOGY-PARIS | 2009年 / 103卷 / 06期

关键词：

EEG; ERP; RP; RQA; Order patterns; N400; QUANTIFICATION ANALYSIS; PLOTS; POTENTIALS; SERIES;

D O I：

10.1016/j.jphysparis.2009.05.007

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：315 / 323

页数：9

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