Nonlinear analysis of EEG in chess players View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Pawel Stepien, Wlodzimierz Klonowski, Nikolay Suvorov

ABSTRACT

The chess game is a good example of cognitive task which needs a lot of training and experience. The aim of this work is to compare applicability of two nonlinear methods - Higuchi Fractal Dimension and Empirical Mode Decomposition - in analysis of EEG data recorded during chess match. We analyzed data of three master chess players registered during their matches with computer program. We used two nonlinear methods: Higuchi Fractal Dimension that is a good and fast tool for analyzing signal complexity and modification of Empirical Mode Decomposition, called Sliding Window Empirical Mode Decomposition, that breaks down a signal into its monocomponents. Obtained results are compared with the resting state i.e. EEG during relax witch closed eyes. The analysis shows higher values of Higuchi Fractal Dimension during the thinking over chess moves than in the players’ rest state. There are no statistically significant differences in contribution of EEG bands to total power of EEG calculated with Sliding Window Empirical Mode Decomposition. Our results show beter applicability of Higuchi Fractal Dimension method for analysis of EEG signals related to chess tasks than that of Sliding Window Empirical Mode Decomposition. More... »

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URI

http://scigraph.springernature.com/pub.10.1140/epjnbp/s40366-015-0016-2

DOI

http://dx.doi.org/10.1140/epjnbp/s40366-015-0016-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050881876


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