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  4. Significance of EEG-electrode combinations while calculating filters with common spatial patterns Signifikanz von EEG-Elektroden-Kombinationen bei der Berechnung von Filtern mittels Common Spatial Patterns
 
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2024
Journal Article
Title

Significance of EEG-electrode combinations while calculating filters with common spatial patterns Signifikanz von EEG-Elektroden-Kombinationen bei der Berechnung von Filtern mittels Common Spatial Patterns

Abstract
Objective: Common spatial pattern (CSP) is a common filter technique used for pre-processing of electroencephalography (EEG) signals for imaginary movement classification tasks. It is crucial to reduce the amount of features especially in cases where few data is available. Therefore, different approaches to reduce the amount of electrodes used for CSP calculation are tried in this research. Methods: Freely available EEG datasets are used for the evaluation. To evaluate the approaches a simple classification pipeline consisting mainly of the CSP calculation and linear discriminant analysis for classification is used. A baseline over all electrodes is calculated and compared against the results of the approaches. Results: The most promising approach is to use the ability of CSP to provide information about the origin of the created filter. An algorithm that extracts the important electrodes from the CSP utilizing these information is proposed. The results show that using subject specific electrode positions has a positive impact on accuracy for the classification task. Further, it is shown that good performing electrode combinations in one session are not necessarily good performing electrodes in another session of the same subject. In addition to the combinations calculated using the de-veloped algorithm, 26 additional electrode combinations are proposed. These can be taken into account when selecting well-performing electrode combinations. In this research we could achieve an accuracy im-provement of over 10%. Conclusions: Carefully selecting the correct electrode combination can improve accuracy for classifying an imaginary movement task.
Author(s)
Wetzel, Dominik
Westsächsische Hochschule Zwickau (FH)
Jacobs, Paul Philipp
Universität Leipzig
Winkler, Dirk
Universität Leipzig
Grunert, Ronny  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Journal
Gms German Medical Science
DOI
10.3205/000334
Language
German
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Keyword(s)
  • algorithms

  • electroencephalography

  • statistics

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