Spatial Filter Feature Extraction Methods for P300 BCI Speller: A Comparison

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Abstract

Brain Computer Interface (BCI) systems enable subjects affected by neuromuscular disorders to interact with the outside world. A P300 speller uses Event Related Potential (ERP) components, generated in the brain in the presence of a target
stimulus, to extract information about the user’s intent. Several methods have been proposed for spatial filtering and classification of the P300 components. In this study, xDAWN algorithm, Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methods are used and evaluated based on the classification performance of two different classifiers, namely the Support Vector Machine (SVM) and Fisher’s Linear Discriminant Analysis (FLDA). In addition, it is shown that the incorporation of some prior knowledge regarding the location of P300 elicitation on the scalp can reduce the computational load while maintaining or even improving the classification performance.
Original languageEnglish
Title of host publicationProceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics
Number of pages5
PublisherIEEE
Publication date2017
Pages003859-003863
ISBN (Print)978-1-5090-1897-0
DOIs
Publication statusPublished - 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016
http://smc2016.org/

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics
CountryHungary
CityBudapest
Period09/10/201612/10/2016
Internet address

Keywords

  • Brain Computer Interface (BCI)
  • P300-speller
  • Event Related Potential (ERP)
  • xDAWN
  • Principal Component Analysis (PCA)
  • Independent Component Analysis (ICA)
  • Fisher’s Linear Discriminant Analysis (FLDA)
  • Support Vector Machine (SVM)

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