Paper Info
Title
Spatial Filter Feature Extraction Methods For P300 Bci Speller: A Comparison
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.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
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)
Field
DocType
ISSN
Computer science,Brain–computer interface,Event-related potential,Artificial intelligence,Algorithm design,Pattern recognition,Support vector machine,Speech recognition,Feature extraction,Independent component analysis,Linear discriminant analysis,Principal component analysis,Machine learning
Conference
1062-922X
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Eleni Chiou100.34
Sadasivan Puthusserypady218127.49