JOURNAL ARTICLE

Visual modifications on the P300 speller BCI paradigm

Mathew SalvarisFrancisco Sepulveda

Year: 2009 Journal:   Journal of Neural Engineering Vol: 6 (4)Pages: 046011-046011   Publisher: IOP Publishing

Abstract

The best known P300 speller brain-computer interface (BCI) paradigm is the Farwell and Donchin paradigm. In this paper, various changes to the visual aspects of this protocol are explored as well as their effects on classification. Changes to the dimensions of the symbols, the distance between the symbols and the colours used were tested. The purpose of the present work was not to achieve the highest possible accuracy results, but to ascertain whether these simple modifications to the visual protocol will provide classification differences between them and what these differences will be. Eight subjects were used, with each subject carrying out a total of six different experiments. In each experiment, the user spelt a total of 39 characters. Two types of classifiers were trained and tested to determine whether the results were classifier dependant. These were a support vector machine (SVM) with a radial basis function (RBF) kernel and Fisher's linear discriminant (FLD). The single-trial classification results and multiple-trial classification results were recorded and compared. Although no visual protocol was the best for all subjects, the best performances, across both classifiers, were obtained with the white background (WB) visual protocol. The worst performance was obtained with the small symbol size (SSS) visual protocol.

Keywords:
Brain–computer interface Linear discriminant analysis Computer science Support vector machine Pattern recognition (psychology) Artificial intelligence Protocol (science) Classifier (UML) Linear classifier Speech recognition Electroencephalography Psychology

Metrics

192
Cited By
7.51
FWCI (Field Weighted Citation Impact)
25
Refs
0.98
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

EEG and Brain-Computer Interfaces
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Neural dynamics and brain function
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Blind Source Separation Techniques
Physical Sciences →  Computer Science →  Signal Processing

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