This off-line study aims to assess the performance of five classifiers commonly used in the brain-computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fisher's linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analysis (BLDA), linear support vector machine (LSVM) and Gaussian supported vector machine (GSVM). Moreover, different values for the decimation of the training dataset were tested. The results were evaluated both in terms of accuracy and written symbol rate with the data of 19 healthy subjects. No significant differences among the considered classifiers were found. The optimal decimation factor spanned a range from 3 to 24 (12 to 94 ms long bins). Nevertheless, performance on individually optimized classification parameters is not significantly different from a classification with general parameters (i.e. using an LDA classifier, about 48 ms long bins). © 2012 IOP Publishing Ltd.

A comparison of classification techniques for a gaze-independent P300-based brain-computer interface / F., Aloise; Schettini, Francesca; Aricò, Pietro; Salinari, Serenella; Babiloni, Fabio; Cincotti, Febo. - In: JOURNAL OF NEURAL ENGINEERING. - ISSN 1741-2560. - 9:4(2012), p. 045012. [10.1088/1741-2560/9/4/045012]

A comparison of classification techniques for a gaze-independent P300-based brain-computer interface

SCHETTINI, FRANCESCA;Aricò, Pietro;SALINARI, Serenella;BABILONI, Fabio;CINCOTTI, FEBO
2012

Abstract

This off-line study aims to assess the performance of five classifiers commonly used in the brain-computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fisher's linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analysis (BLDA), linear support vector machine (LSVM) and Gaussian supported vector machine (GSVM). Moreover, different values for the decimation of the training dataset were tested. The results were evaluated both in terms of accuracy and written symbol rate with the data of 19 healthy subjects. No significant differences among the considered classifiers were found. The optimal decimation factor spanned a range from 3 to 24 (12 to 94 ms long bins). Nevertheless, performance on individually optimized classification parameters is not significantly different from a classification with general parameters (i.e. using an LDA classifier, about 48 ms long bins). © 2012 IOP Publishing Ltd.
2012
01 Pubblicazione su rivista::01a Articolo in rivista
A comparison of classification techniques for a gaze-independent P300-based brain-computer interface / F., Aloise; Schettini, Francesca; Aricò, Pietro; Salinari, Serenella; Babiloni, Fabio; Cincotti, Febo. - In: JOURNAL OF NEURAL ENGINEERING. - ISSN 1741-2560. - 9:4(2012), p. 045012. [10.1088/1741-2560/9/4/045012]
File allegati a questo prodotto
File Dimensione Formato  
VE_2012_11573-478952.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.73 MB
Formato Adobe PDF
1.73 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/478952
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 7
  • Scopus 33
  • ???jsp.display-item.citation.isi??? 34
social impact