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

被引：37

作者：

Aloise, F. ^{[1
,2
]}

Schettini, F. ^{[1
,2
]}

Arico, P. ^{[1
,2
]}

Salinari, S. ^{[2
]}

Babiloni, F. ^{[1
,3
]}

Cincotti, F. ^{[1
]}

机构：

[1] Fdn Santa Lucia IRCCS, Neuroelect Imaging & BCI Lab, Rome, Italy

[2] Univ Roma La Sapienza, Deparment Comp & Syst Sci, Rome, Italy

[3] Univ Roma La Sapienza, Dept Human Physiol & Pharmacol, Rome, Italy

来源：

JOURNAL OF NEURAL ENGINEERING | 2012年 / 9卷 / 04期

关键词：

ATTENTION; COMMUNICATION;

D O I：

10.1088/1741-2560/9/4/045012

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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).

引用

页数：9