Using mental tasks transitions detection to improve spontaneous mental activity classification

被引：25
作者：
Galan, Ferran
Oliva, Francesc
Guardia, Joan
机构：
[1] Univ Barcelona, Fac Psicol, Dept Metodol Ciencies Comportament, Barcelona, Spain
[2] Univ Barcelona, Fac Biol, Dept Estadist, Barcelona, Spain
来源：
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING | 2007年 / 45卷 / 06期
关键词：
electroencephalogram; brain-computer interface; asynchronous protocol; canonical variates transformation; distance based discriminant analysis; mental tasks transitions detection;
D O I：
10.1007/s11517-007-0197-7
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents an algorithm based on canonical variates transformation (CVT) and distance based discriminant analysis (DBDA) combined with a mental tasks transitions detector (MTTD) to classify spontaneous mental activities in order to operate a brain-computer interface working under an asynchronous protocol. The algorithm won the BCI Competition III -Data Set V: Multiclass Problem, Continous EEG- achieving an averaged classification accuracy over three subjects of 68.65% (79.60, 70.31 and 56.02%, respectively) in a three-class problem.
引用
收藏
页码：603 / 609
页数：7
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