Using mental tasks transitions detection to improve spontaneous mental activity classification.
Med Biol Eng Comput
; 45(6): 603-9, 2007 Jun.
Article
em En
| MEDLINE
| ID: mdl-17541665
ABSTRACT
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, Continuous 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.
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Base de dados:
MEDLINE
Assunto principal:
Processos Mentais
Idioma:
En
Ano de publicação:
2007
Tipo de documento:
Article