Using mental tasks transitions detection to improve spontaneous mental activity classification.
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.
Galán
Ferran
F
Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, Universitat de Barcelona, Barcelona, Spain. fgalan@ub.edu
Oliva
Francesc
F
Guàrdia
Joan
J
D000465:Algorithms; D001921:Brain / Q000502:physiology; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods; D006225:Hand / Q000502:physiology; D006801:Humans; D007092:Imagination; D008606:Mental Processes / Q000145:classification / Q000502:physiology*; D009068:Movement / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface