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, 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.
mental tasks transitions detection canonical variates transformation distance based discriminant analysis brain-computer interface asynchronous protocol electroencephalogram