Using mental tasks transitions detection to improve spontaneous mental activity classification

Short Communication


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.


Electroencephalogram Brain-computer interface Asynchronous protocol Canonical variates transformation Distance based discriminant analysis Mental tasks transitions detection 


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Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  1. 1.Departament de Metodologia de les Ciències del Comportament, Facultat de PsicologiaUniversitat de BarcelonaBarcelonaSpain
  2. 2.IDIAP Research InstituteMartignySwitzerland
  3. 3.Departament d’Estadística, Facultat de BiologiaUniversitat de BarcelonaBarcelonaSpain
  4. 4.Departament de Metodologia de les Ciències del Comportament, Facultat de PsicologiaUniversitat de BarcelonaBarcelonaSpain

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