Using mental tasks transitions detection to improve spontaneous mental activity classification
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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.
KeywordsElectroencephalogram Brain-computer interface Asynchronous protocol Canonical variates transformation Distance based discriminant analysis Mental tasks transitions detection
- 1.Blankertz B, Dornhege G, Lemm S, Krauledat M, Curio G, Müller K-R (2006) The Berlin brain-computer interface: machine learning based detection of user specific brain states. J Univers Comput Sci 12:581–607Google Scholar
- 7.Millán J del R (2004) On the need for on-line learning in brain-computer interfaces.In: Proceedings of international joint conference on neural networksGoogle Scholar
- 9.Vaughan TM, Wolpaw JR (2006) The third international meeting on brain-computer interface technology: making a difference. IEEE Trans Rehab Eng 14:126–127Google Scholar