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Improving Motor Imagery Classification With a New BCI Design Using Neuro-Fuzzy S-dFasArt

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3 Author(s)
Cano-Izquierdo, J.-M. ; ETSI Ind., Univ. Politec. de Cartagena, Cartagena, Spain ; Ibarrola, J. ; Almonacid, M.

This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.

Published in:

Neural Systems and Rehabilitation Engineering, IEEE Transactions on  (Volume:20 ,  Issue: 1 )

Date of Publication:

Jan. 2012

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