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Asymmetric hemisphere modeling in an offline brain-computer interface

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4 Author(s)
B. Obermaier ; Inst. of Biomed. Eng., Graz Univ. of Technol., Austria ; C. Munteanu ; A. Rosa ; G. Pfurtscheller

Classification of the electroencephalogram (EEG) during motor imagery of the left or right hand can be performed using a classifier comprising two hidden Markov models (HMMs) describing the spatio-temporal patterns related to the imagination. Due to the known asymmetries during motor imagery of rightand left-hand movement, an HMM-based classifier allowing asymmetrical structures is introduced. The comparison between such a system and a symmetrical one is based on the error rate of classification. The results for EEG data collected during 20 sessions from five subjects demonstrate a significant improvement of 9% for the classification accuracy for the asymmetric classifiers. The selection of the DAM for classification is done using a variant of genetic algorithms (GAs); namely, the adaptive reservoir genetic algorithm (ARGA)

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)  (Volume:31 ,  Issue: 4 )