Adaptive Reservoir Genetic Algorithm with On-Line Decision Making
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
Ensemble learning methods for classifying EEG signals
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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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)