Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI

This paper presents the influence of the voting strategy to enhance the classification rates in motor imagery of brain-computer interface (BCI) systems. The motor imagery is the three-class problem of left-hand movement imagination, right-hand movement imagination, and word generation. An algorithm based on neural networks and fuzzy theory (S-dFasArt) is used to classify spontaneous mental activities from electroencephalogram signals, in order to operate a noninvasive BCI. This algorithm allows obtaining several prediction models. The voting among these prediction results improves the success rates of the classifier method. The number of models and the size of the data set have been analyzed obtaining some recommendation rules for practitioners. An improvement of more than 12% can be expected.