Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI
作者： Miguel Almonacid , Julio Ibarrola , Jose-Manuel Cano-Izquierdo
DOI: 10.1109/JSYST.2014.2360433
关键词:
摘要: This paper presents the influence of voting strategy to enhance classification rates in motor imagery brain–computer interface (BCI) systems. The is three-class problem left-hand movement imagination, right-hand and word generation. An algorithm based on neural networks fuzzy theory (S-dFasArt) used classify spontaneous mental activities from electroencephalogram signals, order operate a noninvasive BCI. allows obtaining several prediction models. among these results improves success classifier method. number models size data set have been analyzed some recommendation rules for practitioners. improvement more than 12% can be expected.
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