Improving Motor Imagery Classification With a New BCI Design Using Neuro-Fuzzy S-dFasArt
作者： J.-M Cano-Izquierdo , J. Ibarrola , M. Almonacid
DOI: 10.1109/TNSRE.2011.2169991
关键词:
摘要: 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 operate a noninvasive brain-computer interface. The focus is placed the three-class problem, left-hand movement imagination, right imagination word generation. allows supervised classification of temporal patterns improving rates BCI Competition III (Data Set V: multiclass continuous EEG). Using precomputed data supplied for competition following rules established there, new method S-dFasArt, along with rule prune voting strategy proposed. results have been compared other published methods their success rates.
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