title: Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt.
abstract: 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 to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.
authors: josem.cano@upct.es
authors: Jose-Manuel
authors: JM
authors: Cano-Izquierdo
authors: Julio
authors: J
authors: Ibarrola
authors: Miguel
authors: M
authors: Almonacid
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D002965:Classification; D004569:Electroencephalography / Q000706:statistics & numerical data; D007839:Functional Laterality / Q000502:physiology; D017143:Fuzzy Logic; D006225:Hand / Q000294:innervation / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology*; D057567:Memory, Long-Term / Q000502:physiology; D008570:Memory, Short-Term; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009473:Neuronal Plasticity; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface