Studying the use of fuzzy inference systems for motor imagery classification.
This paper studies the use of fuzzy inference systems (FISs) for motor imagery classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The results of the four studies achieved are promising as, on the analysed data, the used FIS was efficient, interpretable, showed good capabilities of rejecting outliers and offered the possibility of using a priori knowledge.
Fabien
Lotte
L
IRISA Rennes, F-35 042 Rennes, France. flotte@irisa.fr
Anatole
Lécuyer
L
Fabrice
Lamarche
L
Bruno
Arnaldi
A
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D017143:Fuzzy Logic; D006801:Humans; D007092:Imagination / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface