Studying the Use of Fuzzy Inference Systems for Motor Imagery Classification

This paper studies the use of fuzzy inference systems (FIS) for motor imagery classification in electroencephalography (EEG)-based brain-computer interfaces (BCI). The results of the four studies achieved are promising as, on the analysed data, the used FIS was efficient, interpretable, showed good...

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Bibliographic Details
Published inIEEE transactions on neural systems and rehabilitation engineering Vol. 15; no. 2; pp. 322 - 324
Main Authors Fabien, Lotte, Anatole, Lcuyer, Fabrice, Lamarche, Bruno, Arnaldi
Format Journal Article
LanguageEnglish
Published United States IEEE 01.06.2007
Subjects
Algorithms
Biomedical measurements
Brain - physiology
Brain computer interfaces
Brain-computer interface (BCI)
classification
Classification algorithms
Clustering algorithms
Data analysis
Data mining
Electroencephalography
electroencephalography (EEG)
Electroencephalography - methods
Evoked Potentials, Motor - physiology
fuzzy inference system
Fuzzy Logic
Fuzzy systems
Humans
Imagination - physiology
Inference algorithms
motor imagery
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Training data
User-Computer Interface
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Summary:This paper studies the use of fuzzy inference systems (FIS) for motor imagery classification in electroencephalography (EEG)-based brain-computer interfaces (BCI). 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.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2007.897032