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Motor imagery EEG-based BCI has advantages in the assistance of human control of peripheral devices, such as the mobile robot or wheelchair, because the subject is not exposed to any stimulation and suffers no risk of fatigue. However, the intensive training necessary to recognize the numerous classes of data makes it hard to control these nonholonomic mobile systems accurately and effectively. This paper proposes a new approach which combines motor imagery EEG with the Adaptive Neural Fuzzydoi:10.3233/bme-151409 pmid:26405870 fatcat:npzegabc2jgkjfhdxs4hqj3num