Motor imagery EEG feature extraction based on fuzzy entropy

Fuzzy entropy has been applied to the classification of motor imagery. The fuzzy entropy features was able to indicate the event-related synchmnization/event-related desynchronization (ERS/ERD) time sequence changes effectively, and the difference between C3 and C4 EEG (encephalon electrical signal) fuzzy entropy was used as the feature of the left/right hand motor imagery tasks. Then, a linear fisher criterion classifier method was applied to classify the left/right hand motor imagery tasks. The results of simulation on the data of the 2003 BCI contest show that the best accuracy is 87.22%. The classification of consciousness tasks will be more effective by using the feature based on fuzzy entropy of EEG.