Motor imagery EEG feature extraction based on fuzzy entropy

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作者：
机构：
[1] Tian, Jing
[2] Luo, Zhizeng
来源：
Tian, J. (lansessl@gmail.com) | 1600年 / Huazhong University of Science and Technology卷 / 41期
关键词：
Biomedical signal processing - Extraction - Classification (of information) - Entropy - Image classification;
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摘要：
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.
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