Classification of Motor Imagery Based on Sample Entropy

The classification method of motor imagery based on sample entropy(SampEn) of electroencephalogram(EEG) is proposed.The SampEn of EEG in primary sensorimotor area and its dynamic properties during left-right hand motor imagination are analyzed.Experiment results show that SampEn can reflect the EEG pattern changes of left-right hand motor imageries and have clear physiological explanation.Fisher LDA(Linear Discriminant Analysis) is used to dynamically classify the left-right hand movement imageries based on SampEn features,and an average maximum classification accuracy of 87.8% is obtained.Finally,a fast algorithm of SampEn with minimum computation cost and high speed is introduced,which can meet the requirements of real-time brain-computer interface(BCI) system.