EEG Recognition Algorithm of Motor Imagery Based on Fuzzy Symbolic complexity

A method of Electroencephalogram(EEG)feature extraction and recognition of motor imagery based on fuzzy symbolic complexity is proposed.Introduce Fuzzy algorithm in the EEG complexity fine-grained and multi-symbol metrics,fuzzy processing with the sigmoid function,and calculate fuzzy symbolic complexity by logical judgment.Select the fine graining index n as 2,extract fuzzy symbolic complexity as a characteristic value,and finally use the Support Vector Machine to classify EEG consciousness task of motor imagery.The experimental result shows that the average classification accuracy of EEG of two hands motor imagery can reach 88.67% to the highest owing to the classification method featured by fuzzy symbolic complexity,which excels the algorithm of binary quantification Lempel-Ziv complexity.