Research on Movement Intentions of Human's Left and Right Legs Based on EEG Signals

Abstract

Active rehabilitation training method can help stroke patients recover better and faster. However, the lower limb rehabilitation robot based on electroencephalogram (EEG) has low recognition accuracy now. A classification method based on EEG signals of motor imagery is proposed to enable patients to accurately control their left and right legs. Firstly, aiming at the unstable characteristics of EEG signals, an experimental protocl of motor imagery was constructed based on multi-joint motion coupling of left and right legs. The signals with time-frequency analysis and ERD/S analysis have proved the reliability and validity of the collected EEG signals. Then, the EEG signals generated by the protocol were preprocessed and Common Space Pattern (CSP) was used to extract their features. Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are adapted and their accuracy of classification results are compared. Finally, on the basis of the proposed classifier with excellent performance, the classifier is used in the active control strategy of the lower limb rehabilitation robot, and the experiment verified that the average accuracy of two volunteers in controlling the lower limb rehabilitation robot reached 95.1%. This research provides a good theoretical basis for the realization and application of brain-computer interface in rehabilitation training.