EEG and sEMG Decoding of Gait Spatiotemporal Parameters Based on Bidirectional Long Short-Term Memory Neural Network

To solve the problem of low correlation between continuous gait trajectory decoding results and actual trajectory by electroencephalography(EEG)signals, a gait parameter decoding method based on bidirectional long short-term memory(BiLSTM)neural network is proposed. Firstly, a gait spatiotemporal parameter decoding model based on this neural network is constructed, and the hyperparameters of the decoding model are designed according to the characteristics of EEG and surface electromyography(sEMG). Secondly, EEG, lower limb movement-related sEMG and lower limb joint movement signals are collected synchronously, and gait features of EEG and sEMG signals are analyzed. Thirdly, multi-channel EEG and lower limb movement-related sEMG signals are used as input of the decoding model, and gait related features are extracted automatically from EEG and sEMG fusion signals, and the nonlinear regression model between ankle joint motion and gait related features is constructed. Finally, a nonlinear regression model between gait related EEG signals and sEMG signals is constructed with multi-channel EEG as the input of the decoding model. The results show that compared with traditional support vector machines, the Pearson correlation coefficient of shape similarity between decoded trajectory and measured trajectory is improved by 0.12. Compared with the decoding methods using EEG, sEMG and fusion average absolute value of EEG-sEMG, the proposed method improves the Pearson correlation coefficient of shape similarity between decoded trajectory and measured trajectory by 0.81, 0.19 and 0.63, respectively. Our decoding method can realize decoding of part of sEMG waveform, the average Pearson correlation coefficient of decoded waveform and measured waveform is close to 0.5. It shows that the sEMG signals can be decoded from EEG signal which provides a new idea for the application of active continuous control of lower extremity exoskeleton. © 2022 Xi'an Jiaotong University. All rights reserved.