AUTHOR=Zhang Xiaodong , Li Hanzhe , Lu Zhufeng , Yin Gui 

TITLE=Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2021.642607

DOI=10.3389/fnbot.2021.642607

ISSN=1662-5218

ABSTRACT=One of the most exciting areas of robot is lower limb exoskeleton robot, which translate human’s limb movement intention by electroencephalography (EEG) or electromyography (EMG) signals into control commands that could operate robot. However, the existing detection methods of lower limb voluntary movement intention have an obstacle because its performance. In this paper, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted to analyze EEG and EMG signals. A mathematical model of EEG and EMG was built based on its mechanism, which consists of neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model and musculoskeletal biomechanical model. Both of the mechanism analysis and simulation results demonstrated that EEG and EMG signals were both excited by the same movement intention with a response time difference. To assess the efficiency of proposed model, a synchronous acquisition system for EEG and EMG was constructed to analysis the homology and response time difference from EEG and EMG signals in the limb movement intention. An effective method of wavelet coherence was used to analysis the internal correlation between EEG and EMG signals in the same limb movement intention. To further prove the effectiveness of the hypothesis in this paper, 6 subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1Hz around movement onset, and the phase of EEG was leading of EMG. Both of the simulation and experimental results revealed that EEG and EMG is homologous, and the response time of the EEG signals are earlier than EMG signals during the limb movement intention. This work can provide a theoretical basis for feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection.