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

• Published in: Frontiers in neurorobotics 2021-06, Vol.15, p.642607-642607
• Main Authors: Zhang, Xiaodong, Li, Hanzhe, Lu, Zhufeng, Yin, Gui
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Brain research; Cognitive models; coherence; EEG; Electroencephalography; Electromyography; EMG; Exoskeleton; Homology; homology analysis; Mathematical models; Mechanical properties; Motion detection; Nervous system; Neuroscience; numerical simulation; Perception; Robotics; Simulation
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2021.642607

In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing the linkage of exoskeleton robots to humans via electroencephalography (EEG) and electromyography (EMG). However, even the state of the art performance of lower limb voluntary movement intention decoding still faces many obstacles. In the following work, focusing on the perspective of the inner mechanism, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted. A mathematical model of EEG and EMG was built based on its mechanism, which consists of a neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model, and musculoskeletal biomechanical model. 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 the proposed model, a synchronous acquisition system for EEG and EMG was constructed to analyze 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 analyze 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, six subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1 Hz around movement onset, and the phase of EEG was leading the EMG. Both the simulation and experimental results revealed that EEG and EMG are 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 the feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection.