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. 2021 Jun 18:15:642607.
doi: 10.3389/fnbot.2021.642607. eCollection 2021.

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

Xiaodong Zhang  1   2 , Hanzhe Li  1 , Zhufeng Lu  1 , Gui Yin  1
Affiliations

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

Xiaodong Zhang et al. Front Neurorobot. .

Abstract

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.

Keywords: EEG; EMG; coherence; homology analysis; numerical simulation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Brain cognitive model with multiple-input multiple-output.
Figure 2
Figure 2
Neural pathway of lower limb voluntary movement.
Figure 3
Figure 3
Mathematical model of EEG and EMG for movement intention.
Figure 4
Figure 4
Simulation results of EEG. (A) The simulation results of the EEG signals, (B) The RP response from stimulation.
Figure 5
Figure 5
Simulation of the neuromuscular junction model. (A) The activation function of the model; (B) Simulation result of this model.
Figure 6
Figure 6
Simulation result of EMG. (A) EMG simulation signals, (B) Spectrum analysis of EMG simulation signals.
Figure 7
Figure 7
The Wavelet coherence of the simulation of EEG and EMG. (A) EEG-EMG coherence at early EMG response, (B) EEG-EMG coherence at middle EMG response, (C) EEG-EMG coherence at late EMG response.
Figure 8
Figure 8
The schematic description of the EEG and EMG acquisition system.
Figure 9
Figure 9
Overview of the time series of one session.
Figure 10
Figure 10
Preprocessed EEG and EMG signals from right leg movement. (A) Preprocessed EEG (representative channel FC1) and EMG signals, (B) Single and averaged RP of FC1.
Figure 11
Figure 11
The wavelet coherence of EEG and EMG from representative subject (S1). (A) EEG (FC1) and EMG signals from right leg movement, (B) EEG (C4) and EMG signals from left leg movement.
Figure 12
Figure 12
The wavelet coherence of EEG and EMG from other subjects. (A) Wavelet coherence of EEG (C1) and EMG signals from S2, (B) Wavelet coherence of EEG (C1) and EMG signals from S3, (C) Wavelet coherence of EEG (CP1) and EMG signals from S4, (D) Wavelet coherence of EEG (FC2) and EMG signals from S5.
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