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.

中文翻译：

---

下肢自愿运动意图的脑电图和肌电图的同源性

下肢外骨骼机器人是机器人最令人兴奋的领域之一，它通过脑电图（EEG）或肌电图（EMG）信号将人的肢体运动意图转换为可以操作机器人的控制命令。然而，现有的下肢自愿运动意图的检测方法由于其性能而存在障碍。本文针对下肢自愿运动意图进行了脑电图和肌电图的同源性分析，以分析脑电图和肌电图信号。建立了脑电和肌电的数学模型，包括神经质量模型，神经肌肉连接模型，肌电产生模型，解码模型和骨骼肌生物力学模型。机理分析和仿真结果均表明，EEG和EMG信号均被相同的运动意图所激发，并具有响应时间差。为了评估所提出模型的效率，构建了一个用于脑电图和肌电图的同步采集系统，以分析肢体运动意图中脑电图和肌电图信号的同源性和响应时间差。小波相干的有效方法被用来分析同一肢体运动意图中脑电信号和肌电信号之间的内部相关性。为了进一步证明该假设的有效性，本实验涉及6位受试者。实验结果表明，运动开始时在1Hz附近有很强的EEG-EMG相干性，并且EEG的相位领先于EMG。仿真和实验结果均表明，EEG和EMG是同源的，在肢体运动意图期间，EEG信号的响应时间早于EMG信号。这项工作可以为基于EEG的脑电和EMG在人体运动检测中的预感知和融合感知的可行性提供理论依据。