Motor brain-computer interfaces (BCIs) enable the control of external devices by decoding neural signals. However, most existing systems rely on a direct "brain-machine" mapping, overlooking the hierarchical physiological pathway of natural movement, namely the "brain-muscle-joint" cascade. Due to the lack of explicit modeling and enhancement of this pathway, current systems are often constrained by the low amplitude and high noise of EEG signals, resulting in motor outputs that are unstable, discontinuous, and insufficiently natural.To address these limitations, this study introduces the concept of a brain-muscle atlas, designed to systematically characterize the mapping between motor cortical activity and corresponding muscle activation, thereby establishing a movement decoding framework that better aligns with neuromuscular physiology. Using synchronously recorded EEG-EMG data, we constructed the first brain-muscle atlas for elbow flexion-extension, achieving a structured mapping from cortical activity to muscle activation.Offline experiments demonstrate that the proposed atlas accurately reconstructs the temporal activation patterns of primary elbow agonists, achieving a maximum correlation coefficient of 0.8314, thereby validating its ability to capture cortical-muscular mapping. Furthermore, by leveraging atlas-derived muscle activation representations, we enabled continuous real-time control of a virtual elbow joint. All ten participants successfully completed the online flexion-extension task, indicating that the system robustly extracts motor intent even under low-SNR EEG conditions.

翻译：运动脑机接口通过解码神经信号实现对外部设备的控制。然而，现有系统大多依赖于直接的“脑-机”映射，忽略了自然运动的层级生理通路，即“脑-肌-关节”级联过程。由于缺乏对该通路的显式建模与增强，当前系统常受限于脑电信号的低幅值与高噪声，导致运动输出不稳定、不连续且自然度不足。为突破这些局限，本研究提出脑肌图谱概念，旨在系统表征运动皮层活动与对应肌肉激活之间的映射关系，从而建立更符合神经肌肉生理机制的运动解码框架。利用同步采集的脑电-肌电数据，我们构建了首个肘关节屈伸运动的脑肌图谱，实现了从皮层活动到肌肉激活的结构化映射。离线实验表明，所提图谱能准确重建主要肘部主动肌的时序激活模式，最高相关系数达0.8314，验证了其捕捉皮层-肌肉映射的能力。进一步地，通过利用图谱衍生的肌肉激活表征，我们实现了对虚拟肘关节的连续实时控制。十名受试者均成功完成在线屈伸任务，表明该系统即使在低信噪比脑电条件下仍能稳健提取运动意图。