Brain-Muscle Atlas: A novel framework for Motor Brain-Computer Interfaces

TL;DR

The study introduces the Brain-Muscle Atlas (BMA) to bridge the gap between cortical signals and muscle activation by explicitly modeling the brain–muscle–joint pathway. Using a sliding-window Transformer, EEG-to-EMG mappings are learned with EMG supervision, enabling online EEG to generate EMG-like representations that drive a virtual elbow through the Brain-Muscle-Elbow Interface (BMEI). Offline validation shows physiologically meaningful cortex–muscle mappings (mean SCC ~0.28, with higher coefficients in key muscles) and contributions from sensorimotor regions consistent with neurophysiology. Online experiments with ten participants demonstrate real-time, continuous elbow control, supporting the potential of this physiologically grounded, hierarchical decoding approach for next-generation non-invasive BCIs. The framework lays the groundwork for scalable, multi-joint Brain-Muscle Atlas systems that mimic natural motor control more closely than direct brain-to-machine mappings.

Abstract

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.

Figures (9)

• Figure 1: Figure 1. The concept of the Brain-Muscle Atlas and the corresponding architecture of the Brain-Muscle-Elbow Interface
• Figure 2: Figure 2. Electrode distribution
• Figure 3: Figure 3. Brain-muscle interface paradigm. (A)The situation of the subjects at the time of collection and the distribution of electrodes in the arm; (B)Distribution of electrodes for electroencephalogram and electromyography acquisition; (C)Experimental paradigm
• Figure 4: Figure 4. The framework of Brain-Muscle Atlas
• Figure 5: Figure 5. Direction-proportional electromyography control method