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

TL;DR

This paper introduces a brain-muscle atlas framework that models the hierarchical pathway of movement from brain to muscle, improving decoding accuracy and enabling natural, real-time motor control in BCIs.

Contribution

The study presents the first brain-muscle atlas constructed from EEG-EMG data, enhancing motor decoding by explicitly modeling cortical-muscular mappings based on neuromuscular physiology.

Findings

Achieved a maximum correlation coefficient of 0.8314 in reconstructing muscle activation.

Successfully enabled real-time virtual elbow joint control with all participants.

Validated the atlas's ability to capture cortical-muscular mapping in offline experiments.

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.