EEG2EMG-Net: A Deep Learning Framework for Continuous EEG-to-EMG Estimation for Real-World Applications

Estimating muscle activity from invasive neural recordings has been researched for decades with encouraging results. Such brain-computer interfaces (BCIs) are promising, as they offer an alternative pathway for motor control in patients with spinal cord injury. However, this application remains largely unexplored with non-invasive techniques, such as electroencephalography (EEG). Current non-invasive approaches typically rely on within-subject training, requiring both input (EEG) and output (electromyography, EMG) signals for calibration. This strategy is impractical for paralyzed patients unable to produce the required EMG. To address this, we developed EEG2EMG-Net a deep learning framework for continious EEG-to-EMG estimation
tailored for real-world clinical use. Our approach introduces a subject-adaptation strategy using synthetic EMG templates, eliminating the need for patient-specific
muscle recordings during calibration. Results from 20 healthy participants show that the fine-tuning method allows accurate estimation of the EMG (pearson correlation
coefficient: 0.69±0.32) while suppressing spurious activations during resting-state. These findings open new research avenues for non-invasive BCI alternatives aimed
at assisting motor function in individuals with severe impairments.