Real-time Hand Motion Reconstruction System for Trans-Humeral Amputees Using EEG and EMG

Predicting a hand’s position using only biosignals is a complex problem that has not been completely solved. The only reliable solution currently available requires invasive surgery. The attempts using non-invasive technologies are rare, and usually have led to lower correlation values (CVs) between the real and the reconstructed position than those required for real-world applications. In this study, we propose a solution for reconstructing the hand’s position in three dimensions using electroencephalography (EEG) and electromyography (EMG) to detect from the shoulder area. This approach would be valid for most trans-humeral amputees. In order to find the best solution, we tested four different architectures for the system based on artificial neural networks. Our results show that it is possible to reconstruct the hand’s motion trajectory with a CV up to 0.809 compared to a typical value in the literature of 0.6. We also demonstrated that both EEG and EMG contribute jointly to the motion reconstruction. Furthermore, we discovered that the system architectures do not change the results radically. In addition, our results suggest that different motions may have different brain activity patterns that could be detected through EEG. Finally, we suggest a method to study non-linear relations in the brain through the EEG signals, which may lead to a more accurate system.