Direct Learning of Neuronal Firing Representations for Long-Term Motor Intent Predictions

Accurate hand movement prediction plays a pivotal role in advancing robotic control technologies. Neuronal firing signals, as the driving representation of motor intentions, offer a physiologically meaningful approach to decode motor commands. These representations are typically extracted using blind source separation techniques. However, the high computational intensity of these methods limits practical applications. Therefore, we directly learned neuronal firing representations from surface electromyogram (sEMG) signals via an efficient deep forest (DF) framework. Specifically, we first obtained populational neuronal firing rate signals as the ground truth. The DF model was trained to map sEMG signals directly to populational neuronal firing rate. To enable robust and continuous finger force predictions, we evaluated the DF framework on data obtained across multiple sessions, with an average session interval of 6.58 days. Our results revealed that the DF framework accurately maps sEMG amplitudes to neuronal firing representations, achieving comparable accuracy to source-separation-based method with significantly reduced computational time. The developed DF model also outperformed neural network models and other decision-tree-based ensemble methods. Furthermore, despite utilizing the same input features, the DF framework significantly outperformed the sEMG-amplitude approach, showcasing its capacity to capture complex neural drive information for more accurate finger force predictions. Moreover, the robustness test against noise interference revealed that the DF framework maintained stable performance under different noise levels. These findings highlight the potential of DF framework as an efficient solution for real-time robotic control applications.