A generic neural network model to estimate populational neural activity for robust neural decoding. (May 2022) - Roy, Rinku
Abstract: Background: Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational motoneuron firing activities. Method: We implemented convolutional neural networks (CNNs) to learn the mapping from high-density electromyogram (HD-EMG) signals of forearm muscles to populational motoneuron firing frequency. We first extracted the spatiotemporal features of EMG energy and frequency maps to improve learning efficiency, given that EMG signals are intrinsically stochastic. We then established a generic neural network model by training on the populational neuron firing activities of multiple participants. Using a regression model, we continuously predicted individual finger forces in real-time. We compared the force prediction performance with two state-of-the-art approaches: a neuron-decomposition method and a classic EMG-amplitude method. Results: Our results showed that the generic CNN model outperformed the subject-specific neuron-decomposition method and the EMG-amplitude method, as demonstrated by a higher correlation coefficient between the measured and predicted forces, and a lower force prediction error. In addition, the CNN model revealed more stable force prediction performance over time. Conclusions: Overall, our approach provides a generic and efficient continuous neural decoding approach for real-time and robust human-robot interactions. Highlights: We implemented a convolutional neural network model to continuously decode fingertip forces. Neural network model predicted populational motoneuron firing activities based on high-density electromyogram signals. The developed model leads to accurate and stable force predictions over time. The developed model is generalizable to different participants, and is computationally efficient. The approach enables continuous neural decoding for real-time and robust human-robot interactions.