Heterogeneous Population Encoding for Multi-joint Regression using sEMG Signals

TL;DR

This paper investigates how heterogeneous neural population encoding of sEMG signals enhances the robustness and scalability of multi-joint movement regression for human-machine interfaces, emphasizing the role of variability and population size.

Contribution

It introduces a feature-based encoding approach using diverse LIF neuron populations, demonstrating that variability improves decoding robustness over single-neuron encoders.

Findings

Variability in neuron parameters enhances robustness.

Heterogeneous populations outperform homogeneous ones.

Optimizing population size improves decoding performance.

Abstract

Regression-based decoding of continuous movements is essential for human-machine interfaces (HMIs), such as prosthetic control. This study explores a feature-based approach to encoding Surface Electromyography (sEMG) signals, focusing on the role of variability in neural-inspired population encoding. By employing heterogeneous populations of Leaky Integrate-and- Fire (LIF) neurons with varying sizes and diverse parameter distributions, we investigate how population size and variability in encoding parameters, such as membrane time constants and thresholds, influence decoding performance. Using a simple linear readout, we demonstrate that variability improves robustness and generalizability compared to single-neuron encoders. These findings emphasize the importance of optimizing variability and population size for efficient and scalable regression tasks in spiking neural networks (SNNs), paving the way for robust, low-power HMI implementations.