# Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

arXiv: https://arxiv.org/abs/2106.11169 · HTML: https://mlsanity.com/paper/2106.11169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Reservoir Computing Benchmarks: a tutorial review and critique](https://mlsanity.com/paper/2405.06561/) (2024)
- [Low Power Neuromorphic EMG Gesture Classification](https://mlsanity.com/paper/2206.02061/) (2022)
- [Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing](https://mlsanity.com/paper/2601.00020/) (2025)
- [TRIPROBE: Probing Task Separability Beyond Classification for XAI](https://mlsanity.com/paper/2609.18525/) (2026)

## Abstract (arXiv)

Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-the-art convolutional neural networks. The reservoir performance with regulated activity was found to be 89.72% for the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset. Therefore, the biologically-inspired computing paradigm, which is known for being power efficient, also proves to have a great potential when compared with conventional AI algorithms.