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

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