Neuromorphic Decoding of Spinal Motor Neuron Behaviour During Natural Hand Movements for a New Generation of Wearable Neural Interfaces

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached
${0}.{95} \pm {0}.{14}$
for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN.