Spiking neural network decoders of finger forces from high-density intramuscular microelectrode arrays (opens in new tab)

Assistive technologies for restoring naturalistic finger control require continuous and robust decoding of motor intent, with high accuracy and low latency. Here, we present a spike-based decoding framework that exploits the dynamics of spiking neural networks (SNNs) to efficiently process motor unit activity extracted from high-density intramuscular microelectrode arrays. Using this framework, we demonstrate simultaneous and proportional decoding of individual finger forces from motor unit s...