206 Real-Time, High-Velocity Prosthetic Finger Movements Using Brain-Machine Interfaces with Biomimetic Artificial Neural Networks

Abstract

INTRODUCTION: Injuries that cause hand paralysis are devastating, but brain-machine interfaces (BMIs) offer hope to those with lost function. Unfortunately, brain-controlled prostheses have yet to function at speeds similar to the native limb. METHODS: Two male rhesus macaques were implanted with Utah arrays in motor cortex and trained to perform a two-dimensional finger task using a manipulandum. The spike-band power, a low power proxy for spiking rate, informed a five-layer neural network decoder that is first trained in manipulandum-control mode. NN was then used to decode finger trials and further refined by the ReFIT technique developed for the Kalman filter. Over 4700 trials, across 8 days, were conducted using either the ReFIT neural network (RN), NN, or RK decoders, impemented without online hyperparameter tuning, and performance was compared. RESULTS: The RN decoder produced finger velocities, 1.35 ± 0.04 u/sec (mean ± SEM) for Mky N and 0.94 ± 0.04 u/sec for Mky W, that were higher than RK velocities of 0.55 ± 0.02 u/sec for Mky N and 0.39 ± 0.04 u/sec for Mky W, where u denotes arbitrary distance such that 1 was full flexion and 0 full extension. Averaging across days, trials were completed in 1270 ± 30 ms (Mky N) and 2220 ± 70 ms (Mky W) for RN vs. 1940 ± 50 ms (Mky N) and 3310 ± 130 ms (Mky W) for RK. CONCLUSION: Using an architecture loosely inspired by biological motor pathways, this novel neural network decoder substantially outperforms the current state-of-the-art by achieving higher velocities more similar to naturalistic finger movements.