Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods

Abstract

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by "decoding" neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.

A flexible intracortical brain-computer interface for typing using finger movements

“…The second problem is the non-linear representation of finger movements - the magnitude of neural activity is non-linearly related to the finger velocities (Willsey et al 2022; Temmar et al 2024), and the activities of individual fingers sum pseudo-linearly during simultaneous finger movements (Shah et al 2023). This calls for using a non-linear model to decode attempted finger velocities from neural activity.…”