Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity

TLDR

Neurological injury can severely impair walking, yet brain‑machine interfaces have only been tested for upper‑limb prostheses and not for restoring gait. The study proposes that brain‑machine interfaces could eventually enable paralyzed patients to walk again. Chronic recordings from cortical neuron ensembles were used to predict 3‑D leg joint coordinates and EMG activity during bipedal walking in rhesus macaques, with linear decoders and a switching decoder improving accuracy, especially as gait complexity increased and larger neuronal populations were required.

Abstract

The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain-machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real-time. Linear decoders extracted 3D coordinates of leg joints and leg muscle EMGs from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again.