Decoding of the Walking States and Step Rates from Cortical Electrocorticogram Signals

TL;DR

This study demonstrates that electrocorticogram signals can accurately decode walking states and step rates, advancing brain-computer interface capabilities for restoring complex gait functions in individuals with paralysis.

Contribution

The paper introduces a novel decoder using ECoG signals to predict walking states and step rates with high accuracy, enabling more effective control of prosthetic walking devices.

Findings

Decoding accuracy for walking state reached 99.8%.

Average correlation coefficient for step rate decoding was 0.934.

ECoG signals can reliably predict complex gait parameters.

Abstract

Brain-computer interfaces (BCIs) have shown promising results in restoring motor function to individuals with spinal cord injury. These systems have traditionally focused on the restoration of upper extremity function; however, the lower extremities have received relatively little attention. Early feasibility studies used noninvasive electroencephalogram (EEG)-based BCIs to restore walking function to people with paraplegia. However, the limited spatiotemporal resolution of EEG signals restricted the application of these BCIs to elementary gait tasks, such as the initiation and termination of walking. To restore more complex gait functions, BCIs must accurately decode additional degrees of freedom from brain signals. In this study, we used subdurally recorded electrocorticogram (ECoG) signals from able-bodied subjects to design a decoder capable of predicting the walking state and step rate information. We recorded ECoG signals from the motor cortices of two individuals as they walked on a treadmill at different speeds. Our offline analysis demonstrated that the state information could be decoded from >16 minutes of ECoG data with an unprecedented accuracy of 99.8%. Additionally, using a Bayesian filter approach, we achieved an average correlation coefficient between the decoded and true step rates of 0.934. When combined, these decoders may yield decoding accuracies sufficient to safely operate present-day walking prostheses.