  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.
