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

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