Decoding of walking kinematics from non-invasively acquired electroencephalographic signals in stroke patients

Abstract

Our group has recently shown the feasibility of decoding kinematics of controlled walking from the lower frequency range of electroencephalographic (EEG) signals during a precision walking task. Here, we turn our attention to stroke survivors who have had

lesions resulting in hemiparetic gait. We recorded the EEG of stroke recovery patients during a precision treadmill walking task while tracking bilaterally the kinematics of the hips, knees, and ankles. In offline analyses, we applied a Wiener Filter and two unscented

Kalman filters of 1st and 10th orders to predict estimates of the kinematic parameters from scalp EEG. Decoding accuracies from four patients who have had cortical and subcortical strokes were comparable with previous studies in healthy subjects. With improved

decoding of EEG signals from damaged brains, we hope we can soon correlate activity to more intentional and normal-form walking that can guide users of a powered lower-body prosthetic or exoskeleton.