In [1] Contreras-Vidal and colleagues have shown the feasibility of inferring the linear and angular kinematics of treadmill walking from scalp EEG. Here, we apply a discrete approach to the same problem of decoding the human gait. By reducing the gait process to a mere succession of Stance and Swing phases for each foot, the average decoding accuracy reached 93.71%. This is sufficient to design a gait descriptor that relies only on this classification of two possible states for each foot over time as input, which could complement the model-based continuous decoding method that lacks in some aspects (foot placement at landing, weight acceptance, etc.)[5]. A final implementation of this method could be used in a powered exoskeleton to help impaired people regain walking capability.
CITATION STYLE
Jorquera, F. S. M., Grassi, S., Farine, P. A., & Contreras-Vidal, J. L. (2013). Classification of stance and swing gait states during treadmill walking from non-invasive scalp electroencephalographic (EEG) signals. In Biosystems and Biorobotics (Vol. 1, pp. 507–511). Springer International Publishing. https://doi.org/10.1007/978-3-642-34546-3_81
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