A Noninvasive Neural Interface for Control of a Powered Lower Limb Prosthesis



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Limb amputation results in a physical disability that causes activities of daily living to become difficult or impossible for the amputee. Current lower-limb prostheses provide limited control and result in only modest improvements in mobility for the amputee. Recent advancements in powered lower limb prostheses allow for more intelligent control and better walking functions. The incorporation of neural signals, specifically muscle and brain, may offer a viable method for improved volitional control by directly interpreting signals from muscle and cortical brain activations. In this study, we employ a multimodal neuroimaging approach to determine if noninvasively recorded brain signals can be used within a lower limb prosthesis control scheme. First, we use a mobile brain/body imaging (MoBI) approach to identify the neural correlates of walking during terrain transitions between level ground and stair ascent. These data are then used to demonstrate the feasibility of predicting the two terrains directly from EEG signals, with cross-validation accuracies achieving greater than 80% in offline decoding. Second, we utilize functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to study the brain of amputees during isolated movements of the intact and phantom limb. These data are used to: (1) identify neural correlates of movement during isolated limb movements in the amputee population, and (2) demonstrate the feasibility of control of a powered lower limb prosthesis using neural signals from the brain and muscles. We observed that the representation of the phantom limb is preserved in the deprived cortex. Additionally, using a Kalman Filtering approach, we achieved moderate reconstruction accuracy for predicting movements of the phantom and intact limb directly from EEG. The work in this dissertation contributes to a greater understanding of the neural signals associated with phantom limb movements in lower limb amputees and presents a strategy for neural control of powered prostheses.



EEG, Brain-machine interface, Amputee, Lower limb prosthesis


Portions of this document appear in: Brantley, Justin A., Trieu Phat Luu, Sho Nakagome, Fangshi Zhu, and Jose L. Contreras-Vidal. "Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground." Scientific data 5 (2018): 180133.