LAUSR

One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.