Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation.

Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation.

Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial.

This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control.