%0 Journal Article %J NeuroImage: Clinical %D 2020 %T Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation %A Nikunj A. Bhagat %A Nuray Yozbatiran %A Jennifer L. Sullivan %A Ruta Paranjape %A Colin Losey %A Zachary Hernandez %A Zafer Keser %A Robert Grossman %A Gerard E. Francisco %A Marcia K. O'Malley %A Jose L. Contreras-Vidal %K Brain-machine interface %K Clinical trial %K Exoskeletons %K Movement related cortical potentials %K stroke rehabilitation %X

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. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants’ movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.

%B NeuroImage: Clinical %V 28 %P 102502 %G eng %U http://www.sciencedirect.com/science/article/pii/S2213158220303399 %R https://doi.org/10.1016/j.nicl.2020.102502 %> https://mahilab.rice.edu/sites/default/files/publications/NeuroImage_2020_Bhagat_BMI_EEG_exo.pdf %0 Conference Proceedings %B 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society %D 2014 %T Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke %A N. A. Bhagat %A J. French %A A. Venkatakrishnan %A N. Yozbatiran %A G. E. Francisco %A M. K. O'Malley %A J. L. Contreras-Vidal %K Accuracy %K Adult %K bioelectric potentials %K brain-computer interfaces %K closed loop systems %K closed-loop brain-machine interfaces %K Computer-Assisted %K diseases %K electroencephalography %K Electromyography %K Exoskeletons %K hemiparesis %K Humans %K Male %K medical robotics %K medical signal detection %K medical signal processing %K Middle Aged %K Movement %K movement intent detection %K neurophysiology %K Paresis %K Patient rehabilitation %K Robotics %K Robots %K scalp electroencephalography %K Signal Processing %K stroke %K stroke rehabilitation %K Support Vector Machine %K Support vector machines %K training %K Upper Extremity %K upper extremity dysfunction %K upper limb robotic rehabilitation system %K Young Adult %B 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society %8 08/2014 %G eng %R 10.1109/EMBC.2014.6944532 %> https://mahilab.rice.edu/sites/default/files/publications/bhagat2014ieee.pdf %0 Conference Proceedings %B Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on %D 2013 %T System characterization of RiceWrist-S: A forearm-wrist exoskeleton for upper extremity rehabilitation %A Pehlivan, Ali Utku %A Rose, Chad G. %A O'Malley, Marcia K. %K Actuators %K closed loop position performance %K closed loop systems %K distal joints %K Exoskeletons %K forearm rehabilitation %K forearm-wrist exoskeleton %K Friction %K haptic interface design %K Joints %K medical robotics %K neurological lesions %K neurophysiology %K Patient rehabilitation %K position control %K prosthetics %K RiceWrist-S %K robotic rehabilitation %K Robots %K serial mechanisms %K spatial resolution %K spinal cord injury %K spinal cord injury rehabilitation %K stroke %K stroke rehabilitation %K system characterization %K Torque %K torque output %K upper extremity rehabilitation %K Wrist %K wrist rehabilitation %B Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on %8 June %G eng %R 10.1109/ICORR.2013.6650462 %> https://mahilab.rice.edu/sites/default/files/publications/Pehlivan_RW-S_ICORR2013.pdf