%0 Journal Article %J Current Opinion in Biomedical Engineering %D 2021 %T Myoelectric Control and Neuromusculoskeletal Modeling: Complementary Technologies for Rehabilitation Robotics %A Jeffrey Berning %A Gerard E. Francisco %A Shuo-Hsiu Chang %A Benjamin J. Fregly %A Marcia K. O'Malley %K Electromyography %K neuromusculoskeletal modeling %K robotic rehabilitation %K upper limb motor impairment %X

Stroke and spinal cord injury (SCI) are a leading cause of disability in the United States, and researchers have pursued using robotic devices to aid rehabilitation efforts for resulting upper-extremity impairments. To date, however, robotic rehabilitation of the upper limb has produced only limited improvement in functional outcomes compared to traditional therapy. This paper explores the potential of myoelectric control and neuromusculoskeletal modeling for robotic rehabilitation using the current state of the art of each individual field as evidence. Continuing advances in the fields of myoelectric control and neuromusculoskeletal modeling offer opportunities for further improvements of rehabilitation robot control strategies. Specifically, personalized neuromusculoskeletal models driven by a subject’s electromyography signals may provide accurate predictions of the subject’s muscle forces and joint moments which, when used to design novel control strategies, could yield new approaches to robotic therapy for stroke and SCI that surpass the efficacy of traditional therapy.

%B Current Opinion in Biomedical Engineering %P 100313 %G eng %U https://www.sciencedirect.com/science/article/pii/S2468451121000532 %R https://doi.org/10.1016/j.cobme.2021.100313 %> https://mahilab.rice.edu/sites/default/files/publications/BerningCOBME2021_preprint.pdf %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 Journal Article %J NeuroRehabilitation %D 2016 %T Transcranial direct current stimulation (tDCS) of the primary motor cortex and robot-assisted arm training in chronic incomplete cervical spinal cord injury: A proof of concept sham-randomized clinical study %A Nuray Yozbatirana %A Zafer Keser %A Matthew Davis %A Argyrios Stampas %A Marcia K. O’Malley %A Catherine Cooper-Hay %A Joel Fronteraa %A Felipe Fregni %A Gerard E. Francisco %B NeuroRehabilitation %V 39 %P 401–411 %G eng %> https://mahilab.rice.edu/sites/default/files/publications/TDCS_2016_Neurorehab.pdf %0 Conference Proceedings %B IEEE EMBS Conference on Neural Engineering %D 2013 %T A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton %A Amy Blank %A Marcia K. O’Malley %A Gerard E. Francisco %A Jose L. Contreras-Vidal %B IEEE EMBS Conference on Neural Engineering %P 1159-1162 %8 2013 %G eng %> https://mahilab.rice.edu/sites/default/files/publications/BMI-EXO_2013_NER.pdf %0 Journal Article %J Journal of Rehabilitation Medicine %D 2012 %T Robotic training and clinical assessment of upper extremity movements after spinal cord injury; a single case report %A Yozbatiran, Nuray %A Berliner, J. %A O'Malley, M.K. %A Pehlivan, A.U. %A Z. Kadivar %A Boake, Corwin %A Gerard E. Francisco %B Journal of Rehabilitation Medicine %V 44 %P 186-188 %8 01/2012 %G eng %& 186 %> https://mahilab.rice.edu/sites/default/files/publications/J_Rehab_Medicine_2012_Final_press_version.pdf