This research aims to accelerate the development, efficacy and use of robotic rehabilitation after stroke by capitalizing on the benefits of patient intent and real-time assessment of impairment. Validation will occur using the MAHI EXO-II exoskeleton robot at The Institute for Rehabilitation and Research (TIRR) in Houston, Texas. Robotic rehabilitation is an effective platform for sensorimotor training in stroke patients. A robotic device enables accurate positioning of the impaired limb while simultaneously providing assistance & resistance forces and collection of motion data that can be used to characterize the quality of the patient's movements. The MAHI EXO-II, a physical human-robot interface, will be augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy 'active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. This approach capitalizes on the known benefits of patient intent in movement initiation observed in other clinical studies of robotic rehabilitation and on the beneficial effects of BMI use on cortical plasticity. Robotic measures of motor impairment, derived from real-time data acquired from sensors on the MAHI EXO-II and from the BMI, will drive patient-specific therapy sessions adapted to the capabilities ofthe individual, with the robot providing assistance or challenging the participant as appropriate, in order to maximize rehabilitation outcomes. Assist-as-needed paradigms in robotic rehabilitation have been shown to be efficacious; however, such paradigms are passive and driven by performance metrics that have not been sufficiently validated and verified. Additionally, intense practice and continual 'challenge' during therapy is known to improve rehabilitation outcomes. Key contributions: 1) Adapting most advanced EEG- BMI methods to stroke patients and developing a BMI for the control of the MAHI EXO-II that will a) increase upper limb function, b) advance understanding of brain plasticity, and c) innovate rehabilitation; 2) Determining appropriate robotic and electrophysiological measures of motor impairment and associated control algorithms for patient-specific therapy; and 3) Clinical validation in pilot studies to evaluate the proposed approach.
This research is clinically relevant to stroke patients (~ 1.25 million in the US alone according to the Christopher Reeves Foundation). Stroke is the leading cause of neurological disability in the United States and accounts for the poor physical health evident in survivors. The proposed research will develop, validate and translate novel user-inspired neurorobotic BMI technology for use in rehabilitation robotics.
| Sullivan, Jennifer L; Bhagat, Nikunj A; Yozbatiran, Nuray et al. (2017) Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial. IEEE Int Conf Rehabil Robot 2017:122-127 |
| Bhagat, Nikunj A; Venkatakrishnan, Anusha; Abibullaev, Berdakh et al. (2016) Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors. Front Neurosci 10:122 |
| French, James A; Rose, Chad G; O'Malley, Marcia K (2014) System Characterization of MAHI EXO-II: A Robotic Exoskeleton for Upper Extremity Rehabilitation. Proc ASME Dyn Syst Control Conf 2014: |
| Blank, Amy A; French, James A; Pehlivan, Ali Utku et al. (2014) Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy. Curr Phys Med Rehabil Rep 2:184-195 |
| Venkatakrishnan, Anusha; Francisco, Gerard E; Contreras-Vidal, Jose L (2014) Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation. Curr Phys Med Rehabil Rep 2:93-105 |
| Contreras-Vidal, Jose L (2014) Identifying Engineering, Clinical and Patient's Metrics for Evaluating and Quantifying Performance of Brain-Machine Interface (BMI) Systems. Conf Proc IEEE Int Conf Syst Man Cybern 2014:1489-1492 |
| Bhagat, Nikunj A; French, James; Venkatakrishnan, Anusha et al. (2014) Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke. Conf Proc IEEE Eng Med Biol Soc 2014:4127-4130 |
| Blank, Amy; O'Malley, Marcia K; Francisco, Gerard E et al. (2013) A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton. Int IEEE EMBS Conf Neural Eng :1159-1162 |
| Pehlivan, Ali Utku; Sergi, Fabrizio; O'Malley, Marcia K (2013) Adaptive control of a serial-in-parallel robotic rehabilitation device. IEEE Int Conf Rehabil Robot 2013:6650412 |
| Agashe, Harshavardhan A; Contreras-Vidal, Jose L (2013) Decoding the evolving grasping gesture from electroencephalographic (EEG) activity. Conf Proc IEEE Eng Med Biol Soc 2013:5590-3 |
