The broad, long-term goal of this project is to develop novel noninvasive neuroprosthetics for restoration and/or rehabilitation of bipedal locomotion in patients with spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), subcortical stroke or lower limb amputations. The control of bipedal locomotion is of great interest to the fields of brain machine interfaces (BMIs), i.e. devices that utilize neural activity to control limb prosthesis and gait rehabilitation. Since locomotion deficits are commonly associated with SCI and neurodegenerative diseases, there is also a need to investigate new potential therapies to restore gait control in such patients. While the feasibility of a BMI for upper limbs has been demonstrated in studies in monkeys and humans, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This project builds upon findings from non-invasive neural decoding of movements in our laboratory, and follows a principled, step-by-step, experimental and computational approach to neural decoding of human bipedal locomotion from scalp EEG and the development of brain-computer interfaces for gait rehabilitation.
The specific aims of this project are: 1) to investigate what gait parameters are best predicted from brain activity acquired with scalp EEG;2) to examine longitudinally the changes in the cortical representation of gait during adaptation to virtual cortical lesions or virtual perturbations of gait kinematics using a closed-loop BCI environment. This will be the first time-resolved examination of how cortical networks may adapt to changes in the neural representation of gait in healthy subjects, and may have implications for studying cortical plasticity after brain injury or physical disability, and for the development of BMIs for gait restoration. This research is clinically significant to patients with impaired gait function, as in the case of stroke patients, Parkinson's disease, SCI and lower-limb amputees, as BMIs may one day help restore gait function.
In the United States, there are approximately 1.7 million people persons living with limb loss (2008 National Limb Loss Information Center). In addition, spinal cord injury, ALS and stroke affect gait capabilities of about 2 million people in the USA. This research will provide the foundations for the development of noninvasive neuroprosthetics for restoration and rehabilitation of gait thereby increasing the quality of life of patients while reducing the socioeconomic burden of lower limb disabilities.
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|Luu, Trieu Phat; Nakagome, Sho; He, Yongtian et al. (2017) Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking. Sci Rep 7:8895|
|Contreras-Vidal, Jose L; A Bhagat, Nikunj; Brantley, Justin et al. (2016) Powered exoskeletons for bipedal locomotion after spinal cord injury. J Neural Eng 13:031001|
|Trieu Phat Luu; Yongtian He; Nakagame, Sho et al. (2016) Unscented Kalman filter for neural decoding of human treadmill walking from non-invasive electroencephalography. Conf Proc IEEE Eng Med Biol Soc 2016:1548-1551|
|Luu, Trieu Phat; He, Yongtian; Brown, Samuel et al. (2016) Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar. J Neural Eng 13:036006|
|Kilicarslan, Atilla; Grossman, Robert G; Contreras-Vidal, Jose Luis (2016) A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. J Neural Eng 13:026013|
|Nathan, Kevin; Contreras-Vidal, Jose L (2015) Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking. Front Hum Neurosci 9:708|
|Luu, Trieu Phat; He, Yongtian; Brown, Samuel et al. (2015) A Closed-loop Brain Computer Interface to a Virtual Reality Avatar: Gait Adaptation to Visual Kinematic Perturbations. Int Conf Virtual Rehabil 2015:30-37|
|Cruz-Garza, Jesus G; Hernandez, Zachery R; Nepaul, Sargoon et al. (2014) Neural decoding of expressive human movement from scalp electroencephalography (EEG). Front Hum Neurosci 8:188|
|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|
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