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.

Public Health Relevance

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.

National Institute of Health (NIH)
Research Project (R01)
Project #
Application #
Study Section
Motor Function, Speech and Rehabilitation Study Section (MFSR)
Program Officer
Ludwig, Kip A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Houston
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
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
Bulea, Thomas C; Kilicarslan, Atilla; Ozdemir, Recep et al. (2013) Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding. J Vis Exp :
Contreras-Vidal, Jose L; Grossman, Robert G (2013) NeuroRex: a clinical neural interface roadmap for EEG-based brain machine interfaces to a lower body robotic exoskeleton. Conf Proc IEEE Eng Med Biol Soc 2013:1579-82
Bulea, Thomas C; Prasad, Saurabh; Kilicarslan, Atilla et al. (2013) Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction. Conf Proc IEEE Eng Med Biol Soc 2013:6341-4
Kilicarslan, Atilla; Prasad, Saurabh; Grossman, Robert G et al. (2013) High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton. Conf Proc IEEE Eng Med Biol Soc 2013:5606-9
Contreras-Vidal, Jose; Presacco, Alessandro; Agashe, Harshavardhan et al. (2012) Restoration of whole body movement: toward a noninvasive brain-machine interface system. IEEE Pulse 3:34-7
Presacco, Alessandro; Forrester, Larry W; Contreras-Vidal, Jose L (2012) Decoding intra-limb and inter-limb kinematics during treadmill walking from scalp electroencephalographic (EEG) signals. IEEE Trans Neural Syst Rehabil Eng 20:212-9
Presacco, Alessandro; Goodman, Ronald; Forrester, Larry et al. (2011) Neural decoding of treadmill walking from noninvasive electroencephalographic signals. J Neurophysiol 106:1875-87