Objective: Investigate the clinical impact of brain-machine interface (BMI) integrated neurorehabilitation on paraplegic patients with chronic spinal cord injury using EEG-controlled robotic body weight support gait training
This is a Brain-Controlled Robotic Body Weight Support Gait Training protocol using human as the model organism. The procedure involves 17 procedural steps, 6 equipment items, 3 materials. Extracted from a 2016 paper published in Scientific Reports.
Model and subjects
human • N/A • unknown • Not specified • 50-80 kg (exoskeleton accommodation range) • 8
Study window
Estimated timing pending
Core workflow
Patient enrollment and ethical approval • Component 1: Immersive virtual reality avatar control (seated) • Component 2: Virtual reality avatar control (upright)
Primary readouts
Key equipment and reagents
Verified items
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Eight paraplegic patients with chronic spinal cord injury (>1 year) were enrolled. Protocol approved by local ethics committee (Associação de Assistência à Criança Deficiente, Sao Paulo) and Brazilian federal government ethics committee (CONEP). All participants signed written informed consent.
Note: Seven patients with complete SCI, one with incomplete SCI
“Eight paraplegic patients, suffering from chronic (>1 year) spinal cord injury (SCI, seven complete and one incomplete) were followed by a multidisciplinary rehabilitation team during the 12 months of 2014”
Seated patients employ brain activity recorded via 16-channel EEG to control movements of a human body avatar in immersive virtual reality environment. Patients receive visuo-tactile feedback via haptic display on forearms.
Note: First BMI paradigm: patients imagine arm movements to generate high-level motor commands ('walk' or 'stop'). Confirmation via isometric triceps contraction.
“an immersive virtual reality environment in which a seated patient employed his/her brain activity, recorded via a 16-channel EEG, to control the movements of a human body avatar, while receiving visuo-tactile feedback”
Identical interaction with virtual environment and BMI protocol as Component 1, but with patients in upright position supported by stand-in-table device.
Note: Progression from seated to upright position
“identical interaction with the same virtual environment and BMI protocol while patients were upright, supported by a stand-in-table device”
Training on Lokomat robotic body weight support gait system on treadmill. No BMI control or tactile feedback provided for this component.
Note: Traditional robotic gait training without brain-machine interface
“training on a robotic body weight support (BWS) gait system on a treadmill (Lokomat, Hocoma AG, Switzerland)”
Training with body weight support gait system fixed on overground track with overhead fixed track. No BMI control or tactile feedback provided for this component.
Note: Overground training without mechanical barriers between patient and physical therapist
“training with a BWS gait system fixed on an overground track (ZeroG, Aretech LLC., Ashburn, VA)”
Training on brain-controlled robotic body weight support gait system on treadmill using EEG-based control. Patients receive tactile feedback via haptic display.
Note: Integration of BMI with Lokomat system; second BMI paradigm: patients imagine leg movements to control individual avatar/robotic leg stepping
“training with a brain-controlled robotic BWS gait system on a treadmill”
Gait training with brain-controlled, sensorized 12 degrees of freedom robotic exoskeleton. Exoskeleton used in conjunction with ZeroG overground body weight support system. Patients receive tactile feedback via haptic display.
Note: Most challenging training component requiring postural/trunk control, upper limb strength, and dynamic balance. No mechanical barriers between patient and therapist.
“gait training with a brain-controlled, sensorized 12 degrees of freedom robotic exoskeleton”
Complexity of activities increased over time to ensure cardiovascular stability and better postural control. Training progression: orthostatic training at stand-in-table → different gait training robotic systems.
Note: Gradual progression based on patient tolerance and clinical status
“the complexity of activities was increased over time to ensure cardiovascular system stability and better patient postural control; starting with orthostatic training at a stand-in-table and progressing all the way to the different gait training robotic systems”
Further gait training performed using lower limb orthosis and walking assistive devices including hip-knee-ankle-foot orthosis or ankle-foot orthosis with knee extension splint and wheeled triangular walker.
Note: Additional training modality integrated into protocol
“Further gait training was performed by having subjects utilize a lower limb orthosis and walking assistive devices (hip-knee-ankle-foot orthosis or ankle-foot orthosis with knee extension splint and wheeled triangular walker)”
Before and after every activity, routine general clinical evaluations performed including cardiovascular function assessment, intestinal and urinary emptying evaluation, skin inspection, and spasticity handling.
Note: Ongoing monitoring throughout training
“In addition to routine general clinical evaluations (i.e. cardiovascular function, intestinal and urinary emptying, skin inspection, spasticity handling), before and after every activity”
Comprehensive clinical evaluation performed on first day of training including: ASIA Impairment Scale, Semmes-Weinstein Monofilament Test, temperature/vibration/proprioception/deep pressure sensitivity evaluation, muscle strength test (Lokomat L-force Evaluation), Thoracic-Lumbar Scale, WISCI II, SCIM III, McGill Pain Questionnaire, Visual Analogue Scale, range of motion assessment, Modified Ashworth Scale, Lokomat L-stiff Evaluation, WHOQoL-Bref, Rosenberg Self-Esteem Scale, and Beck Depression Inventory.
Note: Comprehensive baseline assessment
“Such clinical evaluation started on the first day patients began training (Day 0)”
Identical comprehensive clinical evaluations repeated at 4, 7, 10, and 12 months to identify changes in neurological status and assess psychological and physical conditions.
Note: Longitudinal assessment of clinical outcomes
“were repeated after 4, 7, 10, and 12 months”
Long-term treatment of osteoporosis provided throughout the study period.
Note: Preventive medical management
“a long-term treatment of osteoporosis”
Patients instructed to imagine movements of their own legs while EEG signals recorded from 11 scalp electrodes over leg primary somatosensory and motor cortical areas. Recordings performed before and after training months.
Note: Longitudinal analysis of EEG recordings to evaluate functional cortical plasticity
“patients were instructed to imagine movements of their own legs while EEG signals from 11 scalp electrodes were recorded over the leg primary somatosensory and motor cortical areas”
ICA employed to determine potential cortical sources represented by individual independent components (ICs) of novel leg representations in primary motor and somatosensory cortices and to detect functional changes of these representations over time.
Note: Statistical analysis of EEG data
“Independent Component Analysis (ICA) was employed to determine potential cortical sources, represented by individual independent components (ICs), of novel leg representations in the primary motor and somatosensory cortices”
For each independent component, Event Related Spectral Perturbations calculated with respect to baseline of 1 second prior to event and normalized by average power across trials at each frequency.
Note: Quantification of brain dynamics modulation
“we calculated for each IC the Event Related Spectral Perturbations (ERSPs) with respect to a baseline of 1 second prior to the event and normalized by the average power across trials at each frequency”
Event Related Potentials sampled from two EEG electrodes located over leg representation area, averaged over all patients before and after training, calculated and used for statistical comparison.
Note: Comparison of brain responses before and after intervention
“Event Related Potentials (ERPs), sampled from two EEG electrodes located over the leg representation area, averaged over all patients, before and after training, were also calculated and used for statistical comparison”
This section explains what the experiment is doing, which readouts matter, what the data artifacts usually look like, and how the analysis should flow from raw capture to reported result.
Investigate the clinical impact of brain-machine interface (BMI) integrated neurorehabilitation on paraplegic patients with chronic spinal cord injury using EEG-controlled robotic body weight support gait training
Objective
Investigate the clinical impact of brain-machine interface (BMI) integrated neurorehabilitation on paraplegic patients with chronic spinal cord injury using EEG-controlled robotic body weight support gait training
Subjects
From paperhuman • N/A • unknown • Not specified • 50-80 kg (exoskeleton accommodation range)
Sample count
From paper8
Cohort notes
From paperParaplegic patients with chronic (>1 year) spinal cord injury; seven complete and one incomplete SCI; all participants signed written informed consent
Patient enrollment and ethical approval (12 months total study period)
Component 1: Immersive virtual reality avatar control (seated) (Not specified)
Component 2: Virtual reality avatar control (upright) (Not specified)
Component 3: Lokomat robotic gait training (Not specified)
American Spinal Injury Association (ASIA) Impairment Scale
From paperIndependent Component Analysis (ICA) employed to determine cortical sources of leg representations.
Artifact type
Longitudinal gait metrics and per-animal performance tables
Comparison focus
Compare recovery trajectory across post-injury timepoints and treatment conditions
Semmes-Weinstein Monofilament Test
From paperIndependent Component Analysis (ICA) employed to determine cortical sources of leg representations.
Artifact type
Longitudinal gait metrics and per-animal performance tables
Comparison focus
Compare recovery trajectory across post-injury timepoints and treatment conditions
Temperature, vibration, proprioception, and deep pressure sensitivity
From paperIndependent Component Analysis (ICA) employed to determine cortical sources of leg representations.
Artifact type
Longitudinal gait metrics and per-animal performance tables
Comparison focus
Compare recovery trajectory across post-injury timepoints and treatment conditions
Muscle strength (Lokomat L-force Evaluation)
From paperIndependent Component Analysis (ICA) employed to determine cortical sources of leg representations.
Artifact type
Longitudinal gait metrics and per-animal performance tables
Comparison focus
Compare recovery trajectory across post-injury timepoints and treatment conditions
American Spinal Injury Association (ASIA) Impairment Scale
From paperRaw artifact
Per-run gait capture with paw placement, timing, and stride features for each animal
Processed artifact
Cleaned gait metrics table and recovery trend summary across timepoints
Final reported form
Group comparisons of gait indices, stride metrics, or recovery curves
Semmes-Weinstein Monofilament Test
From paperRaw artifact
Per-run gait capture with paw placement, timing, and stride features for each animal
Processed artifact
Cleaned gait metrics table and recovery trend summary across timepoints
Final reported form
Group comparisons of gait indices, stride metrics, or recovery curves
Temperature, vibration, proprioception, and deep pressure sensitivity
From paperRaw artifact
Per-run gait capture with paw placement, timing, and stride features for each animal
Processed artifact
Cleaned gait metrics table and recovery trend summary across timepoints
Final reported form
Group comparisons of gait indices, stride metrics, or recovery curves
Muscle strength (Lokomat L-force Evaluation)
From paperRaw artifact
Per-run gait capture with paw placement, timing, and stride features for each animal
Processed artifact
Cleaned gait metrics table and recovery trend summary across timepoints
Final reported form
Group comparisons of gait indices, stride metrics, or recovery curves
Acquisition
Capture run-level gait data for each animal and preserve the timepoint or treatment labeling.
Preprocessing / cleaning
Independent Component Analysis (ICA) employed to determine cortical sources of leg representations.
Scoring or quantification
Quantify the primary readouts for this experiment: American Spinal Injury Association (ASIA) Impairment Scale; Semmes-Weinstein Monofilament Test; Temperature, vibration, proprioception, and deep pressure sensitivity; Muscle strength (Lokomat L-force Evaluation).
Statistical comparison
Statistical method not yet structured for this page.
Reporting output
Report representative outputs alongside summary comparisons for American Spinal Injury Association (ASIA) Impairment Scale, Semmes-Weinstein Monofilament Test, Temperature, vibration, proprioception, and deep pressure sensitivity, Muscle strength (Lokomat L-force Evaluation).
Source links and direct wording from the methods section for validation and deeper review.
Citation
Ana R. C. Donati et al. (2016). Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. Scientific Reports
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Direct vendor pages are linked from the protocol above. This section stays focused on the full comparison view and the prep checklist.
Gather these items before starting the experiment. Check off items as you prepare.
Hocoma AG • Lokomat • Not specified • N/A
Aretech LLC. • ZeroG • Not specified • N/A
Custom built (research team) • Not specified • Not specified • N/A
Not specified • Not specified • Not specified • N/A
Oculus VR • Oculus Rift • Not specified • N/A
Hocoma AG (integrated with Lokomat) • Not specified • Not specified • N/A
Not specified • Not specified • Not specified • N/A
Not specified • Not specified • Not specified • N/A
Not specified • Not specified • Not specified • N/A
Autodesk • N/A
Not specified (statistical analysis method) • N/A
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Current status surfaces were computed from experiment data updated Feb 28, 2026.
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