Objective: To investigate the clinical impact of brain-machine interface (BMI) integrated neurorehabilitation on gait training in paraplegic patients with chronic spinal cord injury using a brain-controlled robotic exoskeleton and related training systems
This is a Brain-Controlled Robotic Exoskeleton Gait Training protocol using human as the model organism. The procedure involves 12 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 • 8
Study window
Estimated timing pending
Core workflow
Patient enrollment and informed consent • Component 1: Immersive virtual reality BMI training (seated) • Component 2: Virtual reality BMI training (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. Each participant signed written informed consent before enrolling in the study. Protocol approved by local ethics committee and Brazilian federal government ethics committee.
Note: Seven patients with complete SCI, one with incomplete SCI. Weight range 50-80 kg.
“Eight paraplegic patients, suffering from chronic (>1 year) spinal cord injury (SCI, seven complete and one incomplete)”
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'). Tactile stimulation on forearm given in accordance with rolling of ipsilateral virtual feet on ground.
“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: Continues same BMI strategies and tactile feedback as Component 1
“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 gait device with body weight support system integrated with treadmill. No tactile feedback provided in this component.
Note: Lokomat manufactured by Hocoma AG, Switzerland. This is one of two components without tactile feedback.
“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. ZeroG system contains overhead fixed track with no mechanical barriers between patient and physical therapist.
Note: No tactile feedback provided. Offers more challenges than off-the-shelf devices by requiring postural/trunk control, upper limb strength, and dynamic balance.
“training with a BWS gait system fixed on an overground track (ZeroG, Aretech LLC., Ashburn, VA)”
Training with brain-controlled body weight support gait system on treadmill. Patients use EEG-based BMI to control gait.
Note: Combines BMI control with robotic gait training on treadmill
“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 has autonomous power, self-stabilization, and full lower limb hydraulic actuation. Used in conjunction with ZeroG overground BWS system.
Note: Custom-built exoskeleton accommodates weight range 50-80 kg without requiring crutches. Provides tactile feedback via haptic display.
“gait training with a brain-controlled, sensorized 12 degrees of freedom robotic exoskeleton”
Two BMI strategies employed throughout training. Initially, patients imagine arm movements to modulate EEG activity for high-level commands. After mastering first method, patients learn to use EEG signals to control individual avatar/robotic leg stepping by imagining leg movements.
Note: For first paradigm, patients confirm choice by performing isometric triceps contraction
“Initially, patients were required to imagine movement of the arms to modulate EEG activity so that they could generate high level motor commands such as 'walk' or 'stop'. Once patients mastered this first method, they learned to use EEG signals to control individual avatar/robotic leg stepping by imagining movements of their own legs”
Complexity of activities increased over time to ensure cardiovascular stability and better postural control. Training progresses from orthostatic training at stand-in-table to different gait training robotic systems.
Note: Progression ensures patient safety and optimal postural control development
“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”
Before and after every activity, routine general clinical evaluations performed including cardiovascular function, intestinal and urinary emptying, skin inspection, and spasticity handling.
Note: Long-term osteoporosis treatment also provided
“In addition to routine general clinical evaluations (i.e. cardiovascular function, intestinal and urinary emptying, skin inspection, spasticity handling), before and after every activity”
Multiple clinical evaluations performed at Day 0 (baseline), and after 4, 7, 10, and 12 months to identify changes in neurological status and assess psychological and physical conditions.
Note: Comprehensive battery of standardized clinical assessments
“Such clinical evaluation started on the first day patients began training (Day 0), and were repeated after 4, 7, 10, and 12 months”
Patients instructed to imagine movements of their own legs while EEG signals from 11 scalp electrodes recorded over leg primary somatosensory and motor cortical areas. Recordings performed before and after training to evaluate functional cortical plasticity.
Note: 11 electrodes positioned over leg representation areas
“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”
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.
To investigate the clinical impact of brain-machine interface (BMI) integrated neurorehabilitation on gait training in paraplegic patients with chronic spinal cord injury using a brain-controlled robotic exoskeleton and related training systems
Objective
To investigate the clinical impact of brain-machine interface (BMI) integrated neurorehabilitation on gait training in paraplegic patients with chronic spinal cord injury using a brain-controlled robotic exoskeleton and related training systems
Subjects
From paperhuman • N/A • unknown • Not specified • 50-80 kg
Sample count
From paper8
Cohort notes
From paperParaplegic patients with chronic (>1 year) spinal cord injury; seven complete and one incomplete SCI
Patient enrollment and informed consent (12 months total study period)
Component 1: Immersive virtual reality BMI training (seated) (Not specified)
Component 2: Virtual reality BMI training (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 potential cortical sources (individual independent components) of novel leg representations in primary motor and somatosensory cortices and to detect functional changes over time.
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 potential cortical sources (individual independent components) of novel leg representations in primary motor and somatosensory cortices and to detect functional changes over time.
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 evaluation
From paperIndependent Component Analysis (ICA) employed to determine potential cortical sources (individual independent components) of novel leg representations in primary motor and somatosensory cortices and to detect functional changes over time.
Artifact type
Longitudinal gait metrics and per-animal performance tables
Comparison focus
Compare recovery trajectory across post-injury timepoints and treatment conditions
Muscle strength test (Lokomat L-force Evaluation)
From paperIndependent Component Analysis (ICA) employed to determine potential cortical sources (individual independent components) of novel leg representations in primary motor and somatosensory cortices and to detect functional changes over time.
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 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
Muscle strength test (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 potential cortical sources (individual independent components) of novel leg representations in primary motor and somatosensory cortices and to detect functional changes over time.
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 evaluation; Muscle strength test (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 evaluation, Muscle strength test (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 (as part of Lokomat system) • 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
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Evidence Quotes
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Protocol Items
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Evidence
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