ArticleFrontiers in neuroscience2016
Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 6 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 6 syntheses or guidelines pooled it, 126 citations in OpenAlex.
- Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.Frontiers in human neuroscience · 2024Pooled it
- Satisfaction analysis of overground gait exoskeletons in people with neurological pathology. a systematic review.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Pooled it
- A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation.Frontiers in neuroscience · 2020Pooled it
- EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.Sensors (Basel, Switzerland) · 2018Pooled it
- Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study.Scientific reports · 2022Trial
- Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface.BioMed research international · 2017Trial
- Robotic Rehabilitation in Spinal Cord Injury: Neurophysiological Basis and Severity-Based Clinical Framework.Brain sciences · 2026Review
- EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.Scientific data · 2026Article
- Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026Review
- Hybrid EEG-EMG intention decoding for real-time triggering of a lower-limb exoskeleton.Frontiers in neurorobotics · 2026Article
- Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs.Sensors (Basel, Switzerland) · 2025Article
- Consumer views of functional electrical stimulation and robotic exoskeleton in SCI rehabilitation: A mini review.Artificial organs · 2025Review
- 5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.Journal of neural engineering · 2025Article
- Extracting Sensory Preferability from Motor Streams.Sensors (Basel, Switzerland) · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury.Frontiers in neuroscience · 2025Article
- Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.Biomedicines · 2024Review
- The Influence of Body Position on the Resting Motor Threshold of Posterior Root-Muscle Reflexes Evoked via Transcutaneous Spinal Cord Stimulation.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.