Evidence map›Paper›PMID 27536214›Full record

ArticleFrontiers in neuroscience2016

Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation.

Eduardo López-Larraz, Fernando Trincado-Alonso, Vijaykumar Rajasekaran, Soraya Pérez-Nombela, Antonio J Del-Ama, Joan Aranda, Javier Minguez, Angel Gil-Agudo, Luis Montesano

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 6 pooled it
7.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 6 syntheses or guidelines pooled it, 126 citations in OpenAlex.

  1. Pooled it
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  10. Article
  11. Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026
    Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Extracting Sensory Preferability from Motor Streams.Sensors (Basel, Switzerland) · 2025
    Article
  17. Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025
    Review
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Eduardo López-LarrazDepartamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain.
Fernando Trincado-AlonsoBiomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain.
Vijaykumar RajasekaranInstitute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain.
Soraya Pérez-NombelaBiomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain.
Antonio J Del-AmaBiomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain.
Joan ArandaInstitute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain.
Javier MinguezDepartamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain; Bit & Brain TechnologiesZaragoza, Spain.
Angel Gil-AgudoBiomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain.
Luis MontesanoDepartamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain.
Hospital Nacional de Parapléjicos · ESUniversidad de Zaragoza · ESUniversitat Politècnica de Catalunya · ESInstituto de Investigación Sanitaria Aragón · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

ambulatory exoskeletonsbrain machine interfaces (BMI)electroencephalography (EEG)event related desynchronization (ERD)gait rehabilitationmovement intention decodingmovement related cortical potentials (MRCP)spinal cord injury (SCI)

Identifiers

PMID27536214
PMCPMC4971110
OpenAlexW2478630298

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.