Evidence map›Paper›PMID 33467420›Full record

ArticleSensors (Basel, Switzerland)2021

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton.

Mads Jochumsen, Taha Al Muhammadee Janjua, Juan Carlos Arceo, Jimmy Lauber, Emilie Simoneau Buessinger, Rasmus Leck Kæseler

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.0field-weighted citation impact, top 15% 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

10 citing papers in PubMed, 23 citations in OpenAlex.

  1. Building and Sustaining Open-Source Medical Device Projects.IEEE transactions on bio-medical engineering · 2025
    Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Technologies in Home-Based Digital Rehabilitation: Scoping Review.JMIR rehabilitation and assistive technologies · 2023
    Article
  7. Review
  8. Article
  9. Article
  10. 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

6 authors at 2 institutions in 2 countries.

Mads JochumsenDepartment of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.ORCID 0000-0001-7729-4359
Taha Al Muhammadee JanjuaDepartment of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.ORCID 0000-0003-0815-2597
Juan Carlos ArceoLAMIH UMR CNRS 8201, INSA Hauts de France, Université Polytechnique Hauts de France, F-59313 Valenciennes, France.ORCID 0000-0002-5324-5488
Jimmy LauberLAMIH UMR CNRS 8201, INSA Hauts de France, Université Polytechnique Hauts de France, F-59313 Valenciennes, France.ORCID 0000-0003-4174-3065
Emilie Simoneau BuessingerLAMIH UMR CNRS 8201, INSA Hauts de France, Université Polytechnique Hauts de France, F-59313 Valenciennes, France.ORCID 0000-0003-2059-884X
Rasmus Leck KæselerDepartment of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.
Aalborg University · DKCentre National de la Recherche Scientifique · FR

Funding

VELUX FONDEN 22357
6 · The paper itself

Abstract

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

Indexed as

Brain-Computer InterfacesExoskeleton DeviceNeuronal PlasticityPrinting, Three-DimensionalElectroencephalographyHumansWristbrain–computer interfaceexoskeletonmotor imaginationneural plasticityneurorehabilitation

Identifiers

PMID33467420
PMCPMC7830618
OpenAlexW3119628395

What OpenQuestion holds

Textmetadata
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.