Evidence map›Paper›PMID 31275126›Full record

ArticleFrontiers in human neuroscience2019

Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients.

Athanasios Vourvopoulos, Octavio Marin Pardo, Stéphanie Lefebvre, Meghan Neureither, David Saldana, Esther Jahng, Sook-Lei Liew

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

58 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Review
  6. Article
  7. Article
  8. Review
  9. Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026
    Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025
    Review
  16. Review
  17. Article
  18. Review
  19. EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024
    Article
  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

7 authors.

Athanasios VourvopoulosNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
Octavio Marin PardoNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
Stéphanie LefebvreNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
Meghan NeureitherNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
David SaldanaNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
Esther JahngNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
Sook-Lei LiewNeural Plasticity and Neurorehabilitation Laboratory, Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.

Funding

Big Data Neuroimaging to Predict Motor Behavior After StrokeK01HD091283 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LIEW, SOOK-LEI · 2017 to 2020
$802k
NICHD NIH HHS K01 HD091283
6 · The paper itself

Abstract

Rehabilitation for stroke patients with severe motor impairments (e.g., inability to perform wrist or finger extension on the affected side) is burdensome and difficult because most current rehabilitation options require some volitional movement to retrain the affected side. However, although these patients participate in therapy requiring volitional movement, previous research has shown that they may receive modest benefits from action observation, virtual reality (VR), and brain-computer interfaces (BCIs). These approaches have shown some success in strengthening key motor pathways thought to support motor recovery after stroke, in the absence of volitional movement. The purpose of this study was to combine the principles of VR and BCI in a platform called REINVENT and assess its effects on four chronic stroke patients across different levels of motor impairment. REINVENT acquires post-stroke EEG signals that indicate an attempt to move and drives the movement of a virtual avatar arm, allowing patient-driven action observation neurofeedback in VR. In addition, synchronous electromyography (EMG) data were also captured to monitor overt muscle activity. Here we tested four chronic stroke survivors and show that this EEG-based BCI can be safely used over repeated sessions by stroke survivors across a wide range of motor disabilities. Finally, individual results suggest that patients with more severe motor impairments may benefit the most from EEG-based neurofeedback, while patients with more mild impairments may benefit more from EMG-based feedback, harnessing existing sensorimotor pathways. We note that although this work is promising, due to the small sample size, these results are preliminary. Future research is needed to confirm these findings in a larger and more diverse population.

Indexed as

action observationbrain-computer interfacesneurorehabilitationstrokevirtual reality

Identifiers

PMID31275126
PMCPMC6593205

What OpenQuestion holds

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LicenceCC BY
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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.