Evidence map›Paper›PMID 39218590›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2024

[Virtual reality-brain computer interface hand function enhancement rehabilitation system incorporating multi-sensory stimulation].

Xiening Shao, Yiying Zhang, Dong Zhang, Yandi Men, Zilong Wang, Xiaoling Chen, Ping Xie

Abstract readEnglish Abstract
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Article
  2. Application and Research Progress of BCI in Post-Stroke Psychiatric Disorders: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2026
    Review
  3. Review
  4. [A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Article
  5. [Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Article
  6. 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.

Xiening ShaoCollege of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.
Yiying ZhangSchool of Design and Art, Beijing Institute of Technology, Beijing 100081, P. R. China.
Dong ZhangCollege of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.
Yandi MenCollege of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.
Zilong WangCollege of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.
Xiaoling ChenCollege of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.
Ping XieCollege of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.

Indexed as

Brain-Computer InterfacesElectroencephalographyHandStroke RehabilitationVirtual RealityFeedback, SensoryHumansMotor CortexUser-Computer InterfaceBrain-computer interfaceHand functional rehabilitationHaptic feedbackMulti-sensory stimulation feedbackVirtual reality

Identifiers

PMID39218590
PMCPMC11366477

What OpenQuestion holds

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Registered trials

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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.