Evidence map›Paper›PMID 39145233›Full record

ArticlePeerJ. Computer science2024

Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.

Kishor Lakshminarayanan, Vadivelan Ramu, Rakshit Shah, Md Samiul Haque Sunny, Deepa Madathil, Brahim Brahmi, Inga Wang, Raouf Fareh, Mohammad Habibur Rahman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 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

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
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.

Kishor LakshminarayananDepartment of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu, India.
Vadivelan RamuDepartment of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu, India.
Rakshit ShahDepartment of Orthopaedic Surgery, University of Arizona, Tucson, AZ, United States of America.
Md Samiul Haque SunnyDepartment of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America.
Deepa MadathilJindal Institute of Behavioural Sciences, O.P. Jindal Global University, Haryana, India.
Brahim BrahmiElectrical Engineering, Collège Ahuntsic, Montreal, QC, Canada.
Inga WangDepartment of Occupational Science & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America.
Raouf FarehDepartment of Electrical and Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates.
Mohammad Habibur RahmanDepartment of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured Results: Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion: The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.

Indexed as

Brain-computer interfaceEEGMotor imageryRehabilitation

Identifiers

PMID39145233
PMCPMC11323104

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.