Evidence map›Paper›PMID 36171400›Full record

ArticleScientific reports2022

Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface.

Salem Mansour, Joshua Giles, Kai Keng Ang, Krishnan P S Nair, Kok Soon Phua, Mahnaz Arvaneh

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In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Salem MansourDepartment of Automatic Control and Systems Engineering, University of Sheffield, Mapping Street, Sheffield, S13JD, UK. sslmansour1@sheffield.ac.uk.
Joshua GilesDepartment of Automatic Control and Systems Engineering, University of Sheffield, Mapping Street, Sheffield, S13JD, UK.
Kai Keng AngAgency for Science Technology and Research, Institute for Infocomm Research, Singapore, Singapore.
Krishnan P S NairNeurology, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust and The University of Sheffield, Sheffield, UK.
Kok Soon PhuaAgency for Science Technology and Research, Institute for Infocomm Research, Singapore, Singapore.
Mahnaz ArvanehDepartment of Automatic Control and Systems Engineering, University of Sheffield, Mapping Street, Sheffield, S13JD, UK.

Funding

Medical Research Council MC-PC-19051Medical Research Council MC_PC_19051
6 · The paper itself

Abstract

Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI.

Indexed as

Brain-Computer InterfacesStrokeStroke RehabilitationHumansSurvivorsUpper Extremity

Identifiers

PMID36171400
PMCPMC9519575

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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.