Evidence map›Paper›PMID 33414824›Full record

Trial reportNeural plasticity2020

BCI-Based Rehabilitation on the Stroke in Sequela Stage.

Yangyang Miao, Shugeng Chen, Xinru Zhang, Jing Jin, Ren Xu, Ian Daly, Jie Jia, Xingyu Wang, Andrzej Cichocki, Tzyy-Ping Jung

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Neural plasticity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 13 pooled it
4.8field-weighted citation impact, top 4% 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

51 citing papers in PubMed, 13 syntheses or guidelines pooled it, 106 citations in OpenAlex.

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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

10 authors at 6 institutions in 6 countries.

Yangyang MiaoKey Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Shugeng ChenDepartment of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China.
Xinru ZhangKey Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Jing JinKey Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China.ORCID 0000-0002-6133-5491
Ren XuGuger Technologies OG, Austria.
Ian DalyBrain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK.
Jie JiaDepartment of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China.ORCID 0000-0002-4516-4629
Xingyu WangKey Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Andrzej CichockiSkolkowo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia.
Tzyy-Ping JungInstitute for Neural Computation and Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.
East China University of Science and Technology · CNFudan University · CNGuger Technologies (Austria) · ATSkolkovo Institute of Science and Technology · RUUniversity of California San Diego · USUniversity of Essex · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method: Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function. Results: The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions: Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.

Indexed as

Brain-Computer InterfacesAdultAgedFemaleHumansMaleMiddle AgedMotor ActivityRecovery of FunctionStrokeStroke RehabilitationTreatment OutcomeUpper ExtremityYoung Adult

Identifiers

PMID33414824
PMCPMC7752268
OpenAlexW3112880097

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.