Evidence map›Paper›PMID 35548781›Full record

ArticleFrontiers in neurorobotics2022

Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept.

Akshay Kumar, Lin Gao, Jiaming Li, Jiaxin Ma, Jianming Fu, Xudong Gu, Seedahmed S Mahmoud, Qiang Fang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurorobotics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 3 institutions in 2 countries.

Akshay KumarDepartment of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
Lin GaoDepartment of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
Jiaming LiDepartment of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
Jiaxin MaOMRON SINIC X Corporation, Tokyo, Japan.
Jianming Fu2nd Hospital of Jiaxing, Jiaxing, China.
Xudong Gu2nd Hospital of Jiaxing, Jiaxing, China.
Seedahmed S MahmoudDepartment of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
Qiang FangDepartment of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
Shantou University · CNFirst Hospital of Jiaxing · CNOmron (Japan) · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional rehabilitation systems typically execute a fixed set of programs that most motor-impaired stroke patients undergo. In these systems, the brain, which is embodied in the body, is often left out. Including the brains of stroke patients in the control loop of a rehabilitation system can be worthwhile as the system can be tailored to each participant and, thus, be more effective. Here, we propose a novel brain-computer interface (BCI)-based robot-assisted stroke rehabilitation system (RASRS), which takes inputs from the patient's intrinsic feedback mechanism to adapt the assistance level of the RASRS. The proposed system will utilize the patients' consciousness about their performance decoded through their error-related negativity signals. As a proof-of-concept, we experimented on 12 healthy people in which we recorded their electroencephalogram (EEG) signals while performing a standard rehabilitation exercise. We set the performance requirements beforehand and observed participants' neural responses when they failed/met the set requirements and found a statistically significant (

Indexed as

assist-as-needed (AAN)brain-computer interface (BCI)error-related potentials (ErrP)robot-therapysingle-trial classificationstroke rehabilitationTraining-ErrPs

Identifiers

PMID35548781
PMCPMC9085417
OpenAlexW4224325345

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.