Evidence map›Paper›PMID 41886100›Full record

ReviewNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026

A GRASS-guided phased progressive brain-computer interface approach for post-infarction hand motor recovery.

Guidi Zou, Liyi Chen, Huihong Tan, Feixiang Zeng, Anqin Dong, Gufeng Li, Jianghong Fu, Kewei Yu, Liang Du, Qiang Liu and 2 more

Abstract readCase ReportsReview
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In one paragraph

Review in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. 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
–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

0 citing papers in PubMed.

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

12 authors.

Guidi Zou *The Affiliated Huizhou Hospital, Guangzhou Medical University, No.1 Qiaodong Xuebei Street, Huicheng District, Huizhou City, Guangdong Province, 516002, China.
Liyi Chen *Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK.
Huihong Tan *The Affiliated Huizhou Hospital, Guangzhou Medical University, No.1 Qiaodong Xuebei Street, Huicheng District, Huizhou City, Guangdong Province, 516002, China.
Feixiang ZengThe Affiliated Huizhou Hospital, Guangzhou Medical University, No.1 Qiaodong Xuebei Street, Huicheng District, Huizhou City, Guangdong Province, 516002, China.
Anqin DongDepartment of Rehabilitation Medicine, The Fifth Affiliated Hospital of Zhengzhou University, Henan Province, 450052, China.
Gufeng LiDepartment of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China.
Jianghong FuDepartment of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China.
Kewei YuDepartment of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China.
Liang DuDepartment of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China.
Qiang LiuDepartment of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China.
Xiaokai ChenThe Affiliated Huizhou Hospital, Guangzhou Medical University, No.1 Qiaodong Xuebei Street, Huicheng District, Huizhou City, Guangdong Province, 516002, China. hzcxk1968@163.com.
Hewei WangDepartment of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Shanghai, 200040, China. wanghew@fudan.edu.cn.ORCID http://orcid.org/0000-0001-7632-0607

Funding

the 2023 Guangdong Provincial Administration of Traditional Chinese Medicine Scientific Research Project 20231345the 2024 Huizhou Science and Technology Bureau Scientific Research Project 2024CZ010198the National Natural Science Foundation of China 82102665the Shanghai Sailing Program 21YF1404600
6 · The paper itself

Abstract

backgroundHand dysfunction is a disabling sequela of cerebral infarction, often refractory to conventional rehabilitation. Brain–computer interface (BCI) interventions that combine motor imagery (MI) and action observation (AO) demonstrate potential, but heterogeneous reporting restricts evidence synthesis. This article reports a phased progressive BCI protocol guided by the GRASS (Guidelines for Reporting Action Simulation Studies) framework. METHODS AND

resultsA 60‑year‑old male with right hemiparesis following basal ganglia infarction underwent a three‑phase intervention (Central Activation, Reinforcement, and Enhancement) using synchronous AO–MI tasks to trigger a robotic exoskeleton in addition to conventional therapy. Key GRASS elements, including participant details, instructions, and dosage, were transparently reported. After 45 sessions, the Fugl–Meyer Assessment–Upper Extremity score improved from 8/66 to 45/66, the Action Research Arm Test score increased from 1/57 to 18/57, and BCI accuracy reached 95%. To contextualize these findings, a focused literature review situates this case within emerging GRASS informed research, illustrating how standardized reporting addresses methodological heterogeneity identified in recent studies.

conclusionThis case report and literature review illustrate the feasibility of a GRASS-guided BCI rehabilitation approach and highlight the potential value of standardized reporting for improving reproducibility in post-stroke neurorehabilitation research.

Indexed as

Brain-Computer InterfacesCerebral InfarctionHandParesisRecovery of FunctionStroke RehabilitationHumansMaleMiddle AgedAction observationBrain-computer interfaceGRASS guidelinesHand functionMotor imageryStroke

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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.