Evidence map›Paper›PMID 42751597›Full record

ArticleNeurophotonics2026

fNIRS dataset of motor hand-gripping activity using the NIRSport2 system.

Jamila Akhter, Hammad Nazeer, Noman Naseer

Abstract read
In one paragraph

Article in Neurophotonics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

3 authors.

Jamila AkhterAir University Islamabad, Neuroimaging Research Group, Mechatronics and Biomedical Engineering Department, Islamabad, Pakistan.ORCID https://orcid.org/0009-0006-2403-7395
Hammad NazeerAir University Islamabad, Neuroimaging Research Group, Mechatronics and Biomedical Engineering Department, Islamabad, Pakistan.ORCID https://orcid.org/0000-0002-2036-9601
Noman NaseerAir University Islamabad, Neuroimaging Research Group, Mechatronics and Biomedical Engineering Department, Islamabad, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: The functional near-infrared spectroscopy (fNIRS) dataset acquired with the NIRSport2 device provides noninvasive recordings. The analysis of the fNIRS dataset can be used to refine existing models or propose new models to understand the motor cortex during voluntary motor activities, such as neuroplasticity and task-specific neural activation patterns. Aim: The hand-gripping dataset provides open-access fNIRS recordings of motor task-related brain activity to support the development and refinement of signal processing and machine learning models in brain-computer interface (BCI) research. Approach: Twenty healthy right-handed participants' fNIRS data is acquired during a hand-gripping task from the motor cortex using an Results: This dataset is intended to explore patterns of brain activation during hand gripping, contributing to research on rehabilitation, motor learning, and neuroplasticity, and could be used to develop and validate classification algorithms, contributing to the field of BCI. Conclusions: These results demonstrate that the dataset is reliable and suitable for motor task analysis, benchmarking, and fNIRS-based machine learning studies.

Indexed as

brain-computer interface (BCI)functional near-infrared spectroscopy (fNIRS) datasethand grippingmotor activity

Identifiers

PMID42751597
PMCPMC13580398

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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.