Evidence map›Paper›PMID 42041413›Full record

ArticleBiosensors2026

A Miniaturized and Modular Wearable Functional Near-Infrared Spectroscopy (fNIRS) Sensing Module for High-Density Cerebral Hemodynamic Monitoring.

Mengjie Fang, Xinlong Liu, Bowen Ji, Le Li, Kunpeng Gao

Abstract read
In one paragraph

Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Mengjie FangSchool of Information and Intelligent Science, Donghua University, Shanghai 201620, China.
Xinlong LiuSchool of Information and Intelligent Science, Donghua University, Shanghai 201620, China.
Bowen JiNational Key Laboratory of Unmanned Aerial Vehicle Technology, Unmanned System Research Institute, School of Artificial Intelligence, Northwestern Polytechnical University, Xi'an 710072, China.ORCID 0000-0002-2655-4840
Le LiSchool of Life Science and Technology, Northwestern Polytechnical University, Xi'an 710129, China.ORCID 0000-0002-9340-9061
Kunpeng GaoSchool of Information and Intelligent Science, Donghua University, Shanghai 201620, China.ORCID 0000-0003-2317-6263

Funding

Fundamental Research Funds for the Central Universities 223202100019National Natural Science Foundation of China 62106041National Natural Science Foundation of China 62204204
6 · The paper itself

Abstract

This study presents a modular and scalable wearable functional near-infrared spectroscopy (fNIRS) system for high-resolution cerebral hemodynamic signal acquisition. The system is based on compact optoelectronic modules and supports mixed measurements using short-separation and long-separation channels, offering good scalability and spatial adaptability. The integrated quartz light guide structure improves optical coupling efficiency between the probe and scalp. A series of in vivo experiments validated system performance. In a forearm arterial occlusion experiment, the system accurately captured concentration changes in oxygenated and deoxygenated hemoglobin during blood flow blockade and reperfusion, with large effect sizes (Cohen's d > 0.9). In a prefrontal cortex Valsalva experiment, the biphasic response characteristic of neurovascular coupling was successfully resolved. In a 2-back working memory task, the system identified a task-related frequency component (0.0227 Hz) and right-lateralized prefrontal cortex activation (

Indexed as

BrainHemodynamicsWearable Electronic DevicesHumansSpectroscopy, Near-Infraredbrain functional imagingfNIRShemoglobinmodular designneurovascular couplingwearable

Identifiers

PMID42041413
PMCPMC13114163

What OpenQuestion holds

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Registered trials

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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.