Evidence map›Paper›PMID 42783145›Full record

ArticleBiosensors2026

A Low-Cost Wearable Multimodal Brain Signal Acquisition System Integrating EEG and fNIRS for Depression Detection.

Zihan Fei, Hao Li, Zhongyuan Ying, Xingxing Li, Yuezhou Zhang, Qizhi Zhao, Bin Lian, Weiming Cai, Jialin Cui, Tao Yu and 6 more

Abstract read
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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. 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

16 authors.

Zihan FeiCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Hao LiCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Zhongyuan YingCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Xingxing LiThe Affiliated Kangning Hospital of Ningbo University, Ningbo 315201, China.
Yuezhou ZhangDepartment of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London WC2R 2LS, UK.ORCID 0000-0003-4817-3835
Qizhi ZhaoCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.ORCID 0009-0007-2350-3750
Bin LianCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Weiming CaiCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.ORCID 0009-0002-5352-752X
Jialin CuiCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Tao YuCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Xianghong ZhaoCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Shuhao LvCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Zhengxiang YuCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Guanxiang DingCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Yuzhou YingCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Yuhang ZhuCollege of Information Science and Engineering, NingboTech University, Ningbo 315100, China.

Funding

2023 Ningbo Key Research Plan No. 2023Z040Major Technological Innovation Projects of Ningbo High tech Zone No. 2022BCX050002National Natural Science Foundation of China No. 61972350Ningbo Science and Technology Innovation Yongjiang 2035 Key Technology Breakthrough Plan 2024Z261the Ningbo 2025 Major Project of Science and Technology Innovation No. 2021Z109The Ningbo Natural Science Foundation project No. 2024J215Zhejiang Provincial Natural Science Foundation No. LY23F020013
6 · The paper itself

Abstract

Wearable brain-imaging devices have been developed to meet the growing demand in the healthcare industry for long-term monitoring of brain signals in natural conditions, such as monitoring brain diseases and emotions. However, conventional EEG and fNIRS (functional near-infrared spectroscopy) devices are often expensive, bulky and difficult to operate, making it difficult to monitor patients for long periods in natural conditions. To address these issues, this article proposes a low-cost, portable and multimodal wearable brain signal acquisition scheme. It combines EEG (electroencephalography) and fNIRS to reflect brain activity from different perspectives. In order to make it more wearable, a conductive rubber material is used as the electrode for the EEG. In this study, the corresponding experiments were used to verify the performance of the device. The first is the measurement of internal system noise, which satisfies the data acquisition of EEG and fNIRS at different gain levels. The

Indexed as

BrainDepressionElectroencephalographyWearable Electronic DevicesAlgorithmsFemaleHumansMaleSpectroscopy, Near-Infraredconductive rubber materialdepression detectionEEGfNIRSmultimodal data acquisitionVMDwearable brain-imaging devices

Identifiers

PMID42783145
PMCPMC13604232

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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.