Evidence map›Paper›PMID 41667762›Full record

ArticleScientific reports2026

A comprehensive evaluation framework for consumer-grade EEG devices: signal quality, robustness, and usability.

Yeeun Lee, Daeun Gwon, Kiyoun Kim, Seokhwan Park, Semin Sohn, Minuck Choi, Minho Choi, Jang-Han Bae, Minkyu Ahn

Abstract read
In one paragraph

Article in Scientific reports, 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

9 authors.

Yeeun Lee *Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.
Daeun Gwon *Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.
Kiyoun KimSchool of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.
Seokhwan ParkSchool of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.
Semin SohnSchool of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.
Minuck ChoiSchool of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.
Minho ChoiDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Jang-Han BaeDigital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Minkyu AhnDepartment of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea. minkyuahn@handong.edu.

Funding

Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) RS-2025-25443930the Korea Institute of Oriental Medicine KSN2313022the National Research Council of Science and Technology (NST) Aging Convergence Research Center CRC22014-500the National Research Foundation of Korea 2021R1I1A3060828
6 · The paper itself

Abstract

The widespread adoption of consumer-grade electroencephalogram (EEG) devices has introduced new opportunities for applications beyond traditional clinical and laboratory settings. However, the lack of standardized evaluation methodologies for these devices raises concerns regarding their signal validity and usability. This study proposes a comprehensive evaluation paradigm for consumer-grade EEG devices, encompassing three levels of signal assessment: the detection of non-neural physiological artefacts signals, the validation of brain waves, and robustness to noise. Using a participant pool of 30 individuals, we assessed four popular consumer-grade EEG devices—BrainLink Pro, NeuroNicle FX2, Mindwave Mobile2, and Muse2—against a research-grade reference device, DSI-24 (Wearable Sensing Inc.). Experimental paradigms involved tasks such as eye blinking, jaw clenching, eyes-open/closed conditions for brain wave detection, and controlled head movements. The results indicate that all tested devices successfully detected both non-neural physiological artefacts and brain wave signals, with consumer-grade devices displaying comparable alpha rhythm characteristics and noise robustness to the research-grade device. User experience was evaluated through a structured questionnaire, revealing significantly higher usability scores for consumer-grade devices, particularly Mindwave Mobile2. The findings highlight the feasibility of using consumer-grade EEG devices for practical applications, provided that validation is performed using structured evaluation protocols as proposed.

Indexed as

BrainElectroencephalographyAdultArtifactsFemaleHumansMaleSignal Processing, Computer-AssistedBrain wavesConsumer-Grade EEGEEG deviceElectroencephalogram

Identifiers

PMID41667762
PMCPMC12972051

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.