Evidence map›Paper›PMID 39319310›Full record

ArticleFrontiers in neuroscience2024

Signal quality evaluation of an in-ear EEG device in comparison to a conventional cap system.

Hanane Moumane, Jérémy Pazuelo, Mérie Nassar, Jose Yesith Juez, Mario Valderrama, Michel Le Van Quyen

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. [Research on auditory neurofeedback technology and its multi-disciplinary applications].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
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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

6 authors.

Hanane MoumaneLaboratoire d'Imagerie Biomédicale (LIB), Inserm U1146, Sorbonne Université, CNRS UMR7371, 15 rue de l'Ecole de Médecine, Paris, France.
Jérémy PazueloLaboratoire d'Imagerie Biomédicale (LIB), Inserm U1146, Sorbonne Université, CNRS UMR7371, 15 rue de l'Ecole de Médecine, Paris, France.
Mérie NassarLaboratoire d'Imagerie Biomédicale (LIB), Inserm U1146, Sorbonne Université, CNRS UMR7371, 15 rue de l'Ecole de Médecine, Paris, France.
Jose Yesith JuezLaboratoire d'Imagerie Biomédicale (LIB), Inserm U1146, Sorbonne Université, CNRS UMR7371, 15 rue de l'Ecole de Médecine, Paris, France.
Mario ValderramaDepartment of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia.
Michel Le Van QuyenLaboratoire d'Imagerie Biomédicale (LIB), Inserm U1146, Sorbonne Université, CNRS UMR7371, 15 rue de l'Ecole de Médecine, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. Methods: The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. Results: During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG ( Conclusion: Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness.

Indexed as

EEGin-ear devicescalp EEGsignal qualitywearable technology

Identifiers

PMID39319310
PMCPMC11420159

What OpenQuestion holds

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LicenceCC BY
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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.