Evidence map›Paper›PMID 41318658›Full record

ArticleScientific reports2025

EEG glasses for real-time brain electrical activity monitoring.

Renato Zanetti, Amir Aminifar, David Atienza

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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. Review
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.

Renato ZanettiDepartment of Electronics and Biomedical Engineering, Federal Center For Technological Education of Minas Gerais (CEFET-MG), Belo Horizonte, 30510-000, Brazil. zanetti@cefetmg.br.
Amir AminifarDepartment of Electrical and Information Technology, Lund University, Lund, 22100, Sweden.
David AtienzaInstitute of Electrical and Micro Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, 1015, Switzerland.

Funding

EPFL Enable No. 6.1828H2020 Marie Skłodowska-Curie Actions 754354Innovation and Technology Commission of the Hong Kong Special Administrative Region Government ACCESS - AI Chip Center for Emerging Smart SystemsSwiss NSF 10.002.812
6 · The paper itself

Abstract

Wearable devices are becoming a cornerstone for personalized and long-term health monitoring, enabling early intervention and data-driven medical decisions. In this work, we present e-Glass, a state-of-the-art smart wearable device that enables unobtrusive real-time electroencephalography (EEG) monitoring. Our evaluation shows that e-Glass adheres to the established international guidelines for clinical EEG recordings. Moreover, the acquired data presents a Pearson's correlation of 0.93 relative to recordings obtained from the Biopac research-grade EEG system . The proposed EEG acquisition device concept is evaluated in two application domains: epileptic seizure detection and cognitive workload monitoring (CWM). First, we present a lightweight edge machine-learning scheme, designed specifically for e-Glass, achieving overall sensitivity of 64% (100% sensitivity in 11 out of 24 subjects) and 2.35 false-alarms per day, when tested on 982.9 hours of EEG data from the CHB-MIT dataset. Similarly, an CWM strategy with e-Glass reaches an accuracy of 74.5% on unseen data. These results demonstrate that e-Glass is capable of unobtrusive and real-time subject monitoring in outpatient conditions, not only in epileptic seizure detection but also in monitoring the subject's cognitive state.

Indexed as

BrainElectroencephalographyWearable Electronic DevicesAdultEpilepsyFemaleHumansMachine LearningMaleMonitoring, PhysiologicSeizures

Identifiers

PMID41318658
PMCPMC12698764

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.