EEG glasses for real-time brain electrical activity monitoring

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.

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Publication Details

Journal
Scientific Reports
Published
2025-11-29
DOI
https://doi.org/10.1038/s41598-025-29893-4
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

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article

EEG glasses for real-time brain electrical activity monitoring

Amir Aminifar, Renato Zanetti, David Atienza
1 citations
Scientific Reports
EEG and Brain-Computer Interfaces
1.43
article

EEG glasses for real-time brain electrical activity monitoring

Amir Aminifar, Renato Zanetti, David Atienza
article en
1 citations

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.

Scientific ReportsVol. 15(1)
Lund University (SE), Federal Center for Technological Education of Minas Gerais (BR), Informa (Sweden) (SE), École Polytechnique Fédérale de Lausanne (CH)
National Science Foundation, European Commission, Innovation and Technology Commission, Vetenskapsrådet
Openalex Percentile: Top 14%
EEG and Brain-Computer Interfaces
1.43
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EEG glasses for real-time brain electrical activity monitoring — Amir Aminifar, Renato Zanetti, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS