ArticleSensors (Basel, Switzerland)2022
A Personalized User Authentication System Based on EEG Signals.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Interpretable Spectral Features for Cross-Session EEG Biometric Identification and Verification.Sensors (Basel, Switzerland) · 2026Article
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers.Bioengineering (Basel, Switzerland) · 2026Article
- Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication.International journal of telemedicine and applications · 2025Review
- Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.Sensors (Basel, Switzerland) · 2024Article
- Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.
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Registered trials
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.