Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.

Overview

1. School of Physical and Applied Sciences, Goa University, Taleigao 403206, Goa, India; (M.L.d.A.); (N.V.); (K.P.); (R.G.)
2. SAFE Center, Norwegian University of Science and Technology (NTNU), 7491 Gjøvik, Norway

Abstract

Electroencephalography (EEG) has emerged as a promising modality for biometric user authentication due to its inherent uniqueness and resistance to spoofing attacks. Significant advances in brain wave signal analysis over recent years have reinforced its potential as a distinctive and reliable biometric trait. However, a comprehensive evaluation of the overall progress in this field remains limited. To address this gap, this paper presents an in-depth survey of EEG-based user authentication systems. The survey begins with a comprehensive overview of the human brain’s structure and functional organization, followed by a discussion of EEG signal acquisition principles and commonly used recording devices. It provides a detailed review of data acquisition protocols, publicly and proprietary available EEG databases, and essential preprocessing techniques required for effective signal refinement. The paper further examines feature extraction strategies and classification algorithms employed in EEG-based biometric authentication. In addition to reviewing existing methodologies, the survey identifies key challenges and future considerations in EEG biometrics, such as signal variability, age, mental health conditions, inter-session and inter-subject variability, etc, to establish stable and robust algorithms. This work serves as a foundational reference for researchers, outlining current progress and presenting a structured roadmap for future advancements in EEG-based biometric systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• archive.ics.uci.edu/dataset/121, at archive.ics.uci.edu; found in “6.1.5. UCI EEG Database Dataset”
• bbci.de/competition/iii, at bbci.de; found in “6.1.3. BCI Competition III Dataset”
• bbci.de/competition/iv, at bbci.de; found in “6.1.4. BCI Competition IV Datasets”
• data.mendeley.com/datasets/s7chktmb6x, at Mendeley Data; found in “6.2.4. BIOMEX-DB”
• kaggle.com/datasets/harshilgupta28, at Kaggle; found in “6.1.6. DEAP Dataset”
• openneuro:ds004395, at OpenNeuro; found in “6.1.8. Penn Electrophysiology of Encoding and…”
• physionet.org/content/eegmmidb, at PhysioNet; found in “6.1.1. Physionet EEG Motor Movement Imagery…”
• zenodo:4309472, at Zenodo; found in “6.2.3. Brain Wave-Based EEG Database (BED)”

Data Availability Statement

No new data were created or analyzed in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 12 keywords, 6 MeSH terms, 217 references.

Cite

This paper

de Ataide, M. L., Vetrekar, N., Patel, K., Gad, R., & Ramachandra, R. (2026). Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey. Sensors (Basel, Switzerland), 26(13), 4045. https://doi.org/10.3390/s26134045

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