EEG-Based Biometrics

Gondesen, Florian; Marx, Matthias; Gollmann, Dieter

doi:10.1007/978-3-319-98734-7_11

Cited by 5 publications

(1 citation statement)

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“…EEG signals are contaminated by unwanted artefacts caused by, e.g., ocular, muscle, cardiac and respiratory activity, requiring a relatively intensive preprocessing of the signal. Hence, the time it takes to perform EEG-based user authentication is substantially higher than the time required by other competing biometric authentication schemes [10]. In addition, high precision devices are generally expensive, and require specific capturing protocols to ensure an adequate placement of the electrodes and the absence of electrical and electromagnetic interference.…”

Section: Introductionmentioning

confidence: 99%

BED: A New Data Set for EEG-Based Biometrics

Arnau-González

Katsigiannis

Arevalillo‐Herráez

et al. 2021

IEEE Internet Things J.

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Section: Introductionmentioning

confidence: 99%

BED: A New Data Set for EEG-Based Biometrics

Arnau-González

Katsigiannis

Arevalillo‐Herráez

et al. 2021

IEEE Internet Things J.

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Biometrics for Industry 4.0: a survey of recent applications

Cascone

Nappi

2023

J Ambient Intell Human Comput

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The Fourth Industrial Revolution, also known as Industry 4.0, represents the rise of digital industrial technology that is propagating at an exponential rate compared to the previous three revolutions. Interoperability is a basis of production, where there is a continuous exchange of information between machines and production units that act autonomously and intelligently. Workers play a central role in making autonomous decisions and using advanced technological tools. It may involve using measures that distinguish individuals, and their behaviours and reactions. Increasing the level of security, allowing only authorized personnel access to designated areas, and promoting worker welfare can have a positive impact on the entire assembly line. Thus, capturing biometric information, with or without individuals’ knowledge, could allow identity verification and monitoring of of their emotional and cognitive states during the daily actions of work life. From the study of the literature, we outline three macro categories in which the principles of Industry 4.0 are merged and the functionalities of biometric systems are exploited: security, health monitoring, and quality work life analysis. In this review, we present an overview of all biometric features used in the context of Industry 4.0 with a focus on their advantages, limitations, and practical use. Attention is also paid to future research directions for which new answers are being explored.

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An EEG-Based User Authentication System Using Event-Related Potentials and Ensemble Learning

Bolouri,

Shukla

2024

2024 Cyber Awareness and Research Symposium (CARS)

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EEG-Based Biometrics

Cited by 5 publications

References 59 publications

BED: A New Data Set for EEG-Based Biometrics

BED: A New Data Set for EEG-Based Biometrics

Biometrics for Industry 4.0: a survey of recent applications

An EEG-Based User Authentication System Using Event-Related Potentials and Ensemble Learning

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