Brain–Computer Interface for EEG-Based Authentication: Advancements and Practical Implications

Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We co...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 16; S. 4946
Hauptverfasser: Alahaideb, Lamia, Al-Nafjan, Abeer, Aljumah, Hessah, Aldayel, Mashael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 10.08.2025
MDPI
Schlagworte:
Accuracy
Adaptive technology
Algorithms
Analysis
authentication
Biometric identification
Biometrics
Brain research
Brain-Computer Interfaces
brain–computer interface (BCI)
Classification
Computer Security
convolutional neural networks (CNN)
Data encryption chips
Datasets
Electroencephalography
electroencephalography (EEG)
Electroencephalography - methods
event-related potentials (ERP)
Fourier transforms
Humans
Literature reviews
Machine learning
Neural Networks, Computer
Neurophysiology
Rapid serial visual presentation
Safety and security measures
Security management
Security systems
Signal processing
Support Vector Machine
Support vector machines
Systematic review
Systems and data security software
brain–computer interface (BCI)
electroencephalography (EEG)
event-related potentials (ERP)
convolutional neural networks (CNN)
authentication
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25164946