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

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 16; p. 4946
• Main Authors: Alahaideb, Lamia, Al-Nafjan, Abeer, Aljumah, Hessah, Aldayel, Mashael
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10.08.2025; MDPI
• Subjects: Accuracy; Adaptive technology; Algorithms; Biometric identification; Biometrics; Brain research; Classification; Datasets; Electroencephalography; Fourier transforms; Literature reviews; Machine learning; Security systems; Signal processing; Support vector machines; Systematic review
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25164946

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.