Alahaideb Lamia, Al-Nafjan Abeer, Aljumah Hessah, Aldayel Mashael
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2025 Aug 10;25(16):4946. 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.
身份验证是数字安全的关键组成部分，而传统方法往往存在重大漏洞和局限性。本研究涉及基于脑电图的身份验证系统这一新兴领域，突出了它们的理论进展和实际适用性。我们对现有文献进行了系统综述，随后进行了实验评估，以评估这些系统在现实场景中的可行性、局限性和可扩展性。使用各种方法从九名受试者收集了数据。我们的结果表明，卷积神经网络（CNN）模型达到了99%的最高准确率，而随机森林（RF）和梯度提升（GB）分类器也分别以94%和93%的准确率表现出强大性能。相比之下，支持向量机（SVM）和K近邻（KNN）等分类器的有效性明显较低，凸显了它们在捕捉脑电图数据复杂性方面的局限性。研究结果表明，基于脑电图的身份验证系统在增强安全措施方面具有巨大潜力，为传统方法提供了一个有前景的替代方案，并为更强大、用户友好的身份验证解决方案铺平了道路。
