Conventional biometrics have been employed in high security user authentication systems for over 20 years now. However, some of these modalities face low security issues in common practice. Brain wave based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG) based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from the three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy 95,6%), while at the same time poses as a viable option for real time applications, as the total time of the training procedure was kept under one minute.

翻译：20多年来,在高安全用户认证系统中采用了常规生物鉴别技术,但其中一些模式在常见做法中面临低安全度问题。脑波用户认证作为一种有希望的替代方法已经出现,因为它克服了其中一些缺陷,并允许用户持续认证。在本研究中,我们通过提出基于数据驱动电子脑学的认证方法来解决个人用户变异性问题。我们引入了机器学习技术,以便以快速、高效的方式披露最适合每个用户数据的最佳分类算法。一套15个功率光谱特征(delta、theta、低阿尔法、高阿尔法和阿尔法)是从三个EEG频道提取的。结果显示,我们的方法可以可靠地允许或拒绝用户访问(平均准确度95.6 % ),同时作为实时应用的一个可行选项,因为培训程序的总时间保持在一分钟以下。