Brain-Computer Interfaces (BCIs) are used in various application scenarios allowing direct communication between the brain and computers. Specifically, electroencephalography (EEG) is one of the most common techniques for obtaining evoked potentials resulting from external stimuli, as the P300 potential is elicited from known images. The combination of Machine Learning (ML) and P300 potentials is promising for authenticating subjects since the brain waves generated by each person when facing a particular stimulus are unique. However, existing authentication solutions do not extensively explore P300 potentials and fail when analyzing the most suitable processing and ML-based classification techniques. Thus, this work proposes i) a framework for authenticating BCI users using the P300 potential; ii) the validation of the framework on ten subjects creating an experimental scenario employing a non-invasive EEG-based BCI; and iii) the evaluation of the framework performance defining two experiments (binary and multiclass ML classification) and three testing configurations incrementally analyzing the performance of different processing techniques and the differences between classifying with epochs or statistical values. This framework achieved a performance close to 100\% f1-score in both experiments for the best classifier, highlighting its effectiveness in accurately authenticating users and demonstrating the feasibility of performing EEG-based authentication using P300 potentials.

翻译：脑机接口（BCI）被应用于多种场景，实现大脑与计算机的直接通信。具体而言，脑电图（EEG）是获取由外部刺激诱发电位的最常用技术之一，例如通过已知图像诱发的P300电位。机器学习（ML）与P300电位的结合在身份认证领域具有广阔前景，因为每个个体在特定刺激下产生的脑电波具有独特性。然而，现有身份认证方案对P300电位的探索尚不充分，且未深入分析最适宜的ML分类与处理技术。为此，本文提出：i) 基于P300电位的BCI用户身份认证框架；ii) 通过十名受试者构建实验场景，验证采用非侵入式EEG的BCI框架有效性；iii) 通过两类实验（二分类与多分类ML任务）及三种递增式测试配置，系统评估不同处理技术性能、以及基于时间窗与统计特征分类的差异。该框架在两类实验中均以最佳分类器实现了接近100%的F1分数，充分验证了其精准身份认证能力，并证明了基于P300电位的EEG身份认证方法的可行性。