Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain–Computer Interface (BCI) is a tool that provides direct human–computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

由于信息技术领域的巨大进步，对更可靠的身份验证系统的需求迅速增长，以保护个人或组织资产免受在线欺诈。过去，已经提出了许多身份验证技术，例如密码和令牌，但这些技术存在许多缺点，例如离线攻击（猜测）和盗窃。为了克服这些缺点，本文提出了基于脑信号的认证系统。脑机接口 (BCI) 是一种通过分析大脑信号提供直接人机交互的工具。在这项研究中，提出了一种人员身份验证方法，该方法可以通过响应不同对象的图片生成独特的大脑信号特征来有效地识别用户。本研究的重点是用于认证系统设计的 P300 BCI。此外，还测试了三个分类器：二次判别分析 (QDA)、K-最近邻和二次支持向量机。结果表明，与传统范例相比，所提出的以图片为选择属性的视觉刺激获得了显着更高的分类精度（97%）和信息传输率（37.14 位/分钟）。与其他分类器相比，使用 QDA 观察到了最佳性能。这种方法有利于开发基于大脑信号的身份验证应用程序，因为它不能被伪造（如肩部冲浪），并且仍然可以用于大脑处于良好运行状态的残疾用户。