Most of the current Brain–Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject’s information. It means that if EEG were maliciously manipulated, the proper functioning of BCI frameworks could be at risk. Unfortunately, it happens in frameworks sensitive to noise-based cyberattacks, and more efforts are needed to measure the impact of these attacks. This work presents and analyzes the impact of four noise-based cyberattacks attempting to generate fake P300 waves in two different phases of a BCI framework. A set of experiments show that the greater the attacker’s knowledge regarding the P300 waves, processes, and data of the BCI framework, the higher the attack impact. In this sense, the attacker with less knowledge impacts 1% in the acquisition phase and 4% in the processing phase, while the attacker with the most knowledge impacts 22% and 74%, respectively.

当前的大多数脑机接口 (BCI) 应用场景都使用包含受试者信息的脑电图信号 (EEG)。这意味着如果 EEG 被恶意操纵，BCI 框架的正常运行可能会受到威胁。不幸的是，它发生在对基于噪声的网络攻击敏感的框架中，需要付出更多努力来衡量这些攻击的影响。这项工作介绍并分析了四种基于噪声的网络攻击试图在 BCI 框架的两个不同阶段生成假 P300 波的影响。一组实验表明，攻击者对 BCI 框架的 P300 波、过程和数据的了解越多，攻击影响就越大。从这个意义上说，知识较少的攻击者在获取阶段影响 1%，在处理阶段影响 4%，