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在各种事件相关潜能 (ERP) 中基于EEG的身份验证

Abeer Al-Nafjan1, Lamia Alahaideb1, Mashael Aldayel2

  • 1Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括

脑电图 (EEG) 的脑电波模式为安全的用户身份验证提供了一种新的生物识别技术. 一个卷积神经网络 (CNN) 实现了99%的准确性,证明了EEG

关键词:
生物识别身份验证卷积神经网络 (CNN)电脑电图 (EEG)与事件相关的潜力 (ERP)

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科学领域:

  • 神经科学与网络安全
  • 生物识别身份验证
  • 机器学习在安全方面

背景情况:

  • 传统的身份验证方法在安全性和用户方便性方面存在局限性.
  • 生物识别提供了更安全和个性化的替代方案.
  • 脑电图 (EEG) 是一种新的,非侵入性的生物识别方法.

研究的目的:

  • 调查EEG信号对用户身份验证的有效性.
  • 探索脑电波模式作为独特的生物识别.
  • 通过创新的身份验证框架推进网络安全.

主要方法:

  • 使用38名参与者的公开EEG验证数据集.
  • 与事件相关的诱导潜力 (ERP),特别是P300和N400.
  • 应用信号预处理,ERP和功率光谱密度 (PSD) 特性提取.
  • 机器学习 (SVM,RF) 和深度学习 (CNN) 分类器的比较.

主要成果:

  • 拟议的卷积神经网络 (CNN) 模型实现了99%的准确性.
  • 在N400-Faces任务中观察到优异的性能.
  • 在从语义和面部刺激中辨别神经特征的有效性.

结论:

  • 基于EEG的生物识别是可行的安全和非侵入性身份验证方法.
  • 这种方法提高了认证框架的弹性.
  • 有助于开发先进的网络安全解决方案.