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Updated: Sep 9, 2025

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基于EEG的认证的大脑与计算机接口:进步和实际影响

Lamia Alahaideb1, Abeer Al-Nafjan1, Hessah Aljumah1

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

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|August 28, 2025
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概括
此摘要是机器生成的。

基于脑电图 (EEG) 的认证为传统方法提供了安全的替代方案. 一个CNN模型实现了99%的准确性,

关键词:
认证方式大脑与计算机接口 (BCI)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
  • 计算机科学
  • 网络安全

背景情况:

  • 传统的认证方法面临着严重的安全漏洞.
  • 新兴的生物识别技术对于增强数字安全至关重要.
  • 电脑电图 (EEG) 信号是一种新的生物识别方式.

研究的目的:

  • 系统审查和实验评估基于EEG的认证系统.
  • 评估EEG认证的可行性,局限性和可扩展性.
  • 为了比较各种机器学习模型对EEG认证的性能.

主要方法:

  • 对EEG认证的系统文献审查.
  • 使用多种方法从9个受试者收集实验数据.
  • 卷积神经网络 (CNN),随机森林 (RF),梯度提升 (GB),支持向量机 (SVM) 和K-最近邻居 (KNN) 分类器的实施和评估.

主要成果:

  • 美国广播公司的模型达到99%的最高准确度.
  • 射频和GB分类器表现出强的性能,分别达到94%和93%的准确性.
  • 在捕获EEG数据的复杂性方面,SVM和KNN分类器的有效性明显较低.

结论:

  • 基于EEG的身份验证系统显示出增强数字安全的巨大潜力.
  • 这些系统为传统的身份验证方法提供了有前途,强大且易于使用的替代方案.
  • 像CNN这样的先进机器学习模型对于认证的EEG信号处理非常有效.