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PLVNet:使用相锁值连接和深度神经网络进行基于EEG的信任分类.

Julakha Jahan Jui1, Imali T Hettiarachchi1, Asim Bhatti1

  • 1Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, 3216, Victoria, Australia.

Computers in biology and medicine
|November 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了PLVNet,一种新型的深度神经网络,用于客观地监测用户对自动化使用电脑图 (EEG) 功能连接的信心. PLVNet准确地对信任状态进行分类,从而实现了自适应的人机交互.

关键词:
大脑计算机接口 (BCI)认知监测是一种认知监测.深度学习是一种深度学习.这是一个EEGEEGEEGEEGEEGEEGEEG.功能连接性的功能连接性.人类与自动化之间的互动.神经动力学 神经动力学在PLVNet中使用PLVNet.阶段锁定值 (PLV) 是指阶段锁定值.对自动化的信任.

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科学领域:

  • 神经科学是一个神经科学.
  • 人与计算机的交互
  • 机器学习 机器学习

背景情况:

  • 有效的人机交互依赖于准确的用户信任评估.
  • 传统的主观信任指标对于动态的实时评估是不够的.
  • 需要客观的神经生理学标记来捕捉快速的信任波动.

研究的目的:

  • 介绍PLVNet,这是一个深度神经网络,用于使用EEG功能连接来分类信任状态.
  • 评估PLVNet与传统机器学习分类器的性能.
  • 调查对人类自动化系统的信任和不信任的神经相关性.

主要方法:

  • 提取的相锁定值 (PLV) 是从30通道EEG的功能连接特征,跨越6个频段.
  • 开发和评估了PLVNet,一种新的深度神经网络架构.
  • 员工综合,参与者智能化和留下一个主体的交叉验证,以进行可靠的评估.

主要成果:

  • 在信任状态分类中,PLVNet显著超过了CNN,SVM和KNN分类器.
  • 贝塔和低马频段显示出最高的分辨能力.
  • 信任与增强的前额-双肩/尾同步相关,表明全球一体化;不信任显示连接分散.

结论:

  • 为了信任监控,PLVNet有效地捕捉了EEG连接中的非线性相互依赖性.
  • 基于PLV的连接可靠地反映了与用户信任相关的神经动态.
  • PLVNet提供了一种实时客观的信任评估途径,用于适应性人类自动化系统.