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基于情感EEG的交叉会议人身份识别,使用层次图嵌入.

Honggang Liu1,2, Xuanyu Jin1,2, Dongjun Liu1,2

  • 1School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.

Cognitive neurodynamics
|November 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多尺度卷积和图形聚合网络 (MCGP),用于更准确的脑电图 (EEG) 生物识别. 在MCGP模型有效地识别个人尽管情绪状态的变化,增强机密的人身份识别.

关键词:
大脑的生物识别.电脑电图 (EEG) 是一个电脑电图.嵌入矢量是一个嵌入矢量.图形神经网络是一个神经网络.个人身份识别 个人身份识别

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科学领域:

  • 神经科学是一个神经科学.
  • 生物识别信息 生物识别信息
  • 机器学习 机器学习

背景情况:

  • 电脑电图 (EEG) 信号被用于机密的人身识别.
  • 情感 (情感) 状态的变化对基于EEG的生物识别构成了重大挑战,原因是EEG信号的非静止性.
  • 精确的基于EEG的人身识别需要强大的方法来影响情绪状态的波动.

研究的目的:

  • 开发和评估一个新的深度学习网络,即多尺度卷积和图形聚合网络 (MCGP),用于基于EEG的强有力的个人识别.
  • 为了减轻情绪状态变化的影响,对EEG生物识别的准确性.
  • 评估MCGP在不同数据集和实验条件的表现,包括混合和单一情绪状态的跨会话识别.

主要方法:

  • 采用了一个集成的多尺度卷积和图形聚合网络 (MCGP).
  • MCGP网络使用了多个不同尺度的多个1D卷积来进行动态特征提取和融合.
  • 一个带有注意力机制的图形聚合层被纳入,以生成层次化的图形嵌入,然后将其输入到一个完全连接的分类层中.

主要成果:

  • 在混合情绪状态的交叉会话条件下,MCGP实现了高平均准确率:在SEED上达到85.51%,在SEED-V数据集上达到88.69%.
  • 在单个情绪状态跨会话场景中,MCGP在相同情绪状态下达到85.75% (SEED) 和88.06% (SEED-V),在不同情绪状态下达到79.57% (SEED) 和84.52% (SEED-V).
  • 结果表明,与基线方法相比,MCGP显著减轻了情绪状态变化的影响.

结论:

  • 拟议的MCGP网络有效地解决了基于EEG的人身份识别中情感状态变化的挑战.
  • MCGP提供了一种有前途的方法来提高利用EEG信号的生物识别系统的机密性和准确性.
  • 在跨会话评估中,在同一情绪状态内识别个人时,与在不同情绪状态中识别个人时的表现略好一些.