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EEG-FRM:基于神经网络的熟悉和不熟悉的脸部EEG识别方法

Chao Chen^1,2, Lingfeng Fan¹, Ying Gao³

¹Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China.

Cognitive neurodynamics

|May 3, 2024

概括

这项研究引入了一种基于EEG的面部识别模型 (EEG-FRM),用于识别熟悉和不熟悉的面部. 该模型准确地识别了使用脑电图数据的个体,显示了各种领域应用的前景.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
生物识别信息生物识别信息

背景情况:

熟悉的面部识别在医学,执法和欺骗检测等领域至关重要.
目前面部识别的现有方法通常依赖于视觉数据,这限制了它们在某些场景中的应用.

研究的目的:

开发和评估一种基于神经网络的新方法,用于使用脑电图 (EEG) 数据进行跨主体熟悉和不熟悉的面部识别.
调查与熟悉的面部识别相关的神经相关物,特别是P300反应.

主要方法:

在一个熟悉和不熟悉的面部识别实验中,使用复杂的试验协议从147名受试者收集EEG数据.
提出了一种基于EEG的新型面部识别模型 (EEG-FRM),集成一个具有最大概率机制的多尺度卷积分类网络.
该模型包含注意力模块和监督对比学习,以提高分类性能.

主要成果:

熟悉的面部刺激引发了显著的P300事件相关潜力,主要是在叶.
拟议的EEG-FRM实现了85.64%的平衡精度,73.23%的真正阳性率和1.96%的虚假阳性率.
与其他现有方法相比,该模型表现出优越的性能.

结论:

开发的EEG-FRM是有效的跨主题熟悉/不熟悉的面部识别.
基于EEG的面部识别,特别是利用P300反应,为传统视觉方法提供了可行的替代方案.
这些发现突出了拟议模型在需要可靠的个人识别的各种应用中的潜力.

关键词:

注意模块的注意力模块.卷积神经网络是一种卷积神经网络.电脑电图 (EEG) 是一个电脑电图.熟悉/不熟悉的面部识别功能监督的对比学习学习

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