相关实验视频

Updated: Aug 5, 2026

fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

人工神经网络模型:在交互式场景中实现基于功能近红外光谱的自发谎言检测

M Raheel Bhutta¹, Muhammad Umair Ali², Amad Zafar²

¹Department of Electrical and Computer Engineering, University of UTAH Asia Campus, Incheon, Republic of Korea.

Frontiers in computational neuroscience

|February 8, 2024

概括

这项研究引入了一种用于谎言检测的新型功能近红外光谱学 (fNIRS) 方法. 在健康受试者中,深度学习模型在区分欺骗与真言中达到高达90%的准确性.

科学领域:

神经科学是一个神经科学.
认知科学认知科学
生物医学工程生物医学工程

背景情况:

欺骗检测对于理解大脑机制至关重要.
功能近红外光谱学 (fNIRS) 提供了有前途的神经学应用.
现有的欺骗检测方法存在局限性.

研究的目的:

开发和评估基于fNIRS的自发谎言检测模型.
应用深度学习技术来分类欺骗和说实话.
将新型深度学习模型的性能与传统分类方法进行比较.

主要方法:

利用功能近红外光谱学 (fNIRS) 监测10名健康受试者在打牌游戏期间的大脑活动.
应用了修改的酒-兰伯特定律,将光密度信号转换为氧-血球蛋白和脱氧-血球蛋白水平.
开发并实施了三种深度学习模型:AlexNet,ResNet和GoogleNet,用于欺骗分类.

主要成果:

提出的深度学习模型实现了高准确度:AlexNet (88.5%),ResNet (88.0%) 和GoogleNet (90.0%).
这些模型的性能优于传统的机器学习分类器,包括k-近邻和各种支持向量机 (SVM) 模型.
这种以GoogleNet为灵感的模型在检测欺骗方面表现出最高的分类准确性.

结论:

fNIRS与深度学习相结合,为自发谎言检测提供了一种可行且有效的方法.
像GoogleNet这样的深度学习架构在分析神经成像数据以识别欺骗时表现出卓越的性能.
这种方法有可能在欺骗研究中推进神经学应用.

关键词:

这是分类分类的分类.欺骗欺骗欺骗深度学习算法深度学习算法功能近红外光谱学 (fNIRS) 是一种自发的谎言检测检测器

更多相关视频

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

相关概念视频

Understanding Deception

Understanding Deception

Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...