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Comparison of different functional connectives based on EEG during concealed information test

机译:隐藏信息测试中基于脑电图的不同功能连接词的比较

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Deception is a complex cognitive process in which liars always want to conceal some information. Concealed information test (CIT) is a useful paradigm which widely used in deception detection. Recent evidences from brain research suggest deception involves various cognitive activities, and most electroencephalograph (EEG) based concealed information test basically focuses on the signals from few electrodes and analyzed separately. In order to investigate the functional connectivity in different brain regions and the features from spatial domain, we applied graph theoretical concept to evaluate the changes of functional brain networks in guilty group compared with innocent in this article. Five different connectivity methods, including linear and nonlinear interdependence analysis, were explored to the multi-channel EEG signals, to explore which method is best for CIT. The result shows deception responses showed an increased connectivity level. Intraregional and interregional connectivity analysis also showed that deception was associated with increased activity in certain brain areas. Statistical analysis of network parameters showed these features form the two groups were significantly different, and mutual information was the best approach during which for network construction in CIT. Simultaneously, the deception response showed increased small-worldness. The results support the hypothesis that deception mainly involved in the process of working memory, which shows an enhanced connectivity and small-world properties. These findings reveal different dynamic networks in deception and truth telling state, and could be used to identify deception in individual subjects. (C) 2018 Elsevier Ltd. All rights reserved.
机译:欺骗是一个复杂的认知过程,骗子总是想隐瞒一些信息。隐藏信息测试(CIT)是一种广泛用于欺骗检测的有用范例。来自大脑研究的最新证据表明,欺骗涉及各种认知活动,并且大多数基于脑电图(EEG)的隐蔽信息测试基本上都集中在来自少数电极的信号上,并分别进行了分析。为了研究不同大脑区域的功能连通性以及空间域的特征,我们应用图论的概念来评估有罪组与无辜组的功能性脑网络的变化。对多通道EEG信号探索了五种不同的连通性方法,包括线性和非线性相互依赖性分析,以探讨哪种方法最适合CIT。结果表明欺骗响应显示出增加的连接级别。区域内和区域间的连通性分析还显示,欺骗与某些大脑区域活动的增加有关。对网络参数的统计分析表明,两组之间的这些特征显着不同,并且相互信息是在CIT中构建网络的最佳方法。同时,欺骗反应显示出小世界的增加。结果支持这样一个假说,即欺骗主要涉及工作记忆的过程,这显示出增强的连通性和小世界特性。这些发现揭示了在欺骗和说真话状态中的不同动态网络,可用于识别单个对象的欺骗。 (C)2018 Elsevier Ltd.保留所有权利。

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