基于单试次脑电解码的类自举法谎言预测研究  

Research on Single-trial EEG Decoding-based Class Bootstrap Method for Lie Prediction

作　　者：白帅帅 陈超 魏玮[3] 代璐瑶 刘烨[4,5] 邱爽 何晖光 BAI Shuai-Shuai;CHEN Chao;WEI Wei;DAI Lu-Yao;LIU Ye;QIU Shuang;HE Hui-Guang(Tianjin Key Laboratory of Complex System Control Theory and Application,Tianjin University of Technology,Tianjin 300384;Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin 300072;Research Center for Brain-Inspired Intelligence,National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190;State Key Laboratory of Brain and Cognitive Science,Institute of Psychology,Chinese Academy of Sciences,Beijing 100101;Department of Psychology,University of Chinese Academy of Sciences,Beijing 100049;School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]天津理工大学复杂系统控制理论与应用天津市重点实验室,天津300384 [2]天津大学医学工程与转化医学研究院,天津300072 [3]中国科学院自动化研究所模式识别国家重点实验室类脑智能研究中心,北京100190 [4]中国科学院心理研究所脑与认知科学国家重点实验室,北京100101 [5]中国科学院大学心理学系,北京100049 [6]中国科学院大学人工智能学院,北京100049

出　　处：《自动化学报》2023年第10期2084-2093,共10页Acta Automatica Sinica

基　　金：国家重点研发计划(2022YFF1202500,2022YFF1202501);国家自然科学基金(62206285,61806146);中国博士后科学基金(2021M703490)资助。

摘　　要：基于脑电(Electroencephalogram,EEG)的谎言检测技术依赖于对事件相关电位(Event-related potential,ERP)的有效解码,当前主要采用手工设计特征进行脑电分析.近年来,单试次脑电分类方法取得了长足进步,其中端到端的脑电分类方法能够实现对脑电的自动特征提取和分类,但在谎言检测中缺乏研究和应用,同时存在无法在测谎场景下直接应用的问题.本研究设计基于复合反应范式(Complex trial protocol,CTP)进行自我面孔信息识别任务的实验,采集了18名被试的脑电数据.研究了不同端到端的单试次ERP分类方法在谎言检测中的应用,同时针对单试次脑电解码方法无法直接实际应用的问题,提出了一种类自举算法.算法基于数据分布假设,通过对比各类刺激图像被视为探针刺激时所训练模型的性能,来推断真正的探针刺激.实验结果表明,在基于自我面孔信息的CTP的谎言预测中,所提出的类自举法性能优于传统探针预测方法,在仅使用少量脑电数据情况下,可实现准确的谎言预测.Lie detection techniques based on electroencephalogram(EEG)rely on the effective decoding of event-related potential(ERP).At present,manual design features are mainly used for EEG analysis.In recent years,the single-trial EEG classification method has made progress.End-to-end EEG classification methods can realize automatically extract features from EEG and classify,which lacks research and application in lie detection,also those methods cannot be directly applied in lie detection.In this study,we designed the autobiographical-based face recognition task based on a complex trial protocol(CTP)and the EEG of 18 subjects was collected.The application of different single-trial ERP classification methods in lie detection are studied.A class bootstrap method is proposed to solve the problem that the single-trial EEG decoding method cannot be applied to practice directly.The class bootstrap method was based on the assumption of data distribution,the probe stimulus was deduced by comparing the classification performance of classifiers that were trained when each category of stimulus images was set as probe stimuli.The experimental results show that the proposed class bootstrap method outperforms the traditional lie detection method and can accurately predict lies when only a small amount of EEG data is used.

关 键 词：脑电 谎言预测 事件相关电位 复合反应范式 类自举法 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]