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基于单试次脑电解码的类自举法谎言预测研究

白帅帅 陈超 魏玮 代璐瑶 刘烨 邱爽 何晖光

白帅帅, 陈超, 魏玮, 代璐瑶, 刘烨, 邱爽, 何晖光. 基于单试次脑电解码的类自举法谎言预测研究. 自动化学报, 2022, 45(x): 1001−1010 doi: 10.16383/j.aas.c220341
引用本文: 白帅帅, 陈超, 魏玮, 代璐瑶, 刘烨, 邱爽, 何晖光. 基于单试次脑电解码的类自举法谎言预测研究. 自动化学报, 2022, 45(x): 1001−1010 doi: 10.16383/j.aas.c220341
Bai Shuai-Shuai, Chen Chao, Wei Wei, Dai Lu-Yao, Liu Ye, Qiu Shuang, He Hui-Guang. Research on single-trial eeg decoding-based class bootstrap method for lie prediction. Acta Automatica Sinica, 2022, 45(x): 1001−1010 doi: 10.16383/j.aas.c220341
Citation: Bai Shuai-Shuai, Chen Chao, Wei Wei, Dai Lu-Yao, Liu Ye, Qiu Shuang, He Hui-Guang. Research on single-trial eeg decoding-based class bootstrap method for lie prediction. Acta Automatica Sinica, 2022, 45(x): 1001−1010 doi: 10.16383/j.aas.c220341

基于单试次脑电解码的类自举法谎言预测研究

doi: 10.16383/j.aas.c220341
基金项目: 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
详细信息
    作者简介:

    白帅帅:天津理工大学硕士研究生. 主要研究方向为ERP分析与解码. E-mail: baishuai@stud.tjut.edu.cn

    陈超:天津理工大学教授. 主要研究方向为脑机接口, 人机交互技术. E-mail: cccovb@hotmail.com

    魏玮:中国科学院自动化研究所助理研究员. 主要研究方向为脑机接口, 脑电处理分析, 模式识别方法. 本文通信作者. E-mail: weiwei2018@ia.ac.cn

    代璐瑶:中国科学院心理研究所硕士. 主要研究方向为欺骗检测, 谎言识别. E-mail: smxdly5@163.com

    刘烨:中国科学院心理研究所副研究员. 主要研究方向为认知心理学, 谎言识别. E-mail: liuye@psych.ac.cn

    邱爽:中国科学院自动化研究所副研究员. 主要研究方向为精细运动想象脑机接口, 神经调控. E-mail: shuang.qiu@ia.ac.cn

    何晖光:中国科学院自动化研究所研究员, 中国科学院大学岗位教授. 主要研究方向为脑机接口, 人工智能, 医学影像分析. E-mail: huiguang.he@ia.ac.cn

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

Funds: Supported by National Key R&D Program of China (2022YFF1202500, 2022YFF1202501), National Natural Science Foundation of China (62206285, 61806146), China Postdoctoral Science Foundation (2021M703490)
More Information
    Author Bio:

    BAI Shuai-Shuai Master student, Tianjin University of Technology. His research interest covers event-related potential analysis and decoding

    CHEN Chao Professor of Tianjin University of Technology. His research interest covers brain-computer interface and human-computer interaction technology

    WEI Wei Assistant research fellow, Institute of Automation, Chinese Academy of Sciences. His research interest covers brain-computer interface, EEG processing and analysis, and pattern recognition. Corresponding author of this paper

    DAI Lu-Yao Master student, Institute of Psychology, Chinese Academy of Sciences. His research interest covers deception detection and lie detection

    LIU Ye Associate research fellow, Institute of Psychology, Chinese Academy of Sciences. Her research interest covers cognitive psychology and lie detection

    QIU Shuang Associate Research Fellow, Institute of Automation, Chinese Academy of Sciences. Her research interest covers fine motor imagination brain-computer interface and neural regulation

    HE Hui-Guang Researcher, Institute of Automation, Chinese Academy of Sciences, professor of Chinese Academy of Science. His research interest covers brain-computer interface, artificial intelligence, and medical image analysis

  • 摘要: 基于脑电(Electroencephalogram, EEG)的谎言预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, 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, 但在谎言预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分类方法在谎言预测中的应用, 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.
  • 图  1  单试次实验流程图

    Fig.  1  Flow chart of a single trial experiment

    图  2  类自举法的分布假设示意图

    Fig.  2  Schematic diagram of distribution hypothesis of the class bootstrap method

    图  3  事件相关电位波形图

    Fig.  3  Event-related potential waveform

    图  4  类自举法中不同脑电标签训练解码模型的特征可视化

    Fig.  4  Feature visualization of decoding models trained with different EEG labels in class bootstrap method

    表  1  单试次脑电解码实验主要参数

    Table  1  Main parameters of single-trial EEG decoding experiment

    方法 主要参数 代码来源
    时域+SVM 时域特征: Pz导联数据SVM分类器:C = 100; kernel = 'rbf' 利用python工具包sklearn实现
    小波+SVM 连续小波变换; wavelet = 'morl'; SVM分类器: C = 100; kernel = 'rbf' 利用python工具包pywt、sklearn实现
    CSP+LDA 空域特征: 全脑64 通道 利用python工具包pyRiemann、sklearn实现
    HDCA 时间窗: 100 ms 根据文章[23]复现
    MDRM xDAWN-Covariances+MDM python工具包pyRiemann复现
    OCLNN Batch size = 8;optimizer = 'adam' lr = 0.0002; betas = (0.9,0.999) weight_decay = 0.001 根据文章[25]利用Pytorch包复现
    EEGNet Batch size = 64; lr = 0.001 optimizer = 'adam'; betas = (0.9,0.99) https://github.com/vlawhern/arleegmodels
    PLNet Batch size = 16; lr = 0.001 optimizer = 'adam'; betas = (0.9,0.99) early stop 根据文章[27]利用tensorflow.keras库复现
    下载: 导出CSV

    表  2  不同方法在不同训练数据量下的分类均衡精度(均值±标准差,%)

    Table  2  Balanced accuracy of different methods under different training data (mean±standard deviation,%)

    方法 模型训练中采用的训练数据量(组)
    1 2 3 4 5
    时域+SVM 57.31±5.21** 59.42±5.25*** 60.51±6.03*** 61.12±6.66*** 61.49±7.08***
    小波+SVM 58.06±5.79*** 58.80±4.97*** 59.40±5.77*** 59.12±5.53*** 59.31±6.10***
    CSP+LDA 59.16±7.04* 63.06±9.14** 65.56±9.11*** 67.66±10.91*** 68.73±10.77***
    HDCA 56.80±5.41** 60.89±5.82** 62.69±7.09*** 64.68±7.34*** 67.42±7.88***
    MDRM 52.52±2.60*** 57.99±6.44*** 62.47±9.04*** 64.91±8.34*** 67.69±8.93***
    OCLNN 55.25±4.88*** 61.32±5.64*** 65.83±7.16*** 68.28±7.40*** 70.88±8.55***
    EEGNet $ \mathbf{61.79\pm7.57} $ 64.75±6.79* 68.72±7.39** 69.59±7.73** 71.71±7.80**
    PLNet 60.57±6.54 $ \mathbf{68.38\pm 7.22} $ $ \mathbf{73.42\pm 8.44} $ $ \mathbf{74.88\pm 8.69} $ $ \mathbf{76.73\pm 8.94} $
    下载: 导出CSV

    表  3  不同方法在不同数据量下的探针预测正确率

    Table  3  Probe prediction accuracy of different methods under different data volume

    方法 使用的总数据量(组)
    2 3 4 5 6
    BAD B-P 61.11% 66.67% 72.22% 72.22% 77.78%
    P-P 61.11% 72.22% 77.78% 77.78% 77.78%
    类自举法 HDCA 27.78% 38.89% 61.11% 77.78% 83.33%
    MDRM 55.56% 50.00% 66.67% 88.89% 88.89%
    OCLNN 88.89% 72.22% 83.33% 88.89% 88.89%
    EEGNet 83.33% 72.22% 83.33% 94.44% 100.00%
    PLNet 88.89% 72.22% 83.33% 94.44% 100.00%
    下载: 导出CSV
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