Lie Detection Method Based on P300 and Machine Learning

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Lie Detection Method Based on P300 and Machine Learning

更新时间：2025-07-09

- Lie Detection Method Based on P300 and Machine Learning
- Vol. 44, Issue 10, Pages: 120-124(2010)
- 作者机构：
  
  西安交通大学生命科学与技术学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： R318
- Online First：10 October 2010，
  
  Published：2010
- 稿件说明：
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Lie Detection Method Based on P300 and Machine Learning[J]. 2010, 44(10): 120-124.
DOI：

Lie Detection Method Based on P300 and Machine Learning[J]. 2010, 44(10): 120-124. DOI：

摘要

为了克服传统测谎方法没有考虑到相同刺激下受试者思维状态变化的缺点

提出了基于P300和机器学习的测谎方法.该方法使用典型的3刺激测谎范式

首先记录30名随机划分的撒谎者和无辜者的12导脑电(EEG)信号

使用独立成分分析方法(ICA)分解由探针刺激产生的脑电信号

利用在Pz电极上分布强度大的独立分量重建Pz波形

将每名受试者的的若干个Pz波形进行平均

提取两步降噪后的每个Pz波形的时域和小波特征.最后

使用分类器区分P300和非P300波形

进一步计算出个体测谎诊断率.实验结果表明

支持向量机(SVM)适合于说谎意识状态的分类

提出的方法可以有效地改进单次刺激记录上的信噪比

提高P300成分的识别率

进而提高测谎诊断率.

Abstract

For solving the unvariability of subject cognitive states under the same kind of stimulus in the conventional machine learning methods

the method based on P300 and machine learning was proposed. The standard three-stimuli protocol was chosen. Thirty guilty and innocent subjects were randomly divided into two groups and their EEG signals were first recoded. Independent component analysis(ICA)was carried out to decompose the datasets in the probe stimuli. The ICs with the largest projection strength at Pz were selected to reconstruct the Pz waveforms. Then small number of Pz waveforms within each subject is further averaged. Afterwards

the time-domain and wavelet features were extracted from each denoised Pz waveforms. In terms of the classifier to identify the P300 and non-P300 waveforms

the individual diagnostic rate was evaluated. The experimental results show that the SVM classifier is suitable to identify the sense states of lying

and the proposed method enables to improve the SNR in single trails

enhancing the accuracy of identifying the P300 and of individual diagnostic rate.

关键词

Keywords

references

ABOOTALEBI V, MORADI M H, KHALILZADEH M A. A comparison of methods for ERP assessment in a P300-based GKT[J]. International Journal of Psychophysiology, 2006,62(2):309-320.

POLICH J. P300 in clinical applications: meaning, method, and measurement[J]. American Journal of EEG Technology, 1991, 31(3):201-231.

ROSENFDELD J P, CANTWELL B, NASMAN V T, et al. A modified event-related potential-based guilty knowledge test[J]. International Journal of Neuroscience, 1988, 42(1/2):157-161.

FARWELL L A, DONCHIN E. The truth will out: interrogative polygraphy(“lie detection”)with event-related potentials[J]. Psychophysiology, 1991, 28(5):531-547.

ABOOTALEBI V, MORADI M H, KHALILZADEH M A. A new approach for EEG feature extraction in P300-based lie detection[J]. Computer Methods and Programs in Biomedicine, 2009, 94(1):48-57.

RÖSCHKE J, MANN K, WAGNER P, et al. An approach to single trial analysis of event-related potentials based on signal detection theory[J]. International Journal of Psychophysiology, 1996, 22(3):155-162.

JUNG T P, MAKEIG S, HUMPHRIES C, et al. Removing electroencephalographic artifacts by blind source separation[J]. Psychophysiology, 2000, 37(2):163-178.

LIN C T, CHUNG I F, KO L W, et al. EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment[J]. IEEE Transactions on Biomedical Engineering, 2007, 54(7):1349-1352.

HERRMANN C S, KNIGHT R T. Mechanisms of human attention: event related potentials and oscillations[J]. Neuroscience and Biobehavioral Reviews, 2001, 25(6):465-476.

HSU W Y, LIN C C, JU M S, et al. Wavelet-based fractal features with active segment selection: application to single-trial EEG data[J]. Journal of Neuroscience Methods, 2007, 163(1):145-160.

周亮, 杨文俊, 廖四照,等,模拟盗窃者与熟悉现场者在测谎实验中事件相关电位的比较研究[J].中国临床心理学杂志,2000, 8(2):86-88.

ZHOU Liang, YANG Wenjun, LIAO Sizhao, et al. Compared study of event-related potentials in lie detection between subjects for simulated crime condition and subjects for visiting condition [J].Chinese Journal of Clinical Psychology, 2000,8(2):86-88.

DELORME A, MAKEIG S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J]. Journal of Neuroscience Methods, 2004, 134(1):9-21.

QUIROGA R Q, SAKOWITZ O W, BASAR E, et al. Wavelet transform in the analysis of the frequency composition of evoked potentials[J]. Brain Research Protocols, 2001, 8(1):16-24.

SHOKER L, SANEI S, CHAMBERS J. Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm[J]. IEEE Signal Processing Letters, 2005, 12(10):721-724.

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