官金安, 段亚峰, 徐世行, 李东阁,印想, 彭翰林, 潘先攀.隐秘信息的脑电检测[J].中南民族大学学报自然科学版,2019,(2):223-226
隐秘信息的脑电检测
EEG detection of secret information
  
DOI:10.12130/znmdzk.20190214
中文关键词: 自我相关程度  隐秘信息  事件相关电位  小波变换
英文关键词: self-relevance degree  hidden information  event-related potential  wavelet transform
基金项目:国家自然科学基金资助项目(91120017);中央高校基本科研业务费资助项目(CZY13031)
作者单位
官金安1,2, 段亚峰1, 徐世行1, 李东阁1,印想1, 彭翰林2, 潘先攀2 1中南民族大学 生物医学工程学院认知科学国家民委重点实验室武汉4300742 中南民族大学 医学信息分析及肿瘤诊疗湖北省重点实验室武汉430074 
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中文摘要:
      为揭示疑犯隐藏的真实信息,检测隐秘信息的脑电,设计了一个猜测受试者真实名字.结果表明:在个体对不同自我相关程度名字产生刺激,在刺激出现后的300~600 ms内,本人名字诱发的正波幅值大于陌生名字刺激.通过小波变换提取特征,用支持向量机进行训练和分类.在进行5个试次叠加平均后,采用PO3通道可将自己的名字分类成功,5位被试平均正确率达98%,该方法可应用于个体隐秘信息的脑电检测.
英文摘要:
      To uncover the hidden information of suspects, electroencephalogram (EEG) containing secret information was measured. An experiment to find the real names of the subjects was designed. It was found that the individuals were stimulated by self-relevant names with different degrees. The amplitude of positive wave induced by one's own name was larger than the other names during the 300—600 ms after the stimulation. The feature points were extracted by wavelet transform, then trained and classified by support vector machine. After 5 trials of superposition averaging, the average accuracy of 5 subjects could reach 98% by using PO3 channel to classify their own names. This method could be applied to EEG detection of individual hidden information.
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