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官金安, 段亞峰, 徐世行, 李東閣,印想, 彭翰林, 潘先攀.隱秘信息的腦電檢測[J].中南民族大學學報自然科學版,2019,(2):223-226
隱秘信息的腦電檢測
EEG detection of secret information
  
DOI:10.12130/znmdzk.20190214
中文關鍵詞: 自我相關程度  隱秘信息  事件相關電位  小波變換
英文關鍵詞: self-relevance degree  hidden information  event-related potential  wavelet transform
基金項目:國家自然科學基金資助項目(91120017);中央高?;究蒲袠I務費資助項目(CZY13031)
作者單位
官金安1,2, 段亞峰1, 徐世行1, 李東閣1,印想1, 彭翰林2, 潘先攀2 1中南民族大學 生物醫學工程學院,認知科學國家民委重點實驗室,武漢430074;2 中南民族大學 醫學信息分析及腫瘤診療湖北省重點實驗室,武漢430074 
摘要點擊次數: 192
全文下載次數: 185
中文摘要:
      為揭示疑犯隱藏的真實信息,檢測隱秘信息的腦電,設計了一個猜測受試者真實名字.結果表明:在個體對不同自我相關程度名字產生刺激,在刺激出現后的300~600 ms內,本人名字誘發的正波幅值大于陌生名字刺激.通過小波變換提取特征,用支持向量機進行訓練和分類.在進行5個試次疊加平均后,采用PO3通道可將自己的名字分類成功,5位被試平均正確率達98%,該方法可應用于個體隱秘信息的腦電檢測.
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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