基于改进超限学习机的N400诱发电位测谎方法
发布时间：2019-01-01 13:04
【摘要】：针对现有测谎方法识别率低的缺陷,将人工免疫算法和超限学习机相结合,提出了一种基于AIA-ELM的N400诱发电位测谎新方法。将24名被试分成犯罪组和对照组,提取多通道的N400峰值、平均幅值、中值频率作为特征向量。采用AIA-ELM算法对被试的探测刺激与无关刺激进行分类,犯罪组被试的识别率为97.60%。实验结果表明,本方法能较有效地进行谎言区分,为N400测谎提供了一种新的参考依据。
[Abstract]:Aiming at the defect of low recognition rate of existing lie detection methods, a new method based on AIA-ELM for N400 evoked potential lie-detection is proposed by combining artificial immune algorithm with over-limit learning machine. 24 subjects were divided into crime group and control group. The peak value of N400, mean amplitude and median frequency of multi-channel were extracted as characteristic vectors. The AIA-ELM algorithm was used to classify the detection stimulus and the unrelated stimulus. The recognition rate of the crime group was 97.60. The experimental results show that this method can effectively distinguish lies and provide a new reference for N400 lie detection.
【作者单位】： 陕西师范大学计算机科学学院;现代教学技术教育部重点实验室;
【基金】：国家自然科学基金(61672021) 陕西省自然科学基金(2017JM6108)
【分类号】：R318.04;TP18
本文编号：2397606
[Abstract]:Aiming at the defect of low recognition rate of existing lie detection methods, a new method based on AIA-ELM for N400 evoked potential lie-detection is proposed by combining artificial immune algorithm with over-limit learning machine. 24 subjects were divided into crime group and control group. The peak value of N400, mean amplitude and median frequency of multi-channel were extracted as characteristic vectors. The AIA-ELM algorithm was used to classify the detection stimulus and the unrelated stimulus. The recognition rate of the crime group was 97.60. The experimental results show that this method can effectively distinguish lies and provide a new reference for N400 lie detection.
【作者单位】： 陕西师范大学计算机科学学院;现代教学技术教育部重点实验室;
【基金】：国家自然科学基金(61672021) 陕西省自然科学基金(2017JM6108)
【分类号】：R318.04;TP18
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，本文编号：2397606
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