电子科技大学学报Issue(2)：301-305,5.DOI:10.3969/j.issn.1001-0548.2014.02.028
基于P300和极限学习机的脑电测谎研究
Lie Detection Study Based on P300 and Extreme Learning Machine
摘要
Abstract
Extreme learning machine (ELM) is a typical SLFN (single layer feedback network) and its efficiency has been proved by many literatures for pattern recognitions. In this paper, ELM is applied in lie detection for the first time in order to overcome the disadvantages of the current lie detection methods such as lower accuracy and slower training speed. ELM is used as a classifier to classify the guilty and innocent subjects. The experimental result is compared with support vector machine (SVM), artificial neural network (ANN) and fisher discrimination analysis (FDA). The comparison results show that the proposed method obtains the highest training and testing accuracy with the fastest training speed.关键词
脑电/极限学习机/测谎/神经网络/P300/支持向量机Key words
EEG/extreme learning machine/lie detection/neural network/P300/support vector machine分类
医药卫生引用本文复制引用
高军峰,张文佳,杨勇,胡佳佳,陶春毅,官金安..基于P300和极限学习机的脑电测谎研究[J].电子科技大学学报,2014,(2):301-305,5.基金项目
国家自然科学基金(81271659，61262034，91120017)；江西省自然科学基金(20114BAB211020,20132BAB201025)；江西省教育厅科技项目(GJJ13302) （81271659，61262034，91120017）
