Zhang Wenjia

South Central University for Nationalities

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Junfeng Gao

State Ethnic Affairs Commission

Yang Yong

Suzhou University of Science and Technology

Nini Rao

University of Electronic Science and Technology of China

Yong Yang

Tianjin Polytechnic University

Jinan Guan

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

Zhao Wang

University of Electronic Science and Technology of China

Chunyi Tao

South Central University for Nationalities

Jianhui Qiu

Hangzhou Dianzi University

Cooperative Institutions

University of Electronic Science and Technology of China

South Central University for Nationalities

State Ethnic Affairs Commission

Hangzhou Dianzi University

Jiangxi University of Finance and Economics

Hengshui University

Dhurakij Pundit University

Xi'an Jiaotong University

Suzhou University of Science and Technology

Chengdu Medical College

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A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

PLoS ONE (2013)

Junfeng Gao Zhao Wang Yong Yang Zhang Wenjia Chunyi Tao

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.

Extreme Learning Machine

Hyperparameter Optimization

F1 score

Cross-validation

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Feature vector

10.1371/journal.pone.0064704

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Feature Optimize and Classification of EEG Signals: Application to Lie Detection Using KPCA and ELM

DOAJ (DOAJ: Directory of Open Access Journals) (2014)

Junfeng Gao Jianhui Qiu Zhang Wenjia Yang Yong

EEG signals had been widely used to detect liars recent years. To overcome the shortcomings of current signals processing, kernel principal component analysis (KPCA) and extreme learning machine (ELM) was combined to detect liars. We recorded the EEG signals at Pz from 30 randomly divided guilty and innocent subjects. Each five Probe responses were averaged within subject and then extracted wavelet features. KPCA was employed to select feature subset with deduced dimensions based on initial wavelet features, which was fed into ELM. To date, there is no perfect solution for the number of its hidden nodes (NHN). We used grid searching algorithm to select simultaneously the optimal values of the dimension of feature subset and NHN based on cross-validation method. The best classification mode was decided with the optimal searching values. Experimental results show that for EEG signals from the experiment of lie detection, KPCA_ELM has higher classification accuracy with faster training speed than other widely-used classification modes, which is especially suitable for online EEG signals processing system. Copyright © 2014 IFSA Publishing, S. L.

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