A novel classification method based on ICA and ELM: a case study in lie detection

Abstract

The classification of EEG tasks has drawn much attention in recent years. In this paper, a novel classification model based on independent component analysis (ICA) and Extreme learning machine (ELM) is proposed to detect lying. Firstly, ICA and its topography information were used to automatically identify the P300 ICs. Then, time and frequency-domain features were extracted from the reconstructed P3 waveforms. Finally, two classes of feature samples were used to train ELM, Back-propagation network (BPNN) and support vector machine (SVM) classifiers for comparison. The optimal number of P3 ICs and the values of classifier parameter were optimized by the cross-validation procedures. Experimental results show that the presented method (ICA_ELM) achieves the highest training accuracy of 95.40% with extremely less training and testing time on detecting P3 components for the guilty and the innocent subjects. The results indicate that the proposed method can be applied in lie detection.