Classification of EEG signals using hybrid combination of features for lie detection

Abstract

The present work demonstrates the effectiveness of the combination of time, frequency, time–frequency, and statistical features extracted from the electroencephalogram (EEG) data, with support vector machine (SVM) for lie detection. Predominantly, the features extracted from the empirical mode decomposition (EMD) of the EEG data significantly improve the classification accuracy. A specific number of narrow band oscillatory components, called intrinsic mode functions (IMFs), are obtained after EMD of the data. The first three IMFs are selected to extract three time and three frequency domain statistical features corresponding to each IMF. These features are chosen due to the strong data adaptation capability of EMD for the transient signals such as an EEG. Furthermore, the features are selected keeping in mind the differences in the distribution, average value, and regularity of the guilty and innocent subjects’ brain signals. The proposed combination of extracted features with customized SVM demonstrates better accuracy than the other state-of-the-art feature extraction methods reported earlier. The proposed hybrid combination of features prominently distinguishes the guilty and innocent subjects with the classification accuracy of 99.44%.