Lie Detection: Truth Identification from EEG Signal Using Frequency and Time Features with SVM Classifier

Abstract

This study investigated the approach of extracting features from single EEG channels when the minimum number of features in Electroencephalogram (EEG) channels, hence the visibility of using sets of features extracted from a single channel. The feature sets considered in previous studies are utilized to establish a combined set of features extracted from one channel. The feature is the set of statistical moments. Publicly available EEG datasets like the Dryad dataset, obtained from 15 participants, are tested into a support vector machine classifier. The 12 channels were trained separately, where each channel was divided into a different number of blocks, and the results indicated that some channels were bad. Some were very encouraging, reaching 100% in the number of blocks 16 in channels 8 and 12. In this article, the comparison of ANN algorithm test results published in a previous article with SVM algorithm test results for the same tested features and channels will be presented.