A Bispectrum-based Approach for Detecting Deception using EEG Signals

Deception is considered a ubiquitous social phenomenon that has a significant implications in clinical, moral, and law enforcement domains. In this study, we propose a novel electroencephalography (EEG)-based approach for detecting deception. The proposed approach utilizes higher-order spectra (HOS) analysis, namely the bispectrum analysis, to construct a representation of the EEG signals. The constructed bispectrum-based representation is utilized to compute a set of features that can be used to identify deception in EEG signals. Specifically, the extracted features are used to train a support vector machine (SVM) classifier to classify EEG signals into guilty or innocent classes. The performance of the proposed EEG-based deception detection approach was evaluated using EEG signals that were recorded for eleven subjects who participated in a guilty knowledge test (GKT). The results reported in the current study demonstrate the efficacy of the proposed in detecting deception using EEG signals. In particular, the average classification accuracy obtained in differentiating between the innocent and guilty classes is equal to 74%.