A novel method based on variational mode decomposition for lie detection

In this study, a novel method based on Variational Mode Decomposition (VMD) is proposed for lie detection from EEG signals (EEGs). The study was conducted using the LieWaves database, and analyses were performed on 5 −channel EEGs obtained from 27 subjects. The EEGs collected from the subjects during truthful and lying situations were divided into 2-second segments based on the moments when visual stimuli were presented, and a total of 1350 EEG signals were obtained. For lie detection, 3 channels were selected, and EEG signals were processed using the VMD technique and time domain features were extracted from each mode. Extra Trees, Random Forest, K-Nearest Neighbors and Support Vector Machine classification models were used to classify the data. As a result of the tests, the Extra Trees model achieved the highest performance , reaching 100% classification accuracy. The other classification models achieved 99.93%, 99.48% and 64.22% classification accuracy, respectively. These results show that the VMD-based method provides an effective and efficient solution for EEG-based lie detection and it is suitable for real-time applications on portable EEG devices. Moreover, the proposed method is more advantageous than the complex approaches in the literature with its low number of channels and low processing time. The results show that this method has great potential for future studies and applications in the detection of deception.