Deception is a well-known term that refers to acting in a way that causes another person to believe something that is not true. Deception is a rather common occurrence in everyday life and frequently becomes a major national security concern. When investigating crimes, it’s very crucial to be able to detect dishonesty accurately. As a remedy to this problem, deception detection has recently attracted a lot of attention. In this paper, we sought to construct a deception detection system using electroencephalography (EEG) data gathered from a Concealed Information Test (CIT). To improve the system’s performance, we proposed a hybrid TLBO-DE with a novel fitness function to select the best EEG channels. Further, DE mutant vector is modified for faster convergence. In addition a residual Temporal Convolution Network (TCN) is used to reduce model complexity without compromising the performance. The proposed approach is experimented on CIT dataset. The TLBO-DE hybrid channel selection achieved better results in terms of number of channels and accuracy-related measures when compared with TLBO, DEbased channel selection methods. Further, the proposed channel selection model with residual TCN is compared with other deep learning models like LSTM, RNN, and GRUs. The results proved that it not only improves accuracy, sensitivity, f1-score, and specificity but also reduced model complexity. The proposed system’s performance resulted in 96.30 % accuracy and is outperformed compared to State-Of-The-Art (SOTA) models.
Boddu et al. (Fri,) studied this question.