<p>The applications of lie detection are vast, ranging from criminal polygraph testing to employee recruitment screenings. The accuracy of lie detection is crucial; therefore, it is vital to correctly classify whether the person under consideration is lying or not. Traditional polygraph tests monitor the subject’s physiological responses, which can be easily manipulated by the subject, leading to incorrect conclusions. In contrast, EEG (electroencephalography) signal analysis of the subject undergoing the lie detection experiment utilizing the Brain-Computer Interface (BCI) technology has gained popularity in recent years as the subject cannot alter the nature of their EEG signal responses consciously, unlike their physiological responses. This study focuses on acquiring EEG signals from volunteers undergoing the lie detection experiment. Two datasets have been used for training the model, one private and one obtained from an open-source platform, with the dataset size augmented to increase the size of the dataset and for better adaptability. Signal-noising techniques were implemented on the collected signals to stimulate real-world scenarios, thereby generalizing the model. The most significant features were then extracted using a stacked autoencoder model. Finally, a classification algorithm inspired by multiple convolution neural network (CNN) architectures was implemented to distinguish between truth-telling and deceptive responses. To optimize the model’s performance, a novel combination of principal component analysis (PCA) and the genetic algorithm (GA) was applied to fine-tune the model parameters. The study achieved an accuracy of 99.7% on the privately recorded dataset and 100% on the public dataset.</p>