بحوث سكوبس — حمزة وليد حمزة

Lie detection is a well-known word that refers to one person acting in such a way that the other person believes something that is incorrect. Lie detection plays a sensitive part in various Scope including, national security, law enforcement, and psychology. To address this issue, lie detection has received a lot of interest lately. In this research, a deep learning algorithm with a new dataset and protocol is employed to automatically detect truth from electroencephalography (EEG) data. This experiment utilized the OpenBCI Ultracortex "Mark IV" EEG Headset, which acquired 14 channel of EEG data from ten participant. The acquired signal was pre-processing and then inputted individually into three classifiers—MLP, LSTM, and CNN—in order to distinguish between honest or guilty statements in the EEG data and also select the model with the best performance. The indicated manner is non-surgical, effective, and powerful, with least time complication, consequently appropriate for real-time applications. To implement the experiment on EEG signal for deceit detection, a novel dataset and protocol based on video was created. In addition, we compared the outcomes of our method to an existing dataset called Dryad Dataset, which used image protocol. The finding of the proposed system is evaluated using various measures such as accuracy, F1 score, recall, and precision. According to the testing outcomes, the CNN technique achieves the highest incredible accuracy of 99.96% on the EEG data set in our dataset and 99.36% on the Dryad dataset. Finally, the suggested system provides impressive results in comparison with existent algorithms presented in the literature and is precise, scalable, and fault-tolerant. Copyright: ©2025 The authors.