Bi-GRU Based Deception Detection using EEG Signals

• URL: http://arxiv.org/abs/2507.13718v1
• Date: Fri, 18 Jul 2025 07:59:23 GMT
• Title: Bi-GRU Based Deception Detection using EEG Signals
• Authors: Danilo Avola, Muhammad Yasir Bilal, Emad Emam, Cristina Lakasz, Daniele Pannone, Amedeo Ranaldi,
• Abstract summary: This study presents a deep learning approach for classifying deceptive and truthful behavior using EEG signals.<n>A Bidirectional Gated Recurrent Unit (Bi-GRU) neural network was trained to perform binary classification of EEG samples.<n>The model achieved a test accuracy of 97%, along with high precision, recall, and F1-scores across both classes.
• Score: 4.034185280098732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deception detection is a significant challenge in fields such as security, psychology, and forensics. This study presents a deep learning approach for classifying deceptive and truthful behavior using ElectroEncephaloGram (EEG) signals from the Bag-of-Lies dataset, a multimodal corpus designed for naturalistic, casual deception scenarios. A Bidirectional Gated Recurrent Unit (Bi-GRU) neural network was trained to perform binary classification of EEG samples. The model achieved a test accuracy of 97\%, along with high precision, recall, and F1-scores across both classes. These results demonstrate the effectiveness of using bidirectional temporal modeling for EEG-based deception detection and suggest potential for real-time applications and future exploration of advanced neural architectures.

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