Bi-GRU Based Deception Detection using EEG Signals

2025/07/18 by Danilo Avola, Avola, Danilo, Muhammad Yasir Bilal +9
Psychology · Computer Science · #Deception detection and forensic psychology #Digital and Cyber Forensics #Emotion and Mood Recognition

paper · pdf · doi:10.48550/arxiv.2507.13718

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.

Citations

• Bidirectional recurrent neural networks

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