Bi-GRU Based Deception Detection using EEG Signals

2025-07-18

AI Summary

Addressing the challenge of automatic deception detection in security, psychology, and forensic science, this paper proposes a bidirectional gated recurrent unit (Bi-GRU)-based method for EEG signal analysis to classify lying versus truthful statements under naturalistic conditions. Unlike conventional unidirectional sequential modeling, our approach employs bidirectional temporal modeling to more comprehensively capture long-range temporal dynamics in EEG signals. The model is trained and validated on the publicly available Bag-of-Lies dataset, achieving a test accuracy of 97%, with precision, recall, and F1-score all significantly surpassing those of baseline methods. Notably, the architecture balances high discriminative performance with low computational latency, offering a practical, real-time, and non-invasive deep learning solution for deception detection.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Detecting deception using EEG signals
• Classifying truthful vs deceptive behavior
• Applying Bi-GRU for EEG-based binary classification

Innovation

• Bi-GRU neural network for EEG classification
• Bidirectional temporal modeling for deception detection
• Achieves 97% test accuracy in binary classification