Bi-GRU Based Deception Detection using EEG Signals
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915398027313152 |
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| author | Avola, Danilo Bilal, Muhammad Yasir Emam, Emad Lakasz, Cristina Pannone, Daniele Ranaldi, Amedeo |
| author_facet | Avola, Danilo Bilal, Muhammad Yasir Emam, Emad Lakasz, Cristina Pannone, Daniele Ranaldi, Amedeo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bi-GRU Based Deception Detection using EEG Signals Avola, Danilo Bilal, Muhammad Yasir Emam, Emad Lakasz, Cristina Pannone, Daniele Ranaldi, Amedeo Machine Learning 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. |
| title | Bi-GRU Based Deception Detection using EEG Signals |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.13718 |