用脑电波识别说谎，准确率97%
Bi-GRU Based Deception Detection using EEG Signals
- 用双向门控循环单元分析脑电信号
- 测试准确率达97%，各项指标优秀
- 适合安全与心理研究领域的实时检测
说谎检测在安全、心理学和法医学等领域具有重要意义。本研究提出一种深度学习方法，基于袋中谎言（Bag-of-Lies）数据集中的脑电图（EEG）信号，对欺骗与诚实行为进行二分类。采用双向门控循环单元（Bi-GRU）神经网络对EEG样本进行训练，测试准确率达到97%，且在两个类别上均表现出高精确率、召回率和F1分数。结果表明，利用双向时序建模在基于脑电的说谎检测中具有显著效果，为实时应用及未来先进神经架构探索提供了可能。
原文摘要 · Abstract (English)
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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