Bi-GRU Based Deception Detection using EEG Signals
This addresses deception detection for security, psychology, and forensics, but it is incremental as it applies an existing method to a new dataset.
The study tackled deception detection by classifying EEG signals from the Bag-of-Lies dataset using a Bi-GRU neural network, achieving a test accuracy of 97% with high precision, recall, and F1-scores.
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.