Bi-GRU Based Deception Detection using EEG Signals

TL;DR

This paper introduces a Bi-GRU neural network model that effectively classifies deception using EEG signals, achieving high accuracy and demonstrating potential for real-time lie detection applications.

Contribution

It presents a novel deep learning approach employing Bi-GRU for EEG-based deception detection, achieving 97% accuracy on a naturalistic dataset.

Findings

Achieved 97% test accuracy in deception classification

High precision, recall, and F1-scores across classes

Demonstrated effectiveness of bidirectional temporal modeling

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.