Hybrid Deep Learning Approach for Deception Detection on EEG Data Using DWT, FFT and Hyperparameter Tuning

Abstract

Introduction: Electroencephalography (EEG) is a brain imaging technique that records electrical activity via scalp-attached electrodes, widely used for studying brain functions and diagnosing neurological disorders. Its high temporal resolution makes it ideal for real-time analysis, despite lower spatial accuracy than fMRI or PET. Recent advancements integrating deep learning with EEG have significantly improved applications in areas like lie detection and cognitive research.

Objectives: This study aims to improve the precision of deception detection using EEG data by developing and assessing new deep learning and machine learning algorithms. The research also seeks to compare the performance of these newly proposed methods with existing ones in terms of accuracy and practical use in forensic and investigative applications.

Method: A comparative analysis was conducted to evaluate the accuracy of different machine learning and deep learning techniques for brain fingerprinting and detecting deception based on EEG signals. Models like CNN paired with FFT and DWT were compared to existing algorithms to assess their accuracy improvements.

Findings: The results demonstrated that the newly proposed algorithms, particularly the combination of CNN with FFT and DWT, showed a significant increase in accuracy when compared to current methods. For example, the CNN combined with FFT saw an improvement from 94.12% to 98.89% in accuracy, while the new CNN-DWT-FFT combination reached an impressive accuracy of 99.42%. Although there was a slight drop in accuracy when using CNN with DWT alone, from 98.76% to 98.64%, the proposed models generally outperformed the current algorithms in most scenarios.

Novelty: The unique aspect of this study is the application of deep learning techniques, specifically the combination of CNN with both DWT and FFT, which had not previously been explored for lie detection using EEG data. This innovative approach significantly enhances detection accuracy, making a noteworthy contribution to forensic psychology and law enforcement.