Evaluating Explainable Artificial Intelligence in EEG-Based Deception Detection
TL;DR
A systematic review of EEG-based deception detection with a focus on how machine learning and deep learning methods are currently applied and to what extent XAI methods are integrated into these pipelines suggests a growing reliance on deep learning architectures but a limited and unsystematic use of XAI.
Abstract
Electroencephalography (EEG) offers a promising basis for objective deception detection, as it captures covert neural responses that may not be accessible through behavior or self-report. In this context, explainable artificial intelligence (XAI) has the potential to both improve model performance and increase trust by clarifying which EEG features and temporal–spatial patterns drive classification decisions. This work presents a systematic review of EEG-based deception detection with a focus on how machine learning and deep learning methods are currently applied and to what extent XAI methods are integrated into these pipelines. The review organizes recent studies by dataset type, feature extraction methodology, and classification strategy, lie detection paradigms and their experimental and analytical designs. The goal is to identify methodological limitations and research gaps, synthesize current challenges and emerging trends, and propose directions for future work on interpretable EEG-based deception detection. The findings indicate a growing reliance on deep learning architectures but a limited and unsystematic use of XAI, highlighting the need for more principled integration of interpretability into deception detection research.