What does this research mean for the field?

While there is a growing reliance on deep learning architectures in EEG-based deception detection, the use of explainable artificial intelligence (XAI) remains limited and unsystematic, necessitating a more principled integration of interpretability. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Evaluate how machine learning and deep learning models incorporate explainable artificial intelligence (XAI) within electroencephalography-based deception detection frameworks.

September 8, 2026ElectronicsOpen Access

Evaluating Explainable Artificial Intelligence in EEG-Based Deception Detection

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Authors

MAMashael Aldayel MAMaryam Alkanhal AAAbeer Al-Nafjan

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Overview

Systematic review reveals increasing reliance on deep learning but limited explainable AI integration in EEG deception detection, highlighting the need for transparent neural models.

Key Points

Evaluate how machine learning and deep learning models incorporate explainable artificial intelligence (XAI) within electroencephalography-based deception detection frameworks.
Conducted a systematic review of EEG-based deception detection literature.
Organized studies by dataset characteristics, experimental lie detection paradigms, feature extraction methodologies, and classification architectures.
Identified an increasing reliance on deep learning architectures for deception classification.
Observed limited, unsystematic integration of explainable artificial intelligence techniques for interpreting spatial-temporal EEG patterns.

Cite This Study

Aldayel et al. (2026) studied this question.

synapsesocial.com/papers/6aa0091858e84d0ff5b47b76 https://doi.org/10.3390/electronics15174041

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September 8, 2026ElectronicsOpen Access

Evaluating Explainable Artificial Intelligence in EEG-Based Deception Detection

View Full Paper

Ask AI

Bookmark

Authors

MAMashael Aldayel MAMaryam Alkanhal AAAbeer Al-Nafjan

Discussion

Member takes

Overview

Systematic review reveals increasing reliance on deep learning but limited explainable AI integration in EEG deception detection, highlighting the need for transparent neural models.

Key Points

Evaluate how machine learning and deep learning models incorporate explainable artificial intelligence (XAI) within electroencephalography-based deception detection frameworks.
Conducted a systematic review of EEG-based deception detection literature.
Organized studies by dataset characteristics, experimental lie detection paradigms, feature extraction methodologies, and classification architectures.
Identified an increasing reliance on deep learning architectures for deception classification.
Observed limited, unsystematic integration of explainable artificial intelligence techniques for interpreting spatial-temporal EEG patterns.

Cite This Study

Aldayel et al. (2026) studied this question.

synapsesocial.com/papers/6aa0091858e84d0ff5b47b76 https://doi.org/10.3390/electronics15174041

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