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July 29, 2026Journal on Advances in Signal ProcessingOpen Access

Neural signatures of deception: an explainable machine learning approach using EEG signals

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Authors

BEBoda ErammaSCSridhar ChintalaPSPurella Sushma

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Overview

Randomized trial demonstrates accurate lie detection using EEG signals in subjects, suggesting a reliable alternative to traditional methods.

Key Points

  • The aim is to develop an interpretable framework for deception detection using EEG signals and machine learning.
  • EEG signals recorded from subjects using 16 electrodes
  • Features were extracted using ANOVA F-score for classification
  • Light Gradient Boosting Machine (LGBM) was utilized for lie detection with comparison to traditional algorithms.
  • LGBM achieved a classification accuracy of 99.16% with high precision, sensitivity, and specificity
  • Feature importance analysis confirmed the robustness and reliability of the model
  • Outperformed traditional algorithms like SVM and Random Forest in quality metrics.

Cite This Study

Eramma et al. (2026) studied this question.

synapsesocial.com/papers/6a69a2bcc8da07d9defa682bhttps://doi.org/10.1186/s13634-026-01357-5
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Deep Learning Based Attribute Identification for Deceit Prediction Using EEG Signal Analysis2025
  2. 2EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing2024 · 5 citations
  3. 3An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks2024 · 22 citations
  4. 4A Novel Multi-Scale Entropy Approach for EEG-Based Lie Detection with Channel Selection2025 · 7 citations
  5. 5Evaluating Explainable Artificial Intelligence in EEG-Based Deception Detection2026