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August 19, 2025The International Journal of Maritime Engineering

Deep Learning Based Attribute Identification for Deceit Prediction Using EEG Signal Analysis

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Authors

RSR SwethaDEDamodar Reddy Edla

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Overview

Deep learning method identifies deception from EEG signals with 97.66% accuracy, suggesting high potential for real-world applications.

Key Points

  • The algorithm achieved an accuracy of 97.66% in identifying deception from EEG signals, showing promise for practical use.
  • Using Sample Entropy and Recurrence Quantification Analysis, the model captures complex features in EEG data effectively.
  • The approach utilized Independent Component Analysis to reduce EEG channels from 16 to 12, optimizing analysis.
  • This breakthrough in lie detection integrates deep learning with cognitive neuroscience, enhancing existing methods for truth verification.

Cite This Study

Swetha et al. (2025) studied this question.

synapsesocial.com/papers/68af495fad7bf08b1ead5649https://doi.org/10.5750/ijme.v167ia2(s).1649
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Also Consider

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

  1. 1Neural signatures of deception: an explainable machine learning approach using EEG signals2026
  2. 2EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing2024 · 2 citations
  3. 3A Novel Multi-Scale Entropy Approach for EEG-Based Lie Detection with Channel Selection2025 · 7 citations
  4. 4An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks2024 · 21 citations
  5. 5An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals2021 · 25 citations