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<p>Lie detection has become the most inevitable research in recent days since deception acts as a high-level cognitive event, which can be evaluated with the electroencephalogram (EEG) signals that estimate the event-related potential. The existing research planned to achieve significant outcomes in the detection of lies but ended up with certain drawbacks such as lack of intricate details extraction, time complexity, sustainability reduction, low performance with small data, and so on. To deal with the challenges and to detect accurate deception, the Guided and Organized Hunt Optimization-enabled Distributed Capsule Network (GOH-DCsNet) model has emerged in the research, which utilizes the Capsule Network (CsNet) that prevents the loss of information even for minimal data and aids in achieving the hierarchical representations. Further, the integration of the GOH optimization in the model can handle large-scale sensing tasks as well as increase the responsiveness of the model. The feature extraction is carried out to extract the most relevant signal features of the input, which aids in detecting the lie efficiently with the model. Finally, the entire model results in accurate detection of a lie, which is evaluated as 95.812%, 95.901%, 96.059%, and 95.744% with accuracy, F1 score, precision, and recall, respectively.</p>

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Lie Detection from the Electroencephalogram Signal with Guided and Organized Hunt Optimization Enabled a Distributed Capsule Network

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Anand Ashok Ingle,
  • Jayant P. Mehare

摘要

Lie detection has become the most inevitable research in recent days since deception acts as a high-level cognitive event, which can be evaluated with the electroencephalogram (EEG) signals that estimate the event-related potential. The existing research planned to achieve significant outcomes in the detection of lies but ended up with certain drawbacks such as lack of intricate details extraction, time complexity, sustainability reduction, low performance with small data, and so on. To deal with the challenges and to detect accurate deception, the Guided and Organized Hunt Optimization-enabled Distributed Capsule Network (GOH-DCsNet) model has emerged in the research, which utilizes the Capsule Network (CsNet) that prevents the loss of information even for minimal data and aids in achieving the hierarchical representations. Further, the integration of the GOH optimization in the model can handle large-scale sensing tasks as well as increase the responsiveness of the model. The feature extraction is carried out to extract the most relevant signal features of the input, which aids in detecting the lie efficiently with the model. Finally, the entire model results in accurate detection of a lie, which is evaluated as 95.812%, 95.901%, 96.059%, and 95.744% with accuracy, F1 score, precision, and recall, respectively.