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Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based

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This summary is machine-generated.

This review highlights how electroencephalography (EEG) signals, analyzed with artificial intelligence (AI) and brain-computer interface (BCI) technology, can detect pain. Deep learning methods show promise for real-time pain classification in clinical settings.

Keywords:
brain–computer interface (BCI)electroencephalography (EEG)pain assessment

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Objective pain evaluation is critical for effective clinical treatment strategies.
  • Brain-computer interface (BCI) technology shows potential for pain detection and classification.
  • Electroencephalography (EEG) signals offer a viable pathway for non-invasive pain assessment.

Purpose of the Study:

  • To review and analyze machine learning (ML) and deep learning (DL) approaches for EEG-based pain detection.
  • To explore advancements, methodologies, and findings from 20 peer-reviewed articles.
  • To identify challenges and opportunities for BCI applications in clinical pain management.

Main Methods:

  • Systematic review of 20 peer-reviewed articles on EEG-based pain detection.
  • Analysis of various ML techniques (SVM, Random Forests, KNN) and DL models (CNN, RNN, Transformers).
  • Evaluation of AI and BCI integration for real-time pain classification.

Main Results:

  • Deep learning techniques effectively analyze EEG signals to identify pain-related neural patterns.
  • Various ML and DL models demonstrate varying degrees of success in decoding pain signals.
  • The integration of AI with BCI enhances system responsiveness and adaptability for pain detection.

Conclusions:

  • EEG-based pain detection using AI, particularly DL, shows significant potential for clinical applications.
  • Further research is needed to address challenges and optimize BCI systems for accurate pain classification.
  • This review provides insights for researchers and practitioners aiming to advance objective pain assessment technologies.