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Real-time assessment of hypnotic depth, using an EEG-based brain-computer interface: a preliminary study.

Nikita V Obukhov1,2, Peter L N Naish3, Irina E Solnyshkina4

  • 1Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation. onvion24@gmail.com.

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Summary

This study demonstrates that electroencephalography (EEG) can effectively monitor hypnosis depth in real-time. A passive brain-computer interface (pBCI) using EEG data achieved over 85% accuracy in classifying hypnotic states, addressing individual variability.

Keywords:
AwarenessBrain-computer interfaceHypnosisHypnotic depth assessmentSelf-awarenessSupervised machine learning

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Area of Science:

  • Neuroscience
  • Psychophysiology
  • Medical Technology

Background:

  • Hypnosis is a valuable therapeutic tool, yet objective monitoring of its depth remains challenging.
  • Existing methods for assessing hypnotic state lack individualization and real-time capabilities.
  • Electrophysiological correlates of hypnosis show significant inter-individual variability.

Purpose of the Study:

  • To investigate the use of an electroencephalography (EEG)-based passive brain-computer interface (pBCI) for real-time, individualized monitoring of hypnosis depth.
  • To address the challenge of individual variability in electrophysiological markers of hypnosis.
  • To develop a method for continuously estimating the deepening process of hypnosis.

Main Methods:

  • Collected and manually labeled 27 electroencephalographic (EEG) recordings from eight outpatients during hypnosis sessions.
  • Performed spectral analysis to identify stable, patient-specific EEG correlates of deep hypnosis.
  • Trained classification models using EEG data from initial sessions to predict hypnosis depth in subsequent sessions.
  • Evaluated model performance using 10-fold cross-validation and real-time classification accuracy across four frequency bands.

Main Results:

  • EEG correlates of deep hypnosis were found to be stable within individuals but varied between patients.
  • Classification models trained on EEG data achieved over 85% accuracy in 10-fold cross-validation.
  • Real-time classification accuracy exceeded 74% in most sessions, with models using 1.5-14 Hz and 4-15 Hz bands showing the best average accuracy of 82%.

Conclusions:

  • An EEG-based pBCI can provide a reliable, individualized, and real-time method for assessing hypnosis depth.
  • This approach overcomes limitations of previous methods by accounting for inter-individual variability in EEG patterns.
  • The developed pBCI holds promise for enhancing the application and understanding of hypnosis in clinical settings.