Key result
An EEG-based passive brain-computer interface using 1.5-14 Hz and 4-15 Hz frequency bands achieved an average real-time classification accuracy of 82% for assessing deep hypnosis probability.
Why the study?
Although hypnosis is effective for many conditions, existing instrumental approaches fail to address the individual variability of electrophysiological hypnotic correlates to monitor hypnotic depth.
An EEG-based passive brain-computer interface can classify deep hypnosis versus wakefulness with high accuracy, suggesting potential for real-time monitoring of hypnotic depth.
OBJECTIVE: Hypnosis can be an effective treatment for many conditions, and there have been attempts to develop instrumental approaches to continuously monitor hypnotic state level ("depth"). However, there is no method that addresses the individual variability of electrophysiological hypnotic correlates. We explore the possibility of using an EEG-based passive brain-computer interface (pBCI) for real-time, individualised estimation of the hypnosis deepening process. RESULTS: The wakefulness and deep hypnosis intervals were manually defined and labelled in 27 electroencephalographic (EEG) recordings obtained from eight outpatients after hypnosis sessions. Spectral analysis showed that EEG correlates of deep hypnosis were relatively stable in each patient throughout the treatment but varied between patients. Data from each first session was used to train classification models to continuously assess deep hypnosis probability in subsequent sessions. Models trained using four frequency bands (1.5-45, 1.5-8, 1.5-14, and 4-15 Hz) showed accuracy mostly exceeding 85% in a 10-fold cross-validation. Real-time classification accuracy was also acceptable, so at least one of the four bands yielded results exceeding 74% in any session. The best results averaged across all sessions were obtained using 1.5-14 and 4-15 Hz, with an accuracy of 82%. The revealed issues are also discussed.
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Obukhov et al. (2023) studied Hypnosis (n=9). EEG-based passive brain-computer interface (pBCI) was evaluated on Real-time classification accuracy of deep hypnosis probability. An EEG-based passive brain-computer interface using 1.5-14 Hz and 4-15 Hz frequency bands achieved an average real-time classification accuracy of 82% for assessing deep hypnosis probability.
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