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

Autor: Obukhov NV; Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation. onvion24@gmail.com.; Department of Psychotherapy, Academician I.P. Pavlov First St. Petersburg State Medical University, 6-8, L. Tolstoy str, Saint Petersburg, 197022, Russian Federation. onvion24@gmail.com., Naish PLN; Department of Psychology, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK., Solnyshkina IE; Department of Psychotherapy, Academician I.P. Pavlov First St. Petersburg State Medical University, 6-8, L. Tolstoy str, Saint Petersburg, 197022, Russian Federation., Siourdaki TG; Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation., Martynov IA; Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation.
Jazyk: angličtina
Zdroj: BMC research notes [BMC Res Notes] 2023 Oct 24; Vol. 16 (1), pp. 288. Date of Electronic Publication: 2023 Oct 24.
DOI: 10.1186/s13104-023-06553-2
Abstrakt: 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.
(© 2023. BioMed Central Ltd., part of Springer Nature.)
Databáze: MEDLINE
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