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

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.

MECHANISMS OF HYPNOTIC ANALGESIA EXPLAINED BY FUNCTIONAL MAGNETIC RESONANCE (fMRI)

Hypnotic-focused analgesia (HFA) was produced in 20 highly hypnotizable subjects receiving nociceptive stimulations while undergoing functional magnetic resonance imaging (fMRI). The fMRI pattern in brain cortex activation while receiving a painful stimulus was recorded both during nonhypnosis and during HFA. The scanning protocol included the acquisition of a T1-weighted structural scan, 4 functional scans, a T2-weighted axial scan, and a fluid attenuated inversion recovery (FLAIR) scan. Total imaging time, including localization and structural image acquisitions, was approximately 60 minutes. Without HFA, the subjects reported subjective presence of pain, and the cortex primary sensory areas S1, S2, and S3 were activated. During HFA, the subjects reported complete absence of subjective pain and S1, S2, and S3 were deactivated. The findings suggest that HFA may prevent painful stimuli from reaching the sensory brain cortex, possibly through a gate-control mechanism.