Teaching a computer to assess hypnotic depth: A pilot study

Abstract

Having measurable physiological correlates, hypnosis should be measurable generally itself. The precise, continual, quantitative assessment (versus phenomenological one) of a current trance level (i.e., “depth”) is possible only instrumentally. We’ve shown that electrophysiological patterns of a trance are stable from session to session, but significantly vary among subjects. Hence, to measure the trance level individually we proposed the following Brain-Computer interface approach and tested it on the 27 video-EEG recordings of 8 outpatients with anxiety and depressive disorders: on the data of the first session using Common Spatial Pattern filtering and Linear Discriminant Analysis classification, we trained the predictive models to discriminate conditions of “a wakefulness” and “a deep trance” and applied them to the subsequent sessions to predict the deep trance probability (in fact, to measure the trance level). We obtained integrative individualized continuously changing parameter reflecting the hypnosis level graphically online, providing the trance dynamics control. The classification accuracy was high, especially while filtering the signal in 1.5-14 and 4-15 Hz. The applications and perspectives are being discussed.