Certain brain areas predict hypnotic susceptibility better than others
Region-Specific EEG Classification of Hypnotic Susceptibility Using a Sliding-Window 2D CNN: A Subject-Independent Evaluation
Machine Learning
Summary
The study looked at whether brain signals from different regions can tell if someone is more open to hypnosis. By analyzing EEG recordings from many areas on the head, the researchers found that signals from the front part of the brain gave reliable clues that could work across many people. Signals from the back of the brain seemed useful only when looking at the same person's data but not for others. This means using brain data from front regions is better for guessing hypnotizability across different individuals.
What this means in practice
- •For neurotechnology developers: Create devices that assess hypnotic susceptibility using EEG signals focused on frontal brain regions for reliable results across users.
- •For mental health clinicians: Use EEG-based tools targeting anterior brain activity to better screen patients for responsiveness to hypnosis-based therapies.
Authors
Krzysztof Wrobel, Grzegorz M. Wojcik
Abstract
We investigate whether a 2D Convolutional Neural Network (CNN) trained on minimally preprocessed EEG signals can classify hypnotic susceptibility across distinct brain regions. Sixty-two male participants (32 Control, 30 Guided Imagery / Hipno group) underwent a 20-minute EEG recording using a 256-channel EGI cap. A sliding-window approach evaluates all 10-second windows within the recording for each of seven predefined brain regions (prefrontal, frontal, temporal, central, parietal, occipital, and facial), yielding up to 868 independently trained models. Initial screening with a segment-level 90/10 random split showed that five regions exceeded the 0.67 accuracy threshold, with the occipital region achieving the highest mean accuracy (0.721) across 90.3% of windows. Subject-independent evaluation via Leave-One-Subject-Out (LOSO) cross-validation, however, substantially revised this picture: occipital performance collapsed to near-chance (mean ACC 0.453, AUC 0.429), revealing that high random-split accuracy was driven by subject-specific EEG fingerprints rather than genuine group differences. In contrast, the frontal and prefrontal regions maintained above-chance LOSO accuracy (0.646 and 0.624, respectively), demonstrating genuine generalisability. These results underscore the necessity of subject-independent validation in EEG-based classification studies and highlight anterior cortical regions as more reliable neural markers of hypnotic susceptibility.