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arXiv · 2610.27738

EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural Networks

Krzysztof Wrobel·Grzegorz M. Wojcik·Filip Postepski·Andrzej Kawiak·Katarzyna Zemla·Grzegorz Sedek
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Abstract

Guided Imagery (GI) is a mind-based relaxation technique widely used in clinical and therapeutic settings. Objectively assessing whether a patient has entered a relaxed state remains a challenge for clinicians. This study investigates electroencephalography (EEG)-based classification of hypnotic susceptibility using two-dimensional Convolutional Neural Networks (2D CNNs) applied to minimally preprocessed signals, without manual feature extraction. Data were collected from 62 male participants (32 Control, 30 Hypno) using a 256-channel dense array amplifier during Guided Imagery or cognitive task conditions. A sliding-window framework evaluated up to 868 independent models across seven anatomically defined brain regions and 124 ten-second time windows. Segment-level random-split screening was followed by Leave-One-Subject-Out (LOSO) cross-validation to obtain subject-independent performance estimates, and an ensemble approach combined predictions across K = 10 windows. The best single-window model achieved accuracy of 0.82 on the random split. Under LOSO, anterior regions generalised better across subjects: the frontal region achieved mean accuracy of 0.646 (AUC: 0.692). An ensemble of K = 10 windows further improved the prefrontal region to mean accuracy of 0.738. The occipital region, dominant in random-split screening, collapsed to near-chance under LOSO, revealing subject-specific EEG fingerprinting rather than genuine group differences. Bandpass filtering and z-score normalisation suffice for competitive EEG classification without manual feature extraction. Anterior cortical regions are identified as the most generalisable neural markers of hypnotic susceptibility.

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Krzysztof Wrobel, Grzegorz M. Wojcik, Filip Postepski, Andrzej Kawiak, Katarzyna Zemla, Grzegorz Sedek. 2026-10-20. EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural Networks. https://arxiv.org/abs/2610.27738

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