EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural Networks
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.
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Key points
- Guided Imagery (GI) is a mind-based relaxation technique widely used in clinical and therapeutic settings.
- 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.
- Anterior cortical regions are identified as the most generalisable neural markers of hypnotic susceptibility.
Sources (1)
- [1]EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural NetworksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:03 AM
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.
Guided Imagery (GI) is a mind-based relaxation technique widely used in clinical and therapeutic settings.
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