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Enhancing Electroencephalogram-Based Prediction of Posttraumatic Stress Disorder Treatment Response Using Data

Sangha Kim¹, Chaeyeon Yang², Suh-Yeon Dong¹

¹Department of Information Technology Engineering, Sookmyung Women's University, Seoul, Republic of Korea.

Psychiatry Investigation

|August 4, 2025

Summary

This summary is machine-generated.

This study used variational autoencoder (VAE)-based data augmentation to improve electroencephalogram (EEG) analysis for predicting posttraumatic stress disorder (PTSD) treatment response, significantly enhancing prediction accuracy.

Keywords:

Autoencoder Deep learning Electroencephalography Posttraumatic stress disorders

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Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness

Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness

Published on: July 11, 2025

Area of Science:

Neuroscience
Machine Learning
Computational Psychiatry

Background:

Predicting treatment response in posttraumatic stress disorder (PTSD) is challenging due to limited patient data.
Electroencephalogram (EEG) data offers potential biomarkers for neuropsychiatric conditions but often suffers from small sample sizes.
Deep learning models require substantial data for effective training and generalization.

Purpose of the Study:

To enhance the prediction of treatment response in PTSD patients using electroencephalogram (EEG) data.
To apply a variational autoencoder (VAE)-based data augmentation (DA) technique to increase the volume and diversity of EEG data.
To evaluate the impact of VAE-based DA on the performance of deep neural network (DNN) classifiers for treatment response prediction.

Main Methods:

Collected EEG spectrograms from patients diagnosed with PTSD.
Pretrained a VAE model on original EEG spectrograms to generate synthetic augmented data.
Trained a DNN classifier using both original and VAE-augmented EEG data, comparing performance against models trained solely on original data using Area Under the Receiver Operating Characteristic Curve (AUC).

Main Results:

The DNN model trained with VAE-augmented EEG data achieved an AUC of 0.85.
This represents a significant improvement of 0.11 in AUC compared to the model trained without data augmentation.
The VAE-based DA approach demonstrated enhanced classification performance and improved model generalization.

Conclusions:

Variational autoencoder-based data augmentation is an effective strategy for overcoming data limitations in clinical EEG studies for PTSD.
This method significantly improves the performance of deep neural network models in predicting treatment response.
The VAE-DA approach offers a promising avenue for future EEG-based research in neuropsychiatry, particularly with small datasets.