Schizophrenia Detection from EEG Signals: A Transformer Framework with Spectrogram Representation

2026-09-12

AI Summary

本文提出了一种基于EEG信号的转换器框架，通过生成频谱图并结合传统机器学习和深度学习方法来提高精神分裂症检测的准确性。

Abstract

Schizophrenia is a serious psychiatric disorder that affects millions of people worldwide, and its diagnosis remains primarily dependent on clinical assessment. Electroencephalography (EEG) provides a non-invasive approach to investigate brain activity and has shown potential to support automated Schizophrenia detection. However, existing EEG-based classification studies often suffer from limitations including small datasets, inconsistent preprocessing strategies, and evaluation protocols that may not adequately prevent subject-related data leakage. In this study, we propose an EEG-based Schizophrenia classification framework that transforms preprocessed EEG recordings into time-frequency representations using the Short-Time Fourier Transform. The generated spectrogram images are classified using both conventional Machine Learning algorithms, including Support Vector Machines, Random Forests, and XGBoost, and Deep Learning models, including convolutional architectures and CNN-Transformer hybrids. To ensure reliable evaluation, all data partitions are performed at the subject level. Experimental results demonstrate that the proposed approach achieves competitive classification performance, with the CNN-Transformer (CT-SZ) model achieving an AUC-ROC of 88.41% and the CNN + Squeeze and Excitation + Transformer (CST-SZ) achieving an AUC-ROC of 92.88% on the independent test set.

Problem

• Schizophrenia
• EEG
• classification
• data leakage
• spectrogram

Innovation

• Transformer
• Spectrogram Representation
• Schizophrenia Detection
• EEG Signals
• Deep Learning