# P300 Spatiotemporal Prior-Based Transformer-CNN for Auxiliary Diagnosis of PTSD

## TL;DR

This paper introduces a new method using brain signals to help diagnose PTSD more accurately than traditional approaches.

## Contribution

A novel hybrid model combining transformer and CNN architectures with ERP priors for improved PTSD diagnosis.

## Key findings

- P300 signals show significant spatiotemporal differences between PTSD patients and healthy controls.
- The proposed P300-STTCNet model achieved 93.37% classification accuracy in PTSD diagnosis.
- The model outperforms traditional methods by capturing both temporal and spatial features effectively.

## Abstract

Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) priors and facing limitations in spatiotemporal feature modeling. Methods: Using common spatiotemporal pattern (CSTP) analysis and quantitative evaluation, we revealed significant spatiotemporal differences in P300 signals between PTSD patients and healthy controls. ERP prior information was then extracted and integrated into a hybrid architecture combining transformer encoders and a convolutional neural network (CNN), enabling joint modeling of long-range temporal dependencies and local spatial patterns. Results: The proposed P300 spatiotemporal transformer-CNN (P300-STTCNet) achieved a classification accuracy of 93.37% in distinguishing PTSD from healthy controls, markedly outperforming traditional approaches. Conclusions: Significant spatiotemporal differences in P300 signals exist between PTSD and healthy control groups. The P300-STTCNet model effectively captures PTSD-related spatiotemporal features, demonstrating strong potential for electroencephalogram-based objective auxiliary diagnosis.