# Decoding the Stressed Brain with Geometric Machine Learning

arXiv: https://arxiv.org/abs/2506.00587 · HTML: https://mlsanity.com/paper/2506.00587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Graph Neural Networks in EEG-based Emotion Recognition: A Survey](https://mlsanity.com/paper/2402.01138/) (2024)
- [Neural Networks Meet Neural Activity: Utilizing EEG for Mental Workload Estimation](https://mlsanity.com/paper/2408.13930/) (2024)

## Abstract (arXiv)

Stress significantly contributes to both mental and physical disorders, yet traditional self-reported questionnaires are inherently subjective. In this study, we introduce a novel framework that employs geometric machine learning to detect stress from raw EEG recordings. Our approach constructs graphs by integrating structural connectivity (derived from electrode spatial arrangement) with functional connectivity from pairwise signal correlations. A spatio-temporal graph convolutional network (ST-GCN) processes these graphs to capture spatial and temporal dynamics. Experiments on the SAM-40 dataset show that the ST-GCN outperforms standard machine learning models on all key classification metrics and enhances interpretability, explored through ablation analyses of key channels and brain regions. These results pave the way for more objective and accurate stress detection methods.