# EEG Classification based on Image Configuration in Social Anxiety Disorder

## TL;DR

This study introduces a novel EEG classification approach for Social Anxiety Disorder that leverages sensor spatial configuration, demonstrating improved accuracy with CNN models over traditional methods.

## Contribution

The paper proposes a new EEG classification model that exploits sensor spatial configuration, significantly enhancing SAD detection accuracy over non-configurational models.

## Key findings

- Model 2 outperforms Model 1 by 6-7% accuracy across algorithms.
- CNNs outperform SVM and kNN in classification performance.
- Sensor configuration exploitation improves EEG-based SAD detection.

## Abstract

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs.