Multi-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Models

Abstract

Anxiety disorders impact mental and physical health globally. This study classifies anxiety severity levels using Error-Related Negativity (ERN) signals from EEG data, analyzing 163 participants during a go/no-go task. Employing RNN, LSTM, and GRU models, anxiety was categorized as mild, moderate, or severe. GRU achieved 97.6% accuracy under 10-fold cross-validation. Advanced pre-processing and feature extraction ensured robustness. This method outperforms existing techniques, offering a precise, automated approach to anxiety diagnosis using deep learning and EEG analysis.