BrainViT: A Hybrid CNN-Vision Transformer Architecture for Multi-label Affective State Classification Using EEG

This work presents a novel hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for automated mental health assessment using electroencephalogram (EEG) signals. The proposed framework addresses the limitations of traditional deep learning approaches by integrating hierarchical feature extraction with transformer-based sequence modeling. Using a dataset of 48 subjects and a 16-channel dry electrode EEG system, our model simultaneously classifies anxiety, depression, and stress conditions. To address data imbalance, we implement a Conditional Generative Adversarial Network (cGAN) for synthetic data generation. The model achieves remarkable classification performance on the held-out test set, with accuracies of 95.77%, 90.21%, and 90.48% for anxiety, depression, and stress, respectively. SHapley Additive exPlanations (SHAP) analysis reveals distinct EEG feature patterns associated with each condition, providing interpretable insights into the model's decision-making process. Our results demonstrate the potential of hybrid CNNViT architectures in advancing automated mental health diagnostics through EEG analysis.