HCAT-Net a Novel Hierarchical Cross-Attention Transformer Network with Enriched Balanced Ordinal Loss for EEG Emotion Classification

Recognizing human emotions from electroencephalography (EEG) signals is crucial for applications like affective computing and healthcare, yet faces challenges due to the complex, non-stationary nature of EEG data and the inherent ordinal structure of many emotional states. This study introduces the Hierarchical Cross-Attention Transformer Network (HCAT-Net), a novel deep learning architecture specifically designed for improved ordinal emotion classification from 1D EEG time-series. HCAT-Net integrates 1D Residual Convolutional Neural Network blocks with a two-stage hierarchical Transformer encoder, enhanced by Rotary Positional Embeddings (RoPE), to effectively capture dependencies across multiple time scales. The study presents a cross-attention mechanism for improved context integration across Transformer stages and introduces a balanced ordinal loss combining cross-entropy with scaled MAE to preserve label order. Evaluated rigorously on a benchmark EEG emotion dataset, HCAT-Net achieves 99.8% test accuracy, demonstrating its effectiveness. Detailed architectural descriptions and formulations are provided, presenting HCAT-Net as a powerful new approach.