Graph Neural Network Framework for Interpretable EEG-Based Emotion Recognition Using Frequency-Band and Connectivity Analysis

The healthcare field, human-computer interaction, and affective computing development require accurate human emotional recognition through electroencephalography (EEG) signals. EEG signals present intricate spatial temporal and spectral dependencies that both traditional machine learning and deep learning methods fail to capture resulting in restricted cross-subject generalizability and limited understanding. We introduce MER-TFC, a new EEG-based emotion recognition system which uses GraphNeuralNetworks (GNNs) with attention mechanisms to create inter-channel connectivity and temporal models from EEG data while connecting theta, alpha, beta, and gamma frequency bands. The framework is evaluated using leave-one-subject-out cross-validation (LOSOCV) on DEAP and AMIGOS.benchmark datasets. The experimental results show that MER-TFC achieves 88.2 % average accuracy with 87.7 % F1-score and 0.93 ROC-AUC and 87.9 % balanced accuracy which outperforms Transformer-CNN, Multilayer Graph Transformer, Frequency-Adaptive Dynamic Graph Transformer (FreqDGT) and Adaptive Progressive Attention GNN (APAGNN). Attention analysis reveals brain regions that play critical roles and shows how emotional states correspond to functional brain network connections. The proposed framework also maintains high performance when EEG signals experience background noise, which demonstrates its ability to function well on different subjects.