Emotion recognition using spatially unidimensional self-attention with fusion feature of brain effective connectivity...

Electroencephalogram(EEG)-based emotion recognition is crucial for advancing human-computer interaction (HCI), and brain network features have become a key research focus. While existing methods often concatenate brain network features with traditional single-channel features to enhance recognition performance, this direct concatenation undermines the spatial information of brain networks and hinders effective application of deep learning. In this work, we propose a novel feature fusion strategy that effectively combines two-dimensional brain effective connectivity (BEC) network features with one-dimensional spectral power features while preserving spatial information. To leverage the spatial topological properties of brain networks and the one-dimensional correlations in fused features, we further introduce a Dual-channel 1D-CNN based on Spatially Unidimensional Self-Attention (SAD-1D-CNN), designed to extract discriminative features by capturing spatial correlations within the combined data. Results show 90.61% accuracy on SEED and 82.13% on SEED-IV (2.68% higher than state-of-the-art). Comprehensive tests confirm the superiority of our fusion strategy and SAD-1D-CNN in emotion recognition. Parameter visualization reveals the attention module's ability to automatically focus on emotion-related core brain regions, and ablation experiments validate the necessity of each network module. These findings offer new perspectives for advancing emotion recognition research.