Spatial-Energy-Aware Dynamic Filtering with Sparse Graph Convolutions for EEG Emotion Recognition

Jingjing Hu

Abstract:

Accurate recognition of human emotions from EEG
signals plays a critical role in affective computing and
human-computer interaction. However, existing methods
face significant challenges in effectively capturing the
sparse, dynamic, and energy-dependent characteristics
of brain activity during emotional experiences. To
address these challenges, we propose a novel framework,
Spatial-Energy-Aware Dynamic Filtering with Sparse
Graph Convolutions (SEASGC), which rethinks EEG
graph modeling from three perspectives: (1) sparse graph
construction to adaptively capture the essential functional
relationships between brain regions, (2) dynamic and
location-dependent filtering to model nonlinear interactions
between EEG nodes, and (3) energy-aware feature aggregation
to leverage energy changes as critical indicators of emotional
intensity. By explicitly integrating these principles, SEASGC
provides a more comprehensive representation of EEG signals
for emotion recognition. Extensive experiments on benchmark
EEG emotion datasets demonstrate that SEASGC achieves
state-of-the-art performance, highlighting its effectiveness
and generalizability in modeling the complex spatial-spectral
dynamics of EEG signals.