Enhancing EEG-based individual-generic emotion recognition through invariant sparse patterns extracted from ongoing affective processes

Abstract

Emotional responses to stimuli produce distinct brain activity patterns that are often sparse in time and spatial distribution across the cortex. These neural signals also contain individual-specific features, complicating emotion recognition across diverse populations. Current approaches rarely address the dual challenge of capturing sparse emotional patterns while minimizing identity-related biases in individual-generic emotion analysis. To bridge this gap, we propose a graph-based emotion-enhancing network framework that isolates emotion-specific neural signatures by amplifying sparse temporal-spatial features and suppressing person-specific biomarkers. Evaluated on two benchmark databases for binary emotion classification, our model achieved state-of-the-art performance in individual-dependent scenarios with accuracies of 65.76 % and 65.39 % for the arousal scale, and 57.75 % and 66.74 % for the valence scale. In the individual-generic condition, the accuracies were 56.11 % and 61.02 % for arousal, and 55.21 % and 66.17 % for valence. Notably, the model’s temporal and spatial enhancement modules provide interpretable insights into emotion-related neural sparsity through learned feature weights. This framework advances emotion recognition systems by reliably identifying universal emotional patterns across individuals while improving computational generalizability.