Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure

Researchers proposed a graph-regularized deep learning framework for EEG-based emotion recognition that models psychological interdependencies between emotion classes, achieving up to +5.42% accuracy improvement and a 39% reduction in psychologically implausible misclassifications on SEED-IV and SEED-V datasets. The framework, evaluated on three backbone architectures, uses graph-based regularization strategies to enforce emotion topology consistency.

arXiv:2607.07773v1 Announce Type: new Abstract: EEG-based emotion recognition is critical for mental health monitoring and affective brain-computer interfaces, yet existing deep learning approaches often treat emotion classes as isolated labels, ignoring their psychological interdependencies. We propose a graph-regularized learning framework that conceptualizes emotions as nodes in a graph where edges encode proximity based on dimensional emotion theories. We adapt three complementary regularization strategies--Graph Label Smoothing intuitive soft labeling , Commuting distance on graph via Graph Laplacian spectral graph theory , and Sliced Wasserstein Distance optimal transport on graph --ordered by increasing computational complexity. These strategies penalize model predictions that deviate from the established emotion topology. Our framework is evaluated across three representative backbone architectures: AudioTransformer pure transformer , Conformer CNN-transformer hybrid , and DCGNN causal graph neural network , demonstrating architecture-agnostic benefits. Experiments on SEED-IV 4 classes and SEED-V 5 classes datasets show consistent improvements: best case up to +5.42% accuracy and 39% reduction in psychologically implausible misclassifications. Ultimately, our framework help raise the upper bound of performance achievable with standard approaches. Code will be released.