{"slug": "graph-regularized-deep-learning-for-eeg-based-emotion-recognition-with-grounded", "title": "Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure", "summary": "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.", "body_md": "arXiv:2607.07773v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/graph-regularized-deep-learning-for-eeg-based-emotion-recognition-with-grounded", "canonical_source": "https://arxiv.org/abs/2607.07773", "published_at": "2026-07-10 04:00:00+00:00", "updated_at": "2026-07-10 04:17:30.763529+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/graph-regularized-deep-learning-for-eeg-based-emotion-recognition-with-grounded", "markdown": "https://wpnews.pro/news/graph-regularized-deep-learning-for-eeg-based-emotion-recognition-with-grounded.md", "text": "https://wpnews.pro/news/graph-regularized-deep-learning-for-eeg-based-emotion-recognition-with-grounded.txt", "jsonld": "https://wpnews.pro/news/graph-regularized-deep-learning-for-eeg-based-emotion-recognition-with-grounded.jsonld"}}