ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026
Analysis of the generalization ability of graph neural networks in cross-subject EEG emotion recognition.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEmotion decoding from Electroencephalography (EEG) remains limited by cross-subject variability and low structural generalisation. This study aimed to develop a calibration-free (inference without per-subject retraining), entropy-guided graph neural network (EGGN) to enhance accuracy, Architectural interpretability, and fairness in EEG-based emotion recognition.
methodsWe used the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) (n = 15, 3 sessions, 62 channels) and the Database for Emotion Analysis using Physiological Signals (DEAP) (n = 32, 40 trials, 32 channels) datasets, segmented into 3-s epochs with a sampling rate of 128 Hz. Models included Graph Convolutional Network (GCN), Transformer, and Entropy-Guided Graph Neural Network (EGGN), trained using 10-fold cross-validation. Graph edges were derived via entropy gradients; dual-attention pooling and fairness metrics were applied. Saliency and graph fingerprint analyses were performed on SEED; calibration-free and meta-learning evaluations were conducted on DEAP.
resultsEGGN achieved the highest accuracy: 84.2% (valence) and 83.1% (arousal) on SEED, outperforming Transformer (80.5%, 79.8%) and GCN (78.1%, 76.4%). Entropy-based edge modelling improved accuracy by 2.8–3.9%; dual-attention pooling further increased accuracy (+ 3.6–4.1%) and Neuropsychological Alignment Score (NAS) defined by cosine similarity between predicted saliency maps and established emotion-related brain regions (+ 0.14–0.17). Fairness metrics showed minimal gender bias (Disparate Impact Ratio (DIR) = 0.97; Equal Opportunity Difference (EOD) = –1.1%). Graph similarity peaked at 0.74 for high arousal (SEED–DEAP); Uniform Manifold Approximation and Projection (UMAP) silhouette score was 0.64. Calibration-free inference yielded a minor performance drop (EGGN Δ = –1.4% to –0.8%).
conclusionEGGN enables robust, interpretable, and fair emotion decoding from electroencephalography across subjects and datasets.
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