CSTO-Net: cross-view spectral-transport and topology-operator network for EEG emotion recognition

Abstract Objective. Multi-view electroencephalography (EEG) emotion recognition must decide how the spectral graph sets of different connectivity views correspond and how their graph propagation operators interact. We present CSTO-Net, a cross-view spectral-transport and topology-operator network for EEG-based emotion recognition. Approach. CSTO-Net integrates three window-wise views: dynamic differential entropy as node attributes, amplitude-envelope Pearson correlation coefficient (PCC) graphs, and phase-locking value (PLV) graphs. Cross-view spectral transport routing (CSTR) formulates PCC–PLV spectral correspondence as an entropy-regularized optimal-transport problem with explicit marginal constraints, deliberately separating correspondence estimation from band-importance estimation. Directional cross-topology modulation (DCTM) performs ordered composition of the two routed propagation operators on shared node features and encodes order-dependent propagation discrepancy via a provably skew-symmetric graph-operator commutator with signed decomposition. Main results. On the database for emotion analysis using physiological signals dataset, CSTO-Net achieves 93.95 ± 2.50% valence and 94.97 ± 2.72% arousal accuracy under the subject-dependent (SD) setting, and 93.73% and 94.54% under participant-wise five-fold subject-independent cross-validation across 32 participants. On the SJTU Emotion EEG Dataset, it achieves 93.12% under the standard SD split, 91.36% under 15-fold leave-one-subject-out evaluation across 15 participants, and 98.05 ± 2.32% under SD five-fold cross-validation. Ablation studies confirm that both CSTR and DCTM contribute substantially to recognition accuracy. Significance. These results demonstrate that explicit cross-view spectral correspondence and topology-operator-level interaction provide complementary benefits for EEG emotion recognition, offering an interpretable route for modeling order-sensitive interactions between functional connectivity views.