Eeg Coupled Scale-Invariant Dynamics for Emotion Recognition: A Domain Adaptation Approach Publisher



M Tajmirriahi MAHNOOSH ; H Rabbani HOSSEIN
Source: IEEE Transactions on Affective Computing Published:2025

Abstract

In electroencephalogram (EEG)-based emotion recognition, traditional static univariate models often struggle to capture the complex, scale-free dynamics inherent in multivariate neural signals, which hampers generalization across subjects. To address this, we introduce a novel stochastic framework based on operator multifractional Levy stable motion (omLsm) and stochastic differential equations (SDE). This framework effectively captures the dynamic scale-free properties of EEG signals and assesses their local cross-scaling characteristics, revealing dynamic fractal connectivity that correlates with various emotional states. The rationale behind our approach lies in the shared scale-free properties and affective cognitive attributes observed across different subjects within the same emotion categories. Local cross-scaling characteristics expose commonalities in the spatio-temporal and spectral domains, facilitating more robust emotion recognition through a multivariate lens. Furthermore, our framework incorporates domain adaptation strategies that enhance model performance across diverse subject populations. Our results indicate significant differences in scale-free connectivity associated with emotional states, reflecting clear advantages over static univariate approaches. Notably, our detection method achieves maximum accuracy of 98.00% for dominance and 98.41% for arousal recognition, respectively, using the DREMER and DEAP datasets and cross-dataset experiments, demonstrates impressive generalization capabilities of the proposed model. This signifies our method's effectiveness for practical applications in emotion recognition. © 2025 Elsevier B.V., All rights reserved.