Discriminative Knowledge Fuzzy Transfer Learning Guided by Resting-State EEG for Cross-Subject Emotion Recognition

Cross-subject emotion recognition remains a challenge due to intersubject variability, which limits the generalized ability of models to unseen subjects. Existing studies commonly rely on tasking-state EEG data from the target subject for adaptation, which requires additional emotion-elicitation experiments and limits practical deployment. Motivated by findings that resting-state EEG can reflect individual-specific neural characteristics, this study proposes a discriminative knowledge fuzzy transfer learning guided by resting-state EEG (DKFTL-R) for cross-subject emotion recognition without requiring tasking-state EEG data from the target subject. First, resting-state EEG is leveraged to characterize subject-specific neural signatures, by which source-domain selection is informed. Second, an ESPA module is introduced, in which discriminative emotional knowledge and domain-specific style are integrated via adaptive weighting so that a more transferable representation is obtained. Finally, a Takagi–Sugeno–Kang fuzzy classifier is employed to perform fuzzy inference on the transferable representation. Experiments are conducted on DEAP and DENS datasets, where accuracies of 58.79 %, 55.89 %, 62.91 %, and 60.42 % are achieved, respectively, demonstrating competitive performance compared with popular and recent baseline methods. To evaluate practical applicability and deployability, the proposed method is conducted on a self-constructed emotion EEG dataset (BHE-EMO), and it achieves 67.00 % accuracy for two-class classification and 44.92 % for three-class classification tasks, further demonstrating its effectiveness and engineering potential in real-world settings. In conclusion, we propose a new perspective on cross-subject emotion recognition by integrating resting-state EEG information with fuzzy modeling. This study also introduces a new calibration paradigm for affective brain–computer interface systems.