Joint Feature Adaptation and Graph Adaptive Label Propagation for Cross-Subject Emotion Recognition From EEG Signals

• Published in: IEEE transactions on affective computing Vol. 13; no. 4; pp. 1941 - 1958
• Main Authors: Peng, Yong, Wang, Wenjuan, Kong, Wanzeng, Nie, Feiping, Lu, Bao-Liang, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Brain modeling; Data models; Domains; Electroencephalogram (EEG); Electroencephalography; Emotion recognition; Emotional factors; Emotions; feature adaptation; Frequencies; graph learning; Human-computer interface; Invariants; label propagation; Machine learning; Propagation; State estimation; Training
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2022.3189222

Though Electroencephalogram (EEG) could objectively reflect emotional states of our human beings, its weak, non-stationary, and low signal-to-noise properties easily cause the individual differences. To enhance the universality of affective brain-computer interface systems, transfer learning has been widely used to alleviate the data distribution discrepancies among subjects. However, most of existing approaches focused mainly on the domain-invariant feature learning, which is not unified together with the recognition process. In this paper, we propose a joint feature adaptation and graph adaptive label propagation model (JAGP) for cross-subject emotion recognition from EEG signals, which seamlessly unifies the three components of domain-invariant feature learning, emotional state estimation and optimal graph learning together into a single objective. We conduct extensive experiments on two benchmark SEED_IV and SEED_V data sets and the results reveal that 1) the recognition performance is greatly improved, indicating the effectiveness of the triple unification mode; 2) the emotion metric of EEG samples are gradually optimized during model training, showing the necessity of optimal graph learning, and 3) the projection matrix-induced feature importance is obtained based on which the critical frequency bands and brain regions corresponding to subject-invariant features can be automatically identified, demonstrating the superiority of the learned shared subspace.