Abstract
Emotion recognition based on electroencephalogram (EEG) data holds pivotal importance for advancing affective brain-computer interfaces. However, in cross-subject emotion recognition scenarios, negative transfer is likely to happen due to EEG’s individual differences and inherent temporal variability. To solve these issues, this study proposes a novel domain adaptation architecture, named dual filtration subdomain adaptation network (DFSAN), to mitigate negative transfer and align subdomain features at a fine-grained category level. Firstly, the transferability of each subject was assessed to identify those with high transferability to serve as source domains. Then, with the feature alignment through subdomain metric learning, the transferable features could be obtained by dual filtration network. Finally, dual classifiers were employed to mitigate misclassifications near the decision boundary and output the recognition results. Multi-source cross-subject emotion recognition experiments were executed with SEED, SEED-IV, DEAP and SEED-V datasets, achieving recognition accuracy of 88.68 %, 67.61 %, 65.33 % and 65.57 %, respectively. Compared with other state-of-the-art domain adaptation methods, our proposed method achieved better results in cross-subject emotion recognition tasks, demonstrating the effectiveness and feasibility of DFSAN in handling negative transfer under multi-source transfer emotion recognition.