Abstract: Emotion recognition using electroencephalography(EEG) has made significant progress in the field of emotional brain-computer interfaces.However,traditional unsupervised domain adaptation(UDA) methods typically require simultaneous access to both source and target domain data,posing a risk of privacy leakage for source domain subjects.To estimate inter-domain differences and suppress noisy pseudo-labels without the need for source domain samples,this paper proposes a proxy-based source-free domain adaptation for EEG emotion recognition method.This method uses the row vectors of the classifier weight matrix from a pre-trained source model as class prototypes,based on which the nearest neighbor samples in the target domain are selected to construct a class-balanced proxy source domain.Subsequently,this proxy source domain is used to train the target model,and the quality of the proxy domain is improved by optimizing the class prototypes and sample selection process.Addi-tionally,the Mixup algorithm is employed to mix the features extracted from the target domain to enhance feature representation capability.A pseudo-label weighting and correction strategy is also proposed,which re-weights the classification loss through uncertainty estimation and corrects the pseudo-labels of samples with high uncertainty.Compared to mainstream source-free domain adaptation(SFDA) methods,the proposed approach achieves an average accuracy improvement of 3.24% on the SEED,SEED-IV,and SEED-V datasets,while also demonstrating significant advantages in pseudo-label quality.

Key words: Electroencephalogram, Emotion recognition, Source-free domain adaptation, Privacy-preserving, Deep learning