EEG Emotion Recognition Based on Source-Free Domain Adaptation
Existing domain adaptation methods in EEG emotion recognition utilize source domain data and feature distribution to train the model,which inevitably requires frequent access to the source domain and thus may lead to leakage of private information of the source domain subjects.To address this problem,this paper proposed a source-free domain adaptation EEG emotion recognition method based on the Gaussian mixture model,nuclear-norm maximization,and Tsallis entropy(GNTSFDA).First,based on the source domain data and the CNN and transformer feature mixture(CTFM)network,the source domain model was trained to obtain the source domain model using the cross-entropy loss.Then,the pseudo-labels of the target domain data were generated by clustering with the Gaussian mixture model to construct the classification loss.Finally,based on the pseudo-labels and the classification loss,the source domain model was re-trained on the target domain data to update its parameters to obtain the target domain model,and the nuclear-norm maximization loss was also utilized during the training process to enhance the class discriminative property and the diversity of the model predictions,and Tsallis entropy loss was utilized to reduce the model predictions'uncertainty.The GNTSFDA method was experimented on the SEED(14 subjects in the source domain,1 subject in the target domain),SEED-Ⅳ(14 subjects in the source domain,1 subject in the target domain),and DEAP(31 subjects in the source domain,1 subject in the target domain)public datasets,using a leave-one-subject cross-validation experimental paradigm.The results showed that on the three datasets,the accuracies of emotion recognition of the target domain model was 80.20％,61.20％,and 58.89％,respectively,which was an improvement of 8.98％,7.72％,and 6.54％,respectively,compared with that obtained from the source domain model.The GNTSFDA method only needs to access the source domain model parameters,instead of the source domain,therefore,effectively protected the privacy information of source domain subjects and is of great significance in the practical application of EEG-based emotion recognition.
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