Joint domain symmetry and predictive balance for cross-dataset EEG emotion recognition

Cross-dataset EEG emotion recognition is an extremely challenging task, since data distributions of EEG from different datasets are greatly different, which makes the universal models yield unsatisfactory results. Although there are many methods have been proposed to reduce cross-dataset distribution discrepancies, they still neglected the following two problems. (1) Label space inconsistency: emotional label spaces of subjects from different datasets are different; (2) Uncertainty propagation: the uncertainty of misclassified emotion samples will propagate between datasets.

To solve these problems, we propose a novel method called domain symmetry and predictive balance (DSPB). For the problem of label space inconsistency, a domain symmetry module is designed to make label spaces of source and target domain to be the same, which randomly selects samples from the source domain and put into the target domain. For the problem of uncertainty propagation, a predictive balance module is proposed to reduce the prediction score of incorrect samples and then effectively reduce distribution differences between EEG from different datasets.

Experimental results show that our method achieve 61.48% average accuracies on the three cross-dataset tasks. Moreover, we find that gamma is the most relevant to emotion recognition among the five frequency bands, and the prefrontal and temporal brain regions are the channels carrying the most emotional information among the 62 brain channels.

Compared with the partial domain adaptation method (SPDA) and the unsupervised domain adaptation (MS-MDA), our method improves average accuracies by 15.60% and 23.11%, respectively.

Besides, data distributions of EEG from different datasets but with the same emotional labels have been well aligned, which demonstrates the effectiveness of DSPB.