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As an essential element for the diagnosis and rehabilitation of psychiatricdisorders, the electroencephalogram (EEG) based emotion recognition hasachieved significant progress due to its high precision and reliability.However, one obstacle to practicality lies in the variability between subjectsand sessions. Although several studies have adopted domain adaptation (DA)approaches to tackle this problem, most of them treat multiple EEG data fromdifferent subjects and sessions together as a single source domain fortransfer, which either fails to satisfy the assumption of domain adaptationthat the source has a certain marginal distribution, or increases thedifficulty of adaptation. We therefore propose the multi-source marginaldistribution adaptation (MS-MDA) for EEG emotion recognition, which takes bothdomain-invariant and domain-specific features into consideration. First, weassume that different EEG data share the same low-level features, then weconstruct independent branches for multiple EEG data source domains to adoptone-to-one domain adaptation and extract domain-specific features. Finally, theinference is made by multiple branches. We evaluate our method on SEED andSEED-IV for recognizing three and four emotions, respectively. Experimentalresults show that the MS-MDA outperforms the comparison methods andstate-of-the-art models in cross-session and cross-subject transfer scenariosin our settings. Codes at https://github.com/VoiceBeer/MS-MDA.

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition