Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.

ABSTRACT: Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition. The performances of the DG-integrated deep learning models were quantitatively evaluated using two emotional EEG datasets collected by the authors. Data from each subject were treated as distinct domains in each model. Binar

SUBMITTER: Kim JH 

PROVIDER: S-EPMC12235040 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature