Sera: Separated Coarse-to-fine Representation Alignment for Cross-subject EEG-based Emotion Recognition

Neuropsychology-inspired models have been utilized in recent advances in EEG emotion recognition, such as convolutional networks for spatial features and Transformers for temporal dependencies. While these methods benefit from domain knowledge like frequency-band features and spatial correlations, most overlook the fundamental fact that EEG signals are complex mixtures of neural source activities recorded at the scalp. EEG signals presenting challenges for emotion recognition, particularly in cross-subject scenarios due to significant inter-subject variance. Inspired by neurophysiological principles, we propose a novel framework, named Sera, for EEG-based emotion recognition that explicitly separates source activities and aligns representations across subjects. Sera introduces two key components: (1) a variational autoencoder (VAE) with multiple multi-stage decoders (M2VAE) designed to disentangle EEG signals into independent sources, mimicking the neural generation process, and (2) a coarse-to-fine representation alignment block (CFRA) to mitigate subject-to-subject variability. The coarse alignment employs adversarial training with a domain discriminator, while the fine-grained alignment matches covariance matrices to capture temporal correlations within EEG segments. Extensive experiments demonstrate that Sera outperforms the state-of-the-art methods with improvements ranging from 1% to 5%, averaging 3.14% and 3.05% on the DEAP and DREAMER datasets, respectively, confirming its effectiveness and neurophysiological grounding. The code is available at: https://github.com/JZH98/Sera-code.