PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition

Xin Zhou, Xiang Zhang, Hao Deng, Lijun Yin

Keywords: Emotion Recognition, Semi-supervised Learning, Channel Importance, Cross-subject

Abstract:

Electroencephalogram (EEG) captures endogenous brain activity with high temporal fidelity and holds substantial promise for precise emotion decoding. However, channel redundancy and pronounced inter-subject variability remain key obstacles to scalable generalization. To address these limitations, we propose a novel framework termed PRioritized channel Importance with Semi-supervised doMain adaptation (PRISM), enabling label-efficient cross-subject emotion decoding. On the channel side, PRISM assigns differentiable, data-dependent channel weights via a lightweight expert ensemble, amplifying reliable electrodes while suppressing distractors. On the domain side, PRISM leverages unlabeled data through confidence-filtered pseudo-labels to drive consistency regularization and domain alignment, mitigating subject-specific heterogeneity. Extensive experiments show that PRISM surpasses state-of-the-art time-series baselines on DEAP,
DREAMER, and SEED datasets, achieving robust cross-subject generalization given limited annotations. The code will be released to the research community.

Primary Area: applications to neuroscience & cognitive science

Submission Number: 7849