Rongen Zhang

This study develops novel methods to assess modality importance in multimodal electrophysiology classifiers, finding EEG is globally most important for sleep stages except stage 1, with subject-level differences influenced by sex, medication, and age.

This study classifies sleep stages using multimodal electrophysiology data and introduces an ablation method to assess modality importance, finding EEG is most important for most stages and EOG for REM and nonREM.

This paper introduces a local ablation method to explain multimodal classifiers, showing EEG's importance in sleep staging and identifying demographic effects on classification.

The paper introduces a gradient-based explainability method for multimodal electrophysiology classifiers, showing EEG's dominance in sleep stage classification.