Charles A. Ellis

This paper introduces a local spectral explainability method to analyze deep learning classifiers trained on raw EEG data for sleep stage classification, showing varying frequency band importance over time and within sleep stages.

This study introduces a local spectral explainability method for EEG deep learning classifiers, showing that delta, beta, and gamma bands are most important and that age and sex affect classifier patterns.

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

The paper investigates dynamic functional connectivity (dFC) of the default mode network (DMN) in schizophrenia and links it to symptom severity using resting-state fMRI data from two datasets.

This study examines how dynamic functional network connectivity relates to spatial orientation in healthy young adults using resting-state fMRI and machine learning.