SPICE: A Subject-Preserving Counterfactual Augmentation Framework for EEG Disease Detection

EEG-based disease detection often generalizes poorly to unseen subjects. A key challenge is that each subject contributes recordings with a single diagnostic label, enabling models to exploit subject identity as a shortcut for disease prediction. We propose SPICE (Subject-Preserving Inter-Class Expansion), a counterfactual augmentation framework that challenges this shortcut by generating opposite-class EEG samples while preserving subject-specific characteristics. By providing training subjects with examples of both diagnostic classes, SPICE aims to encourage learning of disease-related features that transfer across individuals. We formulate counterfactual generation as unpaired translation between healthy and disease domains and instantiate the framework using CycleGAN with subject-preservation objectives. These objectives also guide a task-specific selection policy that evaluates subject preservation and diagnostic class change while near-copies are screened out of the training view. Experiments across four EEG datasets demonstrate the potential of subject-preserving inter-class expansion to improve cross-subject disease detection, with benefits that depend on the dataset and classifier.