xSTAE: Explaining Classifier Decisions through EEG Signal Style Transfer Autoencoding

Style transfer methods are a powerful visualization tool that can be used to generate counterfactual explanations, plausible alternatives to the original input that leads to a different classification. In this paper we present xSTAE, a system that restyles a misclassified example into the correct class, in order to help the expert understand what patterns the classifier was looking for to assign the correct class, and failed to see in the instance. The system is based on an Autoencoder trained on a loss function that balances between identity loss (similarity with the original instance) and a classification loss derived from a pre-trained classifier, allowing xSTAE to remain completely agnostic with respect to the internals of the classifier it interprets. We present promising experimental results on sleep-stage classification decisions over EEG data, which validate the core of the idea and show future research.