Improved Techniques for Building EEG Feature Filters

Recent advances in the generative adversarial network (GAN) based image translation have shown its potential of being an image style transformer. Similarly, defined as a style transformer for physiological signals, a feature filter is used to filter privacy-related features while still keeping useful features. However, existing feature filter techniques have three problems: (1) the privacy-related features cannot be filtered out to the extent we need through a simple Conv-Deconv generator structure, and (2) the generator cannot control the semantics (maintain desired features) of given physiological signals. To address these problems, we utilize deeper neural networks and adopt techniques from domain adaptation. This includes semantic loss and a GAN based model structure with two generators, two discriminators and a classifier to form a game of five. Our results on the UCI EEG dataset demonstrate that our model can simultaneously (1) achieve the state-of-the-art accuracy removal for the privacy-related feature, (2) reduce the desired feature removal accuracy drop, and (3) make the filtered signals can be interpreted or visually checked.