Bridging Privacy and Utility: Synthesizing anonymized EEG with constraining utility functions

Electroencephalography (EEG) is widely used for recording brain activity and has seen numerous applications in machine learning, such as detecting sleep stages and neurological disorders. Several studies have successfully shown the potential of EEG data for re-identification and leakage of other personal information. Therefore, the increasing availability of EEG consumer devices raises concerns about user privacy, motivating us to investigate how to safeguard this sensitive data while retaining its utility for EEG applications. To address this challenge, we propose a transformer-based autoencoder to create EEG data that does not allow for subject re-identification while still retaining its utility for specific machine learning tasks. We apply our approach to automatic sleep staging by evaluating the re-identification and utility potential of EEG data before and after anonymization. The results show that the re-identifiability of the EEG signal can be substantially reduced while preserving its utility for machine learning.