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

arXiv: https://arxiv.org/abs/2509.20454 · HTML: https://mlsanity.com/paper/2509.20454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [RobustSleepNet: Transfer learning for automated sleep staging at scale](https://mlsanity.com/paper/2101.02452/) (2021)
- [User Identity Protection in EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.09854/) (2024)

## Built on by (most cited first, 2 of 2)

- [Let EEG Models Learn EEG](https://mlsanity.com/paper/2605.21280/) (2026)
- [Pretrained, Frozen, Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Models](https://mlsanity.com/paper/2606.09189/) (2026)

## Abstract (arXiv)

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.