Disguising Personal Identity Information in EEG Signals

TL;DR

This paper introduces a CycleGAN-based method to disguise personal identity information in EEG signals by transforming original data into dummy identities, effectively protecting privacy while maintaining key signal features.

Contribution

The paper presents a novel EEG disguising approach using dummy identities and a CycleGAN model with constraints to preserve key features and hide personal identity information.

Findings

Hides about 90% of personal identity information

Preserves most key EEG features

Effective against high-accuracy identity classifiers

Abstract

There is a need to protect the personal identity information in public EEG datasets. However, it is challenging to remove such information that has infinite classes (open set). We propose an approach to disguise the identity information in EEG signals with dummy identities, while preserving the key features. The dummy identities are obtained by applying grand average on EEG spectrums across the subjects within a group that have common attributes. The personal identity information in original EEGs are transformed into disguised ones with a CycleGANbased EEG disguising model. With the constraints added to the model, the features of interest in EEG signals can be preserved. We evaluate the model by performing classification tasks on both the original and the disguised EEG and compare the results. For evaluation, we also experiment with ResNet classifiers, which perform well especially on the identity recognition task with an accuracy of 98.4%. The results show that our EEG disguising model can hide about 90% of personal identity information and can preserve most of the other key features.