Disguising Personal Identity Information in EEG Signals
This addresses privacy concerns for individuals in EEG data sharing, but it is incremental as it builds on existing methods like CycleGAN for a specific domain.
The paper tackles the problem of protecting personal identity information in public EEG datasets by disguising it with dummy identities while preserving key features, achieving about 90% identity hiding and maintaining most other features.
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.