  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.
