Disguising Personal Identity Information in EEG Signals

Disguising Personal Identity Information in EEG Signals

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.

在EEG信号中掩盖个人身份信息

需要保护公共EEG数据集中的个人身份信息。但是，删除具有无限类（开放集）的此类信息具有挑战性。.. 我们提出了一种在保留关键特征的同时伪装具有虚拟身份的EEG信号中的身份信息的方法。通过在具有共同属性的组中的对象之间的脑电图频谱上应用平均数来获得虚拟身份。使用基于CycleGAN的EEG伪装模型将原始EEG中的个人身份信息转换为伪装的信息。通过将约束添加到模型，可以保留EEG信号中感兴趣的特征。我们通过对原始和变相的脑电图执行分类任务来评估模型，并比较结果。为了进行评估，我们还对ResNet分类器进行了实验，这些分类器在身份识别任务上的表现尤其出色，准确度为98.4％。 （阅读更多）