论文标题
在脑电图信号中伪装个人身份信息
Disguising Personal Identity Information in EEG Signals
论文作者
论文摘要
需要保护公共脑电图数据集中的个人身份信息。但是，删除具有无限类（开放集）的此类信息是一项挑战。我们提出了一种方法，以掩盖具有虚拟身份的EEG信号中的身份信息，同时保留关键功能。虚拟身份是通过在具有共同属性的组中的主题中的脑电图频谱上应用大平均值来获得的。原始脑电图中的个人身份信息通过基于自行车的脑电图伪装模型转变为伪装的信息。随着添加到模型的约束，可以保留EEG信号中感兴趣的特征。我们通过对原始和伪装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，我们还尝试了RESNET分类器，这些分类器的精度为98.4％，尤其是在身份识别任务上表现良好。结果表明，我们的脑电图伪装模型可以隐藏约90％的个人身份信息，并可以保留大多数其他关键功能。
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.
