Electroencephalogram (EEG) contains not only decoding task information but also personal identity privacy information. If it is stolen or attacked, the user's brain-computer interaction behavior may be maliciously manipulated. Existing EEG identity privacy protection generally adopts generative or adding tiny perturbation methods, which can protect the identity privacy in EEG signals to some extent. However, these methods also damage the performance of decoding task. In order to solve these problems, this paper proposes an identity removal network (ID-RemovalNet) to achieve EEG privacy protection while improving the classification accuracy of decoding task. Firstly, an identity decorrelation separation module is constructed to accurately remove the identity features to achieve privacy protection while reducing the interference with the task decoding features. Secondly, a multi-domain multi-level fusion feature extraction module is designed to extract the high-quality EEG time-frequency features. Finally, the feature enhancement module is used to compensate for the loss of task decoding features and excitation of dominant feature selection during identity feature removal. The experimental results show that ID-RemoveNet removes identity information to 0.43% on four EEG datasets with two different paradigms, and significantly improves the EEG task decoding accuracy by 3.28%, and achieves the state-of-the-art performance in cross-subject EEG experiment.

Near-infrared transmission through the finger can capture the vein structure for identity recognition. However, in outdoor applications, finger vein imaging is significantly affected by environmental illumination resulting in low recognition performance. Existing methods typically address this issue by constructing multi-illumination models, but collecting multi-illumination images from individual is challenging, and overexposure can cause venous structure distortion. This paper proposes MDA-Net, a Multi-illumination Domain Adaptive Network for finger vein recognition, which is engineered to excel in the dynamic outdoor lighting landscape with various conditions including overexposure, using only data collected under a single illumination for training. Firstly, an Illumination Feature Separation Network(IFSNet) is used to remove the illumination components and obtain illumination-invariant features; Then an Absorption Difference Feature Extraction network(ADFENet) is used to reduce the impact of venous structure distortion under illumination conditions, especially overexposure. To replicate the entire range from low-light to overexposure in outdoor scenarios, a novel Multi-Illumination Finger Vein Dataset (MIFVD) is constructed with significant illumination variations. Experimental results show that MDA-Net significantly improves recognition performance under complex illumination conditions, achieving a state-of-the-art (SOTA) average recognition rate of 91.67% and an average equal error rate (EER) of 0.96%. Further validation on public datasets SDU and USM, demonstrates SOTA EERs of 0.16% and 0.10%, respectively. The License for MIFVD can be accessed at: https://github.com/AHU-MedImagingIJR/MIFVD.