arXiv subjects

Yunlu Tu

Publications and source records attributed to Yunlu Tu.

3 recordsLinked to original sources

arXiv2024

PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding

An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, such systems face at least three major challenges in real-world applications: limited decoding accuracy, poor robustness, and privacy risks. Although prior studies have addressed one or two of these issues, methods that simultaneously improve accuracy, robustness, and privacy remain largely unexplored. In this paper, we propose Privacy-preserving Adversarial Transfer (PAT), a unified training framework that combines data alignment, adversarial training, and privacy-preserving transfer. PAT provides a single pipeline that can be instantiated under three privacy-preserving scenarios, i.e., centralized source-free transfer, federated source-free transfer, and transfer with privacy-preserved source data, while jointly improving accuracy and robustness. Experiments on five public EEG datasets under three privacy-preserving scenarios (centralized source-free transfer, federated source-free transfer, and transfer with privacy-preserved source data) show that PAT outperforms over ten classic and state-of-the-art methods in both accuracy and robustness. PAT also outperformed leading transfer learning approaches that do not incorporate any privacy mechanisms by 9.76% in terms of average accuracy and robustness. To our knowledge, this is the first approach that simultaneously addresses all three major challenges in EEG-based BCIs. We believe this work can help motivate further research on more accurate, robust, and privacy-preserving EEG decoding.

cs.HC↗

arXiv2024

User-wise Perturbations for User Identity Protection in EEG-Based BCIs

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected. Approach: We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected. Main results: Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations. Significance: Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.

cs.HC↗

arXiv2023

Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach

Open Set Domain Adaptation (OSDA) aims to cope with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target domain. Most existing OSDA approaches, depending on the final image feature space of deep models, require manually-tuned thresholds, and may easily misclassify unknown samples as known classes. Mixture-of-Experts (MoE) could be a remedy. Within a MoE, different experts handle distinct input features, producing unique expert routing patterns for various classes in a routing feature space. As a result, unknown class samples may display different expert routing patterns to known classes. In this paper, we propose Dual-Space Detection, which exploits the inconsistencies between the image feature space and the routing feature space to detect unknown class samples without any threshold. Graph Router is further introduced to better make use of the spatial information among image patches. Experiments on three different datasets validated the effectiveness and superiority of our approach.

cs.CV↗