The paper investigates how subject‑level differential privacy (DP) can anonymize EEG‑derived feature representations while preserving clinical utility. It evaluates Gaussian and Laplace perturbations across three deployment scenarios—client‑side, server‑side, and decentralized local training—using statistical utility metrics and a downstream machine‑learning task. Results indicate that DP can be integrated into EEG workflows, but the choice of mechanism, privacy parameters, and sensitivity calibration critically affects data utility, especially in small, imbalanced clinical datasets.
By Noman Sadiq, Mohsen Toorani
arXiv:2606. 20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained.
By Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe
The paper introduces SW-ProxyCE, a zero-query adversarial attack that exploits publicly released EEG foundation encoders to generate transferable adversarial examples for private downstream models. By using a small labeled reference set and shrinkage-whitened class prototypes, the method recovers task-level decision geometry without training a surrogate classifier. Experiments across three EEG tasks and multiple encoders show that SW-ProxyCE consistently outperforms task-agnostic attacks, demonstrating that the strong transferability of EEG foundation models does not guarantee adversarial robustness.
By Linhua Cong, Dingkun Liu, Dongrui Wu
arXiv:2603. 17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction.
By Akshaj Murhekar, Christina Liu, Abhijit Mishra, Shounak Roychowdhury, Jacek Gwizdka
arXiv:2606. 02597v1 Announce Type: new Abstract: The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning.
By Md Fahimul Kabir Chowdhury, Gahangir Hossain
arXiv:2506. 19141v3 Announce Type: replace-cross Abstract: Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task.
By Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, Alexandre R. Franco, Michael P. Milham, Aviv Dotan, Scott Makeig, Alexandre Gramfort, Jean-Remi King, Marie-Constance Corsi, Pedro A. Vald\'es-Sosa, Amit Majumdar, Alan Evans, Terrence J Sejnowski, Oren Shriki, Sylvain Chevallier, Arnaud Delorme
arXiv:2501. 09700v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification.
By Ali Derakhshesh, Zahra Dehghanian, Reza Ebrahimpour, Hamid R. Rabiee
arXiv:2608.28691v1 Announce Type: cross
Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surround...
By Zhimin Li, Pan Wang, Jingxian Chen, Yuantao Tang, Anthony Chen, Qian Lou, Jingtong Hu
arXiv:2607. 28191v1 Announce Type: cross Abstract: Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privacy leakage from individual model updates.
By Pouya Rajabi, Mohsen Toorani
arXiv:2606. 08583v1 Announce Type: new Abstract: Deep learning on physiological time series is interpreted through domain-specific features -- oscillatory rhythms in EEG, morphological complexes in ECG -- yet these signals sit atop a broadband aperiodic 1/f-like envelope that covaries with arousal, age, and pathology.
By Jasmeet Singh Bindra, Siddharth Panwar, Shubhajit Roy Chowdhury
The paper investigates how different gaze data representations affect privacy and utility in extended reality (XR) systems. Three representations—raw gaze, spatial attention heatmaps, and engineered eye‑movement features—are compared using the HoloAssist dataset, evaluating action recognition accuracy and closed‑set user re‑identification. Engineered features preserve about 85% of action‑recognition performance while reducing re‑identification risk by roughly an order of magnitude, yet still leave some identity leakage, indicating that abstraction alone does not guarantee privacy.
By Cory Ilo, Brendan-David John, Doug A. Bowman
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu