Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

Abstract

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs.

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Cited by in corpus (6)

• Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
• Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review
• T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
• Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
• User Identity Protection in EEG-based Brain-Computer Interfaces
• Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces