Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

TL;DR

This systematic review highlights the privacy concerns in brain-computer interfaces (BCIs), discusses potential threats and protection strategies, and outlines future research directions to ensure privacy in BCI development and deployment.

Contribution

It provides the first comprehensive overview of privacy issues, threats, and solutions in BCIs, addressing a significant gap in current research.

Findings

Identifies key privacy threats in BCI systems.

Summarizes existing privacy protection strategies.

Outlines challenges and future research directions.

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.