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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Tiny noise, big mistakes : adversarial perturbations induce errors in brain-computer interface spellers
Authors: Zhang, Xiao
Wu, Dongrui
Ding, Lieyun
Luo, Hanbin
Lin, Chin-Teng
Jung, Tzyy-Ping
Chavarriaga, Ricardo
et. al: No
DOI: 10.1093/nsr/nwaa233
Published in: National Science Review
Issue Date: 30-Jan-2020
Publisher / Ed. Institution: Oxford University Press
ISSN: 2095-5138
Language: English
Subjects: Human-Computer Interaction; Computer Science; Learning
Subject (DDC): 004: Computer science
Abstract: An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g., amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e., they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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