.png){kind=icon}
返回
Adversarial robustness benchmark for EEG-based brain-computer interfaces
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2023.01.028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many machine learning approaches have been successfully applied to electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing approaches focused on making EEG-based BCIs more accurate, but few have considered their security. Recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks. To our knowledge, there is no study on adversarial defenses in EEG-based BCIs. This paper, for the first time, explores multiple classical and state-of-the-art adversarial defense approaches in EEG-based BCIs. Specifically, we demonstrated the performances of nine adversarial defense approaches on three convolutional neural networks and two EEG datasets, and established a comprehensive benchmark to evaluate their effectiveness in BCIs. Based on the evaluation results, we drew some key observations, hoping to inspire future adversarial defense research and attract more attention to the adversarial security of EEG-based BCIs. (c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Brain-computer interface
Electroencephalogram
Adversarial example
Adversarial defense
Security
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
暂无机构信息
引用论文
分享收藏
Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers.png)
NATIONAL SCIENCE REVIEW
OAAI
NATIONAL SCIENCE REVIEWIF17.1
OAAI分享收藏Deep learning models for electrocardiograms are susceptible to adversarial attack心电图的深度学习模型易受对抗性攻击NATURE MEDICINEOAAI
NATURE MEDICINEIF50
OAAI分享收藏A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies模式识别中的强大对抗训练综述: 基础、理论和方法PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Deep Learning With Convolutional Neural Networks for EEG Decoding and VisualizationHUMAN BRAIN MAPPINGOAAI
HUMAN BRAIN MAPPINGIF3.3
OAAI分享收藏
查看更多内容