TY - GEN
T1 - Adversarial Training for the Adversarial Robustness of EEG-Based Brain-Computer Interfaces
AU - Li, Yunhuan
AU - Yu, Xi
AU - Yu, Shujian
AU - Chen, Badong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Electroencephalogram (EEG) based brain-computer interfaces (BCIs) are becoming popular in clinical diagnosis applications. However, this raises a new issue on the robustness of deep neural networks-based BCIs against environmental noise and adversarial attacks. Unfortunately, there is no adversarial defense approach tailored for EEG adversarial robustness so far. In this work, we systematically evaluate the performance of 5 popular adversarial training (AT)-based defense approaches on three large-scale and real-world EEG datasets with 3 popular EEG classification models, under 3 different white-box attacks. Through extensive experiments, we demonstrate that the naïve AT is a promising adversarial defense approach in EEG-based BCIs. However, existing regularization terms originated from vision tasks do not generalize well to EEG signals. Our results shed light on the future development of the EEG adversarial defense approach.
AB - Electroencephalogram (EEG) based brain-computer interfaces (BCIs) are becoming popular in clinical diagnosis applications. However, this raises a new issue on the robustness of deep neural networks-based BCIs against environmental noise and adversarial attacks. Unfortunately, there is no adversarial defense approach tailored for EEG adversarial robustness so far. In this work, we systematically evaluate the performance of 5 popular adversarial training (AT)-based defense approaches on three large-scale and real-world EEG datasets with 3 popular EEG classification models, under 3 different white-box attacks. Through extensive experiments, we demonstrate that the naïve AT is a promising adversarial defense approach in EEG-based BCIs. However, existing regularization terms originated from vision tasks do not generalize well to EEG signals. Our results shed light on the future development of the EEG adversarial defense approach.
KW - Adversarial attack
KW - Adversarial defense
KW - Brain-computer interface
KW - Electroen-cephalogram
UR - https://www.scopus.com/pages/publications/85142685967
U2 - 10.1109/MLSP55214.2022.9943479
DO - 10.1109/MLSP55214.2022.9943479
M3 - 会议稿件
AN - SCOPUS:85142685967
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
PB - IEEE Computer Society
T2 - 32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022
Y2 - 22 August 2022 through 25 August 2022
ER -