On the Vulnerability of CNN Classifiers in EEG-Based BCIs

@article{Zhang2019OnTV,
  title={On the Vulnerability of CNN Classifiers in EEG-Based BCIs},
  author={Xiao Zhang and Dongrui Wu},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  year={2019},
  volume={27},
  pages={814-825}
}
  • Xiao Zhang, Dongrui Wu
  • Published 2019
  • Computer Science, Medicine, Mathematics
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate… Expand
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References

SHOWING 1-10 OF 51 REFERENCES
A novel deep learning approach for classification of EEG motor imagery signals.
  • 366
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
  • 376
  • PDF
Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
  • 50
  • PDF
Deep Learning in Physiological Signal Data: A Survey
  • 30
  • PDF
Deep learning with convolutional neural networks for EEG decoding and visualization
  • 628
  • Highly Influential
  • PDF
Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)
  • 46
  • PDF
One Pixel Attack for Fooling Deep Neural Networks
  • 911
  • PDF
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization
  • 47
  • PDF
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
  • 27
  • PDF
...
1
2
3
4
5
...