34 citations · 122 across the 10 of their papers we have counts for
11 papers
Active Poisoning: Efficient Backdoor Attacks on Transfer Learning-Based Brain-Computer Interfaces
X. Jiang, L. Meng, S. Li +1
Transfer learning (TL) has been widely used in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) for reducing calibration efforts. However, backdoor attacks could b…
User Identity Protection in EEG-based Brain-Computer Interfaces
L. Meng, X. Jiang, J. Huang +3
A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in…
Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces
Lubin Meng, Xue Jiang, Xiaoqing Chen +3
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its con…
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
Tianwang Jia, Lubin Meng, Siyang Li +2
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical con…
Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces
Lubin Meng, Xue Jiang, Tianwang Jia +1
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCI…
CSP-Net: Common Spatial Pattern Empowered Neural Networks for EEG-Based Motor Imagery Classification
Xue Jiang, Lubin Meng, Xinru Chen +2
Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain-computer interfaces. Common spatial pattern (CSP), which exploits differ…
Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces
Xiaoqing Chen, Dongrui Wu
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI research focused on improving its accuracy, but…
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Lubin Meng, Jian Huang, Zhigang Zeng +6
Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adopti…
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Zihan Liu, Lubin Meng, Xiao Zhang +2
Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been foun…
White-Box Target Attack for EEG-Based BCI Regression Problems
Lubin Meng, Chin-Teng Lin, Tzyy-Ring Jung +1
Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning…
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
Guilei Hu, Yang Xiao, Zhiguo Cao +4
Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc. Although numerous of effort…