135 citations · 232 across the 12 of their papers we have counts for
12 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…
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition
Yifan Xu, Xue Jiang, Dongrui Wu
Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled d…
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…
Self-supervised Contrastive Learning for EEG-based Sleep Staging
Xue Jiang, Jianhui Zhao, Bo Du +1
EEG signals are usually simple to obtain but expensive to label. Although supervised learning has been widely used in the field of EEG signal analysis, its generalization performan…
MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging
Xue Jiang
Sleep staging based on electroencephalogram (EEG) plays an important role in the clinical diagnosis and treatment of sleep disorders. In order to emancipate human experts from heav…
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…
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
Dongrui Wu, Xue Jiang, Ruimin Peng +3
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promis…
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)
Ziang Liu, Xue Jiang, Hanbin Luo +3
Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most…
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces
Xue Jiang, Xiao Zhang, Dongrui Wu
Deep learning has made significant breakthroughs in many fields, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, deep learning models are vuln…