400 citations · 1.6k across the 69 of their papers we have counts for
70 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…
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Siyang Li, Ziwei Wang, Hanbin Luo +2
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and…
Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability
Haoyi Xiong, Xuhong Li, Boyang Yu +3
Random label noises (or observational noises) widely exist in practical machine learning settings. While previous studies primarily focus on the affects of label noises to the perf…
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…
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
Siyang Li, Yifan Xu, Huanyu Wu +4
Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle…
PyTSK: A Python Toolbox for TSK Fuzzy Systems
Yuqi Cui, Dongrui Wu, Xue Jiang +1
This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy syste…
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow
Haiyan Jiang, Haoyi Xiong, Dongrui Wu +2
Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size (HDLSS) sett…
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Haoran Liu, Haoyi Xiong, Yaqing Wang +3
We find that different Deep Neural Networks (DNNs) trained with the same dataset share a common principal subspace in latent spaces, no matter in which architectures (e.g., Convolu…
Optimization Variance: Exploring Generalization Properties of DNNs
Xiao Zhang, Dongrui Wu, Haoyi Xiong +1
Unlike the conventional wisdom in statistical learning theory, the test error of a deep neural network (DNN) often demonstrates double descent: as the model complexity increases, i…
Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions
Yuqi Cui, Dongrui Wu, Yifan Xu
Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty…
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review
Dongrui Wu, Jiaxin Xu, Weili Fang +5
Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, ad…
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…
FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief
Zhenhua Shi, Dongrui Wu, Chenfeng Guo +3
To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorit…
A Survey on Negative Transfer
Wen Zhang, Lingfei Deng, Lei Zhang +1
Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain ha…
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…
Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNs
Xiao Zhang, Haoyi Xiong, Dongrui Wu
Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance tra…
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016
Dongrui Wu, Yifan Xu, Bao-Liang Lu
A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG),…
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…
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression
Ziang Liu, Dongrui Wu
In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approac…
MBGD-RDA Training and Rule Pruning for Concise TSK Fuzzy Regression Models
Dongrui Wu
To effectively train Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a Mini-Batch Gradient Descent with Regularization, DropRule, and AdaBound (MBGD-RDA) algorithm…
BoostTree and BoostForest for Ensemble Learning
Changming Zhao, Dongrui Wu, Jian Huang +4
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and…
Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers
Yuqi Cui, Huidong Wang, Dongrui Wu
Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each clust…
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
Xiaotong Gu, Zehong Cao, Alireza Jolfaei +4
Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with th…