82 citations · 256 across the 20 of their papers we have counts for
22 papers
Feature Weighting Improves Pool-Based Sequential Active Learning for Regression
Dongrui Wu
Pool-based sequential active learning for regression (ALR) optimally selects a small number of samples sequentially from a large pool of unlabeled samples to label, so that a more…
Temporal Out-of-Distribution Detection for Asynchronous Motor Imagery Brain-Computer Interfaces
Chenhao Liu, Siyang Li, Luofei Tan +1
Real online brain--computer interfaces operate on continuous electroencephalography (EEG) streams, where users are usually at rest and enter motor-imagery task states only intermit…
Spiking Neural Network for Intra-cortical Brain Signal Decoding
Song Yang, Haotian Fu, Herui Zhang +3
Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features…
Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies
Siyang Li, Hongbin Wang, Xiaoqing Chen +1
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs…
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
Ziwei Wang, Siyang Li, Xiaoqing Chen +1
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has…
Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks
Haotian Fu, Peng Zhang, Song Yang +3
A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in…
Privacy-Preserving Brain-Computer Interfaces: A Systematic Review
K. Xia, W. Duch, Y. Sun +8
A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, ed…
Front-end Replication Dynamic Window (FRDW) for Online Motor Imagery Classification
X. Chen, J. An, H. Wu +3
Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successf…
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
Huanyu Wu, Siyang Li, Dongrui Wu
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous system…
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…
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
Ziwei Wang, Siyang Li, Jingwei Luo +2
A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular f…
Semi-Supervised Transfer Boosting (SS-TrBoosting)
Lingfei Deng, Changming Zhao, Zhenbang Du +2
Semi-supervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxi…
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…
Gated Parametric Neuron for Spike-based Audio Recognition
Haoran Wang, Herui Zhang, Siyang Li +1
Spiking neural networks (SNNs) aim to simulate real neural networks in the human brain with biologically plausible neurons. The leaky integrate-and-fire (LIF) neuron is one of the…
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…
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment
Z. Wang, S. Li, Dongrui Wu
Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindere…
PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding
Xiaoqing Chen, Tianwang Jia, Yunlu Tu +1
An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, such systems face at least three ma…
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…
User-wise Perturbations for User Identity Protection in EEG-Based BCIs
Xiaoqing Chen, Siyang Li, Yunlu Tu +2
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied…
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs
Xiaoqing Chen, Ziwei Wang, Dongrui Wu
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accura…
Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach
Zhenbang Du, Jiayu An, Yunlu Tu +2
Open Set Domain Adaptation (OSDA) aims to cope with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for k…