82 citations · 256 across the 20 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…