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20242026
most citedFederated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

25 citations · 26 across the 6 of their papers we have counts for

collaborators

8 papers

eess.SP2026

FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding

Ziwei Wang, Xingyi He, Tianwang Jia +2

Auditory attention decoding (AAD) aims to infer the attended speaker from neural responses in multi-speaker acoustic environments and is a key problem for neuro-steered hearing sys…

cs.LG2026

Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions

Ziwei Wang, Zhentao He, Xingyi He +6

Deep learning has achieved transformative performance across diverse domains, largely driven by large-scale and high-quality training data. In contrast, the development of brain-co…

cs.HC2026★ 1 cited

SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces

Tianwang Jia, Xiaoqing Chen, Dongrui Wu

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) are widely adopted due to their efficiency and portability; however, their decoding algorithms still face multiple…

cs.HC2026

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

Siyang Li, Jiayi Ouyang, Zhenyao Cui +4

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computationa…

cs.LG2025

DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding

Ziwei Wang, Hongbin Wang, Tianwang Jia +3

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolution…

cs.LG2024★ 25 cited

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…

cs.HC2024

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…

cs.HC2024

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…