15 citations · 17 across the 21 of their papers we have counts for
23 papers
RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection
Xingyi He, Ziwei Wang, Dongrui Wu
Auditory attention decoding (AAD) identifies the attended speaker from physiological signals, supporting neuro-steered hearing devices and natural human-machine interaction. Electr…
Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding
Ziwei Wang, Xingyi He, Hongbin Wang +3
Multimodal brain state decoding has largely focused on fusing paired modalities for prediction, but has rarely explored how their correspondence can be further exploited to enrich…
HaptiNet: Networked Haptic Robots Enable Physical Co-presence in Geographically-Unconstrained Rehabilitation
Chenyang Sun, Mingjie Dong, Haodong Deng +18
Cooperative rehabilitation enhances engagement, task performance, and social-motor interaction, yet it demands physical co-presence: users must transmit forces, coordinate movement…
SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval
Zhenyao Cui, Siyuan Kan, Siyang Li +2
Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potenti…
Beyond Trial Averaging: Anchoring Neural and Visual Representations for Few-Repetition Brain-to-Image Retrieval
Zhenyao Cui, Siyuan Kan, Dingkun Liu +1
Decoding visual information from brain signals probes neural representations and enables neuro-rehabilitation and dream decoding. Recent brain-to-image retrieval approaches have ac…
SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models
Linhua Cong, Dingkun Liu, Dongrui Wu
Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale heterogeneous ne…
STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
Zhu Chen, Dingkun Liu, Yuheng Chen +1
Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios. However, conventional neural signal decod…
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…
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…
StackingNet: Collective Inference Across Independent AI Foundation Models
Siyang Li, Chenhao Liu, Dongrui Wu +2
Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readil…
RAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection
Siyang Li, Zhuoya Wang, Xiyan Gui +4
Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding method…
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…
EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models
Dingkun Liu, Yuheng Chen, Zhu Chen +5
Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representation…
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…
FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation
Yuheng Chen, Dingkun Liu, Xinyao Yang +3
Brain-computer interfaces (BCIs) provide potential for applications ranging from medical rehabilitation to cognitive state assessment by establishing direct communication pathways…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification
Dingkun Liu, Zhu Chen, Jingwei Luo +2
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. Recent EEG foundation models aim to learn generalized representations across di…
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Xiaoqing Chen, Siyang Li, Dongrui Wu
Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-s…
CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm
Dingkun Liu, Zhu Chen, Dongrui Wu
The construction of large-scale, high-quality datasets is a fundamental prerequisite for developing robust and generalizable foundation models in motor imagery (MI)-based brain-com…
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…
SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding
Hongbin Wang, Zhihong Jia, Yuanzhong Shen +5
Speech disorders such as dysarthria and anarthria can severely impair the patient's ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potent…
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
Dingkun Liu, Siyang Li, Ziwei Wang +2
A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding…
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces
Dongrui Wu
Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibrati…