1 citations · 1 across the 9 of their papers we have counts for
10 papers
Completely parabolic surfaces are Veech
Inbo Gottlieb Fenves
We prove that completely parabolic translation surfaces are Veech, answering a question of Smillie-Weiss.
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…
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…
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…
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…
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…