1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…