1 citations · 3 across the 6 of their papers we have counts for
7 papers
Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations
Zhige Chen, Shu Peng, Chengxuan Qin +4
Electroencephalography (EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines and their prospects for furt…
EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark Construction
Chengxuan Qin, Zhige Chen, Shu Peng +9
Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that…
Fragile by Design: On the Limits of Adversarial Defenses in Personalized Generation
Zhen Chen, Yi Zhang, Xiangyu Yin +4
Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but also raise significant privacy concerns, particularly the risk of…
HEAR: An EEG Foundation Model with Heterogeneous Electrode Adaptive Representation
Zhige Chen, Chengxuan Qin, Wenlong You +5
Electroencephalography (EEG) is an essential technique for neuroscience research and brain-computer interface (BCI) applications. Recently, large-scale EEG foundation models have b…
From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios
Chen Zhige, Qin Chengxuan
The individual variabilities of electroencephalogram signals pose great challenges to cross-subject motor imagery (MI) classification, especially for the data-scarce single-source…
EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model
Chengxuan Qin, Rui Yang, Wenlong You +4
The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to ma…
BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain
Zhige Chen, Rui Yang, Mengjie Huang +2
Because of "the non-repeatability of the experiment settings and conditions" and "the variability of brain patterns among subjects", the data distributions across sessions and elec…