5 citations · 7 across the 10 of their papers we have counts for
10 papers
Understanding and Correcting Low-Frequency Bias in EEG Foundation Model
Junjie Yu, Zihan Deng, Jianyu Zhang +8
Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bias in representations learned…
Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics
Zhoujie Hou, Song Wang, Kexin Lou +3
Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals inc…
OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models
Ziling Lu, Zongsheng Li, Xinke Shen +11
Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. EEG foundation models are eme…
A geometry aware framework enhances noninvasive mapping of whole human brain dynamics
Song Wang, Kexin Lou, Chen Wei +8
Non-invasive electrophysiology lacks methods that accurately reconstruct whole-brain spatiotemporal dynamics while incorporating individual cortical geometry, leaving current elect…
Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data
Yinuo Zhang, Demao Liu, Zhichao Liang +6
Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''.…
Controlling network-coupled neural dynamics with nonlinear network control theory
Zhongye Xia, Weibin Li, Zhichao Liang +2
This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics. Based on the L…
Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions
Song Wang, Chen Wei, Kexin Lou +2
Electroencephalography (EEG) and Magnetoencephalography (MEG) are pivotal in understanding brain activity but are limited by their poor spatial resolution. EEG/MEG source imaging (…
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Peizhen Yang, Xinke Shen, Zongsheng Li +3
Identifying causal relationships among distinct brain areas, known as effective connectivity, holds key insights into the brain's information processing and cognitive functions. El…
Embedding Decomposition for Artifacts Removal in EEG Signals
Junjie Yu, Chenyi Li, Kexin Lou +2
Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of…
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization
Chen Wei, Kexin Lou, Zhengyang Wang +3
EEG source localization is an important technical issue in EEG analysis. Despite many numerical methods existed for EEG source localization, they all rely on strong priors and the…