5 papers
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Feng Wu, Harsh Deep, Eric Lehman +6
Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent…
SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
Jingying Ma, Feng Wu, Yucheng Xing +5
Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in realistic settings remains chall…
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Jingying Ma, Feng Wu, Qika Lin +4
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to a…
EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning
Fengcheng Wu, Zhenxi Song, Guoyang Xu +4
Robust and interpretable dementia diagnosis from noisy, non-stationary electroencephalography (EEG) is clinically essential yet remains challenging. To this end, we propose SeeGrap…
GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
Xiang Lan, Feng Wu, Kai He +3
While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, they still face two key limitations: (1) insufficient multimodal synergy between t…