6 papers
Rethinking EEG Spatial Super-Resolution as Dynamic State Decomposition and Filtering
Hongjun Liu, Leyu Zhou, Chang Ge +2
EEG spatial super-resolution aims to recover high-density neural signals from sparse electrode observations. Existing methods have achieved substantial progress through spatial mod…
Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation
Hongjun Liu, Leyu Zhou, Zijianghao Yang +1
EEG spatial super-resolution (EEGSR) in real deployments is challenged by random channel missingness, unstable electrode quality, and changing visible-channel patterns caused by ba…
Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities
Sha Tao, Jiao Pan, Yu Guo +1
Multimodal magnetic resonance imaging (MRI) is crucial for brain tumor segmentation, with many methods leveraging its four key modalities to capture complementary information for e…
NESA: Non-stationary Evidence-aware Semantic Alignment for Robust ECG--Text Representation Pretraining
Hongjun Liu, Rujun Han, Leyu Zhou +1
ECG-text pretraining offers transferable electrocardiogram (ECG) representations for zero-shot diagnosis, but its supervision is inherently asymmetric: clinical reports contain spa…
CAFE: Channel-Autoregressive Factorized Encoding for Robust Biosignal Spatial Super-Resolution
Hongjun Liu, Leyu Zhou, Zijianghao Yang +4
High-density biosignal recordings are critical for neural decoding and clinical monitoring, yet real-world deployments often rely on low-density (LD) montages due to hardware and o…
Step-Aware Residual-Guided Diffusion for EEG Spatial Super-Resolution
Hongjun Liu, Leyu Zhou, Zijianghao Yang +1
For real-world BCI applications, lightweight Electroencephalography (EEG) systems offer the best cost-deployment balance. However, such spatial sparsity of EEG limits spatial fidel…