5 papers
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…
Semantic Compensation via Adversarial Removal for Robust Zero-Shot ECG Diagnosis
Hongjun Liu, Rujun Han, Leyu Zhou +1
Recent ECG--language pretraining methods enable zero-shot diagnosis by aligning cardiac signals with clinical text, but they do not explicitly model robustness to partial observati…
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…
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…