4 papers
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities
Wei-Bang Jiang, Xi Fu, Yi Ding +1
Multimodal physiological signals, such as EEG, ECG, EOG, and EMG, are crucial for healthcare and brain-computer interfaces. While existing methods rely on specialized architectures…
EEG-to-Gait Decoding via Phase-Aware Representation Learning
Xi Fu, Weibang Jiang, Rui Liu +2
Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This stud…
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding
Xi Fu, Rui Liu, Aung Aung Phyo Wai +4
Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feat…
Task-Oriented Learning for Automatic EEG Denoising
Tian-Yu Xiang, Zheng Lei, Xiao-Hu Zhou +7
Electroencephalography (EEG) denoising methods typically depend on manual intervention or clean reference signals. This work introduces a task-oriented learning framework for autom…