Zhongke Gao

Graph diffusion convolution on multivariate weighted joint recurrence networks for seizure-state classification in intracranial EEG.

Discriminating interictal and ictal brain states is important for both seizure-state classification and the characterization of seizure-related network reorganization in drug-resistant epilepsy. Intracranial electroencephalography (iEEG) provides high-resolution recordings of brain activity, but its sparse, irregular,...

MTGNet: a task-oriented and spectrally guided framework for EEG denoising

The proposed MTGNet framework provides a practical approach to task-aware EEG denoising, while future work should further validate its applicability across real-world EEG settings involving diverse tasks, artifact types, and acquisition conditions.

Graph convolution neural network channel selection with attention for motor imagery EEG decoding.

Experimental results demonstrate that the proposed approach achieves performance comparable to that obtained with all channels while using significantly fewer electrodes, and UniEEG-Net exhibits classification accuracy surpassing current state-of-the-art models.

Multi-scale diffusion model for gas–solid flow mass flow rate prediction based on electrostatic sensing