Zhongke Gao

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

OBJECTIVE Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs), but its microvolt-level signals are easily contaminated by electromyography (EMG), electrooculography (EOG), and mixed physiological artifacts. This study develops an EEG denoising framework that suppresses artifacts while preser...

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

Accurate decoding of motor imagery electroencephalography (MI-EEG) signals is critical for practical brain-computer interface (BCI) systems. However, conventional approaches typically rely on dense multi-channel recordings, which not only introduce data redundancy but may also incorporate noise, thereby hindering real-...

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