In this paper, we propose a novel dynamic frequency domain graph convolution network (DFDGCN) to capture spatial dependencies. Specifically, we mitigate the effects of time-shift by Fourier transform, ...

However, these methods often overlook cross-frequency spatial interactions, which are crucial for capturing comprehensive neural dynamics. To address the issues mentions above, we propose a new model, ...