4 accepted papers
While Physics-Informed Neural Networks (PINNs) offer a mesh-free approach to solving PDEs, standard point-wise residual minimization suffers from convergence pathologies in topologically complex domains like Triply Periodic Minimal Surfaces (TPMS). The locality bias of point-wise constraints fails t…
Motor Imagery (MI) is essential in Brain-Computer Interfaces (BCIs), highlighting the central role of electroencephalography (EEG) in this technology. However, the amount of raw EEG data is often limited. Raw EEG data contains significant noise caused by individual and task-specific differences. The…
A brain-computer interface (BCI) offers an innovative solution for facilitating communication and control in individuals with paralysis. BCI reflects brain activity by decoding electroencephalogram (EEG) signals. Despite numerous techniques for classifying motor imagery (MI) EEG signals, challenges…
Nonlocal neural networks have been proposed and shown to be effective in several computer vision tasks, where the nonlocal operations can directly capture long-range dependencies in the feature space. In this paper, we study the nature of diffusion and damping effect of nonlocal networks by doing sp…