The field of neuroimaging and EEG analysis is rapidly advancing with the development of innovative machine learning approaches and techniques. Researchers are focusing on creating more accurate and robust methods for analyzing EEG data, including the use of manifold learning, graph neural networks, and explainable AI. These advances have the potential to improve our understanding of brain function and behavior, and to develop more effective treatments for neurological and psychiatric disorders. Notable papers in this area include those that propose novel architectures for EEG classification, such as the use of hyperbolic graph neural networks and multi-scale wavelet transforms, as well as those that develop more explainable and interpretable models, such as xEEGNet and KnowEEG. These papers demonstrate the power of combining machine learning and neuroscience to drive innovation and discovery in the field.