The field of biomedical signal processing is witnessing significant advancements with the integration of neural networks and innovative signal processing techniques. Researchers are focusing on developing more accurate and efficient methods for analyzing complex biomedical signals, such as EEG and PET images. Notably, graph neural networks are being explored for their potential in modeling dynamic brain networks and capturing statistical interdependencies in spatio-temporal signals. Additionally, novel feature selection methods and kernel-based approaches are being proposed to improve the performance of neural networks in biomedical applications. These advancements have the potential to improve diagnostic accuracy, enable early intervention, and enhance our understanding of complex biological systems.

Noteworthy papers include: IEFS-GMB, which proposes a novel feature selection method guided by a Gradient Memory Bank, achieving accuracy improvements of up to 6.45% over baseline models. EvoBrain, a dynamic multi-channel EEG graph modeling approach that integrates a two-stream architecture with a GCN, resulting in a 23% improvement in AUROC and a 30% improvement in F1 score. Graph Variate Neural Networks, which introduce a unified framework for modeling dynamically evolving spatio-temporal signals, outperforming strong graph-based baselines and sequence models like LSTMs and Transformers.