Electrophysiological Data

Electrophysiological data analysis focuses on extracting meaningful information from recordings of electrical activity in biological systems, primarily the brain and heart, to understand their function and dysfunction. Current research emphasizes developing advanced computational methods, including deep learning (e.g., transformers, convolutional neural networks), optimal transport, and physics-informed neural networks, to address challenges like data heterogeneity, limited sample sizes, and the ill-posed nature of inverse problems in reconstructing underlying sources. These advancements are improving the accuracy and efficiency of analyses, leading to better diagnostic tools, personalized treatments (e.g., cardiac digital twins), and a deeper understanding of complex biological processes. The ultimate goal is to translate these insights into improved clinical practices and technological applications.