Graph Theory in Neural Signal Processing

This lecture covers topics in neural signal processing, including Partial Least-Squares Correlation (PLSC), Independent Component Analysis (ICA), Central Limit Theorem, and the relevance of multivariate methods. It explores the impact of permutations, symmetry of ICA, and the distinction between PCA and ICA. The instructor discusses the application of graph theory in modeling brain networks, graph measures, connectomics, and graph partitioning. The lecture delves into the analysis of multi-voxel patterns in fMRI data, linear classification, loss functions, and cross-validation techniques.