Neural Signals and Signal Processing: Methods and Designs

This lecture covers the subtraction method in neural signal processing, the presentation of conditions in experiments, and the design considerations for block-related designs in fMRI. It also explores the importance of baseline conditions, the role of rest in brain activity, and the preprocessing steps in fMRI data analysis, such as slice timing correction, motion correction, and normalization. The lecture delves into the concept of the general linear model for statistical analysis in fMRI data, including task regressors and nuisance regressors, and the fitting of the GLM. Various aspects of spatial smoothing, image conversion, and quality control in fMRI data processing are also discussed.