Neural Signals and Signal Processing: Electrophysiology to Brain Imaging

This lecture by the instructor covers the popularity of linear classifiers in fMRI analysis, focusing on multivariate analyses and the use of linear classifiers to interpret brain activity. It explores the spatial scales exploited by MVPA, the sensitivity to dipole orientation in EEG and MEG, and the analysis of electromagnetic field signals. The lecture also delves into the properties of field signals, the transformation from sensor to source space, and the challenges in EEG data preprocessing. Additionally, it discusses blind source separation in EEG, event-related potential analysis, and spectral analysis using power spectral density plots. The assessment methods for the course are also outlined, including graded mini-projects, a final exam, and teaser exam questions.