Neural Signals and Signal Processing

This lecture covers the relationship between neural activity in the brain and electromagnetic field signals, the advantages of EEG and MEG for measuring electrophysiological signals, the history of electrophysiological brain signal discovery, the properties of field signals, and the analysis methods used. It also explains the process of going from sensor to source space, the sources of noise in EEG data, preprocessing steps, event-related potential analysis, spectral analysis, and multivariate approaches like microstates and independent component analysis. The lecture concludes with decoding and neurofeedback using fast EEG signals and a comparison between M/EEG and fMRI signals.