EEG- and MEG-signals produced by neurophysiological processes. The signals that are measured using EEG-electrodes or MEG-sensors are produced by neurophysiological processes in the neuropil. In this part of the course, we describe these processes, the electrical currents they produce, and how these currents produce measurable EEG- and MEG-signals.
Signal processing of electrophysiological data. Often, the relevant aspect in the electrophysiological data is a modulation of oscillatory components. To identify these components, we need a representation in the frequency domain. In this part of the course, we present the frequency domain methods that are most prominent in current research.
Source reconstruction. The gold standard in cognitive neuroscience are measurements of electrophysiological signals at locations in the brain from where they originate physiologically. Unfortunately, in human cognitive neuroscience, we can only measure these signals at some distance from their physiological origin, via the EEG or the MEG. Source reconstruction techniques, are then used to infer a signal at the source level (the brain's grey matter) from an observed signal at the sensor level. In this part of the course, we give an introduction to these methods.
Statistical testing of electrophysiological data. One of the challenges in the statistical analysis of electrophysiological data is the high dimensionality of this type of data (many channels, many time points, and many frequencies). We will describe both parametric and nonparametric methods for dealing with this challenge, but the focus will be on the nonparametric methods. We will also introduce methods that do not rely on p-values.