When analyzing EEG or MEG signals, the aim is to investigate the modulation of the measured brain signals with respect to a certain event. Defining data segments of interest can be done according to a specified trigger channel and the use of an array of events.
This tutorial covers how to identify trials using the trigger signal. Preprocessing involves several steps including identifying individual trials (called Epochs in MNE) from the dataset (called Raw), filtering and rejection of bad epochs. In the introduction, the filtering of continuous data is optional. The raw data do not need to fit in memory unless you want to filter continuous data (see below). MNE works by loading data in memory only if needed when extracting epochs. In MNE the preprocessing of data refers to the reading of the data, segmenting the data around interesting events such as triggers, temporal filtering and optionally rereferencing. It explains how to visualize the axial magnetometer and planar gradiometer signals. Subsequently, you will find information how to compute an event related potential (ERP)/ event related field (ERF) and how to handle the three channels that record the signal at each sensor location. This tutorial describes how to define epochs-of-interest (trials) from your recorded MEG-data, and how to apply the different preprocessing steps.