The output of ft_definetrial is a configuration structure containing the field cfg.trl. according to your own criteria when you write your own trial functionĮxamples for both ways are described in this tutorial, and both ways depend on ft_definetrial.according to a specified trigger channel.Defining data segments of interest can be done This tutorial covers how to identify trials using the trigger signal. Preprocessing involves several steps including identifying individual trials from the dataset, filtering and artifact rejections. The approach for reading and filtering continuous data and segmenting afterwards is explained in another tutorial. The remainder of this tutorial explains the second approach, as that is the most appropriate for large data sets such as the MEG data used in this tutorial. The second approach is to first identify the interesting segments, read those segments from the data file and apply the filters to those segments only. The first approach is to read all data from the file into memory, apply filters, and subsequently cut the data into interesting segments. There are largely two alternative approaches for preprocessing, which especially differ in the amount of memory required. The ft_preprocessing function takes care of all these steps, i.e., it reads the data and applies the preprocessing options. In FieldTrip 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. If you want to learn how to segment EEG data into trials, check the tutorial on Preprocessing of EEG data and computing ERPs. There, you can also find information about how to preprocess EEG data. If you are interested in how to do preprocessing on your data prior to segmenting it into trials, you can check the Preprocessing - Reading continuous data tutorial. This tutorial does not show yet how to analyze (e.g., average) your data. This tutorial describes how to define epochs-of-interest (trials) from your recorded MEG-data, and how to apply the different preprocessing steps. Tutorial meg raw preprocessing meg-language Trigger-based trial selection Introduction
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