Update preprocessing_erp.md (#660)

* Update preprocessing_erp.md,  reformatted a bit. I think that the point has been made now.

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Co-authored-by: Jan-Mathijs Schoffelen <[email protected]>

tutorial/preprocessing_erp.md

@@ -9,9 +9,9 @@ tags: [tutorial, eeg, brainvision, preprocessing, trialfun, timelock, eeg-affect
 
 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. The **[ft_preprocessing](/reference/ft_preprocessing)** function takes care of all these steps, i.e., it reads the data and applies the preprocessing options.
 
-There are largely two alternative approaches for preprocessing, which especially differ in the amount of memory required. The first approach is to read all data from the file into memory, apply filters, and subsequently cut the data into interesting segments. 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 remainder of this tutorial explains the second approach, as that is the most appropriate for large data sets such as the EEG data used in this tutorial. The approach for reading and filtering continuous data and segmenting afterwards is explained in [another tutorial](/tutorial/continuous).
+There are largely two alternative approaches for preprocessing, which differ in the amount of memory required in the order of the individual analysis steps. The first approach is to read all data from the file into memory, apply filters, and subsequently cut the data into interesting segments. 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 remainder of this tutorial explains the second approach. This is mainly motivated by the historical development of FieldTrip: in the early days of the toolbox, computer memory as a more limiting factor than nowadays. Also, note, that some (older) datasets may have the data already represented on disk as epoched, which prohibits the treatment of the data as a single continuous data matrix. The approach for reading and filtering continuous data and segmenting afterwards is explained in [another tutorial](/tutorial/continuous).
 
-Preprocessing involves several steps including identifying individual trials from the dataset, filtering and artifact rejections. This tutorial covers how to identify trials using the trigger signal. Defining data segments of interest can be done according to a specified trigger channel or according to your own criteria when you write your own trial function. Examples for both ways are described in this tutorial, and both ways depend on **[ft_definetrial](/reference/ft_definetrial)**.
+Preprocessing involves several steps, including identifying individual trials from the dataset, filtering and artifact rejections. This tutorial covers how to identify trials using the trigger signal. Defining data segments of interest can be done according to a specified trigger channel or according to your own criteria when you write your own trial function. Examples for both ways are described in this tutorial, and both ways depend on **[ft_definetrial](/reference/ft_definetrial)**.
 
 The output of ft_definetrial is a configuration structure containing the field cfg.trl. This is a matrix representing the relevant parts of the raw datafile which are to be selected for further processing. Each row in the `trl` matrix represents a single epoch-of-interest, and the `trl` matrix has at least 3 columns. The first column defines (in samples) the beginpoint of each epoch with respect to how the data are stored in the raw datafile. The second column defines (in samples) the endpoint of each epoch, and the third column specifies the offset (in samples) of the first sample within each epoch with respect to timepoint 0 within that epoch.
 
@@ -25,7 +25,7 @@ The EEG dataset used in this script is available [here](https://download.fieldtr
 
 Make sure that all files that you have downloaded are unzipped and are located in the present working directory in MATLAB. In the command window, you can type [pwd](http://www.mathworks.nl/help/techdoc/ref/pwd.html) to see what the present directory is, and you can type [dir](http://www.mathworks.nl/help/techdoc/ref/dir.html) to see the content of the working directory.
 
-For memory efficiency (especially relevant for large datasets), with FieldTrip we commonly use the strategy to only read in those segments of data that are of interest. This requires first to define the segments of interest (the trials) and subsequently to read them in and preprocess them. It is also possible to read in the whole continuous data, and segment the data in memory [(see here)](/tutorial/continuous).
+For memory efficiency (especially relevant for large datasets in comparison to the available RAM), with FieldTrip we historically commonly use the strategy to only read in those segments of data that are of interest. This requires first to define the segments of interest (the trials) and subsequently to read them in and preprocess them. It is also possible to read in the whole continuous data, and segment the data in memory [(see here)](/tutorial/continuous). 
 
 Instead of using the default 'trialfun_general' function with **[ft_definetrial](/reference/ft_definetrial)**, we will use a custom 'trialfun_affcog' that has been written specifically for this experiment. This custom function reads markers from the EEG record and identifies trials that belong to condition 1 (positive-negative judgement) or 2 (animal-human judgement). The function is available along with the data.
 
@@ -72,6 +72,8 @@ Now call pre-processing using the cfg output that resulted from **[ft_definetria
 
     data = ft_preprocessing(cfg);
 
+As was already mentioned in the background section, the order of the steps taken above, i.e. using the output of **[ft_definetrial](/reference/ft_definetrial)** with predefined epoch boundaries will force subsequent data reading and preprocessing steps to be applied to data epochs. If you wish preprocessing to be applied to continuous data (this is recommended for high-pass filtering for example), you may want to read in the data as a single continuous matrix, preprocess it, and then use  **[ft_redefinetrial](/reference/ft_redefinetrial)**. This order of steps is explained on this [page](https://www.fieldtriptoolbox.org/tutorial/continuous/).
+
 Try **[ft_databrowser](/reference/ft_databrowser)** now to visualize the data segments that were read into memory.
 
     cfg = [];  % use only default options