We will show an example of this later in the tutorial. To use ERPLAB, first create an EventList for the EEG stored in your dataset. In the ERPLAB. ERPLAB Toolbox is a free, open-source Matlab package for analyzing ERP data. It is tightly integrated with EEGLAB Toolbox, extending. Colgate ERP Lab Resources Data Processing & Viewing. NetStation Waveform Tools Technical Manual · NetStation Data Viewer Tutorial.
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Because this is a first release, bugs are inevitable. In some cases, errors will occur leading to a message that instructs you to eplab the error to the EEGLAB developers. This version is designed to provide most basic ERP processing functions; enhanced versions of these functions and additional functions will be forthcoming over the next several years. Feel free to make your own modifications and additions to the toolbox, and send them to us if you yutorial they would be of value to other users.
Some features are present in this version that have not yet been documented, and some features may have changed since this document was released. In addition, some of the screen shots shown in this document may reflect previous versions and may not conform with the text. The documentation, like the software, is a work in progress. This tutorial was written for a beta release that preceded the official public version release, and a few small changes were made for version We will provide an updated tutorial soon.
The goal of this tutorial is to provide step- by- step instructions for the processing of a typical data set. A Frequently Asked Questions document is also available.
We have set up three web- based discussion groups. Another is designed for reporting bugs and describing work- arounds. The third is designed to give users the opportunity to make suggestions about modifications and new features.
There is also an list, that you can use to communicate with the developers and with other users. If you would like to join this list, you should go to Please keep in mind that this is FREE software, and we do not have the resources to provide the level of support that commercial software vendors can provide.
Overview of Tutorial This tutorial takes you step by step through the analysis of a set of ERP data from an actual published experiment Luck et al. It focuses on giving you experience with the specific steps involved in analyzing a simple prototypical ERP experiment. To do this, close the main EEGLAB window and type clear all and then eeglab in the Matlab command window each Matlab command should be followed with the return or enter key. Next you need to put the various tutorial files on your hard drive, preferably in a folder named Tutorial.
The data from the first subject we will be processing is in a subfolder named S1, and you should make this folder the current directory in the Matlab command window. At the top of the Matlab command window, there is a drop- down menu displaying the current directory see screenshot below.
ERPLAB: an open-source toolbox for the analysis of event-related potentials
Click on the browse button labeled with to the right of the current directly and choose the S1 folder. The top of the window will then look something like this although the details depend on what operating system you are using: EEGLAB prints some information in the command window while it is processing the file, finishing with Done when the file has finished loading. If you try to start the next procedure before the current procedure is done, you will cause problems that will ultimately slow you down.
This is especially true of procedures that create or load datasets. So just take a sip of coffee and wait until you see Done in the command window before you take the next step. When the dataset erplqb finished loading, it will be visible in the Datasets menu, as shown in the following screenshot. In this section, we will provide a basic overview of the experiment, along with a few of the details you will need to know to analyze it.
For a complete description of the experimental paradigm, see the publication Luck et al. This study examines the P3 erplsb and the lateralized readiness component LRP in a group of schizophrenia patients and a group of matched control subjects. This tutorial focuses on the data from 6 of the control subjects in the folders named S1 S6; but note that some releases contain only the data from subject S1. All subject identity information has been stripped from the data files. In this experiment, a letter or digit was presented every ms.
Subjects were instructed tjtorial press a button with one hand for digits and with the other hand for letters. Thus, the stimulus category and the probability were counterbalanced. The probability manipulation was designed to isolate the probability- sensitive P3b component. Different event codes were used for the digits when they were rare, the digits when they were frequent, the letters when they were rare, and the letters when they were frequent.
The experiment also included a condition in which the two categories were equiprobable, but that eprlab is not included in this tutorial. The experiment was also designed to isolate the LRP by counterbalancing the stimulus category and probability with the left and right hand responses. That is, subjects responded to letters with the left hand and digits with the right hand for half of the experiment, and they responded to digits with the left hand and letters with the right hand for the other half.
This gives erolab user more power, but it tutorixl gives the user responsibility for using the event codes in a sensible way. However, the combination of the stimulus event code and the response event code makes it possible to determine whether the subject pressed the left button or the right button.
Here is a table showing the stimulus event codes. Each scalp site was recorded relative to a right- earlobe reference. Recordings were also obtained from a vertical electrooculogram VEOG electrode located beneath the left eye and from an electrode on the left earlobe both referenced to the right earlobe.
A horizontal electrooculogram HEOG signal erlpab recorded as the potential between electrodes located just lateral to each eye. Letters and digits were presented in an unpredictable order within each trial block, but separate blocks were used for each combination of probability response assignment. That is, there were separate trial blocks for: Each block was originally recorded in a separate file. The procedure you follow will depend on your hutorial acquisition system.
Note that EEGLAB places a boundary event code at the boundary between the original files to mark the fact that a temporal discontinuity was present at those times. This is important, because some functions e. Specifically, we will add location coordinates for each electrode site see the EEGLAB documentation for more information.
These coordinates are not guaranteed to be exact, but they are a reasonable approximation for simple procedures such as plotting scalp maps. If you want exact electrode locations, you will need to use a 3- D digitizer and measure the locations for each subject. Click Ok, and you will see the window shown here: For simple data files such as the one used in this tutorial, you can accept all of the default values and simply click Ok.
This will add the coordinates to the current dataset. Click OK and you will see the following plot: Later parts of this tutorial will make use of this dataset, so you should save it to disk. Click Ok, and then look in the Datasets menu.
ERPLAB TOOLBOX TUTORIAL – PDF
We recommend that you give the file the same name as the dataset, but with a. Datasets, ERPsets, and bins There are a few key concepts you need to understand before we go any further. A dataset typically stores the data from a single subject, either a single block of trials or an entire session.
Whenever you run a routine that changes the data in a dataset, a new dataset is created. You can see the currently available datasets in the Datasets menu. One tugorial is currently active, and any routines that you run will typically be applied to the current dataset. When a new dataset is created, it becomes the current dataset, but you can make a different dataset active by selecting it in the Datasets menu.
This provides a very convenient workflow. A bin is a set of averaged ERP waveforms, one for each electrode site, which were created by averaging together a specific set of EEG epochs. A simple oddball experiment, for example, might have one bin for the infrequent targets and another bin for the frequent standards.
However, a sophisticated experiment might have dozens of different bins, with a given bin being tutorlal like Digits preceded by letters followed by a correct response between and ms, in a condition in which digits are rare and tutoriql are frequent. However, this can lead to a huge number of different files for each subject, making it difficult to keep track of everything.
This saves time and reduces errors. We will begin with some housekeeping, then do artifact rejection, and finally averaging. To make it easier to see and manipulate the event codes, ERPLAB creates an EventList, which is a list of all of the event codes and associated information. But the Tuttorial can also be saved in a text file, where you can easily erplav and manipulate the event information.
If you make any changes, you can import it back into the EEG structure. We will show an example of this later in the tutorial. The advanced version allows you to define a text label for each numeric event code or vice versawhich helps to avoid the errors that often occur in experiments with many different event codes.
It also allows you to define the bins that you will use to sort your EEG epochs for averaging. You will use the advanced method for creating events if you want to create event labels from numeric event codes or if you want to assign the events to bins, and you will use the basic method if you don t want event labels and you will be using BINLISTER to assign events to bins. If your data acquisition system uses text- based labels instead of tugorial codes, you must define a numeric event code for tutoria text- based event label.
If your data acquisition system uses numeric codes, you are not required to create text- based labels for each code, but it s a good idea. You can also define a bin for all or some of your event codes, along with a label for that bin. To keep our example simple, we will start by defining this information erplabb only one of the trial blocks in the tutorail experiment.
In this block, letters were rare event code 12 and numbers were frequent event code You can use the Event Info area of the window to define the event codes, event labels, bin numbers, and bin descriptions for each event. If you then click Update Line, this definition will appear in the table at the left of the window.
We will be computing stimulus- locked averages rather than response- locked averages in this example, so we do not need to define bins for the response event codes. However, it will still be useful to define labels for the response codes so that we will not have to remember that code 9 denotes a correct response and code 8 denotes an incorrect response. The table at the left of the window in the screenshot below shows how to define these events. Don t forget to click Update Line after entering the information for each type of event or the information will not be added to the table.
Also, your bin numbers must start at 1 and be consecutive with no breaks but event codes need not be consecutive, and they can be any integer number that Matlab can represent. Because you will need to do this step for every subject, you can save the table of event information in a text file by clicking save list and then load it in again for the next subject by clicking open list.
To do this, check the box labeled EventList Text File and click the Browse button to save the file as elist. By checking this box, you will have the opportunity to transfer your newly created labels into EEG. Once you have entered all of the necessary information into the window, as shown in the screenshot above, click the APPLY button to create the EventList and add it to the current dataset.