S.J. Luck ERP Boot Camp All rights reserved

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1 All rights reserved ERP Boot Camp: Data Analysis Tutorials (for use with BrainVision Analyzer-2 Software) Preparation of these tutorials was made possible by NIH grant R25MH Emily S. Kappenman, Marissa L. Gamble, and Steven J. Luck Center for Mind & Brain and Department of Psychology University of California, Davis Contact information for corresponding author: Emily S. Kappenman UC-Davis Center for Mind & Brain 267 Cousteau Place Davis, CA (phone) (fax)

2 All rights reserved Activity 1 Learning Brain Vision Analyzer Activity 1 Learning the Boot Camp Data Analysis Software In the first activity we will overview the basic structure of the Analyzer program, including how to navigate the workspace. There are many features available in Brain Vision Analyzer, but due to time constraints we are only going to review the basic features that are most applicable to the boot camp lectures. You are welcome to work on your own to explore the additional features of Analyzer that we will not cover. To explore these options or for all of your general questions about Analyzer, we have provided the Analyzer manual at: C:\Bootcamp_Analyzer2\Documentation\Analyzer.pdf Workspace Analyzer uses workspaces to keep track of files and analysis steps. Typically, you would create a separate workspace for each experiment you conduct. We have already created a workspace for you with the raw files we will be using for the demos. Launch the Analyzer2 program by double clicking on the Vision Analyzer icon on the desktop. To load the workspace we created, go to File > Workspace > Open and click on Bootcamp_Analyzer2.wksp (located in C:\Bootcamp_Analyzer2). On the far left side of the screen is a list of the main files in the workspace. You should see four items listed, each with a name and an icon that looks like a little book. The first one labeled AnalyzerIntro can be used for this section. Analyzer adds new nodes under the original node for each step in the analysis to create a tree of nodes. In order to expand the tree for a particular item, click on the + sign next to the icon. You can also expand the tree by right clicking and selecting Expand All. Do this now for the AnalyzerIntro tree. You should see a number of nodes that we will now take you through. Your workspace should look like this:. First let s make sure that we are viewing the data in the same way. We are a positive-up lab. To properly compare the work that you do and the pictures you see in this manual, you need to have the same polarity configuration. To check the polarity, go to File > Configuration > Preferences and click on the Scaling tab. Make sure the box that says Polarity Positive Down is unchecked. 1

3 All rights reserved Activity 1 Raw Files The first node in the AnalyzerIntro tree is labeled Raw_Data. There will be a node labeled Raw_Data for each of the demos that contains the raw data for that demo collected with the BioSemi system; however, Raw_Data does not contain the correct event code information. In Analyzer, BioSemi data files have to be decoded with a macro in order for the program to reflect the event code information in a numerical way. We have already completed this decoding step for you for each of the Demos. For the present demo, there is a node labeled AnalyzerIntro_Raw below the Raw_Data node. If you double click on this AnalyzerIntro_Raw node you can see the raw EEG data, decoded with numerical event codes (you may have to move forward in the data to see the event codes). We will be ignoring the Raw_Data node for each demo and will instead use this second raw data node for each of the demos (in this case, AnalyzerIntro_Raw). Take a minute to play around with the raw data and become familiar with the information available. The channel labels are on the left side of the screen and event codes appear on the bottom of the screen. You can also see the time relative to the beginning of the data file at the bottom right of the screen. This is important, as many of the demos will require you to find a particular time point in a particular raw file. You can move forward or backward in a raw file by clicking on the blue arrow buttons at the bottom left of the screen. If you place the cursor over a particular point on one of the lines of EEG, you can see the voltage, channel name, and time in seconds of that particular point in the bottom right of the screen. It is always a good idea to fully examine the raw EEG from each of your subjects before performing any filtering, artifact rejection, or data analysis to ensure that there were no problems during recording (we will discuss this more in later tutorials). Segmented Files The third node in the AnalyzerIntro tree is labeled Segmentation1. This node contains segments of data (known as epochs in some analysis programs). These segments correspond to trials from the experiment, and they were created using particular event codes (we will show you how to do this later). If you want to know what processing steps were performed on a particular node in a tree in Analyzer, you can find this information by right clicking on the name of the node and selecting Operation Infos. This will tell you all of the processing steps completed on this node. If you do this for the segmented data node labeled Segmentation1, you should be able to tell which event codes (also known as reference markers) were used to create the segments, the starting point, the ending point, whether or not overlapped segments were allowed and the total number of segments that fit those parameters. If you double click on the node Segmentation1, you can see that each of the channels is displayed in a separate box. If you wish to view a close-up of a given channel, you can double click on the channel name in the upper left part of the box (e.g., C3) and that channel will expand to fill the screen. To view all of the channels simultaneously again, simply double click on the channel name again. Just like you saw with the raw data file, there is information about the segmented data at the bottom of the screen. Here you can see the total number of segments in the node. Right now we are on segment 1 out of 256 segments. To scroll through the segments, you simply click on the arrow buttons, as you did to scroll through the raw EEG data. 2

4 All rights reserved Activity 1 Now go to the next node, BaselineCorrection1. One useful feature of Analyzer is the ability to overlay information from different nodes or different channels. For example, if we want to overlay channels C3 and C4, you can simply drag the C4 box onto the C3 box, and the waveform for channel C4 will appear in a different color overlaid with channel C3. To remove the overlaid waveform, press the Clear Overlays button. In addition, Analyzer allows you to overlay waveforms from different nodes in a tree. For example, if we want to overlay two sets of segments (in this case, BaselineCorrection1 and BaselineCorrection2), we first double click to select BaselineCorrection1. The waveforms should now be visible. Next, drag the BaselineCorrection2 node, which can be found under Analyzer Intro -- Raw Data -- AnalyzerIntro_Raw -- Segmentation2 -- BaselineCorrection2, onto the waveforms. The waveforms from these two nodes should now be overlaid. Feel free to go back and right click on the segmentation nodes and select Operation Infos. See if you can figure out what the difference is between the two nodes that you have overlaid. Other Useful Points It can sometimes be a little difficult to select a particular node, so make sure to double click carefully on the node you wish to select. If you perform an operation and the results do not look like the pictures in this manual, it is likely that a different node was selected when you performed the operation. 3

5 All rights reserved Activity 1 If this happens or you make another type of error or wish to delete a step in the analysis tree, you can simply right click on the n2de and select Delete. Let s try this now. Delete the BaselineCorrection2 node by right clicking and selecting Delete. Now let s recreate the node we just deleted. In other words, let s create a baseline correction node for Segmentation2, so that we have the same analysis steps for Segmentation1 and Segmentation2. Analyzer allows you to duplicate operations performed on one node on a second node very easily. This allows you to save a lot of time if you are analyzing multiple subjects or data sets. To do this, simply drag the node that has the operation you want to duplicate onto the node on which you want to perform the operation. For example, to perform the baseline correction we did for Segmentation1 on Segmentation2, we simply drag the BaselineCorrection1 node onto the Segmentation2 node. Do this now. Analyzer performs the operation from the first node on the second node for us. In addition, if there had been subsequent steps after baseline correction, it would have performed all of those as well. Just make sure to rename the new nodes to keep track of them. For example, Analyzer named the new baseline correction node you created, BaselineCorrection1. You should rename this node BaselineCorrection2 by right clicking and selecting Rename, or by clicking once on the name, pausing, and clicking a second time. Most of the activities in this tutorial will require that you use some type of transformation. These can be found underneath the Transformations tab the top of the Analyzer window. The transformations are then grouped into Dataset Preprocessing, Artifact Rejection/Reduction, Frequency and Component Analysis, Segment Analysis Functions, Comparison and Statistics, Special Signal Processing, and Others. You may need to click on another button underneath these initial function class buttons to see more options. If you see a little arrow, it indicates that more options can be seen if you click on that transformation. As described above, overlaying data is a great feature of Analyzer that really allows you to inspect your data. However, you may find that sometimes if you try to overlay certain types of data, you are unable to see both sets of data in the window. This occurs whenever the offsets of the data are drastically different and the scaling does not allow both sets of data to be visible at the same time. For instance, if you try to overlay segments that have not been baseline corrected or if you overlay data with different high-pass filter cutoffs, the differences in the offsets of the data may make it hard to see the original and overlaid data simultaneously. In such cases, you can view the data by using the Windows > Tile Horizontal or Tile Vertical options to do sideby-side comparisons. 4

6 All rights reserved Activity 2 Activity 2 Re-referencing For this next tutorial you will be using the Demo1 tree. The data in this demo are from the Monster paradigm used during the data collection activity and discussed in the Boot Camp lecture. There will also be a description of the Monster paradigm and an explanation of the event code scheme in later tutorials. To view the first raw EEG file, double click on the Demo1_Raw node. If you do not see Demo1_Raw listed under the Demo1 file header, press the + next to Demo1, which should produce a file called Raw Data. Press the + next to Raw Data and this should expose the Demo1_Raw node. Notice that you have 38 EEG channels. If you cannot see all 38 EEG channels, click on the button until all channels are visible. The 38 channels include 32 EEG scalp sites labeled with the corresponding electrode system label (FP1, Fz, etc.) and six external electrodes labeled EXG1-EXG6. EXG1-EXG6 correspond to the following positions: EXG1- Horizontal EOG Right (HEOG_R) EXG2- Horizontal EOG Left (HEOG_L) EXG3- Vertical EOG Lower (VEOG_Lower) EXG4- Vertical EOG Upper (VEOG_Upper) EXG5- Left Mastoid (LM) EXG6- Right Mastoid (RM) Once you have examined the raw EEG data, you are ready to start processing the data. Remember, it is always a good idea to thoroughly examine the raw EEG data from each subject before beginning data analysis. This allows you to identify any weird artifacts or segments of data that need to be excluded. This is especially important for re-referencing the data, because any artifacts present in the reference channels will be propagated to all of your channels when you re-reference the data. Our first goal in this activity will be to re-reference the EEG channels to the average of the left and right mastoid channels. Our second goal will be to create a bipolar HEOG channel (HEOG_R minus HEOG_L) and a bipolar VEOG channel (VEOG_Lower minus VEOG_Upper). Creating bipolar channels for the EOG signals will help in the identification of eyeblink and eye movement artifacts, as you will see in the Artifact Rejection tutorial. The data files we will be using throughout these demos were collected with the Biosemi Active- Two system. Unlike traditional EEG systems that record data in a differential mode, the Biosemi system records in what is known as single-ended mode. This means that each site is recorded as the potential between that site and the common mode sense (CMS) electrode site (which is analogous to a ground electrode). Conventional systems record the potential between an active site and the ground electrode and the voltage between a reference site and a ground electrode, and then electronically form the difference between the active and reference sites online. With the Biosemi system we perform this subtraction offline during data analysis. First let s rereference the data recorded in single-ended mode. Later we will discuss how to re-reference data collected in differential mode. 1

7 All rights reserved Activity 2 Double-click on the Demo1_Raw file. To re-reference the data, select Transformations > Dataset Preprocessing > Linear Derivation. The Linear Derivation command is used to create new EEG channels that are linear (summed and scaled) combinations of the existing EEG channels. However, this way of referencing data can be prone to mistakes and can be rather tedious to set up, particularly if you are using a dataset with many channels. However, manually creating re-referenced data using the linear derivation tool is a great exercise to show precisely what is being done to the data during re-referencing. Therefore, we will work through the logic of re-referencing our data for one channel of EEG using the linear derivation tool to work through the math involved. Let s do this for the FP1 channel. Click on the button Load from File, and when it prompts you for the text file, load C:\Bootcamp_Analyzer2\workfiles\Reference_FP1_sem.txt. The file should look like this: Notice that the leftmost column of the spreadsheet contains the names of the output channels (i.e., the channels that we will create). The top row contains the names of the input channels (i.e., the original channels from the demo1_raw file). Each cell in the matrix contains a number that indicates the scaling of the input channel that will be used in computing the output channel. In this case, we are re-referencing channel FP1 to the average of the left and right mastoids. Most of the values in the matrix are set to 0, but if you look closely you will see a 1 wherever a particular electrode site in the input channel column is also present in the output channel column, in this case channel FP1. In the EXG5 and EXG6 columns (which represent the left mastoid and right mastoid, respectively), you will see that each of these columns has a coefficient of Let s work through the logic of this. Originally, the FP1 electrode site was recorded in single- 2

8 All rights reserved Activity 2 ended mode as the potential between FP1 and CMS. Now we want to re-reference FP1 to the left and right mastoids by subtracting the average of channels EXG5 and EXG6 from channel FP1. Thus, we want to compute the following: FP1 referenced = FP1 original [(EXG5 + EXG6) 2] which is the same as: FP1 referenced = FP1 original+ (-0.5 x EXG5) + (-0.5 x EXG6) Click OK and a new node will appear under Demo1_raw labeled Linear Derivation. Re-label this new node Demo1_FP1_sem. FP1 has now been re-referenced to the average of the two mastoids (i.e. EXG5 and EXG6). Of course, we need to re-reference all of our data channels and not just FP1. Now that we understand how re-referencing works, instead of doing this using the linear derivation method for each channel of data, we will re-reference the entire data set in one step with the re-referencing option in Analyzer. Let s go back to our original unreferenced raw EEG data, node Demo1_Raw. Go to Transformations > Dataset Preprocessing > Channel Preprocessing > New Reference. The window should look like this: 3

9 All rights reserved Activity 2 Select the two mastoid channels (EXG5 and EXG6). You will see that there is a checkbox for whether to include an implicit reference into the calculation of the new reference. The Include Implicit Reference into Calculation of the New Reference button should be unchecked for rereferencing data collected in single-ended mode, because there is no real implicit reference channel. Click Next. In the next window you need to indicate the channels to which you would like the new reference to be applied. We will re-reference all 32 of the EEG data channels, but we will not re-reference the external eye or mastoid channels or the Status channel (which contains the event code information). Click Next. It will then ask you the name of the new channel (leave this blank). Click Finish and a new node will be created named New Reference. Rename this node to Demo1_ref_sem. Now we have re-referenced the EEG data channels. Before we create the bipolar EOG channels, let s work through the logic of re-referencing for data collected in differential mode, which many of you may be using in your own labs. Re-referencing Data in Differential Mode Unlike the Biosemi system, most systems record the data online as referenced to a specific site, like a mastoid, an earlobe, or the nose. If the data are originally collected with a reference on one side of the head (like a mastoid or earlobe), it is usually desirable to re-reference the data offline to the average of sites over both sides of the head (e.g., to the average of the left and right mastoids). This is conceptually the same as the process we just completed in which we rereferenced offline to the average of the two mastoids, but we need to take into account that the data are already referenced to one of the mastoids. How exactly does this work? Let s imagine that we collected our data online referenced to the left mastoid. This means that each of our data channels and the right mastoid were all collected as the difference between the data channel and the left mastoid. For the Fp1 site, for example, this is literally equal to Fp1 Left Mastoid. Now we want to re-reference our data so that each channel is re-referenced to the average of the two mastoids. If you work through the algebra, it turns out that in order to re-reference our data to the average of the left and right mastoids, you simply need to subtract out half of the right mastoid. If you would like to work through this algebraically, the equations can be found on page 108 of Luck, 2005 or in your boot camp lecture notes. To see how this works, go to Transformations > Dataset Preprocessing > Linear Derivation and load the text file C:\Bootcamp_Analyzer2\workfiles\Reference_FP1_dm.txt. You can see that for the new channel FP1 there is a -0.5 for EXG6 (right mastoid) and a 1 for FP1 just like we had for the data collected in single-ended mode. However, because in this example the data were collected (hypothetically) as referenced to the left mastoid online, there is a zero in the EXG5 spot (remember, the data were already collected against this reference online, hypothetically). 4

10 All rights reserved Activity 2 With this derivation we will end up with FP1 re-referenced to the average of the right and left mastoid. Once again, we can do this for all of our EEG channels in one step using the re-referencing tool in Analyzer (feel free to hit Cancel on the linear derivation for FP1). Make sure that you have selected the unreferenced raw data, Demo1_Raw, and then go to Transformations > Dataset Preprocessing > Channel Preprocessing > New Reference. This time you will just select the right mastoid as a reference (EXG6). You will also need to make sure that you DO check the box for including an implicit reference into the calculation of the new reference, since these data were (hypothetically) referenced online to the left mastoid. Checking this box means that Analyzer will subtract out half of EXG5 from the EEG channels to which you choose to apply the selected reference. Leaving this box unchecked would mean that Analyzer would subtract out the entire channel from each of the selected EEG channels, and that would not result in an average mastoid reference. Again, we will re-reference all 32 of the EEG data channels. Rename the new node to Demo1_ref_dm. Bipolar channels In addition to referencing the scalp sites to the average of the mastoids, you may also want to create bipolar channels from the ocular electrodes. We will discuss the usefulness of these bipolar channels more in the tutorial on artifact rejection. Double click on Demo1_ref_sem and select Transformations > Dataset Preprocessing > Linear Derivation and load file C:\Bootcamp_Analyzer2\workfiles\Bipolar.txt. If you scroll down to the bottom of the Linear Derivations window, you will see a channel labeled HEOGR-L, which is created by taking channel EXG1 minus channel EXG2 (HEOG_R minus HEOG_L). This will create a bipolar channel. By creating a channel like this, all of the EEG that is the same at the two HEOG sites will be subtracted away, making it easier to see the horizontal eye movements, which are opposite in polarity at the HEOG_L and HEOG_R sites (this will become more important later on in the tutorials). In addition to the bipolar HEOG channel, we have also created a bipolar VEOG channel (VEOGL-U). This channel is the subtraction of VEOG_Lower minus VEOG-Upper (EXG3 minus EXG4). This subtracts away all of the brain activity in common to these two sites, leaving only the difference (which is large for blinks, which are opposite in polarity below versus above the eyes). Click OK and a new node will appear under the Demo1_ref_sem labeled Linear Derivation. Rename this new node Demo1_ref_sem_bp. The re-referenced data should look like the screen shot below: 5

11 All rights reserved Activity 2 FP1 Fz F3 F7 T7 C3 Cz P1 P3 P5 P7 P9 PO7 PO3 O1 Iz Fp2 F4 F8 T8 C4 Pz P2 P4 P6 P8 P10 PO8 PO4 POz Oz O2 EXG1 EXG2 EXG3 EXG4 EXG5 EXG6 50 µv HEOGR-L VEOGL-U ActiveTwo CMS Start in Epoch Range ActiveTwo CMS MK2 31in Range 5MK Next try to re-reference the data for demo2. Since we have already done this on the first demo, you can click on the Demo1_ref_sem node and drag it down onto Demo2_Raw. One of the most convenient features of Analyzer is that when you click on something in a tree and drag it onto a different node, it will replicate the operation you performed on the first node on the second node. Very cool! Another nice feature of Analyzer is that it keeps track of the manipulations that you have performed. This information is available if you right click on the history node and select Operation Infos. Another way to keep track of all the operations you perform on the data is to rename the nodes with more informative names, which is what we have started to do. Go ahead and rename the referenced nodes under demo2 with Demo2_ref_sem and Demo2_ref_sem_bp. Now you have re-referenced both data sets and you are ready to move on to the next tutorial. 6

12 All rights reserved Activity 3 Activity 3 Segmentation The next step in our data analysis is to create segments of data corresponding to the trials in our experiment. Data segmentation, which is known as epoching in some data analysis systems, simply means chopping up the continuous EEG data into small time periods (e.g., 1000 ms). The most common way to do this is to extract segments surrounding the event codes from your experiment (e.g., from 200 ms prior to the event code until 800 ms after the event code), although there are other ways to segment data in Analyzer (e.g., creating segments at particular times, manually selecting segments, etc.). Before we segment the data, let s review a little bit about the MONSTER experiment. In this experiment, subjects are presented with a set of stimuli on each trial, composed of a black letter (A or B) and a white letter (also A or B), with one letter on each side of fixation. Subjects are told to attend to either the white or black letter for a block of trials, counterbalancing whether the participant presses the left button for A and the right button for B or vice versa. We have also manipulated the probability of the stimuli, such that in some blocks A is 80% probable and B is 20% probable, and in other blocks B is 80% probable and A is 20% probable. In addition to the target and non-target presented on each trial, there are also black-and-white checkerboards that are presented either in the upper visual field or the lower visual field. A key element of the MONSTER paradigm is that it contains several (mostly) orthogonal experimental manipulations that allow different ERP components to be isolated by means of various difference waves. The four main components we will isolate are: 1) The C1 Wave, which comes from primary visual cortex. Due to the folding pattern of the calcarine fissure, the C1 is opposite in polarity for upper vs. lower field stimuli, with a negative voltage for the upper field and a positive voltage for the lower field. To isolate the C1 from the overlapping components, we will make an upper-minus-lower difference wave corresponding to the location of the checkerboard. 2) The lateralized readiness potential (LRP) reflects preparation of a response, and it is observed over the hemisphere contralateral to the hand being prepared. We will isolate it with a contralateral-minus-ipsilateral difference wave, relative to the response hand. 3) The P3 wave is larger for rare stimulus categories than for frequent stimulus categories, and it provides a measure of the time required to categorize a stimulus. 4) The N2pc component reflects the focusing of attention onto a lateralized target, and it is negative over the hemisphere contralateral to the target. We will isolate it with a contralateral-minus-ipsilateral difference wave, just like the LRP, but it will be contra vs. ipsi to the target side rather than the response hand. There is actually a fifth factor in the design, which is the compatibility between the target and the nontarget on the other side of the fixation point (e.g., whether the nontarget is an A or B when the target is an A). If you have time, a good exercise would be to figure out how to isolate the effect of that factor (which might be expected to influence the N2 component over central scalp sites). 1

13 All rights reserved Activity 3 The MONSTER paradigm is extremely efficient because we can collapse across all the other factors when we are examining a given component. For example, when we isolate the C1 wave, we make one average for all the trials with checkerboards in the upper visual field and another average for all the trials with checkerboards in the lower visual field, irrespective of the target side, what the target is, what the response is, etc. The same set of trials can be subdivided in several different ways to isolate different components. Although this ignores any interactions between the factors, that s OK as a first approximation. You might want to figure out how to assess these interactions if you have time at the end (e.g., is the N2pc larger when the checkerboards are in the lower visual field?). Event Codes for the MONSTER Paradigm To make all of these orthogonal difference waves, you first need to understand the scheme we used for the event codes. The event codes created by the MATLAB program range between 1 and 255, and we will use the different digits to represent different aspects of the experimental design. If we had enough digits in the event codes, we could use a separate digit to represent each factor. Indeed, the Biosemi digitization software allows event codes up to 65536, which would make it easy to represent all of our factors in different digits. Unfortunately, most stimulus presentation systems (and EEG acquisition systems) are limited to a range of or even In the MONSTER paradigm, we use the digit in the hundreds place to represent whether subjects are attending to the white or black stimulus in the current trial block, with codes of 0xx for attend-black and 1xx for attend-white. We use the digit in the tens place to represent whether the target is on the left (a value of x1x,x2x, x5x, or x6x) or on the right (a value of x3x, x4x, x7x, or x8x), and whether the checkerboards are in the upper field (x1x, x3x, x5x, or x7x) or in the lower field (x2x, x4x, x6x, x8x). In addition the tens place also indicates whether A = left response (x1x, x2x, x3x, or x4x) or A = right response (x5x, x6x, x7x, or x8x). For example, an event code of 12x means that white was attended, that the white target was on the left side, that the checkerboards were in the lower visual field and the participant should press the left button for A and press the right button for B. The digit in the ones place codes the identity of the target stimulus, the identity of the nontarget stimulus (both of which can independently be A or B), and the probability of the stimuli, as indicated by the table below: Stimulus Event Codes Hundreds place 0 = attend black 1 = attend white Tens place 1 = left side target, upper checkerboards, A=Left Response 2 = left side target, lower checkerboards, A=Left Response 3 = right side target, upper checkerboards, A=Left Response 4 = right side target, lower checkerboards, A=Left Response 5 = left side target, upper checkerboards, A=Right Response 6 = left side target, lower checkerboards, A=Right Response 7 = right side target, upper checkerboards, A=Right Response 8 = right side target, lower checkerboards, A=Right Response 2

14 All rights reserved Activity 3 Ones place 1 = A target (A is Frequent), A nontarget 2 = A target (A is Frequent), B nontarget 3 = A target (A is Rare), A nontarget 4 = A target (A is Rare), B nontarget 5 = B target (B is Frequent), A nontarget 6 = B target (B is Frequent), B nontarget 7 = B target (B is Rare), A nontarget 8 = B target (B is Rare), B nontarget Response Event Codes Responses 5 = Left Response 6 = Right Response For example, an event code of 137 means that subjects were attending to white, the white item was on the right side, the checkerboards were in the upper field, A = left response, the white target item was a B, which was 20% probable, and the black nontarget item was an A. Note that a left hand response produces an event code of 5 and a right hand response produces an event code of 6. Scroll through the data in Demo1_raw and make sure that you see the event codes, denoted with a vertical marker through the EEG and the event code value at the bottom of the screen. You may notice an occasional event code of 7 in the data file, which does not map on to the event codes described above. The 7s occur whenever both the left and right response buttons are pressed simultaneously. For all of our data analysis activities, you can just ignore the 7s. Segmentation Now that we know about the monster paradigm, let s use this information to segment the data for the different ERP components of interest. Let s start with the C1 analysis. We want to separate the data into two different types of segments: targets with checkerboards in the upper visual field and targets with checkerboards in the lower visual field. If you look at the chart above you should be able to figure out which event codes belong in the Upper segment and which belong in the Lower segment. Selecting the event codes in creating segments of data is one of the easiest places to make a mistake during ERP data analysis. Make sure to double-check the event codes you select, and look in the file: C:\Bootcamp_Analyzer2\Documentation\event codes if you want to compare your selected event codes with the appropriate event codes. This will make sure that your data match the screenshots throughout the tutorials. To create the segments for the C1 wave, select Demo1_ref_sem_bp and then go to Transformations > Segment Analysis Function > Segmentation. A box like the one below will appear. Note that Analyzer2 uses the term marker for event codes. 3

15 All rights reserved Activity 3 Select Create new Segments based on a marker position and then press Next. Another box will appear. On the left will be the available markers (all of the event codes in the data file). This will include the response event codes, which it may label as stimulus codes, but that is ok--the software cannot make the distinction between different types of event codes. Select all of the markers that belong to the Upper C1 segments and click the Add button. This will move these markers to the right of the box under the Selected Markers area. Please note that the C:\Bootcamp_Analyzer2\Documentation\event codes file lists all possible event codes for a particular segmentation, and some of those event codes may not appear in a given data file due to the randomization of trials. 4

16 All rights reserved Activity 3 Notice that there is a box in the window called Advanced Boolean Expression. In this box you can indicate other parameters for segmentation selection. For example, if we wanted to only select those segments that are followed by a left response event code (event code 5) within 1000 ms of the stimulus event code, we would put 5(200, 1000) in the Advanced Boolean Expression box. This expression simply indicates that the selected stimulus event codes in Selected Markers should only be included in the segmentation process if there was an event code 5 within 200 ms to 1000 ms following the stimulus event code. Normally we would only want to include trials with correct responses. This would actually require us to initially create two different segmentations for the Upper checkerboards. Think about why this might be. For now, however, we are going to accept all segments regardless of accuracy (although we will modify this for isolating the LRP component). Click Next to continue with segmentation. A new box will pop up that lets you specify the start and end of the segments based on either time (in ms) or data points. Select Based on Time and specify -200 ms for Start time and 800 ms for the End time. This will provide a pre-stimulus baseline period of 200 ms and a post-stimulus period of 800 ms, for a total segment length of 1000 ms. As discussed in the boot camp lectures, the prestimulus interval should be sufficiently long to assess overlapping activity from the previous trial as well as the background noise level. For some experiments, you may wish to use a prestimulus interval that is much longer than 200 ms. 5

17 All rights reserved Activity 3 There are two more options in this window. The first, Allow Overlapped Segments should be checked, whereas the Skip Bad Intervals option should be unchecked. The option to allow overlapped segments just means that if one of our segments overlaps in time with the next one, we will include both segments. This actually won t affect our results for this experiment, since there will be no overlapping segments. The skip bad intervals option rejects intervals that have been marked with artifacts. However, since we haven t done any artifact rejection yet, this should not matter either. The box should look like the image above. Click Finish and a new node will be created called Segmentation. Rename this node Upper, to indicate that these segments correspond to trials in which the checkerboard stimuli were in the upper visual field. Go back to Demo1_ref_sem_bp and go through segmentation for the checkerboards in the lower visual field. You should be able to figure out these event codes from the information above (or check in the event codes file). Name this node Lower. Now we have the segments we need to create the C1. But first let s create the segments we need for the LRP. Go back and double click on Demo1_ref_sem_bp. We are now going to create Right_Resp and Left_Resp segmentation files. These segments are going to be for the LRP analysis, so we are looking for those conditions where the participant responded with the right hand (correct right responses when A = Right hand and B = Right hand for the A and B stimuli, respectively) and when the subject responded with the left hand (correct left responses when A = Left hand and B = Left hand for 6

18 All rights reserved Activity 3 the A and B stimuli, respectively). At this point, however, we will be time-locking to the stimulus preceding the response rather than computing response-locked averages. Figure out which event codes belong in each condition and then double check with the event codes file. For this particular segmentation, we only want to isolate the instances where the participant made the correct responses. Remember that a left hand response is indicated by an event code of 5, and a right hand response is indicated by an event code of 6. See if you can figure out the correct way to make these segmentations based on what we have learned before moving on to the next paragraph. Go to Transformations > Segment Analysis Function > Segmentation and select the appropriate markers for Left_Resp. In the Advanced Boolean Expression box type 5(200, 1000), to specify that an event code of 5 should occur within ms after the stimulus event code. Go ahead and create the segments for Right_Resp (remember to change the response code to 6!). Don t forget to rename the nodes with the appropriate names. Baseline Correction We have one more step before we move on to artifact rejection. We must baseline-correct the segmented data. Baseline correction involves calculating the mean voltage over a specified window and subtracting that value from each point in the segment. By subtracting this voltage, you will remove any large voltage offsets from each segment and set a new zero voltage level. It is very important to keep in mind that baseline correction is a manipulation of the data and will influence every subsequent step of processing and analysis. Double click on Upper and select Transformations > Segment Analysis Functions > Baseline Correction. A small window will pop up asking for the Range for Mean Value Calculation. Enter -200 ms for the Begin, and 0 ms for the End time points. This will give you a good range for the baseline correction calculation. Press OK and rename the segment Upper_bc. Do this for the lower segments and the LRP segments as well. As we will discuss in the next tutorial, baseline correcting the segmented data allows us to identify artifacts in the data based on voltages. We have found for most of our studies that a baseline window of 200 ms prestimulus works well. However, this is dependent on the 200 ms prestimulus being relatively flat and free from noise and other artifacts. Regardless of which baseline interval you choose to use, it is a good idea to look at the data and make sure that the baseline period you have chosen is relatively flat and free from noise contamination before baseline correcting. Think about why this is so important. 7

19 All rights reserved Activity 4 Activity 4 Artifacts and Averaging Now that we have segmented the data we are ready to perform artifact rejection. Artifact rejection is a very important part of ERP data analysis, as it allows us to clean up our data to ensure that we are getting a good signal from the data we collected. But keep in mind that artifact rejection cannot save noisy data, and it is always best to begin with clean data containing minimal artifacts. There is no substitute for clean data!!! There are a number of common artifacts that you will see in nearly every EEG data file. These include eyeblinks, slow voltage changes (caused mostly by skin potentials), muscle activity (from moving the head or tensing up the muscles in the face or neck), horizontal eye movements, and various types of C.R.A.P. (Commonly Recorded Artifactual Potentials). Although we usually perform artifact rejection on the segmented data, it s a good idea to examine the raw unsegmented EEG data first. You can usually identify patterns of artifacts, make sure there were no errors in the file, etc., more easily with the raw data. Therefore, we ll start by taking a look at some artifacts in a raw EEG data file recorded with the MONSTER paradigm. For each of the following steps, you should go to the specified time by scrolling on the arrows or or by dragging the blue cursor at the bottom of the screen, to get to the desired time. For most of the figures we will be looking at a time frame of about two seconds. You can change the amount of time displayed in the Analyzer window by clicking on the Set Interval or Reset Interval buttons would like to see in the window. and indicating the length of time that you Now lets take a look at some artifacts. Select demo2 > RawData > Demo2_Raw > Demo2_ref_sem > Demo2_ref_sem_bp. We will use the data in Demo2 for artifact identification. Blinks Eye blinks are one of the most common artifacts in EEG data. To see an example of an eye blink, scroll to the 00:01:50 time point in the data file (with a display interval of 2 seconds), so that the center marker is between event codes 6 and 219. It should look like this: 1

20 All rights reserved Activity 4 FP1 Fz F3 F7 T7 C3 Cz P1 P3 P5 P7 P9 PO7 PO3 O1 Iz Fp2 F4 F8 T8 C4 Pz P2 P4 P6 P8 P10 PO8 PO4 POz Oz O2 EXG1 EXG2 EXG3 EXG4 EXG5 EXG6 50 µv HEOGR-L VEOGL-U Blinks. 00:01:50 with display at 2 seconds, Demo2_ref_sem_bp You can clearly see a blink in the VEOG Channel (you might want to decrease the vertical scaling a bit by clicking the button). Notice that you can also see the eye blink in some of the EEG channels as well (it is particularly large at sites FP1 and FP2). This propagation of blink-related activity across the scalp is why we measure eyeblinks and reject segments that have eyeblink artifacts. If you look at the EXG4 (VEOG_Upper) and EXG3 (VEOG_Lower) channels, you can see that the eye blink appears as a negative-going deflection under the eye (EXG3) and a positive-going deflection above the eye (EXG4). Although the bipolar VEOG channel is good for identifying eye blinks (see next paragraph), the upper and lower VEOG channels help to distinguish between 2

21 All rights reserved Activity 4 eye related activity and ERPs in the averaged waveforms. Once you average the segments of data, cortically generated ERPs should be visible in the VEOG_Upper and VEOG_Lower channels, because ERPs are visible over the entire surface of the head. If you see an experimental effect in your average data that goes in opposite directions under vs. over the eyes, it is very likely that you are merely seeing differences in blinking between conditions and not a real ERP effect. We have taken advantage of the opposite polarity above and below the eye by creating a channel called VEOGL-U, in which the VEOG_Upper signal is subtracted from the VEOG_Lower signal. This is exactly what you would get by recording a bipolar signal between the VEOG_Lower site and the VEOG_Upper site (with VEOG_Upper as the reference). Because the blink signal is opposite in polarity at these two sites, subtracting the two signals leads to an increase in the size of the blink without increasing non-blink activity. This makes it easier to reject trials with blinks without also rejecting trials with non-blink activity. However, it is also useful to retain the original VEOG_Lower and VEOG_Upper signals so that we can assess polarity inversions in the averaged waveforms, as described in the previous paragraph. Horizontal Eye Movements Another common artifact in EEG data arises from horizontal eye movements, although these artifacts are subtler and harder to detect than eye blinks (unless the eye movements are very large). Whenever stimuli are presented away from the center of the screen, subjects are likely to make eye movements toward the stimuli. A horizontal eye movement can be seen in the data file at 00:03:19. However, this is a very large horizontal eye movement, and in most cases the eye movements will be much smaller and harder to distinguish (we will look at some examples of typical horizontal eye movements later). The eye movement should look like this: 3

22 All rights reserved Activity 4 FP1 Fz F3 F7 T7 C3 Cz P1 P3 P5 P7 P9 PO7 PO3 O1 Iz Fp2 F4 F8 T8 C4 Pz P2 P4 P6 P8 P10 PO8 PO4 POz Oz O2 EXG1 EXG2 EXG3 EXG4 EXG5 EXG6 50 µv HEOGR-L VEOGL-U Horizontal eye movement in the HEOG channel. 00:03:19. Horizontal eye movements are most easily seen in the HEOGR-L channel, which shows the difference in voltage between the HEOG_R (EXG1) and HEOG_L (EXG2) sites. Saccadic eye movements have a distinctive boxcar shape, with a sudden shift in one direction, a flat period, and then often a sudden shift back toward the original value. This reflects the eyes suddenly moving from one place to another, holding steady for a period of time, and then possibly shifting back to the original position. The eyes cannot move gradually unless they are tracking a smoothly moving object (these are called smooth pursuit eye movements, in contrast to the sudden saccadic eye movements seen here). The eyes contain a dipole that points from the back of the eye to the front of the eye, with positive at the front. When the eyes point to the right, this causes the positive end of the dipole to point toward the right side of the head and the negative end of the dipole to point to the left side of the head. You can see that in the example above. The HEOG_L electrode shows a negative-going voltage deflection, whereas the HEOG_R electrode shows a positive-going 4

23 All rights reserved Activity 4 deflection. The HEOGR-L signal combines these two into a single, double-sized deflection that is easier to separate from other noise signals. There isn t usually any reason to keep the original HEOG_L and HEOG_R signals, but we retain them in the Boot Camp examples for educational purposes. Over the next few seconds in the data file, you will see many horizontal eye movements. Some of these eye movements will have the boxcar positive, and in others the boxcar will be negative. Given what you now know about the position of the dipole in the eyes AND how the HEOGR-L channel is subtracted (right-minus-left), you should try to figure out which way the eyes were moving for each of the artifact examples. You may want to increase the scale of the HEOGR-L channel on your computer screen to compare it to the figures below. HEOGR-L 200 µv Small (typical) eye movement 00:03:13 5

24 All rights reserved Activity 4 HEOGR-L 500 µv Large (atypical) eye movement preceded by a small eye movement 00:03:16. Try to scan throughout other sections of the dataset to find small horizontal eye movements. Note that there is often a small, sharp voltage spike at the beginning of an eye movement; this is EMG activity from the extraocular muscles that cause the eyes to rotate. These muscles contract briefly to rotate the eyes, but they do not continue to contract once the eyes have moved (the eyes stay in the new position without any significant muscle activity). Skin Potentials Skin potentials are another common artifact. Skin potentials are caused by small voltage changes at an electrode site and are commonly caused by the subject sweating (often imperceptibly). There are sweat glands located all over the head, so it is very important to try to minimize sweating by keeping the recording chamber cool and dry. If you change the scale of the window from 2 seconds to 10 seconds you can see voltage changes that are happening over longer periods of time. If you look at time point 00:06:12 you will see drifting in electrodes Fp1, Fp2, PO7, as well as some eye channels. This drifting is most likely caused by skin potentials. 6

25 All rights reserved Activity 4 C.R.A.P. Another artifact we commonly see in EEG data is what we call C.R.A.P. (or Commonly Recorded Artifactual Potentials). These are miscellaneous artifacts, usually of unknown origin. You can see an example in channel O2 during the time frame in which the skin potentials are happening. If you saw an artifact like this during data collection, you would want to pause the experiment and fix the electrode. Scroll to time point 00:06:26. Notice the oscillations in channel F8. If you change the display interval to 100 ms (.1 s), you can see that there are 6 peaks in 100 ms, which is equal to 60 peaks in 1000 ms (or 60 Hz). Thus, for some reason, 60 Hz noise was being picked up by the F8 electrode at that time. Muscle Activity Muscle activity (EMG) is another artifact that you often see. It is very fast activity that has both positive and negative spikes. Scroll to time point 00:05:51. Change the interval back to two seconds and start scrolling through the data. You will notice that a good portion of the channels are very clean; however, there is quite a bit of muscle activity in the eye channels and FP1 and FP2. Muscle activity consists of fairly irregular high-frequency noise (little spikes of varying sizes). Before you read on, think of a reason why you might see muscle activity in these particular channels are there muscles nearby? When participants are concentrating or are tired and trying to stay awake, you may notice a lot of activity in FP1 and FP2. This is because they are lifting their eyebrows or scrunching up the forehead in an attempt to focus. By making the participants aware of this activity and telling them to relax the forehead, you can usually reduce these artifacts. Scroll to time point 00:08:09. Take a close look at this muscle activity. You may notice that a lot of the muscle activity takes place at T7/T8 and F7/F8, as well as some of the eye areas. This muscle activity is due to jaw clenching. The very powerful jaw muscles are close to these electrodes, and when a subject is tense, this can cause C.R.A.P.py data. The T7/T8 sites usually exhibit some visible EMG activity, even if the subject tries very hard to relax the jaw muscles. Scroll through the next few minutes of data and you will see lot of muscle activity, concentrated around the eyes. In this case, the muscle activity is caused by smiling (not a common problem in actual experiments!). 7