To improve the transparency of what we do in our laboratory, and to help others interpret our results, we provide here, a step-by-step description of our analysis pipeline. There are two key components to our analysis: first, the raw data analysis using Brain Vision Analyzer 2.1, and next, statistical analysis. What you see below can also act as a tutorial for those looking to develop their skills and familiarity with EEG outputs and analysis, and a sample data set can be downloaded for those seeking to practice their use of Analyzer. The experiment below consists of an oddball task, where participants were exposed to either a blue 'target' circle or green 'control' circle located either side of a central focus. As the target and control stimuli were presented, participants were instructed to mentally count the number of target circles that appeared among the series of circles. At the end of each testing block participants were instructed to record the number of target circles they encountered on the electronic device housing the Oddball task.
CONVERTINg muse .csv files FOR ANALYZER
When exporting MUSE data from your device, you will be left with a comma separated value file containing raw MUSE EEG data. Analyzer can not read .csv files, and so one of our PhD students Chad Williams, has created a Matlab script to convert this data. Posted below is a zip file containing this script as well as a link to his GitHub repository.
https://github.com/chadcwilliams/Convert_Muse_Files/blob/master/Convert%20Muse%20PEER/convert_Peer_to_BV.m
https://github.com/chadcwilliams/Convert_Muse_Files/blob/master/Convert%20Muse%20PEER/convert_Peer_to_BV.m
| muse_conversion_zip_file.zip |
Analyzer Set-Up
Creating a Storage Location
Create four files labeled: Export, History, Raw and Workspace on your desktop. Inside the Export and History folders create a folder labeled Export_Lastname and History_Lastname respectively.
Create four files labeled: Export, History, Raw and Workspace on your desktop. Inside the Export and History folders create a folder labeled Export_Lastname and History_Lastname respectively.
Open Analyzer
Open BrainVision Analyzer by clicking on the red icon.
Open BrainVision Analyzer by clicking on the red icon.
Create a Workspace
In the File tab, select New. A new workspace will appear asking you to select locations for Raw, History, and Export files. Using the browse function, select the folders you have created as destinations for each file type. Click ok and save the workspace to your Workspace folder.
In the File tab, select New. A new workspace will appear asking you to select locations for Raw, History, and Export files. Using the browse function, select the folders you have created as destinations for each file type. Click ok and save the workspace to your Workspace folder.
Open Your Data
Go to the File tab and click Open. Find your data file and open it. Your raw data will now be in the Analyzer window.
Go to the File tab and click Open. Find your data file and open it. Your raw data will now be in the Analyzer window.
Starting Analysis
Step 1: Data FILTERING
Step 4) Data Filtering
Under the Transformations tab, click Data Filtering and select IIR Filters. Enable the Low Cutoff and insert 0.1 as the frequency. Enable the High Cutoff and insert 15 as the frequency. Enable the Notch and select 60 as the notch frequency. Hit OK.
Under the Transformations tab, click Data Filtering and select IIR Filters. Enable the Low Cutoff and insert 0.1 as the frequency. Enable the High Cutoff and insert 15 as the frequency. Enable the Notch and select 60 as the notch frequency. Hit OK.
Step 2: Segmentation
The purpose of segmentation is to extract the 'Target' and 'Control' stimuli of the experiment from the remainder of the data collected. Under the Transformations tab, click Segmentation. In the first window, select Create new Segments based on a marker position. Select Cache data to a permanent file. Hit Next. In the next window, select the markers of interest (S 6 and S 5 in this oddball paradigm) add them to the right-hand column, then click Next. In the third window, select Based on Time and insert -200 in the start box and 600 in the end box. Click Finish.
Step 3: Baseline CORRECTION
Under the Transformations tab, click Baseline Correction. In the pop-up window, insert -200 in the Begin space and 0 in the End space. Click OK.
STEP 4: ARTIFACT REJECTION
I. In the pop-up window select Automatic Segment Selection under the Inspection Method tab. Uncheck all other options under this tab.
II. Move to the Channels tab. Here, select Enable All.
III. Move to the Criteria tab and in the Gradient sub-tab select Check Gradient and insert 10 into the Maximal Allowed Voltage text box. In the Before Event space insert 200. Do the same in the After Event text box.
IV. Now move to the Min-Max sub-tab and select Maximal Allowed Absolute Difference. In the following text box insert 100. In the Interval Length text box insert the length of your segment (e.g. 800ms: see above). In the Before Event space insert 200. Do the same in the After Event text box. Finally, finish up artifact rejection by clicking OK.
II. Move to the Channels tab. Here, select Enable All.
III. Move to the Criteria tab and in the Gradient sub-tab select Check Gradient and insert 10 into the Maximal Allowed Voltage text box. In the Before Event space insert 200. Do the same in the After Event text box.
IV. Now move to the Min-Max sub-tab and select Maximal Allowed Absolute Difference. In the following text box insert 100. In the Interval Length text box insert the length of your segment (e.g. 800ms: see above). In the Before Event space insert 200. Do the same in the After Event text box. Finally, finish up artifact rejection by clicking OK.
STEP 4: CONDITION SEGMENTATION
Now you must segment your data into the different conditions of your study (e.g. oddball and control). Under the Transformations tab, click Segmentation. In the first window, select Create new Segments based on a marker position. Select Cache data to a permanent file. Hit Next. In the next window select the markers of interest (S6 for the oddball target or S5 for control) and add them to the right-hand column then click Next. In the third window select Based on Time and insert -200 in the start box and 600 in the end box. Note: The segment length here is dependent of the ERP component of interest. For the P1/N1 a segment length of 600 ms is all you need: -200 to 400 ms. For the FRN/RP 800 ms, -200 to 600 ms, for the P300 you may need a segment length of 1000 ms: -200 to 800 ms. Click Finish. This step should be repeated for each marker of interest (oddball marker S 6 and control marker S 5).
STEP 5: New Reference
Re-Referencing allows you to form a new reference from the average of any grouping of channels in your data you select. As part of our analysis we re-reference to the mastoids (TP9 & TP10).
Step 6: Averaging
The ERP waveform is the average of all the segments for a condition. As such, once artifacts are removed one simply needs to generate the mean across the segments to get the ERP waveforms for each channel and condition. It is worth noting here, that in our laboratory we typically collapse across the two frontal channels (AF7,AF8) and the two ear channels (TP9, TP10) to create a pooled or average channel (AF, TP).