The MATLAB script in this repository enable the semi-automatic identification of Global Field Power (GFP) peaks on Event-Related Potentials (ERP) files.
The method is based on: Wicht, C.A., De Pretto, M. & Spierer L.; Neural correlates of expectations-induced effects of caffeine intake on executive functions; Cortex Registered Reports, stage 1 in principle acceptance
C.A. Wicht, GFPPeaks, (2020), GitHub repository, https://github.com/CorentinWicht/GFPPeaks
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When on the GFPPeaks startup page, start by clicking on ⬇️ Code on the top right of the screen and then Download ZIP to download the whole repository (alternatively you can also clone it).
Then, you will need to run the following script: GFPPeaks.m
You will find below a step by step explanation on how to run each script in MATLAB (by clicking on the ▶️ Run button or on your keyboard's button ⌨️ F5).
You can define first define the extension of the files you would like to load (currently .ep or .eph).
Then, you can choose between a :
- semi-automated
- manual processing modes
- or loading previous data
The semi-automated method looks for the GFP peak inside each component's upper and lower bounds as defined in Chapter 8, see Chapter 10.1 for an example of the main GFP Peaks window.
The manual method requires you to enter manually the GFP peaks for each files (identified for e.g. with CARTOOL) in the table detailed in Chapter 10.2. Loading data will require you to have ran the script previously to get the following .mat file(s):
Here you are asked to provide the most upper folder containing all your ERP files with .ep extension.
The third prompt enables the definition of the statistical design.
These are the designs that are currently accepted:
* 1 between-subjects factor
* 1 within-subjects factor
* 1 between- & 1 within-subjects factors
* 2 between-subjects factors
There can only be up to 3 levels for each factor !
There is already an example that shows you how to fill the first two columns, namely on the... :
* First line you should indicate whether Factor1 is a Between-subjects (B) or Within-subjects (W) factor
* Second line you should give a name to the Factor
* Third and + lines you need to define the name of each levels.\
This is case-sensitive since the name you pick as levels should match either a pattern in your EEG files or the name of subfolders in which you stored the files.
Additionally, you have the possibility to ignore specific folder(s) and/or file(s) that are inside the EEG files-containing upper folder.
For example, if you would like to ignore every EEG files that contains the pattern _EXCLUDE, just write this pattern in the first box under the IgnoreFiles header.
Once you are done, press on the cross X on the top-right corner and the code will resume
Since the individual GFP peaks will be averaged for each factor's level, you can here unblind the conditions assignment.
You further need to determine whether you want to replace the factor's levels names by:
- their unblinded names for all files (All):
- or each individual file need to be renamed separately (Individual, ! requires Excel !):
CAREFUL NOT TO CLOSE THE EXCEL FILE, JUST PRESS "OK" WHEN DONE
Here you have the possibility whether you would like to process all the data in the folder path you provided in Chapter 1 or only a subset of the files. \
Here you have the possibility to decide which data you like to process.
Close the window once you are satisfied with your choice.
In MATLAB's command window you will see the list of correctly loaded files according to your selection in the previous prompt.
In this prompt you need to:
1) define the epoching interval of your files (e.g. -100 to 700ms), in MILLISECONDS (!).
2) report the sampling rate (e.g. 1024Hz).
3) Define a folder name where to save the results (e.g. "Output").
This is one of the most important prompt !
On each of the four lines you have the possibility to define a specific ERP component of interest.
Each column should be filled accordingly:
Column 1: Type in the name of the component (e.g. N2)
Column 2: Define the lower bound in ms for the component of interest (e.g. 200ms)
Column 3: define the upper bound in ms for the component of interest (e.g. 350ms)
Column 4: determine the channel number which needs to be visually higlighted (may be usefull for the identification of specific components based on topography)
Column 5: determine if subset of electrodes need to be cluster to compute the average voltage amplitude (empty = all electrodes)
These informations will be used in the GFP peaks main display in Chapter 10.1
Finally, you have the possibility to define which file format you would like to export the figures (i.e., PDF, EMF and EPS are vector graphics format).
This is the main window which will enable you to determine whether the preselected GFP peak in the data table (see Chapter 10.2) is correct.
The window displays from left to right, top to bottom:
1) The ERP including all channels (each channel is represented as one line and electrodes selected for visualisation are highlighted in colors) [top left].
2) The GFP activity for epoch of interest [bottom left].
3) The topographical plot (topoplot) [right].
The vertical line represent, for all graphs, the position of the maximum GFP identified by the script.
Next to the vertical line, you have the exact peak position in ms and in time-frames (TF; i.e. dependent of your sampling rate).
The vertical blue bar represents the component of interest's time range.
On the far right you will find the topography at the GFP peak (indicated in TF in the title).
You have the possibility to use the slide bar below to inspect the topography of the whole ERP period while this will also adjust the vertical line in all other graphs.
This table will popup next the main figure from Chapter 10.1.
The GFP peak for the current file will be prefilled if you selected the semi-automatic method while the table will be completely empty if you selected the manual method (see Chapter 2).
For the semi-automatic method, either you leave the prefilled value if you think it is correct or you can change it manually. \
Once you are satisfied with your results you can close the data table and the results will be saved.
You are now done with the script and based on what you gave in Chapter 7 as folder name for the outputs, you fill find the figure above.
This figure shows the mean GFP activity for the provided ERP epoch, separated by conditions/groups (i.e. depending on your design).
The vertical red line represents the peak (i.e. maximum) of the mean of each individual GFP, while the red rectangle represents +- 1 Standard Deviation (SD) over the each individual GFP.
Finally, below the GFP graph you will find the topography averaged over the period included in +- 1 Standard Deviation (SD) around the mean peak (i.e. time range of averaging indicated in TF in the title).
Corentin Wicht
SNSF Doc.CH PhD student
corentin.wicht@unifr.ch, corentinw.lcns@gmail.com
Laboratory for Neurorehabilitation Science
University of Fribourg, Switzerland
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
See the LICENSE.md file for details
PD Dr. Lucas Spierer, Director of the Laboratory for Neurorehabilitation Science (LNS), Section of Medicine, Faculty of Science and Medicine, University of Fribourg, Switzerland provided substantial support and advices regarding theoretical conceptualization as well as access to the workplace and the infrastructure required to successfully complete the project.
This project was supported by Swiss National Science Foundation grants:
- #P0LAP1_181689 to Corentin Wicht
- #320030_175469 to PD Dr. Lucas Spierer