a basic tutorial...

In this section, two brief analysis tutorials will be given to illustrate the EMEGS processing facilities. A 70-sensor EEG data file, acquired with a simple passive viewing paradigm  including 44 trials in each of three experimental conditions (3 different neutral faces), presented in random order, willl be analyzed. The processed data is included in an EMEGS download so readers can follow along using the same data set.
In a first part, standard ERP preprocessing steps, including segmentation, filtering, correction of eye movements, artifact detection, event-related averaging and basic data display are described. In the second part, using the result files from the ERP analysis (that is, the clean single trial data epochs), the Fieldtrip plug-in is used to perform a time-frequency analysis.
Please note that the figures referenced can be found in the article:

Peyk, P., De Cesarei, A., & Junghöfer, M. (2011). Electro Magneto Encephalograhy Software: overview and integration with other EEG/MEG toolboxes. Computational Intelligence and Neuroscience, Volume 2011, Article ID 861705.

ERP analysis tutorial


Download emegs2.5 from the EMEGS website (http://www.emegs.org). Extract the zip-file and add emegs2.5 and all its subfolders to the MATLAB path. Extract the 7-zip compressed demo data file in the emegs testdata folder (.../emegs2.5/emegs2dTestData/eeg/BinData/Biosemi/EEGTutorialData.7z) to get the “EEGTutorialData.bdf”-file. Then, type “emegs” in the MATLAB command window and press “enter” to open the EMEGS start dialog (called “EMEGS launchpad”, figure 2a). Make sure to activate the “expert”-mode2 on the bottom of the launchpad (figuree 2a-1), then start the “Preprocessing” tool (figure 2a-2). A yellow interface will open (figure 2b), push the “open data file” button at the very top (figure 2b-1) and select the demo datafile. The file dialog will reappear allowing you to select a series of files for batch processing. As in the present case, we have only 1 data file, push “Cancel” to stop the file selection. EMEGS reads the data file header and displays number of channels, data format and recognized predefined sensor configurations (“Biosemi69” for the present example). As EMEGS has no way of automatically determining which channels in a BDF file are EEG channels and which may be irrelevant for ERP analysis (such as ECG- or EDA-channels), EMEGS will look for a text file listing the relevant channels. If this file is not found, as is always the case in a freshly downloaded EMEGS copy, EMEGS displays a corresponding message and lets you select a predefined channel definition file. Push the “Select predefined”-button on this dialog and then select the “DefaultBdfEegChannelsTutorial.txt” file in the folder where the dialog will start. Next, EMEGS will look for sensor configurations to match the EEG channels found in the data file. Again, the configurations are managed by a text file, which in a fresh install is missing too. Another message comes up to inform you about this. Push the “Create”-button on this dialog, to have EMEGS create a default version of this file, which will work for the present data file. Next, enable a high-pass filter (figure 2b-2) which will bring up the filter transfer function dialog to configure the filter (note that the stop band of the filter is displayed in the figure). The default setting is a 0.1Hz highpass filter, which is fine for this example, so just push “Apply” on this dialog figure 2b-3) to set this filter and close the dialog window. The transfer function graph now appears also on the preprocessing window. You can use the “configure”-button below this graph to reconfigure the filter later on. Do not set a lowpass filter, as we will do a wavelet analysis later on and therefore wish to keep high frequencies. Activate the eye blink and eye movement correction button (figure 2b-4), activate the checkbox on the bottom of the preprocessing interface reading “eyecorr with biosig” (not shown) and enter the size of the data epochs to be extracted: set the number of points to be extracted before each trigger to 125 (corresponding around 500ms, figure 2b-5, “PreTrig”) and the number of points to be extracted after each trigger to 150 (corresponding around 600ms, figure 2b-5,  “PostTrig”). Push the “Run”-button on the bottom of the preprocessing window (not shown) to start the analysis. EMEGS will print a few status messages in the command window, read trigger information from the data file and display a trial timing graph (time course of triggers, distance of triggers).  After this, channels need to be rereferenced to Cz3 and again EMEGS does not know, which channel in the data file really is the Cz channel. Therefore, you are prompted to enter the name of the Cz sensor during the first analysis (this information is written to file and remembered in the future). For now, just confirm the label “Cz” on the input dialog using the “OK”-button and finally EMEGS will start the preprocessing. It will write a log file in the folder where the data file resides (called “PrePro.log”) and display this log file when the analysis is finished. EMEGS creates a set of files which are described in detail in the EMEGS help files. Most important is the so-called “session file” which contains the extracted data epochs (“EEGTutorialData.f.ses” for the present example).

Artifact detection
Continue with the artifact detection by pushing the “Edit processed files”-button on the very bottom of the preprocessing window (not shown). The preprocessing window will close and the artifact detection window will come up, remembering the processed files. To start the artifact detection just push the “Abs MaxStd”-button (figure 2c-1), which has EMEGS perform statistical control of artifacts based on the maximum absolute amplitude (“Abs”) and the standard deviation excluding outliers on the maximum side of the trial distribution (“MaxStd”; exclusion on the minimum side is possible too - for instance to exclude flat channels - but hardly ever needed in modern amplifier systems). The program asks you to confirm that you wish to process the files previously preprocessed (choose “Yes”) and will then display histograms of the Abs and MaxStd parameters for each channel figure 2c). Narrow unimodal distributions in nearly all cases indicate good data quality, whereas multimodal and broad distributions nearly always indicate a substantial amount of artifacts in the data. EMEGS automatically sets an individual threshold separating good from bad trials for each channel (red vertical lines), automatically sorts out globally contaminated sensors (figure 2c-2, sensor PO4), but prompts you to set the trial threshold separating good from bad trials manually (figure 2d) by dragging the dotted red line (figure 2d-1) from left to right to the proper position. Each column of the gray background graph codes the four possible statistical parameter values (Abs, Std, Gradient and Flat, arranged as rows) for all channels of one trial and trials are sorted by data quality with the best trials on the far left and the worst trials on the far right. The dotted red line you need to adjust marks the threshold between good and bad trials. For the present data file, data quality is very good, so a far right position indicating mostly good trials and very few bad trials is appropriate. To check the data of a specific trial, right click and select “show trial” from the context menu (figure 2d-2). The data display program comes up and shows the current trial, highlighting good channels in green and bad channels in red. Note that PO4 is correctly highlighted in red (figure 2d-3), but the data quality of all sensors taken together is quite good, justifying it’s center position in the quality sorted display. When you’ve set the threshold properly, push the “accept”-button on the window named “Number of trials per std of approx.:” (figure 2d-4). A graphical display of the chronological sequence of good and bad trials is displayed next (not shown) and global and trial- and channel-specific thresholds are written to disk.

Event related averaging, sensor interpolation and simple data display
Push the “Close all and EmegsAvg”-button on the artifact detection main window (figure 2c-3) to directly start the averaging window for the edited data. The artifact detection window will close and the averaging window (not shown) will come up. The data format is set automatically, and you just need to push the “Run average”-button to start the averaging and approximation of missing sensors. When finished, EMEGS again opens the log file to show you a few trial statistics such as the number of good trials per condition and the average number of good sensors per trial.
Now, you should be able to find the result files in the data folder. Namely the files “EEGTutorialData.f.ses.app1”, “EEGTutorialData.f.ses.app2” and “EEGTutorialData.f.ses.app3” contain the clean data epochs for the conditions 1, 2 and 3, the files “EEGTutorialData.f.at1.ar”, “EEGTutorialData.f.at2.ar” and “EEGTutorialData.f.at2.ar” contain the ERPs.
Push the “Close All and Emegs2d”-button on the bottom right corner of the averaging window and the data display and analysis window will open (figure 2e). Push the the “Open data file”-button (figure 2e-1) and select one of the ERP-files listed above to display it’s data.

Wavelet analysis 

        Download EEGLAB from http://sccn.ucsd.edu/eeglab, extract the zip-archive and add the eeglab folder with all its subfolders to the MATLAB path. Fieltrip is included in this version of EEGLAB , so no other download is required.
After the averaging step described in the ERP tutorial, approximated trial by trial data are stored in .app files, which are now used to run the time-frequency analysis. To display the plug-in interface, either run the plugin from Emegs2d -> Plugin -> Run Fieldtrip TF analysis, or run Plugtfwltgui from the Matlab prompt (Figure 3a-1). In the plugin interface (Figure 3b-2), push the ‘load *.app*’ button and select any of the three newly created .app* files. The file dialog will reappear allowing you to select a series of files for batch processing. As in the present case, we have only 1 data file, push “Cancel” to stop the file selection. Now enter the wavelet analysis parameters: under the “frequency settings” label, set min = 8, step = .05 max = 80, step = 5, to focus on data from 8 to 80 Hz. As frequency resolution is maximal for low frequencies and minimal for high frequencies (Roach & Mathalon, 2008), the step between successive frequencies will vary linearly from 0.5 Hz for the lowest frequencies to 5 Hz for the highest frequencies. To display a diagnostic plot indicating which and how many frequencies will be analyzed, push the “show” button(Figure 3b-3). In the displayed plots, the curve relating absolute frequency to frequency step will be displayed (left), together with a line plot representing the to-be-analyzed frequency spectrum (y axis), with one horizontal line for each frequency which will be analyzed (right). In this sample data, since the pre-trigger and the post-trigger interval is very short, wavelet parameters will be set to m = 4 and c = 2, to obtain a short wavelet which can be repeated several times in the data segment, even at the lowest frequencies. In general, longer baseline and data segments allow for more accurate time-frequency analyses. To display the resulting wavelet, push the “display wavelet” button (Figure 3b-4). Four diagnostic plots will be thus displayed. In the top row, the resulting wavelet at the lowest (8Hz) and at the highest (80Hz) frequency are displayed. The bottom-left panel, the curve relating time and frequency resolution is plotted. Finally, the bottom-left panel relates the wavelet frequency resolution to the frequency steps which were selected by the user. Two reference lines are also displayed, displaying the 1:1 linear relation between frequency resolution and frequency step (purple) and the 2:1 relation between frequency line and step (red). This plot can be used to make sure that the chosen analysis parameters are appropriate, i.e. to check that frequency step is equal, or half of, frequency resolution. In the “time settings” section, specify min = 0, max = 1.1, step = 0.01 to analyze the entire 1100 ms epoch in steps of 10 ms each. Push “Run TF analysis” to start the analysis (Figure 3b-5). Data will be automatically converted in the EEGLAB format, then imported in Fieldtrip and analyzed using the chosen parameters. As a result, a .mat file containing the time-frequency results, in terms of power and phase-locking factor (PLF), will be created in the same folder as the original data.
To display results, choose Emegs2d -> Plugin -> Display TF results (figure 3a-6) or run Plugtfgui from the MATLAB command window. Load the newly created .mat files by selecting “Add file” (Figure 3c-7), then activate “single sensor” from the visualization options and “PO8” as sensor of interest (Figure 3c-8). In the time section, enter min = 0.16, max = 0.94, with baseline correction from 0.18 to 0.45 (as described in the ERP tutorial, data epochs include 500ms of baseline, thus stimulus onset is at 0.5 s). Change the power scale to -6 to +6, and select tf_plot to display a Fieldtrip plot of the chosen sensor (Figure 3c-9). In a similar fashion, whole-head topographies and multiple sensor plots can be easily obtained for both power or PLF data.