artifact detection

Commands: EditAEM, CalcAutoEditMat, CalcAutoEditMEGMat, FindBadChan, FindBadChanTrial, emegs2d

Contents:

statistical artefact detection

manual artefact detection

Statistical artefact detection: The main interface of EMEGS responsible for statistical artifact detection is EditAEM, which you can see in the figure 1 below. It works on the bases of statistical parameters, that have to be calculated for every channel in every trial before using EditAEM. These parameters usually include the maximum absolute amplitude and standard deviation of the signal. They can be calculated in batch mode using PrePro (see section 'segmentation and filtering...') or by running the script 'CalcAutoEditMat' (for EEG data) / 'CalcAutoEditMEGMat' (for MEG data) and by default are stored in the *.AEM / *.TVM and *.AEM.AR /*.TVM.AR -files.

The function of EditAEM  is to allow the user to interactively set the threshold that separates 'good' from 'bad' data. This can be done quickly in a mostly automatized fashion, but also manually for every sensor and threshold type. Generally speaking, EditAEM works by setting 3 types of thresholds. The default procedure is to let EMEGS set the first and third threshold and let the user set the second one.  This is because the first and third threshold type include multiple decisions, which, with a high number of channels and trials, quickly become quite laborious. The threshold types are the following:

1. an overall channel threshold to globally extract bad sensors
2. an overall trial threshold to globally extract bad epochs
3. trial specific channel thresholds, that only extract  a specific sensor in a specific trial

All three threshold types can be seen in figure 1 and will be explained separately:

type 1 thresholds: type 1 threshold can be seen in the two windows titled ' spline weighted deviation from ...'. Both show a channel overview (channel number on the x-axis, average parameter  value (across trials)  on the y-axis), one for the parameter 'maximum absolute ampitude', the other for the 'standard deviation'. The green horizontal line illustrates the mean of all channels, the red line illustrates the threshold level. Thus, all channels above the red line are globally extracted. You can influence this threshold by changing the 'channel exclusion'-value in the EditAEM-menu.The given value is interpreted as absolute distance in standard deviations from the overall mean, thus a value of 2.5 would mean, that all channels, that differ from the overall mean by more than 2.5 standard deviations, are excluded from further analysis.

type 3 thresholds:  the blue bar graphs in figure 1 are trial histogram plots for a chosen parameter ('maximum absolute amplitude' in this example). In each plot there is a dashed red line, that corresponds the type 3 threshold, that EMEGS set automatically for each channel. In cases where there is no line visible, all trials for that sensor were rejected, which can also be seen from the green '0' above the bar plot (the green number corresponds the number of good trials, the red number corresponds the number of bad trials, the cyan number is the good/total ratio, the pink number is the channel number).  You can influence this threshold by changing the 'required signal/noise'-value in the EditAEM-menu. This value does not have such a straight forward meaning as the 'channel exclusion'-value. It is used as an exponent, thus changing it towards 1 will half stronger consequences than lowering it towards 0 by the same amount. A higher value will results in more conservative thresholds, therefore it can be useful to think of it as the amount of signal/noise that you require in your data. As a start you might use 0.2, higher values (e.g. > 0.25 ) would mean, that you only accept high quality data with high signal and little unusual events (noise), low values (e.g.  < 0.08 ) would mean, that you also accept data with a larger amount of variation in it, possibly including artifacts. But remember, this is only meant as a help for understanding the general function of this value. Its true meaning is described in detail in the SCADS-paper below.

type 2 threshold:   the one type 2 threshold is set using the windows titled 'Number of trials per std of approximation' and 'Trials and std of approximation by quality'.  The latter one shows a graphical display of the sorted 'AutoEditMat', the matrix that contains the parameter values for every channel and trial (with a high number of channels and trials, not all channels are displayed, but averaged channel groups, e.g. channel 1:5, channel 6:10 etc). It is sorted by quality, so that the best trials are columns on the left, the worst trials are columns on the right. If forms the greyish background of the window and is subdivided in 4 rows, one for each parameter in the AutoEditMat: the 'maximum absolute amplitude', the 'standard deviation', the 'maximum absolute gradient' and the 'flat'-value. The red line again is the threshold line. The foreground  blue line shows the 'std-of-approximation'-index for every trial on the x-axis. This index codes the data quality of a trial, using the information of the AutoEditMat and the positioning information of good and bad sensors, to estimate how reliably bad sensors can be approximated by surrounding good sensors. Thus, if missing sensors are all clustered together, the index will be higher (the quality is worse) than if the same number of bad sensors are evenly distributed (see the section on the trial quality index below). Please note that the y-axis of this line has nothing to do with the y-axis of the matrixplot in the background. Both displays share only the x-axis (the trial number) and are overlayed only to focus the information on one window. The fact that the blue line is monotonically increasing is due to the fact that, as mentioned above,  the AutoEditMat is sorted by trial quality. The window titled ' Number of trials per std of approximation' now is simply a histogram plot of this index. Therefore, the vertical threshold line in the histogram plot corresponds a horizontal threshold in the AutoEditMat-window. The horizontal and vertical threshold lines in the AutoEditMat-window are linked, thus if one of them is dragged, the other one will follow to keep the crosssing on the blue line. For both windows, trials on the left of the threshold line are accepted (good trials), trials on the right are excluded (bad trials).

Note on parameter distributions/histograms in EditAEM: Looking at trial-frequency-distributions of statistical parameters like maximum absolute amplitude or standard deviation can give you important information about the measured data quality: narrow distributions usually are found only for homogeneous data of good quality (normal signal with only EEG/MEG inherent variation), reflecting the fact that many trials have approximately the same properties and only considerably few trials are 'outliers'. Wide distributions on the other hand, or distributions with several maxima, reflect heterogeneous data with inherent and most likely also external sources of variation like movement-, eyemovement-, eyeblink- or other types of artifacts. Of course, this is only true assuming that the measurement was done trying to maximize the amount of good data. If there are permanent movement artifacts in all segments of the data, this also could end up in a narrow distribution of statistical trial parameters (at a different level).

Note on the trial quality index (approximation error) This index is calculated for a given good/bad-sensor distribution (in a given trial), as the average approximation errors that occurs on a sensor with this good/bad-sensor-configuration when approximating radial dipole testfunctions. These testfunctions are generated, one for each sensor, by calculating the forward solution for an activation with only 1 active radial dipole right underneath the sensor. Approximation of such a test function is most heavily impaired by bad sensors near the generating dipole and much less by bad sensors further away. Thus the average approximation error of all test functions can be used as an index for how well missing data can approximated in a trial on all sensors.

figure 1: artifact detection using EditAEM

The automatized editing process
If you want to use the standard editing procedure (that is to have EMEGS set the type 1 and type 3 thresholds for you, and set the type 2 threshold manually), push the 'Fast Auto 2'-button. It uses two parameters: the maximum absolute amplitude, and the standard deviation. This is sufficient for most cases.  You are then asked to select *.AEM*-files manually or in form a batchfile. When done with the *.AEM*-files, you can also supply *est*-files (by mouse or as batchfile) for every *.AEM*-file given. Choose cancel when you're finished with that, and wait for EditAEM to display the parameterhistograms and one channeloverview for each parameter used and to set the corresponding thresholds (dashed red lines). For FastAuto1, the histograms will be updated twice and two channeloverview windows will appear. Based on these thresholds, EditAEM calculates the 'std-of-approximation'-index for every trial, showing you the progress in a separate window. When done,  the two graphs titled 'Number of trials per std of approximation' and 'Trials and std of approximation by quality' will be displayed. EditAEM now sets a default type 2 threshold in order to give you a valid initial configuration and quickly displays the 3 worst trials for this default threshold as simplified channel status plot (green numbers indicating good sensors, red numbers indicating bad sensors, to give you an quick impression of the of the data quality you will have with this threshold). After that EditAEM waits for you to adjust the threshold according to your wishes.

To do this, drag the threshold line on either of the two latter windows (their lines are linked)  to a position of you choice using the mouse. When releasing the mouse button, EditAEM displays the 3 worst trials for the chosen threshold as simplified channel status plot (green numbers indicating good sensors, red numbers indicating bad sensors), to give you an quick impression of the of the data quality you will have after the editing. EditAEM also displays the number of trials that will be accepted as good  (green number at the top of the 'Number of trials per std of approximation'-figure) and the number of trials that will be considered as bad (red number below).
When you think you have chosen the optimal threshold, click the 'accept'-button.Then EditAEM closes the editing windows and saves your thresholds to disk, creating a *.WE*-file and a *.TVM*-file that contains information from all 3 threshold types . These files can then be used as input for EmegsAVG, which is the tool in EMEGS to average across 'good' trials while interpolating 'bad' sensors by surrounding ones. Moreover, another graph will be displayed showing accepted (green *s) and rejected (red *s) trials in their temporal succession in the presentation. If you are editing in batchmode, EditAEM will then close all the supplementary graphs and jump to the next file. If you are editing file by file manually, you can either load the next data file, close the figures manually or select the 'All' or the 'all expect main' item, from the 'close specific'-dropdownmenu. If you select 'All & EmegsAVG', EditAEM will be closed, EmegsAVG will be started and the edited files will automatically be loaded for averaging.

Diplaying data trials from the AutoEditMat-window
With the considerably abstract diplay of the stastical parameters as a grayscaled surface, it is often hard to judge where good trials end and bad trials begin. One thing to help in this decision it to take a look at the overal shape of the AutoEditMat-display: Light color values represent larg parameter values, darker colors small parameter values. For 'maximum absolute amplitude' and 'standard deviation', unusually large values are mostly caused by artifacts, so that in general, as the AutoEditMat-display is sorted by quality, the surface should become increasingly bright from left to right. The entirely white bars on the right represent trials, that emegs has already extracted due to type 1 and 3 thresholds. They cannot be included in the averaging by selecting a high type 2 threshold. The only way include them anyway, is to alter the settings for the automatized thresholds before initiating the 'AutoEdit'. In most cases, however, you will not want to use those trials, as they contain mostly blinks, head movements and other severe distortions of the signal. However, quite often you can identify a position in the AutoEditMat, where a qualitative change takes place, for instance, where a cluster or bar of bright spots starts, that cannot be found in trials on its left. This suggests, that all trials with this bar are somehow different from most of the other trials, and chances are that they contain artifacts.

figure 2: displaying single trials of the AutoEditMat

Another helpful tool for identifying the right threshold position, is to look at the trial rawdata itselft.  To do this, rightclick over a desired position on the AutoEditMat-graph and select 'show trial'.  EditAEM will open  the emegs2d-console, load the datafile, select the corresponding trial and colorize good and bad sensors in green and red. If you do this for a trial on the far left and a trial on the far right of the AutoEditMat, you should see large differences. Explore trials near your threshold location to get an idea of what kind of data you would be accepting/rejecting.

Editing modes:

mode	fast auto 1	fast auto 2	fast auto 3	fast auto 4	fast auto 5	custom
behavior	?	this is the mode you should choose for standard ERP/ERF designs	?	?	?	?
parameters used	?	maximum absolute amplitude standard deviation	?	?	?	?

Recommended settings: the default values in EditAEM are optimized for a 129-channel-setup. The following table gives you suggestions how to modify these settings for different system:

setup	32 channel EEG	64 channel EEG	129 channel EEG	256 channel EEG	145 channel MEG	280 channel MEG
Std. Exponent [goal function]	?	?	0.25	0.15	?	?
channel exclusion [std]	?	?	2	3	?	?

manual artefact detection: Support for manual artefact detection outside EditAEM is limited in emegs.  You can however globally exclude channels using the continous data display in emegs2d (only for EGI data at the present point). To do this, open emegs2d, select Egis Continuous as data format, and load your data file, you can then rightclick on channel numbers/names and select define as bad to exclude the channel for further processing (see example below). Please note, that this works only when used with PrePro. For manual script-based preprocessing, call AddDefinedBadChan to integrate the continuous electrode status file (*.*cest) with the standard electrode status file (*.*est).