After going through the theory of markers in BrainVision Analyzer 2, let us now move on to some common steps of EEG analysis in which markers help to make our lives easier. Several Analyzer 2 transformations work with different marker types. The ones that create or manipulate markers, as well as those that export marker information shall be described in the following. In addition, many of the solutions in Analyzer 2 make use of the markers for more specific purposes. Let us see how.
Finding a peak
Are you looking for an easy way to find the latency and amplitude of your characteristic ERP component (e.g. P300) or would you like to find the individual alpha frequency for each subject? You are looking for peak detection! The Peak Detection transform (Transformations > Segment Analysis Functions > Result Evaluation > Peak Detection) is used to detect and mark local minima and maxima within an averaged EEG waveform.
In contrast to Peak Detection, the MinMax Markers solution (Solutions > Markers > MinMax Markers) lets you find the minimum and/or maximum amplitude in each segment and in this sense is great for single trial analysis. Here, a marker is set at the time point or frequency bin with the lowest or highest value within a given search window. Additionally, this solution also works on averaged frequency data to find spectral peaks.
Both Peak Detection and MinMax Markers place markers of type Peak. You can select specific channels in which the peaks are to be marked as channel markers or choose a reference channel to place global markers.
Of course, peak processing does not end with marking latencies and amplitudes. Peak Information Export (Export > Multiple Export > Peak Information) and the Peak Export solution (Solutions > Export > Peak Export) help you to export peak marker information by writing it to a *.txt file usable for further processing or statistical analysis. Additionally, peak to peak and area measures can be selected for export. Similarly, to finding peaks, Peak Information Export is applied to averaged data, whereas Peak Export is used for single trial analysis.
Dealing with artifacts
Oh no, artifacts! Generally, you have two options: Reject them or correct them.
Portions of bad data can be marked on continuous data using Raw Data Inspection (Transformations > Artifact Rejection/Reduction > Raw Data Inspection) or on segmented data using Artifact Rejection (Transformations > Artifact Rejection/Reduction > Artifact Rejection). The former allows you to check your data for artifacts and mark respective time periods as Bad Intervals. The same is done in Artifact Rejection but it allows you to not only mark but also remove whole segments if they contain artifacts. The inspection can be carried out manually, semiautomatically or automatically. Markers are of type Bad Interval and indicate to many transformations that the marked intervals should not be considered, unless stated/set differently. Please refer to the first part of this article for the special characteristics of Bad Interval markers.
Rejecting artifacts has the disadvantage that valuable portions of data are lost, while correcting them lets you keep the affected time periods. Thankfully a number of sophisticated correction methods exist for certain kinds of artifacts and are implemented in Analyzer 2. All of these methods include a detection algorithm which can be used to set markers indicating the occurrence of artifacts.
Ocular Correction and Ocular Correction ICA can both reduce the effect of eye movements on the EEG signal using different methodological approaches. However, both can either detect and correct the data in one step or they can be used to set markers of type Blink as either interval or start/stop markers without applying the correction itself. This can be very useful to evaluate the result of the detection method or even to decide which correction method would be the most promising for a particular data set.
If you are dealing with ECG artifacts, the EKG Markers solution (Solutions > EKG > EKG Markers) searches for R-waves in a selected ECG channel. For each component P, Q, R, S, T of the ECG complex, markers of type Peak can be placed. These can be set as either global or channel markers.
Also, the CB Correction transform (CB – cardio ballistic; Transformations > Special Signal Processing > CB Correction) deals with the detection of R-waves but also correction of artifacts originating from the heart beat during combined EEG-fMRI measurements. CB Correction either uses existing R-markers (as from EKG Markers solution) or can set R-markers of type Peak based on a selected ECG channel. Furthermore, it can be used to set R-markers only without data correction.
Another artifact in simultaneous EEG-fMRI recordings is due to gradient coils’ pulse sequences. The MR Correction transform (Transformations > Special Signal Processing > MR Correction) allows the detection and correction of these non-physiological artifacts. It either uses existing volume markers sent from the MR scanner or can set markers based on selected channel(s) and adjustable detection values.
Note that exact timing is crucial for a successful MR correction. Therefore, it should be mentioned here that an easy way to check for potential timing differences is to use the solution Marker Timing (Solutions > Views > Marker Timing). It generates a data set with only one channel containing the time difference between subsequent volume markers. A flat line indicates the constant time difference between markers that is necessary for MR correction.
Data dependent marker placement becomes important when you are looking for neurophysiological correlates between brain activity and behavior. For example, if you have recorded a signal indicating crucial points in time, such as a force sensor, during the task along with your EEG, the Level Trigger transformation (Transformations > Dataset Preprocessing > Level Trigger) can be used to set markers of type Threshold if a certain force (or other) limit is exceeded. These markers can be placed as channel markers or as global markers and can be used as a basis for segmentation.
When working with simultaneous EEG-EMG recordings, you may be interested in finding the onset of a movement on the basis of the EMG signal. The EMG Onset Search solution (Solutions > EMG > EMG Onset Search) does exactly that. This solution searches for EMG onsets in selected channels of segmented data. Markers are of type Peak and are given the name EMG Onset. They can be used as a basis for a subsequent segmentation.
Response and reaction times
In many paradigms it’s all about reaction time. An easy way to export the time between stimulus presentation and reaction (e.g. indicated by a button press) is to use the Write Markers solution (Solutions > Markers > Write Markers). This also allows you to export additional marker information such as type, name, position and duration.
What if you would like to analyze your data depending on different reaction times? In other words, how does the ERP look in trials of slow compared to faster reaction times? For this you can use the Recode Markers solution (Solutions > Markers > Recode Markers). This solution recodes reaction (time) markers according to specified marker properties. Depending on the information provided, the reaction markers are grouped into statistically derived categories. As an example, reaction time markers can easily be distinguished as below or above the median or as lying inside or outside of specified percentiles depending on their latencies. New Comment markers are placed in each trial indicating to which category a trial belongs. This can be used for a subsequent segmentation to compare data between the defined categories.