Get to know BrainVision Analyzer 2.2.2
– Take the next step in EEG-fMRI analysis with Carbon Wire Loop Regression
by Dipl.-Psych. Michael Hoppstädter
Team Leader of Scientific Support (Brain Products)
We are happy to inform you that the new BrainVision Analyzer 2.2.2 has been released. We are also excited about adding a completely new transformation, the CWL Regression for EEG-fMRI data correction. Let me take you on a tour of the new features and enhancements so that you can download Analyzer 2.2.2 from our website today and get started!
Introducing the new Carbon Wire Loop Regression – Correct your EEG-fMRI data even more effectively
Carbon Wire Loops (CWL) are still a rather new solution in our portfolio. One year ago, we introduced them for the BrainCap MR, and now they are also available as an add-on to the new R-Net MR. However, so far, we were missing a part of the equation as we did not have a method in BrainVision Analyzer 2 that would make use of the CWL signals. Luckily, there was already an excellent alternative available with the CWLRegrTool plugin for EEGLAB by Johan van der Meer. However, Brain Products is always striving for a complete solution, and therefore, we are very happy to announce that the newest Analyzer 2 version comes with the missing piece: the CWL Regression transform.
Along with gradient artifacts, cardioballistic and movement-induced artifacts are common types of MR-related data quality issues. However, until a few years ago there wasn’t a straightforward way to deal with them. As you might remember from our webinar “Introduction to Carbon Wire Loops for BrainCap MR” and previous articles on Carbon Wire Loops, motion-related signals can be measured by a loop moving within a magnetic field. Therefore, the CWLs that are fitted to the cap will record small head movements and the signal is stored together with your EEG channels. The CWL channels can then serve as reference signals for regressing out the motion-induced artifacts that are contaminating your EEG channels.
The multiple linear regression analysis used in Analyzer 2.2.2 is based on the published algorithm by van der Meer and colleagues . Using this approach, you can reduce the impact of motion-related and cardioballistic artifacts on your EEG signal significantly (see Fig. 1) in the easy and intuitive Analyzer 2 way that has been appreciated by many users for a long time.
Figure 1: With CWL Regression you can easily attenuate the artifacts related to head motion in EEG-fMRI datasets. The example displays the same portion of data twice, uncorrected with motion artifacts on the left side, and corrected with CWL Regression on the right side.
How to Cite Us – Making software citations easier
When we introduced this feature for BrainVision Recorder in 2020, we had already announced that it’s going to be implemented in the next Analyzer 2 release. Finally, Analyzer 2.2.2 now comes with its own How to Cite Us tool.
As in Recorder, you can find this utility under Help > How to Cite Us. Using it is super easy as the tool just opens a dialog (see Fig 2) with both examples for an in-text citation and a reference list entry. The examples will always reflect the currently used version of Analyzer 2 and you can simply copy and paste the citation or send it to the clipboard.
Figure 2: How to Cite Us provides examples for in-text and reference list citation. The buttons on the right allow you to send the text directly to the clipboard.
Segmentation – Further enhancements to the user interface
We have further improved the interface of our Segmentation transform for a better user experience. With the Analyzer 2.2.1 release, we had already introduced the new standardized interval selection for setting the boundaries of the new segments – which is unarguably the core part of creating a trial-based EEG processing pipeline. In addition, there is another important step when creating your segments based on stimulation: the marker selection. Segmentation is now using our standardized item selection control (see Fig 3) that adds a couple of useful options, like De-/Select All, Swap and a Filter Mask. The latter becomes especially handy when your paradigm includes many markers, and you can scan through them more easily by constraining the selection to a certain Marker Type.
Figure 3: The new standardized item selection control in Segmentation offers some practical new options for marker selection.
IIR Filters – Caching helps minimizing filter transients
If you have been working with Analyzer 2 for a while, you are probably very familiar with one of our most appreciated working concepts, on demand processing. Rather than storing the result of every processing step on hard disk, Analyzer 2 applies all the parameters of your processing pipeline on the fly to the portion of data that you are currently looking at. While this way of processing is highly efficient, it has also some bottlenecks, for instance when dealing with rather resource-intensive processing. Here, caching is our remedy. When the data is cached, it is stored on the hard disk and therefore subsequent processing steps will not have to go back to the raw data and apply all the parameters on the fly. Instead, everything that has happened before the caching is already contained in this stored data and, thus, less on demand processing needs to happen.
You might ask yourself now, what does this have to do with the filters? While the IIR Filters are working resource efficiently, they have to deal with transient phenomena. The filter will request some data before and after each data point in order to compute the filtered data; therefore, it is missing input at the edges of each data segment. This generates filter transient artifacts at the segment boundaries, and is also the reason why it is preferable to apply filters on continuous data. However, when you apply the filters on demand, they are applied only to the data segment that you are displaying; this raises the problem with the filter transients again. While we are doing our best to avoid this issue (Analyzer 2 actually filters a longer section of data than what is displayed), the effects of filter transients cannot always be compensated.
Caching helps here because the entire data is filtered at once and then stored on the hard disk. Later processing steps will thus grab the fully filtered data and thereby completely avoid transients (except for the beginning and end of the whole dataset). Therefore, we offer optional caching directly in the IIR Filters dialog and we recommend using this option, for instance, when applying rather low cutoffs (< 0.5 Hz). At the same time, caching at the level of filters can also speed up later processing steps.
To make a long story short, I strongly recommend caching as it has no disadvantages and is potentially very helpful for your data processing. In Analyzer 2.2.2, we have decided to enable the caching option in IIR Filters by default and encourage using it. This small detail should make your data processing experience even better.
We have implemented some smaller changes in the newest Analyzer 2.2.2 in order to stay compatible with changing infrastructure.
Don’t forget …
You can customize when you want to see the Online Info. Navigate to File > Preferences > General to find the settings. We recommend showing the Online Info at least in case of important changes/news, so that you do not miss anything!
An update to the official Analyzer 2 Solutions package
The solutions have been a part of Analyzer 2 since the beginning, providing you additional functionalities that can be useful in many situations. You can download all official Solutions from our website for free. With this Analyzer 2 release, our Solutions package will grow substantially as we have now added all Solutions that were mentioned in our “Offline sensor data analysis” tutorial and in the recent overview article on Solutions.
Let me just mention a few highlights
Our new Sleep Scoring solution can help you with manual staging of your sleep data, providing informative visualizations and reports. Read Coordinates enables you to import Cartesian channel positions and convert them into the Analyzer 2 standard. With Set Markers you can create new markers based on existing marker positions with either fixed or jittered offset. Lastly, we also included some Solutions that will help you with the analysis of heart rate and electrodermal data.
We have also implemented some enhancements and corrected smaller issues, so make sure to have a look at our dedicated Release Notes for Solutions.
Last but not least
Last but not least, we have published a new version of the BrainVision Analyzer 2 User Manual and additionally an updated version of our Automation Reference Manual. Please check out the Release Notes; they will give you a more detailed overview of all changes in Analyzer 2.2.2.
We hope you will enjoy our newest addition to the BrainVision Analyzer 2 family and we thank you for choosing Analyzer 2 as your tool for EEG and neurophysiological data analysis.
 van der Meer, J. N., Pampel, A., Van Someren, E. J. W., Ramautar, J. R., van der Werf, Y. D., Gomez-Herrero, G., Lepsien, J., Hellrung, L., Hinrichs, H., Moller, H. E., & Walter, M. (2016).
Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections – A validation of a real-time simultaneous EEG/fMRI correction method.
NeuroImage, 125, 880-894.