Fingerprints of excellence: A great journey towards the new features in BrainVision Analyzer 2.2.0

A typical workflow in EEG analysis starts with reading the data. Together with our well-established and widely supported BrainVision data format, Analyzer 2 supports more than 25 other EEG data formats from different manufacturers.

To better support our community of EGI data users, we also provide a completely redesigned EGI reader in Analyzer 2.2.0, expanding its support for continuous and segmented EGI simple binary format (*.raw) with integer precision (format versions 2 and 3) and floating-point single precision (format versions 4 and 5). The new EGI reader also achieves a higher precision of EEG voltage values by supporting the additional channel gain and zero files that comes along with the *.raw file.

After reading and inspecting the data, preprocessing steps usually follow, based on a variety of tools for data cleaning, extraction of the data portions of interest, etc.

In Analyzer 2.2.0 the IIR Filters transform has been extensively rewritten to improve calculations and performance. This module now makes use of a new algorithm integrated in a dedicated filter mathlib where all calculations are standardized. Together with the IIR Filters transform, the data preprocessing modules Edit Channels, Topographic Interpolation and Linear Derivation have undergone important enhancements in their user interface, as well as in the parameter validations in the GUI and template-based computations. Besides, the handling of unsupported conditions, channel properties and data units has been improved in the new version of these transforms.

The event-related potential (ERP) methodology has proven to be a robust approach to elucidate the time course and neural basis of cognitive process. Together with methods for data preprocessing, averaging across segments and across subjects are crucial analysis steps of the ERP methodology. These operations are possible with the transforms Average and Grand Average.

In Analyzer 2.2.0, the Average transform has been rewritten and ported to the .NET platform. Its user interface provides you with a new standard GUI control for segment selection, as well as a robust set of GUI validations to check the integrity of used parameters. Performance of numerical computations across segments is improved with the implementation of new algorithms. The Average – Grand Average workflow has been considerably optimized with the introduction of a new property to store the number of segments used for averaging in each channel. Also, the Operation Infos has been expanded to include detailed information about used parameters and channel specific information.

Grand Average consists of a rather complicated set of operations for selecting input history files, nodes and channels, which lead to the computation of valid output data. In Analyzer 2.2.0, Grand Average has been extensively rewritten to enhance the usability of the user interface and to improve vital aspects of this module. This includes the identification of the reference history file, the selection of valid nodes and channels, the logic of data aggregation when Individual Channel Mode option is on or off, the mathematical computations based on the number of segments/weights and the correct matching of input nodes, channels and units.

The new user interface is utterly improved by a standard GUI control for history file selection, which makes the selection process more flexible and effective. Both parameters in the GUI and data properties during calculations are submitted to a stricter validation, in order to guarantee the validity of Grand Average output data.

You will find a new structure of the Operation Infos, which includes a detailed report of the analysis steps. It addresses relevant questions such as: what’s my reference node? which nodes are included and excluded? how many segments contributed to the grand average calculation, in each channel? why is a particular node excluded?

Alongside this, the Operation Infos includes warnings on minor issues affecting the input data.

While continuing our Analyzer 2.2.0 tour, let’s take a pause to explore what is new regarding visualization tools.

Analyzer 2.2.0 shines with its new set of color maps that will considerably enhance the visual experience of data exploration and representation (see Figure 1). These features are of vital importance in revealing the underlying patterns in your neurophysiological data and to better communicate your research results to the broader scientific community.

Figure 1: Illustration of the newly implemented color maps.

The Standard color map is replaced by its successor, the Brainbow color map (comparable to Jet in MATLAB®), which better represents subtle changes in the underlying data structure. Likewise, the BrownScaling color map is replaced and further improved by the Maretierra color map, which is made available to all kind of Analyzer views.

In addition, two new color maps Paruly (similar to Parula in MATLAB®) and Plasma are implemented in Analyzer 2.2.0. Paruly and Plasma are of superior quality and overcome well-known issues in other color maps. They are more perceptually uniform, colorblind friendly and their grayscale conversion is printer-friendly.

3D data visualization is also improved by replacing the heads (Adam, Anna, Liza and Baby) of the 3D Head View with four newly created heads which have a 4x denser mesh in the scalp area. This change entails a smoother distribution of the data on the head surface, and a homogenous visual experience of light/shading effects across the four heads.

The visualization of the frequency spectrum is also expanded to include spectral values in the negative frequency domain. This new feature makes the visualization of auto- and cross-correlation of spectral data for negative frequency lags possible.

Time-frequency data visualization is significantly enhanced by the new color maps. Besides, if connectivity data (e.g. Coherence) is computed in the time-frequency domain, its time-frequency representation in Analyzer 2.2.0 displays the corresponding connectivity graph for the selected time-frequency point (see section: Connectivity analysis made easy).

For your quick access to all these new visualization features, the user interface of the View Settings has been rearranged and further extended, including a preview of the selected color map.

Decades of EEG research based on the ERP methodology has shown, together with its various merits, that it captures a narrow fragment of the dynamical repertoire of underlying neural activity. As ERP analysis is predicated on the existence of time-locked and phase-locked activity, the pursuit of elucidating the non-stationary, non-phase-locked character of brain oscillatory processes have paved the way to investigate patterns of synchronization and desynchronization of neural activity beyond the ERPs.

In Analyzer 2.2.0, event-related synchronization and desynchronization analysis has been strongly reinforced by providing you with new versions of the modules ERS/ERD and Complex Demodulation, and by incorporating frequently requested features in the Wavelets transform.

Both Complex Demodulation and ERS/ERD have been ported to the .NET platform. Their user interfaces have been redesigned to improve usability, e.g. it makes use of the standard GUI control for segment selection. Moreover, both transforms make use of the new standard filter mathlib to accomplish the underlying IIR filtering operations.

The new version of Complex Demodulation also provides you with a wider spectrum of output options, including Amplitude, Power and Phase.

Likewise, several new options significantly increase the versatility of the ERS/ERD and Wavelets transforms in Analyzer 2.2.0:

One of the dominant hypotheses in modern neuroscience claims that the dynamics of cognitive processes is reflected in (and highly correlated with) the interplay of many neural oscillators which interact and evolve over time. Not surprisingly, we are witnessing an explosion of novel experimental paradigms and connectivity analysis methods which have been advanced to elucidate the complex organization of dynamic neural networks underlying cognitive operations on a timescale of milliseconds.

Along these trends, Analyzer 2.2.0 introduces a major overhaul of the modules devoted to connectivity analysis.

The Covariance transform has been deprecated and will not be further developed. It has been superseded by the new .NET transform Correlation Measures, which includes several covariance and correlation methods. Likewise, the Cross-Correlation transform has been extensively rewritten in order to include new cross-covariance and cross-correlation methods.

The three connectivity transforms Coherence, Correlation Measures and Cross-Correlation have adopted new standard GUI controls for the automatic and manual selection of channel pairs (connectivity graphs or networks). These controls make the selection of predefined networks or the construction of custom network configurations (see Figure 2) very easy. Besides, the handling of unsupported conditions has been improved in the new version of these transforms.

Figure 2: GUI for selection of predefined (left) and manual (right) channel pairs.

A major feature of the Coherence and Correlation Measures transforms is attained by their support of input data in the time-frequency domain. This allows you to compute frequently requested connectivity measures such as time-varying Coherence and Phase Locking Value (PLV). For instance, PLV is easily computed in a workflow consisting of three transforms, namely Wavelets (using the option Wavelet Phase – Complex Values), followed by Correlation Measures (using the option Normalized Correlation) and subsequently Rectify. A demo pipeline illustrating the calculation of PLV is provided as a history template here.

Since the release of Analyzer 1 we have been committed to offer free high-quality scientific support for BrainVision Analyzer to all our customers. Our experience over the past two decades has shown that a successful analysis workflow requires several ingredients such as: quality and integrity of your data, careful selection of the correct Analyzer modules, and identification of optimal parameters for data processing. Hence, your choices can make the difference between a reliable data analysis pipeline and an erroneous approach to your research question.

The new Add In, Troubleshooting, included under the group Diagnostics in Analyzer 2.2.0, aims to help you in this difficult endeavor. You can think of it as an on-site support module, that will help you to scan/diagnose existing workspaces, history trees and history nodes. Troubleshooting contains a set of predefined tests to detect automatically data integrity problems, inconsistencies or wrong settings. In addition, it conveniently reports detected issues in a customizable format and proposes potential solutions. Test reports can be saved to an *.html file and shared with your colleagues or our scientific support team for further inspection.

Troubleshooting can be applied to diagnose complete workspaces, a given history file, or just one history node. In Analyzer 2.2.0, this Add In is released with an initial set of tests including:

• Detection of obsolete V1 modules that have been ported to the .NET platform and should  be replaced.
• Detection of data integrity issues or unsupported conditions.
• Detection of suspended nodes, where the underlying calculations have not been resumed.
• Detection of history nodes that should be reprocessed because of potential issues in the selection of parameters.

Stay tuned for upcoming releases! The set of Troubleshooting tests will increase in Analyzer 2, to cover most of the possible issues and provide potential solutions.

The solution Wavelet Data Export has been commonly applied by our Analyzer 2 users to export the cumulative sum or average data within a given time-frequency range, as generated by the Wavelets transform. Given that the Wavelets transform has been enhanced in Analyzer 2.2.0 to include additional output options, the solution Wavelet Data Export was also updated to assure compatibility with the new Wavelets transform.

In Analyzer 2.2.0 the computations pertaining the solution Phase Locking Factor are integrated within a workflow consisting of three transforms, namely Wavelets (using the option Wavelet Phase – Complex Values), followed by Average and subsequently Rectify. A demo pipeline illustrating this workflow is provided as a history template here. All functionalities existing in the solution Phase Locking Factor are included in this pipeline. Therefore, this solution is deprecated and hence excluded from the official solutions package of Analyzer 2.2.0 and later versions.

Likewise, the solutions Complex Data Measures, Easi Export, ICA BackTransform, ICA Topographies, CBC Parameters, Slice Volume Align and Slice2Volume Triggers have become outdated and/or their functionalities are integrated in existing modules of Analyzer 2. Therefore, they are also excluded from the official solutions package of Analyzer 2.2.0 and later versions.

All excluded solutions in Analyzer 2.2.0 will not be further developed and will be excluded from the Solutions download section. However, they will remain available upon request via our Scientific Support team.

What a journey! From the release of Analyzer 1, to the success of Analyzer 2, to the upcoming release of Analyzer 2.2.0!

We have taken this unique opportunity to frame our recent efforts for Analyzer 2.2.0 in the larger history of our company and our commitment to providing high-quality products. In this spirit, working for Analyzer 2.2.0 has been really thrilling for us. Most of the announced new features and enhancements in Analyzer 2.2.0 have been inspired by your feedback and suggestions. We would like to express our gratitude to all of you who have provided constructive suggestions and requests for improvements over the years.

We are proud of the strength and the merits of our market-leading BrainVision Analyzer 2. Our hard work will pay off if Analyzer 2.2.0 makes you proud as well.

Welcome BrainVision Analyzer 2.2.0!