BESA Statistics

BESA Statistics 2.1 is a specialized software solution for group-level statistical analysis of EEG and MEG data. It provides a user-guided workflow environment for conducting cross-subject comparisons using permutation-based methods that are robust to the multiple comparisons problem. The software supports t-tests, single-factor ANOVA, and correlation analyses, and applies parameter-free cluster permutation statistics to detect significant effects across time, space, and frequency dimensions.

With version 2.1, the software introduces significant functional enhancements over the previous release. These include expanded support for input data formats, improved visualization tools, and performance optimizations for large-scale studies.

BESA Statistics is designed to integrate seamlessly with data exported from BESA Research and BESA Connectivity, while also supporting input from other software platforms that adhere to the BESA Statistics file format.

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Key Features

• Statistical workflows for t-tests, single-factor ANOVA, and correlation analyses
• Parameter-free cluster permutation testing for objective and corrected statistical inference
• Full support for time-domain, time-frequency, image, and connectivity data
• Integrated post-hoc testing for ANOVA/ANCOVA using a non-parametric version of Scheffé’s method with Bonferroni-Holm correction
• Direct compatibility with BESA Research and BESA Connectivity output, as well as native support for BrainVision Analyzer 2 time and time-frequency data

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Statistical Analysis

BESA Statistics uses non-parametric permutation testing methods based on established approaches from the literature (e.g., Maris & Oostenveld, 2007). These tests do not rely on assumptions of normality and are well-suited for high-dimensional neurophysiological data. The software identifies clusters across time, frequency, and spatial dimensions where the null hypothesis of equal group or condition means must be rejected.

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Cluster significance is assessed by comparing observed cluster statistics to the distribution of maximum cluster values obtained from randomized permutations of the data. This ensures rigorous control over false positives without the need for manual threshold adjustments or post-hoc corrections in other analysis environments.

All relevant statistical values, including t-values, F-values, and p-values, are computed and visualized automatically. The results are immediately ready for use in scientific publications or reports.

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Time-Frequency and Connectivity Support

Version 2.1 expands input compatibility and includes:

• Support for connectivity data at both sensor and source levels, enabling statistical analysis of results from BESA Connectivity
• Support for single-trial time-frequency data in t-test workflows (*.tfcs format)
• Full compatibility with logarithmic frequency spacing used in wavelet-based time-frequency analyses
• No practical limit on the number of data files that can be imported into a single analysis workflow

These additions make BESA Statistics suitable for advanced analyses involving dynamic brain activity and functional connectivity, across experimental conditions or subject groups.

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Visualization and Usability Enhancements

Several improvements have been introduced to enhance user interaction and result interpretation:

• Adjustable color themes, including a new BESA White mode
• New color map options for visualizing statistical values
• Configurable slice views for image data, with support for orthogonal orientation
• Data value display on mouse-over for detailed inspection
• Export options including high-resolution SVG format and direct clipboard copying of result views and cluster summaries
• Full support for high-DPI displays

These features improve the clarity and accessibility of visual outputs and streamline the process of including result figures in documentation or presentations.

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Integration and Export

BESA Statistics is fully integrated with the BESA software environment. It reads data directly from BESA Research and BESA Connectivity, including sensor-level and source-level representations. Data exported from other platforms can also be used, provided they are in the required format. Output includes:

• Cluster results and statistical values
• Graphical summaries suitable for publications
• Exported images and videos
• Result data in ASCII or MATLAB-compatible formats