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Related Experiment Video

Updated: Dec 17, 2025

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

SUPFUNSIM: Spatial Filtering Toolbox for EEG.

Krzysztof Rykaczewski^1,2, Jan Nikadon^1,3, Włodzisław Duch^1,4

¹Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Wileńska 4, Toruń, 87-100, Poland.

Neuroinformatics

|June 22, 2020

Summary

This summary is machine-generated.

The SUPFUNSIM MATLAB toolbox offers advanced tools for electroencephalography (EEG) and magnetoencephalography (MEG) signal analysis, enhancing brain activity pattern recognition and source reconstruction with novel spatial filters.

Keywords:

Localization Matlab Object-oriented Reconstruction Toolbox

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Area of Science:

Cognitive Neuroscience
Neuroimaging Analysis
Computational Neuroscience

Background:

Electroencephalography (EEG) and magnetoencephalography (MEG) signal analysis are crucial for understanding brain activity patterns in cognitive neuroscience.
Accurate EEG forward modeling and source reconstruction are essential for reliable interpretation of neuroimaging data.
Existing toolboxes may have limitations in spatial filtering techniques and connectivity analysis.

Purpose of the Study:

Introduce the SUPFUNSIM MATLAB toolbox for advanced EEG and MEG data analysis.
Provide a comprehensive overview of its capabilities, including EEG forward modeling, spatial filtering, and directed connectivity analysis.
Facilitate the use of advanced neuroimaging analysis techniques for researchers.

Main Methods:

Development of the SUPFUNSIM MATLAB toolbox, built upon the FIELDTRIP toolbox.
Implementation of various spatial filters for EEG source reconstruction: Linearly Constrained Minimum Variance (LCMV), Eigenspace LCMV, Nulling (NL), and Minimum-Variance Pseudo-Unbiased Reduced-Rank (MV-PURE).
Integration of source-level directed connectivity analysis using Partial Directed Coherence (PDC).

Main Results:

The SUPFUNSIM toolbox successfully generates accurate EEG forward models.
It implements a comprehensive suite of spatial filters for robust EEG source reconstruction.
The toolbox enables advanced source-level directed connectivity analysis using PDC.
Object-oriented programming ensures modularity and extensibility.

Conclusions:

The SUPFUNSIM toolbox provides a powerful and extensible platform for EEG and MEG data analysis.
It enhances brain activity pattern recognition and source reconstruction capabilities.
Researchers can leverage SUPFUNSIM for sophisticated cognitive neuroscience investigations and connectivity studies.