supFunSim : Spatial Filtering Toolbox for EEG
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SOFTWARE ORIGINAL ARTICLE
SUP F UN S IM : Spatial Filtering Toolbox for EEG Krzysztof Rykaczewski1,2 · Jan Nikadon1,3 · Włodzisław Duch1,4 · Tomasz Piotrowski1,4
© The Author(s) 2020
Abstract Brain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The SUPFUNSIM library is a new MATLAB toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimumvariance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure. The SUPFUNSIM library is based on the well-known FIELDTRIP toolbox for EEG and MEG analysis and is written using object-oriented programming paradigm. The resulting modularity of the toolbox enables its simple extensibility. This paper gives a complete overview of the toolbox from both developer and end-user perspectives, including description of the installation process and use cases. Keywords Matlab · Toolbox · Reconstruction · Localization · Object-oriented
Introduction Neuroimaging and signal processing methods are rapidly evolving, with the ultimate goal of reaching high time and space resolution, allowing for models of functional connectivity, activation of large-scale networks and their rapid dynamic transitions in multiple time scales. Network neuroscience is at present the most promising approach to understand the structure and functions of complex brain networks (Bassett and Sporns 2017). Electroencephalography (EEG) has excellent temporal resolution, is noninvasive and relatively easy to use. Unfortunately, signals observed at the scalp level are difficult to interpret, because they are
Tomasz Piotrowski
[email protected] 1
Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Wile´nska 4, Toru´n, 87-100, Poland
2
Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Chopina 12/18, Toru´n, 87-100, Poland
3
Faculty of Humanities, Nicolaus Copernicus University, Fosa Staromiejska 1a, Toru´n, 87-100, Poland
4
Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudzia¸dzka 5/7, Toru´n, 87-100, Poland
a mixture propagated from many cortical and subcortical sources through multiple layers of the brain with several different volume conduction properties, including the scalp, skull, cerebrospinal fluid (CSF), and brain tissues. Sensors receive corrupted mixed signals from various active brain structures. Therefore, direct scalp-level EEG analysis cannot reflect the underlying neurodynamics. Reconstruction of sources of brain’s electrical activity from EEG or magnetoencephalographic (MEG) recordings, based on spatial filters, also called “beamformers” in array signal processing, may provide meaningful information. Many papers have been written on applicat
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