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 minimum-variance (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.
Highlights
IntroductionReconstruction of sources of brain’s electrical activity from EEG or magnetoencephalographic (MEG) recordings, based on spatial filters, called “beamformers” in array signal processing, may provide meaningful information
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
Spatial filters may be used in a single-trial neural response to maximize the signal to noise (S/N) ratio based on a generalized eigenvalue decomposition (Das et al 2020)
Summary
Reconstruction of sources of brain’s electrical activity from EEG or magnetoencephalographic (MEG) recordings, based on spatial filters, called “beamformers” in array signal processing, may provide meaningful information. The lead-field matrices establishing signal propagation model are estimated on the basis of geometry and electrical conductivity of head compartments together with position of sensors on the scalp We consider these properties to be fixed in time during a single EEG data acquisition session. We assume that orientations of the ECD moments are normal and directed outside with respect to the cortical surface mesh This is in accordance with the widely recognized physiological model of EEG signal origin that considers pyramidal cortical neurons to be the main contributor to the brain’s bioelectrical activity that can be measured on the human scalp (Baillet et al 2001). Having solved the EEG forward problem which introduced, in particular, the lead-field matrices embodying the propagation model of brain’s electromagnetic activity, we are in a position to solve the inverse problem It amounts to reconstruction of time courses of activity of sources at predefined locations. Hb is discussed in the subsequent Section Brain Signals in Sensor Space
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