MV-PURE Spatial Filters With Application to EEG/MEG Source Reconstruction

supFunSim: Spatial Filtering Toolbox for EEG

“… The eigenspace-LCMV filters (Sekihara and Nagarajan 2008 ) exploiting projection of the signal covariance matrix R onto its principal subspace of the forms and where is the orthogonal projection matrix onto subspace spanned by eigenvectors corresponding to — the sig largest eigenvalues of R , where sig is the dimension of signal subspace. The MV-PURE filters, defined as Piotrowski et al ( 2019 ) for the interference-free model, and Piotrowski et al ( 2019 ) for the model in presence of interference. In the above expressions, , for i = 1, 2, 3, are the orthogonal projection matrices onto subspaces spanned by eigenvectors corresponding to the r smallest eigenvalues of symmetric matrices , , , respectively; similarly, , for i = 1, 2, 3, are the orthogonal projection matrices onto subspaces spanned by eigenvectors corresponding to the r smallest eigenvalues of symmetric matrices , , , respectively.…”

“…The MV-PURE filters, defined as Piotrowski et al ( 2019 ) for the interference-free model, and Piotrowski et al ( 2019 ) for the model in presence of interference. In the above expressions, , for i = 1, 2, 3, are the orthogonal projection matrices onto subspaces spanned by eigenvectors corresponding to the r smallest eigenvalues of symmetric matrices , , , respectively; similarly, , for i = 1, 2, 3, are the orthogonal projection matrices onto subspaces spanned by eigenvectors corresponding to the r smallest eigenvalues of symmetric matrices , , , respectively.…”

“…However, to the best of our knowledge, none of them implements up-to-date state-of-the-art spatial filters based on high precision EEG forward models. supFunSim Matlab toolbox (library) described in this paper is an open source software that fills this gap by providing, among others, implementation of various forms of the linearly constrained minimum-variance filters (LCMV) (Frost 1972 ; Van Veen et al 1997 ; Sekihara and Nagarajan 2008 ), eigenspace LCMV (Sekihara and Nagarajan 2008 ), nulling (NL) (Hui et al 2010 ), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters (Piotrowski and Yamada 2008 ; Piotrowski et al 2009 ; Piotrowski et al 2019 ). It also enables source-level directed connectivity analysis using partial directed coherence (PDC) (Baccala and Sameshima 2001a ) and directed transfer function (Kamiński et al 2001 ) measures by applying it to the time series representing reconstructed activity of sources of interest.…”

“…It can be used as an extension (plugin) to FieldTrip . The code of presented library was created to perform experiments for the paper (Piotrowski et al 2019 ) and later refactored and reimplemented in an object-oriented way. The source code of the toolbox is publicly available at https://github.com/nikadon/supFunSim as an Org-mode file, Jupyter notebook, and also as a plain Matlab source code.…”

Task-Specific Stimulation Duration for fNIRS Brain-Computer Interface

“…Many methodologies exist in acquiring brain signals; from the direct collection of neural firing from neurons to a decoding method of motor signals using a nerve sensor. Among these, noninvasive approaches include electroencephalography (EEG) [3]- [5], functional near-infrared spectroscopy (fNIRS) [6]- [12], functional magnetic resonance imaging (fMRI) [13], and magnetoencephalography (MEG) [14], [15]. fNIRS is a newly emerging technique that utilizes near-infrared light within the 650 nm ∼ 1,000 nm range (i.e., in this range, the absorption by water is negligible) to gauge the varieties of regional cerebral blood flows (rCBFs) in the brain [16], [17].…”