eeglab package (matlab) Search Results


96
MathWorks Inc matlab software package
Matlab Software Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc interactive matlab toolbox
Interactive Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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RStudio rstudio 0.98.501
Rstudio 0.98.501, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Compumedics scan packages
Scan Packages, supplied by Compumedics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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brain products gmbh brain vision analyzer package bva
Brain Vision Analyzer Package Bva, supplied by brain products gmbh, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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InfoMax Inc infomax ica
Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with <t>ICA</t> <t>or</t> <t>SOBI</t> . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.
Infomax Ica, supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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96
MathWorks Inc wavelet analyses
Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with <t>ICA</t> <t>or</t> <t>SOBI</t> . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.
Wavelet Analyses, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 96 stars, based on 1 article reviews
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90
SPSS Inc v20.0
Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with <t>ICA</t> <t>or</t> <t>SOBI</t> . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.
V20.0, supplied by SPSS Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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brain products gmbh brainvision analyzer v.2.1
Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with <t>ICA</t> <t>or</t> <t>SOBI</t> . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.
Brainvision Analyzer V.2.1, supplied by brain products gmbh, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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brainvision analyzer v.2.1 - by Bioz Stars, 2026-05
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90
Jackson Laboratory pv-cre: 129p2-pvalbtm1 (cre)arbr/j
Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with <t>ICA</t> <t>or</t> <t>SOBI</t> . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.
Pv Cre: 129p2 Pvalbtm1 (Cre)arbr/J, supplied by Jackson Laboratory, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
pv-cre: 129p2-pvalbtm1 (cre)arbr/j - by Bioz Stars, 2026-05
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93
MathWorks Inc eeglab matlab package
Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with <t>ICA</t> <t>or</t> <t>SOBI</t> . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.
Eeglab Matlab Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/eeglab matlab package/product/MathWorks Inc
Average 93 stars, based on 1 article reviews
eeglab matlab package - by Bioz Stars, 2026-05
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Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with ICA or SOBI . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.

Journal: Frontiers in Neuroscience

Article Title: Group-level component analyses of EEG: validation and evaluation

doi: 10.3389/fnins.2015.00254

Figure Lengend Snippet: Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with ICA or SOBI . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.

Article Snippet: Notable exceptions are the implementation of Infomax ICA, SOBI, as well as functions for data filtering and plotting of EEG topographies; for these tasks, routines of the MATLAB-based open source software package EEGLAB were used (Delorme and Makeig, ).

Techniques: