matlab-based interacting toolbox eeglab Search Results


eeglab  (MathWorks Inc)
96
MathWorks Inc eeglab
Eeglab, 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
https://www.bioz.com/result/eeglab/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
eeglab - by Bioz Stars, 2026-05
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eeglab toolbox  (MathWorks Inc)
98
MathWorks Inc eeglab toolbox
Eeglab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 98/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/eeglab toolbox/product/MathWorks Inc
Average 98 stars, based on 1 article reviews
eeglab toolbox - by Bioz Stars, 2026-05
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algorithm of ica with an extension of a natural gradient  (InfoMax Inc)
90
InfoMax Inc algorithm of ica with an extension of a natural gradient
Algorithm Of Ica With An Extension Of A Natural Gradient, 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
https://www.bioz.com/result/algorithm of ica with an extension of a natural gradient/product/InfoMax Inc
Average 90 stars, based on 1 article reviews
algorithm of ica with an extension of a natural gradient - by Bioz Stars, 2026-05
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infomax ica  (InfoMax Inc)
90
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
https://www.bioz.com/result/infomax ica/product/InfoMax Inc
Average 90 stars, based on 1 article reviews
infomax ica - by Bioz Stars, 2026-05
90/100 stars
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eeglab toolkit  (SciCrunch Inc)
90
SciCrunch Inc eeglab toolkit
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 Toolkit, supplied by SciCrunch Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/eeglab toolkit/product/SciCrunch Inc
Average 90 stars, based on 1 article reviews
eeglab toolkit - by Bioz Stars, 2026-05
90/100 stars
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infomax ica algorithm  (InfoMax Inc)
90
InfoMax Inc infomax ica algorithm
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 Algorithm, 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
https://www.bioz.com/result/infomax ica algorithm/product/InfoMax Inc
Average 90 stars, based on 1 article reviews
infomax ica algorithm - by Bioz Stars, 2026-05
90/100 stars
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runica algorithm  (InforMax Inc)
90
InforMax Inc runica algorithm
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.
Runica Algorithm, supplied by InforMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/runica algorithm/product/InforMax Inc
Average 90 stars, based on 1 article reviews
runica algorithm - by Bioz Stars, 2026-05
90/100 stars
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eeglab tools  (MathWorks Inc)
90
MathWorks Inc eeglab tools
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 Tools, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/eeglab tools/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
eeglab tools - by Bioz Stars, 2026-05
90/100 stars
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based platforms eeglab/erplab  (MathWorks Inc)
90
MathWorks Inc based platforms eeglab/erplab
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.
Based Platforms Eeglab/Erplab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/based platforms eeglab/erplab/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
based platforms eeglab/erplab - by Bioz Stars, 2026-05
90/100 stars
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matlab-based opensource toolbox eeglab  (MathWorks Inc)
90
MathWorks Inc matlab-based opensource toolbox eeglab
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.
Matlab Based Opensource Toolbox Eeglab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab-based opensource toolbox eeglab/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab-based opensource toolbox eeglab - by Bioz Stars, 2026-05
90/100 stars
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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: