eeglab matlab functions Search Results


96
MathWorks Inc eeglab toolbox function pop resample in matlab
Eeglab Toolbox Function Pop Resample In Matlab, 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 r2020a signal processing toolbox
R2020a Signal Processing 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
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MathWorks Inc toolbox function
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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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InfoMax Inc independent component analysis (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.
Independent Component Analysis (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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MathWorks Inc wavelet toolbox
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 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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brain products gmbh brain vision analyser
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.
Brain Vision Analyser, 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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Siemens AG verio fmri scanner (siemens ag)
Combination of the brain and non-brain signals modalities.
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InfoMax Inc runica
Combination of the brain and non-brain signals modalities.
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MathWorks Inc machine learning toolbox
Combination of the brain and non-brain signals modalities.
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neurobehavioral systems inc software and algorithms presentation
KEY <t> RESOURCE </t> TABLE
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MathWorks Inc cpu cores
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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:

Combination of the brain and non-brain signals modalities.

Journal: Frontiers in Human Neuroscience

Article Title: Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

doi: 10.3389/fnhum.2020.613254

Figure Lengend Snippet: Combination of the brain and non-brain signals modalities.

Article Snippet: Tobar et al. ( ) , EEG , Active Two System, BIOSEMI , Verio fMRI Scanner (Siemens AG), EMG BrainAmp (Brain Products) , , 32 , 256 , 10-20 , , ActiView software for EEG signals, SPM8, EEGlab, Matlab.

Techniques: Control, Activation Assay, Activity Assay

Summary table of reviewed articles.

Journal: Frontiers in Human Neuroscience

Article Title: Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

doi: 10.3389/fnhum.2020.613254

Figure Lengend Snippet: Summary table of reviewed articles.

Article Snippet: Tobar et al. ( ) , EEG , Active Two System, BIOSEMI , Verio fMRI Scanner (Siemens AG), EMG BrainAmp (Brain Products) , , 32 , 256 , 10-20 , , ActiView software for EEG signals, SPM8, EEGlab, Matlab.

Techniques: Control, Biomarker Discovery, Blocking Assay, Activity Assay, Extraction, Comparison, Sampling

Hardware and software description used for signals acquisition for gait applications.

Journal: Frontiers in Human Neuroscience

Article Title: Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

doi: 10.3389/fnhum.2020.613254

Figure Lengend Snippet: Hardware and software description used for signals acquisition for gait applications.

Article Snippet: Tobar et al. ( ) , EEG , Active Two System, BIOSEMI , Verio fMRI Scanner (Siemens AG), EMG BrainAmp (Brain Products) , , 32 , 256 , 10-20 , , ActiView software for EEG signals, SPM8, EEGlab, Matlab.

Techniques: Software, Sampling

KEY  RESOURCE  TABLE

Journal: Neuron

Article Title: Neural mechanisms of sustained attention are rhythmic

doi: 10.1016/j.neuron.2018.07.032

Figure Lengend Snippet: KEY RESOURCE TABLE

Article Snippet: Subsequently, we combined z-scores across observers and then turned the averaged z-scores into a probability using the cumulative distribution function φ according to the following formula: p combined = 1 − φ ( ∑ i = 1 : N φ − 1 1 − p i N ) table ft1 table-wrap mode="anchored" t5 REAGENT or RESOURCE SOURCE IDENTIFIER Software and Algorithms Presentation Neurobehavioral Systems Inc. https://www.neurobs.com EPrime Psychology Software Tools Inc. https://pstnet.com/ MATLAB 2015a MathWorks Inc. RRID: SCR_001622 EEGLAB 13_4_4b ( Delorme and Makeig, 2004 ) https://sccn.ucsd.edu/eeglab/index.php FieldTrip 20170912 ( Oostenveld et al., 2011 ) http://www.fieldtriptoolbox.org/ CircStat 2012 ( Berens, 2009 ) https://philippberens.wordpress.com/code/circstats/ IRASA ( Wen and Liu, 2016 ) https://purr.purdue.edu/publications/1987/1 SPM8 ( Penny et al., 2011 ) http://www.fil.ion.ucl.ac.uk/spm/ Freesurfer 5.3.0 ( Dale et al., 1999 ) https://surfer.nmr.mgh.harvard.edu/ ibootci Andrew Penn; MATLAB central https://mathworks.com/matlabcentral/fileexchange/52741-ibootci Open in a separate window KEY RESOURCE TABLE We furthermore used Pearson’s correlation coefficient to delineate functional networks ( / ).

Techniques: Software