matlab-based program ragu Search Results


matlab-based randomization graphical user interface (ragu) software-package  (MathWorks Inc)
90
MathWorks Inc matlab-based randomization graphical user interface (ragu) software-package
Matlab Based Randomization Graphical User Interface (Ragu) Software Package, 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 randomization graphical user interface (ragu) software-package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab-based randomization graphical user interface (ragu) software-package - by Bioz Stars, 2026-03
90/100 stars
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matlab-based program ragu  (MathWorks Inc)
90
MathWorks Inc matlab-based program ragu
Left: The <t>Ragu</t> data import dialog window. The defined tags represent the four conditions considered in the study. The search expression enables the identification of all subjects to be imported. Right: The Montage dialog window. The channel coordinates can be visually verified and adjusted if the channel positions are set incorrectly.
Matlab Based Program Ragu, 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 program ragu/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab-based program ragu - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


Left: The Ragu data import dialog window. The defined tags represent the four conditions considered in the study. The search expression enables the identification of all subjects to be imported. Right: The Montage dialog window. The channel coordinates can be visually verified and adjusted if the channel positions are set incorrectly.

Journal: Frontiers in Neuroscience

Article Title: A Student's Guide to Randomization Statistics for Multichannel Event-Related Potentials Using Ragu

doi: 10.3389/fnins.2018.00355

Figure Lengend Snippet: Left: The Ragu data import dialog window. The defined tags represent the four conditions considered in the study. The search expression enables the identification of all subjects to be imported. Right: The Montage dialog window. The channel coordinates can be visually verified and adjusted if the channel positions are set incorrectly.

Article Snippet: The aim of this article was to illustrate the analysis of ERP data using randomization statistics as implemented in the MATLAB-based program Ragu, using an example study.

Techniques: Expressing

Within-subject design with two orthogonal factors in Ragu. On the upper right side, the two factors and their particular levels (1, 2) are shown. On the bottom, the assignment of conditions to the factor level values is shown. Use “+” and “−” to increase/decrease the assigned factor level.

Journal: Frontiers in Neuroscience

Article Title: A Student's Guide to Randomization Statistics for Multichannel Event-Related Potentials Using Ragu

doi: 10.3389/fnins.2018.00355

Figure Lengend Snippet: Within-subject design with two orthogonal factors in Ragu. On the upper right side, the two factors and their particular levels (1, 2) are shown. On the bottom, the assignment of conditions to the factor level values is shown. Use “+” and “−” to increase/decrease the assigned factor level.

Article Snippet: The aim of this article was to illustrate the analysis of ERP data using randomization statistics as implemented in the MATLAB-based program Ragu, using an example study.

Techniques:

Ragu output for the TCT. Left: The Global Field Power (shown as the black line) of the mean ERP maps on the y-axis for every time point in ms on the x-axis is shown separately for each condition. The red line indicates the p-threshold (0.05). The gray area marks non-significant time points. The height of the gray area indicates the p -value of the TCT (in the white area p < 0.05). Right: By clicking on the graph, the mean ERP map of a specific time point can be shown, as displayed on the right side of the GFP curves. There is a clear contrast between the moments of high GFP (in the first graph; C1) and small GFP (second graph; C2) displayed by a more intense coloring and narrower contour lines. It is possible to compute t-maps and plot three-dimensional models in this dialog window (see picture on the right).

Journal: Frontiers in Neuroscience

Article Title: A Student's Guide to Randomization Statistics for Multichannel Event-Related Potentials Using Ragu

doi: 10.3389/fnins.2018.00355

Figure Lengend Snippet: Ragu output for the TCT. Left: The Global Field Power (shown as the black line) of the mean ERP maps on the y-axis for every time point in ms on the x-axis is shown separately for each condition. The red line indicates the p-threshold (0.05). The gray area marks non-significant time points. The height of the gray area indicates the p -value of the TCT (in the white area p < 0.05). Right: By clicking on the graph, the mean ERP map of a specific time point can be shown, as displayed on the right side of the GFP curves. There is a clear contrast between the moments of high GFP (in the first graph; C1) and small GFP (second graph; C2) displayed by a more intense coloring and narrower contour lines. It is possible to compute t-maps and plot three-dimensional models in this dialog window (see picture on the right).

Article Snippet: The aim of this article was to illustrate the analysis of ERP data using randomization statistics as implemented in the MATLAB-based program Ragu, using an example study.

Techniques:

Left: Dialog window for the computation of microstates in Ragu. Right: The result of the cross validation for the optimization of the number of microstate classes. The x-axis shows the number of classes of the different solutions (3–10). On the y-axis, the explained variance (the fit) is displayed. The left plot shows the explained variance in the learning set, which by nature of the analysis increases monotonically. The left plot shows the explained variance in the test set, which stops increasing after a certain number of microstate classes. The red arrow marks the point in the test set where a plateau of explained variance seems to be reached. The best fitting model seems to be the seven-microstate class solution. However, it may be reasonable to also explore other numbers to ensure that the results do not crucially depend on that particular choice.

Journal: Frontiers in Neuroscience

Article Title: A Student's Guide to Randomization Statistics for Multichannel Event-Related Potentials Using Ragu

doi: 10.3389/fnins.2018.00355

Figure Lengend Snippet: Left: Dialog window for the computation of microstates in Ragu. Right: The result of the cross validation for the optimization of the number of microstate classes. The x-axis shows the number of classes of the different solutions (3–10). On the y-axis, the explained variance (the fit) is displayed. The left plot shows the explained variance in the learning set, which by nature of the analysis increases monotonically. The left plot shows the explained variance in the test set, which stops increasing after a certain number of microstate classes. The red arrow marks the point in the test set where a plateau of explained variance seems to be reached. The best fitting model seems to be the seven-microstate class solution. However, it may be reasonable to also explore other numbers to ensure that the results do not crucially depend on that particular choice.

Article Snippet: The aim of this article was to illustrate the analysis of ERP data using randomization statistics as implemented in the MATLAB-based program Ragu, using an example study.

Techniques: Biomarker Discovery