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matlab-readable format  (MathWorks Inc)


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    MathWorks Inc matlab-readable format
    The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography <t>(EEG)-file</t> selection (see Section “3.1 Loading <t>EEG</t> <t>data”),</t> where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).
    Matlab Readable Format, 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-readable format/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab-readable format - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Robin’s Viewer: Using deep-learning predictions to assist EEG annotation"

    Article Title: Robin’s Viewer: Using deep-learning predictions to assist EEG annotation

    Journal: Frontiers in Neuroinformatics

    doi: 10.3389/fninf.2022.1025847

    The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography (EEG)-file selection (see Section “3.1 Loading EEG data”), where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).
    Figure Legend Snippet: The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography (EEG)-file selection (see Section “3.1 Loading EEG data”), where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).

    Techniques Used: Selection



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    The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography <t>(EEG)-file</t> selection (see Section “3.1 Loading <t>EEG</t> <t>data”),</t> where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).
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    The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography <t>(EEG)-file</t> selection (see Section “3.1 Loading <t>EEG</t> <t>data”),</t> where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).
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    The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography (EEG)-file selection (see Section “3.1 Loading EEG data”), where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).

    Journal: Frontiers in Neuroinformatics

    Article Title: Robin’s Viewer: Using deep-learning predictions to assist EEG annotation

    doi: 10.3389/fninf.2022.1025847

    Figure Lengend Snippet: The “Preprocessing” pop-up window is the initial screen of Robin’s Viewer (RV) and has five distinct sections: First, the electroencephalography (EEG)-file selection (see Section “3.1 Loading EEG data”), where the user can load their EEG recording and display selected statistics about the data once it is loaded. Second, the preprocessing settings (see Section “3.2 Preprocessing and visualization settings”), which are used to set a bandpass filter [finite impulse response (FIR) filter with Blackman window] and a custom reference. Third, the bad-channel handling (see Sections “3.2 Preprocessing and visualization settings” and “3.4 Bad-channel marking”), where the user can decide whether to use automatic bad channel detection, and whether to interpolate bad channels. Fourth, the visualization settings (see Section “3.2 Preprocessing and visualization settings”), comprised of downsampling, custom scaling (by default 1e-6 as RV scales data from volts to microvolts for plotting), the gap between traces (by default 40 (μV); setting this to 0 results in butterfly mode where all traces are collapsed on top of each other; values higher than 40 move traces further apart), segment length to plot [by default 60 (seconds)], whether or not to activate the view-slider, and selection of channels to plot. Visualization settings will only be applied to the data for plotting and hence will not be saved in the save-file (in contrast to the preprocessing settings). Fifth, the deep-learning model settings (see Sections “3.2 Preprocessing and visualization settings” and 3.5 Deep-learning model predictions”), where previously saved model output can be loaded and the integrated deep-learning model can be activated to generate predictions. Clicking the “Plot” button at the bottom will close this window and, after a loading screen (which lasts as long as it takes to plot the data), it will open up the main graphical user interface (GUI).

    Article Snippet: The main working points are the option to add labels to annotations to support multi-class deep-learning predictions and annotations (e.g., for sleep staging), the possibility to save annotated EEG data in a MATLAB-readable format to better facilitate the integration of RV into MATLAB-based EEG toolboxes, as well as functionality to interact with the RV-GUI using keyboard keys and combinations of keys.

    Techniques: Selection