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To visualise some of the choices that were made in the pipeline either create your own plotting functions, or try running the diagnostics script. To learn more about how data structures and calculations see the end of this section.
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**Using the Diagnostics Script**
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Open up a EEG dataset that has complete the dipfit stage of the pipeline to get all of the available figures. Most typically these will have the ending *_dip.set.* Make sure that the diagnostics.m file is on the MATLAB path. In the matlab editor call the diagnostics function in the MATLAB editor:
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Open up a EEG dataset that has complete the dipfit stage of the pipeline to get all of the available figures. Most typically these will have the ending *_dip.set.* Make sure that the diagnostics.m file is on the MATLAB path. Call the diagnostics function in the MATLAB editor:
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```matlab
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diagnostics(ALLEEG,'single');
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```
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diagnostics
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or
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```matlab
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diagnostics(ALLEEG,'study');
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```
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By typing the function name you are calling the diagnostics script. This function does not need any input values but directly starts accessing your loaded dataset. Figures will be generated and you can browse them as you would like. The figures contain some visual representations of the calculations that are being preformed based on the quality of the data file. Follow along below for a more in depth view of some of the figures.
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By typing the function name you are calling the diagnostics script. This function needs two input values.
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* 1. ALLEEG- (structure) - most often pass this function the ALLEEG structure so that it has access to all the datasets you have loaded. You can also use EEG if you are going to only access the current dataset.
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* 2. type - (string) - The type of analysis you want to do
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* 'single' - will preform calculations and plot figures pertaining to the **current** dataset.
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* 'study' - will preform calculations and plot figures pertaining to **all** datasets loaded.
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Figures will be generated and you can browse them as you would like. The figures contain some visual representations of the calculations that are being preformed based on the quality of the data file. Follow along below for a more in depth view of some of the figures.
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# Data Quality
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The first plot is a general overview of how much good data has been found in your data set. If the % of time and the % of channels removed from your data is very high you might want to reconsider your experiment modality. From the figure below:
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