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# Additional Scripts
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overview
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[[_TOC_]]
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# Diagnostics
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Throughout the pipeline the scripts make many decisions that result in marks on both channels and time segments. The criteria for these decisions is laid out in the configuration files, and the results and the intermediate stages are saved in the EEG structure. These matrices can be easily access and plotted to get a visual representation of what is happening.
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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 diagnotics script.
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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. Most typically these will have the ending *_dip.set.*
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Make sure that the diagnostics.m file is on the MATLAB path.
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In the matlab editor call the diagnostics function in the MATLAB editor:
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```
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diagnostics
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```
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By typing the function name you are calling the diagnostics script. This function does not contain 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, and 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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*Time Flags*
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*Channel Flags*
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*Data Quality
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*Component Classification*
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#Creating your own plotting functions
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We are currently working on this section of the Wiki.
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%%%%%%%%%%%%%%%%%%%%%%%%%%
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# Fin Script QA
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* Data Visualisation
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* Component Selection
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# Additional Scripts
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* Dipole Fitting and Interpolation
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* Segmentation
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* Exporting
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# Rebuild
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Due to the nature of the pipeline being lossless we have created a script that essentially rebuilds the raw data from the finished file. There are only three occurrences when the original EEG.data values are changed.
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* Lowpass filter
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* Highpass filter
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* Optional ASR pass
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These pieces can be saved when the pipeline is run so that stats can be preformed on the residuals that are removed.
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These pieces can also be added back to the data if a pipeline user wants to see how the percent change of the data. Dues to rounding errors there data is not exactly the same as it was but is usually has greater than 99% accuracy.
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