adlofts created page: postprocessingscript authored by Andrew Lofts's avatar Andrew Lofts
......@@ -96,6 +96,7 @@ Component classification can only be done on files that have successfully run th
* Heartbeat
* Muscle
* Mixed Signal
These percentages sum to 100% and the category with the highest % is selected as the guessed type. This information is useful in getting a general overview of what type of components can be found in your data, even if you don not completely trust its choices.
![diag3](/uploads/dfa4b9cb0401bc516f5b8ebe9524f874/diag3.png)
......@@ -105,8 +106,20 @@ The pie graph in diagnostics shows the fraction of components that were placed i
These figures can give you a quick idea on how well you are isolating components. Ideally you are looking for components that have have a high % in one category, indicating a clear isolation.
# Creating your own plotting functions
In order to create your own diagnostics tool you should make sure you are familiar with:
* The above content
* [Marks](https://git.sharcnet.ca/bucanl_eeglab_extensions/vised_marks/wikis/home) structure and creating by script.
* [Configuration files](https://git.sharcnet.ca/bucanl_eeglab_extensions/batch_context/wikis/configuration-files)
This section will use these concepts along with [plotting in matlab](https://www.mathworks.com/help/matlab/creating_plots/types-of-matlab-plots.html) and the EEG. structure.
We are currently working on this section of the Wiki.
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Sample matrix representation of the calculate->flag->mark path for a channel mark.
| | Epoch Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | | |
|--------------|------------------|------|------|------|------|------|------|------|------|------|------|----------------|----------------------------|
......@@ -114,9 +127,6 @@ Sample matrix representation of the calculate->flag->mark path for a channel mar
| Flag | Criteria (0.1) | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4/10 (0.4,40%) | Result: Channel is flagged |
We are currently working on this section of the Wiki.
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# Fin Script QA
* Data Visualisation
* Component Selection
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