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# Manual Marks
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:so: :b: Sub structure 1 for each of the info structures is created with the *label* **manual**. This structure is usually a dependent array based off of the indices of the other independent flag structures. See [Creating Marks by Script](https://git.sharcnet.ca/bucanl_eeglab_extensions/vised_marks/wikis/vised) to find the code that does this.
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:so: :b: Sub structure 1 for each of the info structures is created with the *label* **manual**. This structure is usually a dependent array based off of the indices of the other independent flag structures. See [Creating Marks by Script](https://git.sharcnet.ca/bucanl_eeglab_extensions/vised_marks/wikis/vised) to find the code that does this. The examples below show how *manual* is the sum of the other marks.
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***
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... | ... | @@ -60,7 +60,7 @@ An example flag would be to mark the components that do not replicate across par |
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| Flag Criteria 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
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| Manual | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 |
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**Example of Channel Flags**
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**Example of Channel/Component Flags**
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| Channel | Flag Criteria 1 | Flag Criteria 2 | Manual |
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|---------|-----------------|-----------------|--------|
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* eg. (x = length(find(EEG.marks.chan_info(1).flags))) to find out how many channels will be removed.
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You also have the ability in the function call marks_epochs2continuous to 'invert' 'on' and invert all of the 0s to 1s and 1s to 0s for the marks array. This can let you quickly isolate the channel or component in question. For example if you wanted to isolate a certain component like the you could mark it then invert the array to select and 'remove' 'on' to remove all the unwanted components.
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The marks structure can take any value between 0 and 1 as well. If the value is not zero it is considered flagged and as the decimal increases towards one the opacity of the mark is increased so that at 1 the mark is full color and at 0.5 the mark is 50% transparent. This is useful when showing how a particular condition varies throughout time. For example you could normalise the model likelihood values of an AMICA run, so that time periods with low model contribution appear transparent while areas that contribute well to the model are at full color.
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***
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[ :house: **Return to Main Page**](https://git.sharcnet.ca/bucanl_eeglab_extensions/vised_marks/wikis/home)
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