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Readme-optional_cleaning.Rmd
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Readme-optional_cleaning.Rmd
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---
title: "Cleaning optional script"
author: "Douwe John Horsthuis"
date: "`r Sys.Date()`"
output: github_document
editor_options:
markdown:
wrap: 72
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE
)
knitr::opts_chunk$set(fig.align = 'center')
```
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# General intro
The pop_clean_raw function to clean data in EEGLAB lets you use several
settings that determin how much data will be deleted. This script is
created so one can run it and find out how much data can be deleted
without losing too much participants. Since it is a part of the [EEG
pipeline](https://github.com/DouweHorsthuis/EEG_to_ERP_pipeline_stats_R)
we will only focus on what the script does and why it might be useful to
run it before you clean your data.
*If you have questions or suggestions please reach out to
douwehorsthuis\@gmail.com*
**Disclaimer, do check out the EEGLAB tutorials etc. there might be reasons why data behaves as it they do that should be adressed instead of the cleaning functions**
## What is pop_clean_raw
Pop_clean_raw is the suggested cleaning method for data by [the EEGLAB creators](https://eeglab.org/tutorials/06_RejectArtifacts/cleanrawdata.html). This function allows you to delete bad channels based on flatlines, noise, and correlation between channels. It also allows you to deleted bad portions of the data where many channels get noisy without deleting the channels. Lastly it is possible to reconstruct noisy data, so it becomes usable, this is not something that we use.
**If you have experience with the Artifact Subspace Reconstruction (ASR) algorithm, and feel like this should be a part of this script somehow, please let us know either in the issues part or email me at [email protected]
[Back to top](#cleaning-optional-script)
## What does the script do
The script will either load the data, and pre-process it (downsample/high and low pass filter/adding channel info), or if you already have your data ready for the cleaning part, you can just load that data. It then runs the `pop_clean_rawdata` function on a loop.
Every Loop the `ChannelCriterion` and the `BurstCriterion` get changed. And stores how many channels and how much % data got deleted. After this is done for all the participants you have a variable called `quality`. This will be used to create figures that show how much the different settings impacted the data.
[Back to top](#cleaning-optional-script)
## What settings can be changed
Of course everything can be changed, but the main things that are easy and quick are and maybe required:
`subject_list` This is where you store all the Unique ID numbers of your participants.
`home_path` This is where you set the path to the main folder of the data. All data is expected to be in individual subject folders.
`Burst_criteria` This is requires a start point a step size and end point of what you want to use for the threshold for how much data get's deleted. The higher the number the more std the data is allowed to be different, so high number equals less deleted data.
`Channel_criteria` This is requires a start point a step size and end point of what you want to use for the threshold for how much the channels can be correlated, the higher the number equals less channels getting rejected.
[Back to top](#cleaning-optional-script)
## examples
Here are 3 examples, where for the first 2 we used a 1Hz filter and for the last one we used a 0.1Hz filter.
*We used a 1Hz highpass and a 45Hz lowpass filter and use relatively strict burst criteria*
*We used a 1Hz highpass and a 45Hz lowpass filter and use a more liberal burst criteria*
*We used a 0.1Hz highpass and a 45Hz lowpass filter and use a more liberal burst criteria*
It is clean, and probably expected that lowering the highpass filter means that more data will be deleted, because of this is you need different settings.
Next there are 3 more plot that show the same data in a different way.
*We used a 1Hz highpass and a 45Hz lowpass filter and use relatively strict burst criteria*
*We used a 1Hz highpass and a 45Hz lowpass filter and use a more liberal burst criteria*
*We used a 0.1Hz highpass and a 45Hz lowpass filter and use a more liberal burst criteria*