[Mne_analysis] EEG pipeline with MNE

[Alexandre Gramfort]

hi Davide,

answering all your questions in details could be book :)

maybe this preprint https://www.biorxiv.org/content/early/2017/12/28/240044
that comes with code and recommendations to visually assess the quality
of your processing steps can help.

HTH
Alex


On Wed, Apr 4, 2018 at 11:13 PM, Davide Aloi <davide.aloi93 at gmail.com>
wrote:

> Hi there,
> I am analyzing some data from an experiment with children with dyslexia
> and a control group. The experiment was done using a 128 electrodes EEG.
> Basically, children were presented some words which could be wrong spelled
> or correct and were asked to decide whether these words were correct or
> not. The words were 120, 60 correct and 60 incorrect. These words were also
> divided into different categories (included/not included in a previously
> done training).
>
> I would like to see if there are differences between:
> - correct/incorrect words
> - words that were included/not included
> - dyslexic children/control group.
>
> I wanted to be sure that my pipeline was correct, as I am facing some
> difficulties.
>
> 1) I don't have any electrodes marked as eog or emg. I suppose I can just
> rename the two electrodes close to the eyes and the two two at the level of
> the cheekbones?
> 2) Some subjects do not have a reference. Can I procede in this way?
>
>>
>> if len(raw.info['chs']) == 130:
>>     ref_channels=[]
>>     print ('A reference is already present.')
>> else:
>>     print('Data need to be referenced')
>>     raw.set_eeg_reference('average', projection=True)  # set EEG average
>> reference
>>     ref_channels=['Cz']
>>     #raw.apply_proj() #To apply the projection
>
>
>  3) What parameters should I pass to ICA to remove blinks/movement
> automatically? I can do ICA after epoching and then I visually check my
> data again, manually selecting other artifacts that have not been detected?
>
> 4) I want to implement autoreject in my pipeline. When should I use it?
>
> 5) Is it correct to check bad channels visually and using *RANSAC
> algorithm from autoreject* over all the epochs? I also tried to check for
> outliers using the function *is_outlier* from here
> https://stackoverflow.com/questions/22354094/pythonic-way-
> of-detecting-outliers-in-one-dimensional-observation-data. It gives me a
> clue about which electrodes are different from the others, am I correct?
> Often times the ransac algorithm and the is_outlier function gives me a
> similar list of electrodes.
>
> 6)What if I want to use different tmax and tmin for different events when
> creating epochs?
>
> I want to be sure about my pipeline as this is the first eeg analysis I am
> doing.
> - I impor raw data -> I mark bad channels -> I do the reference -> I
> create events and epochs -> I run ICA
> At this point I actually don't understand how to remove artifacts with
> ICA. I just need to create EOG epochs and reject them?
>
> After ICA I visually check the data again and manually remove other
> artifacts.
>
> -> Then I do the ERP for the events I am interested in.
>
> At this point I would like to know how I can make the statistics to see if
> there are differences between conditions and between groups.
>
>
> Suggestions?
> Kind Regards.
>
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