# EEG pipeline with MNE **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [April 4, 2018, 9:13pm UTC](https://mne.discourse.group/t/eeg-pipeline-with-mne/1475 "2018-04-04T21:13:39Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [April 4, 2018, 9:13pm UTC](https://mne.discourse.group/t/eeg-pipeline-with-mne/1475/1 "2018-04-04T21:13:39Z")
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/](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. -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20180404/5f29807a/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20180404/5f29807a/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [April 5, 2018, 7:57pm UTC](https://mne.discourse.group/t/eeg-pipeline-with-mne/1475/2 "2018-04-05T19:57:54Z")
hi Davide, answering all your questions in details could be book 🙂 maybe this preprint [https://www.biorxiv.org/content/early/2017/12/28/240044](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