[Eeglablist] **preprocessing

Jaleh Mohammad alipoor sugmad973 at yahoo.com
Sat Jul 22 01:29:10 PDT 2017

 I’m doing a project on the effects of music on the functional connectivity of the brain using EEG. But l came across a confusing problem during preprocessing of my data!We all know that there isn’t a confirmed and stable pipeline for preprocessing so that all experts agree on. So I tried two different ways to preprocess my data that l’ve explained below but adding to the confusion is the fact that after finishing preprocessing and doing the main process, data acquired by each of these two ways gave me completely different results and now l wonder which of these ways and subsequently final results could be correct!!
 but first l want to give you a little about my data and process. I took a 32-channel continuous data of length 350000 datapoints from each person(my subjects are children between 10 to 13 years old). These data consist of 12 segments during which 12 music excerpts was played. The lengths of these 12 segments vary between 12000 And 27000 datapoints. As you can see below the only major difference between these two pipelines that l’ve taken is doing segmentation as the final step or doing it earlier!!  My reason to abandon the first way and trying the second was the fact that the final segments in first way, after doing all of these steps and after segmentation were sometimes noisy and had noisy channels that made me confused!! And now the two ways l took to clean the data and prepare it for the final process:
First way:Define channel location fileBand-pass filter between 1 to 40 HzRejection of paroxysmal artifactsRemove bad channelsRun ICARun ADJUST toolbox to find bad componentsInterpolate removed channelsRe-reference to averageSegmentation second way:Define channel location fileBand-pass filter between 1 to 40 HzSegmentationAnd doing these works on each segment:Remove paroxysmal artifactsRemove bad channels (but l didn’t remove if bad channels were fp1 and fp2 because l thought they were bad channels because of eye blinks and eye movements (by visual inspection) that ICA would remove them properly and data after running ICA and ADJUST showed me that l thought correct)Run ICARun ADJUSTInterpolate if there were removed channelsRe-reference to averageRe-checking the data for residual artifacts Whether  my reason to not remove bad channels if they are due to eye movements and running ICA  on them is correct?And how can l get sure that my process is correct and the data are really clean?
l would be very grateful for your kind help.  Best regards,Zhaleh 
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