<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">Hi Ahmad,<div class=""> Thanks a lot for the reply. </div><div class=""> My preprocessing methodology is taken from Makoto’s pre-processing pipeline. But there were some posts in eeglablist which suggested to interpolate after ICA, and in doing so I did better components. So I modified the preprocessing methodology to:</div><div class=""><br class=""></div><div class="">1. Downsampling</div><div class="">2. High Pass filter (1 Hz)</div><div class="">3. Read Channel info</div><div class="">4. Use CleanLine</div><div class="">5. Reject bad channels using clean_rawdata</div><div class="">6. Re-reference data to average</div><div class="">7. run ICA</div><div class="">8. Interpolate channels</div><div class=""><br class=""></div><div class="">I will again run ICA after interpolation, with PCA rank reduction methods and compare the results.</div><div class=""><br class=""></div><div class="">Best</div><div class="">Sonia</div><div><blockquote type="cite" class=""><div class="">On 03-Apr-2017, at 13:51, Dr. Sonia Baloni <<a href="mailto:sbaloni@cbcs.ac.in" class="">sbaloni@cbcs.ac.in</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><meta http-equiv="Content-Type" content="text/html charset=utf-8" class=""><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">Hi Tarik,<div class=""> Thanks for the reply. I am referring to the eeglab tutorial available as a pdf in SCCN website and Makoto’s preprocessing pipeline and also previous posts from the eeglablist. I compiled all three sources and so had some doubts. As Makoto’s preprocessing pipeline suggests to interpolate before running ICA.</div><div class="">I checked on again and sorry yes it is clean_rawdata that removed bad channels from the data. I will again read the posts on ranks in eeglablist, maybe I missed some of them to clarify my second doubt.</div><div class=""><br class=""></div><div class="">best</div><div class="">Sonia<br class=""><div class=""><blockquote type="cite" class=""><div class="">On 03-Apr-2017, at 05:35, Tarik S Bel-Bahar <<a href="mailto:tarikbelbahar@gmail.com" class="">tarikbelbahar@gmail.com</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class=""><div class="gmail_default" style="color:rgb(51,51,153)">Hello Sonia, some notes below, best wishes.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br class=""></div><div class="gmail_default" style="color:rgb(51,51,153)">Generally speaking one ICA should be enough if you clean your data appropriately before running ICA. Not sure which tutorial you are referring to. If it is the general eeglab tutorial or Makoto's pipeline (check it out if you have not) remember those are recommendations not golden rules. Many investigators make their own unique choices.</div><div class="gmail_default" style="color:rgb(51,51,153)">Channels should not be interpolated before any ICA.</div><div class="gmail_default" style="color:rgb(51,51,153)">I might be wrong, but cleanline as far as I know is meant to denoise data, not drop channels, so you may want to double check that you are running cleanline and not PREP toolbox or clean_rawdata.</div><div class="gmail_default" style="color:rgb(51,51,153)">Regarding rank see Makoto's pipeline and past discussions and responses on eeglablist.</div><div class="gmail_default" style="color:rgb(51,51,153)">Last, if you have not had a chance to fully process eeglab tutorial data it is highly recommended.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br class=""></div><div class="gmail_default" style="color:rgb(51,51,153)"><br class=""></div><div class="gmail_default" style="color:rgb(51,51,153)"><br class=""></div><div class="gmail_default" style="color:rgb(51,51,153)"><br class=""></div></div><div class="gmail_extra"><br class=""><div class="gmail_quote">On Thu, Mar 30, 2017 at 6:42 AM, Dr. Sonia Baloni <span dir="ltr" class=""><<a href="mailto:sbaloni@cbcs.ac.in" target="_blank" class="">sbaloni@cbcs.ac.in</a>></span> wrote:<br class=""><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word" class="">Hi All,<div class=""><br class=""><div class=""> I am new to EEGlab and trying to work with ICA analysis. I have been reading the eeglab list mails on ICA topic. I have gather few footnotes and few question which I would like to ask:</div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">1. Cleanline algorithm removes bad channels from the data. Posts in EEGlablist suggests that interpolation should be done after ICA.The tutorial suggests that two ICAs should be performed on the same data-set. So if we are performing two ICAs, we should interpolate these channels after second ICA?</div><div class=""><br class=""></div><div class="">2. I assume rank of the data (used in ICA analysis) would be equivalent to the number of channels in data after running clean line function. If there are for example 128 channels with which data was collected and 10 channels were removed with clean line function then the rank of the data would be 118. If we now want to run ICA on this data do we need to add “ ‘pca’,117 “ in command line option - next to ‘extended’, 1 ? as suggested in the tutotrial : <a href="https://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA" target="_blank" class="">https://sccn.ucsd.edu/wiki/<wbr class="">Chapter_09:_Decomposing_Data_<wbr class="">Using_ICA</a>.</div><div class=""> </div><div class=""><br class=""></div><div class="">Thanks a lot.</div><div class=""><br class=""></div><div class="">Best </div><span class="HOEnZb"><font color="#888888" class=""><div class="">Sonia</div><div class=""><br class=""></div><div class=""><br class=""></div></font></span></div></div><br class="">______________________________<wbr class="">_________________<br class="">
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