<div dir="ltr"><div class="gmail_default" style="color:rgb(51,51,153)">Hello Shira, comments below, best wishes.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">***************************************************************</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">1. 1 hz higpass is usually recommended for great ICA decompositions, and is usually not necessary to be lower than 1hz for resting-state data, unless you have specific hypotheses <1hz. Further, after getting a good ICA decomposition, you can apply those ICs to continuous data that has been filtered with a lower highpass filter, and then remove some bad ICs, and then epoch, and then drop epochs that still have clear artifacts.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">2. One should reject bad data before ICA. You can leave blinks and muscle noise in, but big movements, multi-channel artifacts, etc.. should be removed. See the pre-ICA eeglab tutorials, Makotos' eeglab pipeline suggestions, and past notes on the eeglablist on this topic. Then, after ICA, one can epoch, or apply the ICA to the original continuous data, and clean from there via multiple methods.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">3. regarding the hunt for "right criteria". Try playing around with the various artifact-detection and measures, and don't just use only the threshold measure. Ideally you can use a combination of eeglab settings, and have different artifact measures converge on particular epochs to drop. If you do this in eeglab, you will see some epochs marked with multiple colors, which signifies that multiple measures are catching that epoch. If you're running artifact measures after ICA, one can also choose to use the ICA-based artifactual epoch rejection measures rather than the channel-based ones. Overall, a good rule of thumb is that you should be able to keep at least 50% of the recorded data. If not, there is something wrong with the artifact-detection and/or with the general quality of the eeg data.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">4. See also the "reject continuous" data tool in the eeglab gui, which works quite well and works at the frequency level, looking for unique noise in several frequency ranges. Another option is to use the settings you see in a published articles that uses a similar paradigm and recording system. There are also other options within eeglab, such as Kothe's clean data tools available in the gui and the PREP toolbox. See also TAPEEG and other solutions for automatic cleaning of data.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">5. EEG cleaning is still an art, and it depends on how clean your data is to begin with, how many channels you have, and what tools you are using to find bad periods. Since your data is not very long I recommend visual cleaning by human expert, unless you have hundreds or thousands of sessions to deal with.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">6. If you are a beginner with eeg or with cleaning EEG data, one good thing to do is have an expert show you what really clean / good data looks like, and this can be a reference for further cleaning attempts. Another option is to identify the cleanest time periods within one subject and use that information to clean the rest of the subject's data. EEG data often varies in quality across subjects and across recording sessions.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Sat, Aug 5, 2017 at 10:49 AM, shira frances <span dir="ltr"><<a href="mailto:shirafrn@gmail.com" target="_blank">shirafrn@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="rtl"><div dir="ltr">hello all,</div><div dir="ltr">i am analyizing 5 min closed-eyes resting-state data (bdf file) 32 electrodes. i am intersted on the alpha power band. i read old mails and answers on eeglablist and found (thank you!!!) the script for frequncy analysis (<a href="https://sccn.ucsd.edu/wiki/Makoto%27s_useful_EEGLAB_code#How_to_extract_EEG_power_of_frequency_bands" target="_blank">https://sccn.ucsd.edu/wiki/<wbr>Makoto%27s_useful_EEGLAB_code#<wbr>How_to_extract_EEG_power_of_<wbr>frequency_bands</a> .) </div><div dir="ltr">the plan i am trying to do now is this: import the raw data, define channel location, reference to CZ, FIR high-pass 0.5, lowpass 45, import events(from a file i created, an event evey second, only for the epoching, the events has no meaning), after that i run the ICA and removing what looks like eye movement (the oarticipants moved their eyes altough it was eyes-closed condition) , then i epoch the data to 1s epochs (from -500ms to 500ms). now i want to reject artifacts by epochs, i used like i read - "all methods" and then reject by the first option (abnormal values) - i wrote +-100. the problem is it reject about half of the data... i tried it again this time with +-150 and it rejected 13% - is this the right criteria for rejection? (i was instructed to try this because we want to see alpha)</div><div dir="ltr">may i use other criteria?</div><div dir="ltr">and generally how can i know what is the right criteria?</div><div dir="ltr"><br></div><div dir="ltr">thank you vert much</div><div dir="ltr"><br></div><div dir="ltr">shira frances.</div><div dir="ltr">tel-aviv university</div><div dir="ltr"><br></div></div><div id="m_3007892038193664287DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2"><br>
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