<div dir="ltr">Dear Marius,<div><font color="#0000ff"><br></font></div><div><font color="#0000ff">> Data cleaning and source separation is the toughest frontier in MoBI research at the moment I'd say,<br></font></div><div><font color="#0000ff"><br></font></div><div><font color="#000000">Signal processing is a secondary issue. What's most important is the <i>unique questions</i> it can address to make a good science. Consider the fact that designing an experiment has much larger degrees of freedom than the types of signal processing we can perform (more or less successfully).</font></div><div><font color="#0000ff"><br></font></div><div><font color="#0000ff">> if all components are subtracted, there should again be nothing left, or where am I on the wrong track here?</font></div><div><font color="#0000ff"><br></font></div><div><font color="#000000">You are right.</font></div><div><font color="#0000ff"><br></font></div><div><font color="#0000ff">> Also, the backprojection from components to channels is just a matrix multiplication with the remaining components' weights inversed, but if there's no component left, this would be an empty matrix, or how would this work mathematically? I might have gotten something wrong here, though...</font><br></div><div><br></div><div>Hmm I have difficulty in seeing your problem. What do you want to know?</div><div><br></div><div>Makoto</div><div><br></div><div><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Fri, Oct 28, 2016 at 4:55 AM, Marius Klug <span dir="ltr"><<a href="mailto:marius.s.klug@gmail.com" target="_blank">marius.s.klug@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div>Hey Duncan and all!<br></div><div><br>Yeah, you really should ask Prof. Gramann's group ;-) (Duncan was doing an internship with us for a month, I am one of Prof. Gramann's PhD students...) We were discussing this at some point, I saw this mail and thought "Ah that sounds similar to what Duncan asked me about, let's check what the EEGLAB Pros answer!" and only later realized that it was actually you asking this question! So, great to have this discussion here! <br></div><br>I'd also say just try out everything and check, this is going to be interesting. You know that we're still exploring this ourselves, so well... just try out and report to us and the list ;-) We're experiencing heavy artifacts of all kinds in our datasets, which is also due to the Phasespace Mocap system and the HTC Vive VR headset... Basically, we're in the stage of finding suitable solutions for data cleaning and source separation and do it similarly, trying out what works best at this point. Evelyn Jungnickel just told me that she repeatedly did ICA, then checked where the algorithm had troubles separating the sources (e.g. by checking if there are time periods in which all the components have strong activity), then cleaned those parts out of the original data set and then repeated the process until the components appeared comparably clean. ICA and denoising is a delicate process for sure... Data cleaning and source separation is the toughest frontier in MoBI research at the moment I'd say, and hopefully ever more sophisticated solutions will emerge on the research horizon! On another note there's also a more general thing "Joint Decorrelation" (<a href="https://www.ncbi.nlm.nih.gov/pubmed/24990357" target="_blank">https://www.ncbi.nlm.nih.gov/<wbr>pubmed/24990357</a>),
which might turn out to be useful in some cases. But as I said, we're
still on this ourselves... and there's also the valid point you raised once that the
more you clean the data the more signal you are also prone to lose in
the process. No perfect solutions unfotunately!<br><br></div><div>As a question from my part: I thought that the ICA components combined would always contain the whole original dataset, just split up differently, and if you subtract all components you get zero at all channels. I take that is true for the data set from which the ICA was calculated? So, if I understand this correctly, you say that's not the case any more if you take the weights and apply them to longer/other data sets? Really interesting. But I'm afraid I don't fully understand this... Makoto you wrote that the parts unaccounted for by the ICA would be spread through all components, so if all components are subtracted, there should again be nothing left, or where am I on the wrong track here? Also, the backprojection from components to channels is just a matrix multiplication with the remaining components' weights inversed, but if there's no component left, this would be an empty matrix, or how would this work mathematically? I might have gotten something wrong here, though...<br><br></div><div>Cheers,<br></div><div>Marius<br></div><div><br></div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="gmail-m_4039962159299782775gmail-h5">2016-10-21 15:02 GMT+02:00 Tarik S Bel-Bahar <span dir="ltr"><<a href="mailto:tarikbelbahar@gmail.com" target="_blank">tarikbelbahar@gmail.com</a>></span>:<br></div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div><div class="gmail-m_4039962159299782775gmail-h5"><div dir="ltr"><div style="color:rgb(51,51,153)">Hi Duncan, some quick notes about your questions below, hope they are of use. Best wishes, Tarik</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Mobile brain imaging (EEG in not just "passive and sitting-quietly" conditions) is still a developing field. You may benefit from contacting a few researchers who work in this area, including Klaus Gramman and Daniel Ferris, so as to garner their opinions. There are several other groups that should be findable via google. As you're at the frontier of this area,</div><div style="color:rgb(51,51,153)">it's better to test a range of options and make the best informed decision, as there are few consolidated guidelines for this kind of research. There's a range of issues and options, just a few thoughts below for now.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">If you don't give some subset of data to ICA, it's of course not considering it. If you have several ICA decompositions for one person, then there should definitely be similarities across the decompositions. Thus, it's not clear that you would end up with, after subtractions, just, as you said, "<span style="font-size:12.8px;color:rgb(34,34,34)"> </span><span style="font-size:12.8px;color:rgb(34,34,34)"> remaining signal consisting of all the activity that lacked in the passive conditions?". This expectation makes sense, but it's just not published about enough to be sure.</span></div><div style="color:rgb(51,51,153)"><span style="font-size:12.8px;color:rgb(34,34,34)"><br></span></div><div style="color:rgb(51,51,153)"><span style="font-size:12.8px;color:rgb(34,34,34)">And yes, generally, if you are trying to subtract ICs representing neck muscle activity from a dataset that has very little (or a lot of) activity like that, the basic assumptions about subtracting the ICs may not hold. </span></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">In your case, consider yourself first in an exploratory stage. You are considering several important points, so that's good. The field needs to know more about mobile brain imaging problems and how to deal with them. Overall this is an excellent opportunity to make a contribution to real problems in the field.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">You should be able to run ICA on the non-passive conditions and get interpretable ICs that reflect real brain sources. However, they will likely be more mixed with artifact activity than the ICs you get from the passive condition.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">I would recommend running an ICA across all conditions per person, as well as a (separate) ICA for each condition, and review the results for yourself (from single-condition and all-condition ICAs). You may get more "prototypical artifact ICs that are applicable across the conditions. If you have "weak artifact" IC from one condition and apply it to a "receiving set"</div><div style="color:rgb(51,51,153)">it should remove some of that artifact activity, but not necessarily most of it, especially if the IC information from the "donor set" does not well-characterize the artifact dynamics in the "receiving set".</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Another option for future consideration, is to ask people to make a range of stereotyped movements with their bodies, faces, and arms, and use that as a kind of glossary of artifacts for a specific person.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Another option is to compare several conditions, with each condition having "more artifacts". For example, really slow walking, normal walking, and fast walking.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Some of your issues like cable sway and so on from movement may be resolved with wireless caps available from several manufacturers, though I would not recommend dry systems nor "non-research" consumer systems. </div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">You may also want to think about using ASR in eeglab to clean up the data beforehand. See an example of the effects of ASR on EEG with active movement here:</div><div><font color="#333399"><a href="https://www.youtube.com/watch?v=qYC_3SUxE-M" target="_blank">https://www.youtube.com/watch?<wbr>v=qYC_3SUxE-M</a></font><br></div><div><font color="#333399"><br></font></div><div><br></div><div><font color="#333399"><br></font></div><div><font color="#333399"><br></font></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div></div>
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