<div dir="ltr"><img width="0" height="0" class="mailtrack-img" style="float:right;" alt="" src="https://mailtrack.io/trace/mail/d1866b177ed7cd899baee10a0dc0f79d65246da4.png?u=1584927">Dear Marco,<div><br></div><div>Thank you for your valuable comments here. As I can not change the number of channel because my device is fix in that case (Emotiv EPOC headset), from here I can conclude that in my case ADJUST is not recommended. I must say, ADJUST is a nice tool for EEG based research community.</div><div><br></div><div>One thing more, some talk about the value of <i>K. </i>can you advice a specific value of this parameter in my case, As I am confuse how to get this value.</div><div><br></div><div>Thank you for the advice, Yeah I will increase the registration time to get enough training points for ICA decomposition and do the necessary steps for ICA decomposition.</div><div><br></div><div>Thank you. <br></div></div><div class="gmail_extra"><br clear="all"><div><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666">Best Regards,</font><div><br></div><div>Rabnawaz</div></div></div></div></div></div></div></div>
<br><div class="gmail_quote">On Tue, May 9, 2017 at 5:58 PM, Marco Buiatti <span dir="ltr"><<a href="mailto:marco.buiatti@gmail.com" target="_blank">marco.buiatti@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="ltr">Dear Rabnawaz,<div><br></div><div>unfortunately, 14 channels do not have the spatial resolution necessary for ADJUST spatial features to be correctly computed, no matter the time duration of the registration.</div><div><br></div><div>The importance of the duration of EEG recordings is relative to the quality of ICA decomposition. Please follow EEGLAB's indications on ICA decomposition here: <a href="https://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA" target="_blank">https://sccn.ucsd.edu/<wbr>wiki/Chapter_09:_Decomposing_<wbr>Data_Using_ICA</a></div><div>where you can also find a "rule of thumb" for the minimum duration of the recordings:</div><div>"<span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">As a general rule, finding </span><i style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">N</i><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">stable components (from N-channel data) typically requires </span><i style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">more than</i><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px"> </span><i style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">kN^2</i><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px"> data sample points (at each channel), where N^2 is the number of weights in the unmixing matrix that ICA is trying to learn and </span><i style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">k</i><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px"> is a multiplier. In our experience, the value of </span><i style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">k</i><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px"> increases as the number of channels increases. In our example using 32 channels, we have 30800 data points, giving 30800/32^2 = 30 pts/weight points."</span></div><div><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px"><br></span></div><div><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">My advice is:</span></div><div><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">- use longer recordings (whatever measure you plan to use, 180 s are really short for any analysis!)</span></div><div><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">- clean your data from paroxystic, non-stereotyped artifacts (crucial for a good ICA decomposition)</span></div><div><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.8px">- run ICA and test whether it efficiently isolates artifacts from neural-like components.</span></div><div><br></div><div>Good luck,</div><div><br></div><div>Marco</div></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On 9 May 2017 at 03:22, Rabnawaz khan <span dir="ltr"><<a href="mailto:13mseerabnawaz@seecs.edu.pk" target="_blank">13mseerabnawaz@seecs.edu.pk</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><img width="0" height="0" class="m_-6747279061716996050m_-5539155937393255240mailtrack-img" style="float:right" alt="" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7">Dear Marco,<div><br></div><div>Hoping all is well, I would like to get a bit more in my previous query. there are two condition, one is number of channel (which is fix in my case, 14 channels) the second is time duration of data(which I can increase). can you suggest minimum duration of EEG recordings with 14 channels to properly apply ADJUST and get good results.<br></div></div><div class="gmail_extra"><br clear="all"><div><div class="m_-6747279061716996050m_-5539155937393255240gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666">Best Regards,</font><div><br></div><div>Rabnawaz</div></div></div></div></div></div></div></div><div><div class="m_-6747279061716996050h5">
<br><div class="gmail_quote">On Wed, May 3, 2017 at 11:48 AM, Rabnawaz khan <span dir="ltr"><<a href="mailto:13mseerabnawaz@seecs.edu.pk" target="_blank">13mseerabnawaz@seecs.edu.pk</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Dear Armand and Marco,<div><br></div><div>Thank you so much for your valuable comments. Actually my task is to work out for Alpha absolute power values for this data. for that reason i wanted to use automatic artifact removal in my pre-processing phase, but i was surprised after getting un-expecting values for alpha absolute power from my data.</div><div><br></div><div>considering the scenario, there is much noise present in the data. So working by visual inspection and rejecting the the noisy portion from my data now its seems better as I get expected values for alpha band of EEG. Thanks </div><div class="gmail_extra"><br clear="all"><div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666">Best Regards,</font><div><br></div><div>Rabnawaz</div></div></div></div></div></div></div></div><div><div class="m_-6747279061716996050m_-5539155937393255240h5">
<br><div class="gmail_quote">On Tue, May 2, 2017 at 3:05 PM, Marco Buiatti <span dir="ltr"><<a href="mailto:marco.buiatti@gmail.com" target="_blank">marco.buiatti@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="ltr">Dear Rabnawaz,<div><br></div><div>Armand is right, 180 seconds are definitely not enough to obtain a good ICA decomposition, and 14 channels are too few for ADJUST working properly. The reason for this is that ADJUST is based on spatial features which are not correctly computed for montages below 32 channels.</div><div><br></div><div>A final, general consideration: the term "automatic" refers to the fact that no parameter tuning is needed, not that it works magically (neither ADJUST nor any other automatic method pretends to be 100% reliable). When using automatic methods, you should always review the results to double-check their efficacy.</div><div><br></div><div>All the best,</div><div><br></div><div>Marco Buiatti (main ADJUST developer) </div></div><div class="gmail_extra"><div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833h5"><br><div class="gmail_quote">On 29 April 2017 at 13:16, Armand Mensen <span dir="ltr"><<a href="mailto:research.mensen@gmail.com" target="_blank">research.mensen@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="ltr"><div><div><div><div><div>Hi Rabnawaz,<br><br></div>So with only 14 channels and 180 seconds of recording, its going to be very difficult for you to "clean" your data. <br><br>For ICA: While there is no set rule for just how many channels or length of recording will result in a good separation of independent components... considered that the maximum number of independent components you are able to find is equal to the rank of your dataset (in your case 14 channels). Thus it seems highly unlikely that ICA will find a single component that captures the spurious (as you descrive it) artefact without also captures some genuine neural activity with it. <br><br>In terms of time, the longer your recording the better the ICA algorithm can separate truly independent sources. 180 seconds is very little information to go on for the ICA learning. The quality of the resulting independent components will also depend on what sort of sources are in your data. An active component that is consistent throughout the 180 seconds of your recording is more likely to be captures than a few random artefacts etc.<br><br></div>There are complete books and 100s of papers written about ICA, and so I cannot describe all the pitfalls in this email, and I would suggest having a good look through the literature before blindly applying a tool like ADJUST and hoping for the best.<br><br></div>My best advice is that you attempt with different filtering settings to eliminate the artefacts you find in your data. If your filters intrude in your frequencies of interest, then I would suggest rejecting those samples in your data with artefacts and working with the remaining time that is artefact free (although with only 180 seconds, you don't have much to work with).<br><br></div>Good luck!<span class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544HOEnZb"><font color="#888888"><br></font></span></div><span class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544HOEnZb"><font color="#888888">Armand </font></span></div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544HOEnZb"><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544h5"><div class="gmail_extra"><br><div class="gmail_quote">On 29 April 2017 at 07:13, Rabnawaz khan <span dir="ltr"><<a href="mailto:13mseerabnawaz@seecs.edu.pk" target="_blank">13mseerabnawaz@seecs.edu.pk</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><img class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544m_8798760466786640126m_5784034406900150075mailtrack-img" style="float:right" alt="" width="0" height="0">Dear Mensen,<div><br></div><div>Thank you for your response.</div><div><br></div><div>After plotting my data using EEGlab plot (scroll plot option), I can see some portion of my data is very noisy, most of the channels are giving huge peaks for some duration (I am not sure how to differentiate these noise but I can guess these are not the eye or muscle artefacts). Moreover, I am using Emotiv EPOC headset for recordings, which is 14 channels data, and the duration of my recordings is 180sec.</div><div><br></div><div>any good advice dealing with it? </div><div><br></div><div>thanks,</div><div><br></div><div class="gmail_extra"><div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544m_8798760466786640126m_5784034406900150075gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666">Best Regards,</font><div><br></div><div>Rabnawaz</div></div></div></div></div></div></div></div>
<br><div class="gmail_quote"><div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544m_8798760466786640126h5">On Wed, Apr 26, 2017 at 4:49 PM, Armand Mensen <span dir="ltr"><<a href="mailto:research.mensen@gmail.com" target="_blank">research.mensen@gmail.com</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544m_8798760466786640126h5"><div dir="ltr"><div><div><div>Dear Rabnawaz,<br><br></div>A few things here to comment on. <br><br>1) I think you may be expecting too much from any automatic artefact removal (or manual ones for that matter). Which sorts of artefacts any tool can remove from your data may depend on a multitude of factors. For example, ICA is generally quite good at finding eye blinks and eye movements. However even there it depends on the number of channels you recorded, and the length of your recording time, whether those sorts of artefacts can be removed without taking too much good data with them. So what sort of artefacts are you trying to deal with? How many channels do you have? How long is your recording?<br><br></div>2) It is generally unwise to run multiple ICA analysis one after another. There are a couple of reasons for this, but the main one (I think) is that you are reducing the rank of your data each run. This rank reduction is not always so easily or accurately detectable and will lead to problems.<br><br></div>3) I've used ADJUST sparingly in the past, and wasn't overly impressed. While examining components manually does take longer than just running some automatic script... you will get a good feeling for the quality of your data as well as the sorts of strong independent sources that are there (whether artefactual or not). I completely see the utility of using the same artefact criteria and taking some of the subjective decision making out of using ICA to remove artefacts... however only to the extent that these standard criteria are really generalisable, and useful in the first place [apologies to any ADJUST creator or enthusiast; I'd be happy to be convinced otherwise].<br><div><br></div><div>Good luck with your analysis!<br></div><div>Armand<br></div><div><div><div><br><div><div class="gmail_extra"><div class="gmail_quote"><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">---------- Forwarded message ----------<br>From: Rabnawaz khan <<a href="mailto:13mseerabnawaz@seecs.edu.pk" target="_blank">13mseerabnawaz@seecs.edu.pk</a>><br>To: <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>Cc: <br>Bcc: <br>Date: Tue, 25 Apr 2017 18:34:43 +0800<br>Subject: [Eeglablist] Artifact removal using ADJUST plugin<br><div dir="ltr"><img class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544m_8798760466786640126m_5784034406900150075m_2233013525528271147m_-8396571122335288076mailtrack-img" style="float:right" alt="" width="0" height="0"><span style="font-size:12.8px">Dear All,</span><div style="font-size:12.8px"><br></div><div style="font-size:12.8px">I am using ADJUST plugin for artifact removal from raw data. I follow the instruction listed in the ADJUST manual to process the data. after running ADJUST I get the artifacted ICs in a new pop-up window, I mark these ICs for rejection and then I go to tools to remove these ICs (via EEGlab GUI menu>>tools>>remove components). A new dataset is created (eegdata pruned with ICA ), according to ADJUST tutorial this is the clean eeg data, but I see from the plots that there is still artifact present in the data. When I run ICA again and then again run ADJUST I get a new pop-up window in which some other ICs are identified as artifacts. I remove these ICs again. But still, artifact are present in data. I repeat running ADUST many time and each time I get new ICs marked as an artifact. </div><div style="font-size:12.8px"><br></div><div style="font-size:12.8px">I would be happy if you guide me with this problem. </div><div><div class="m_-6747279061716996050m_-5539155937393255240m_8228330165282159833m_854532840399964544m_8798760466786640126m_5784034406900150075m_2233013525528271147m_-8396571122335288076gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666"><br></font></div><div dir="ltr"><font color="#666666">Best Regards,</font><div><br></div><div>Rabnawaz</div></div></div></div></div></div></div></div>
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