<div dir="ltr">Hi Simon, <div><br></div><div>This is an increasingly tricky issue as time goes on, with fewer and fewer computers having parallel ports. Psychophysiology as a field is in need of the next generation of inter-computer communication devices! </div><div><br></div><div>To answer your question, though, there are several options, although I don't know very much about Linux machines, and nothing about Psychtoolbox. However, I've worked with parallel port signalling with the stimulus-presentation programs Inquisit, PsychoPy, ePrime, Presentation, and the data acquisition programs ActiView (from BioSemi) and Acqknowledge (from BioPac), and there are some similarities.</div><div><br></div><div>First, you want to check that your computer actually knows where your parallel port is. If it's natively installed (not installed after-market by you or someone else), that makes it a lot easier. Make sure you know the base address, which I'm not sure how to find out on a Linux machine, but you probably already know what it is (if you don't, an internet search should tell you how to find out this address). You'll probably need to feed this into Psychtoolbox at some "experiment" or "properties" level.</div><div><br></div><div>If you had to install the parallel port card into a PCI or PCI-Express slot, then make sure the computer can use it an recognize it. There are several aspects to this, which I don't have the chance to go into now, but you can respond with more specific questions. There are some utilities out there that you can use to test to see if parallel port signals are getting through, such as "VBPortTest", which you can download online.</div><div><br></div><div>You may also want to reach out to Neuroscan to see if there is a special format in which it needs to receive the TTL signals, such as only through the parallel port's data channels 3:10, or only through channels 2:9, etc. You may need to use a "breakout board" to change which channels/pins are communicating the signal. I've used the PBX-2 from Probotix successfully. <br><div><br></div><div>Good luck!</div><div><br>James</div><div><br></div><div><div class="gmail_extra"><div class="gmail_quote">On Mon, May 16, 2016 at 11:46 AM, <span dir="ltr"><<a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex">Send eeglablist mailing list submissions to<br>
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<br>Today's Topics:<br>
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1. Interpolation of bad channels (DISNAN FEDERICA [PS5100398])<br>
2. Re: Eigenvalues generation (Ibtissem KHOUAJA BENFRADJ)<br>
3. Fwd: How to high-pass filter eeg data containing NaNs?<br>
(leo budinich)<br>
4. Fwd: How to high-pass filter eeg data containing NaNs?<br>
(leo budinich)<br>
5. Re: Eigenvalues generation (Matt Gerhold)<br>
6. Re: How to high-pass filter eeg data containing NaNs?<br>
(Andreas Widmann)<br>
7. Re: Question about ASR (Artifact Subspace Reconstruction)<br>
(Huy Ho?ng Tr?n)<br>
8. sending TTL triggers over parallel port (Simon De Deyne)<br>
<br><br>---------- Forwarded message ----------<br>From: "DISNAN FEDERICA [PS5100398]" <<a href="mailto:FEDERICA.DISNAN@studenti.units.it">FEDERICA.DISNAN@studenti.units.it</a>><br>To: "<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>" <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Cc: <br>Date: Sat, 14 May 2016 16:31:54 +0000<br>Subject: [Eeglablist] Interpolation of bad channels<br>
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<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">Dear users, </span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">I would like to ask you some help with the usage of EEGLab.</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">In detail, I have some questions concerning the interpolation of bad channels. </span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">Before running the ICA on EEG continuous data I follow this steps:</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">- Load Channel configuration</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">- Resampling to 500 Hz</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">- Filtering </span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">- Re-reference</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">- Cleanline</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">Kindly I would like to know when and how it would be more appropriate to interpolate the bad channels.</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">I would really appreciate your suggestions to solve this problem.</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">Many thanks for your support,</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">Best regards</span></font></div>
<div><font face="Arial,sans-serif" size="1" color="#222222"><span style="font-size:12.79px">Federica Disnan</span></font></div>
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<br><br>---------- Forwarded message ----------<br>From: Ibtissem KHOUAJA BENFRADJ <<a href="mailto:ibtissem.khouaja@live.fr">ibtissem.khouaja@live.fr</a>><br>To: Matt Gerhold <<a href="mailto:matt.gerhold@gmail.com">matt.gerhold@gmail.com</a>><br>Cc: "<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>" <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Date: Fri, 13 May 2016 11:57:57 +0100<br>Subject: Re: [Eeglablist] Eigenvalues generation<br>
<div><div dir="ltr"><font face="Times New Roman,sans-serif" color="#000000"><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>Hi Matt, </span></span></font><br></font><br><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>I </span><span>thank you</span> so much <span>for your reply.</span><br><br><span>Really, you have</span> <span>undersatand</span> <span>very well my goal.</span> <span><br>In fact, I</span>'<span>d like to know</span> <span>if you have applied</span> <span>a specific</span> <span>algorithm</span> <span>for the generation of</span> <span>the</span> <span>AR</span> </span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>matrix </span></span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>(A).</span> <br><span>In my work</span><span>, I applied</span> <span>the algorithm</span> <span>'</span><span>arfit</span><span>'</span><span>,</span> <span>and it gives me</span> <span>a</span></span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"> <span>vector </span><span></span> <span>of</span> p <span>AR</span> <span>parameters for each</span> <span>signal.</span><br><br>I<span>n this case,</span> <span>how can</span> I <span>present my</span> <span>matrix</span> <span>to extract</span> <span>the eigenvalues?<br><br>Thank's a lot for your reply, Ibtissem<br><br></span></span></font><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><font style="font-size:10pt;background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">Ibtissem KHOUAJA BENFRADJ</font><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">PhD in computer science</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Speciality: Biomedical Signal Processing</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LTIM, University of Monastir, Tunisia</font></span></div><div><a href="http://www.labtim.org/accueil.php" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://www.labtim.org/accueil.php</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LIGM, Univerisity of Paris-East, France </font></span></div><div><a href="http://ligm.u-pem.fr/" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://ligm.u-pem.fr/</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2"><br></font></span></div><div><span style="font-size:12pt"><br></span></div><br><br><div><hr>Date: Tue, 3 May 2016 21:09:01 +0200<br>Subject: Re: [Eeglablist] Eigenvalues generation<br>From: <a href="mailto:matt.gerhold@gmail.com" target="_blank">matt.gerhold@gmail.com</a><br>To: <a href="mailto:ibtissem.khouaja@live.fr" target="_blank">ibtissem.khouaja@live.fr</a><br>CC: <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br><br><div dir="ltr">Hi,<br><br>One would compute the eigenvalues for the autoregressive parameter matrix--your estimated beta coefficients. This would be to assess the stability of the system. One only needs to compute these eigenvalues for this matrix; nothing more nothing less... Then take the modulus of the eigenvalues, they should all be less than one. Following Lutkepohl (2005), a simple way to do this is: <br><br>if A = autoregressive parameters (K x K), where K is number of data-channels: <br><br>>> eig_vals = abs(diag(jordan(A))) % jordan canonical form of A<br>>> eig_vals < 1 % should give a vector of 1's if the system is stable<br><br>Regards,<br><br>Matthew<br><br></div><div><br><div>On Mon, May 2, 2016 at 6:20 PM, Ibtissem KHOUAJA BENFRADJ <span dir="ltr"><<a href="mailto:ibtissem.khouaja@live.fr" target="_blank">ibtissem.khouaja@live.fr</a>></span> wrote:<br><blockquote style="border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex">
<div><div dir="ltr"><font face="Times New Roman,sans-serif" color="#000000"><font style="font-size:16pt" size="4">Dear list, </font><br><br></font><font face="Times New Roman,sans-serif"><br></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>I applied</span> <span>the ARFIT</span> <span>algorithm</span> <span>in the generation of</span> <span>autoregressive</span> <span>parameters</span><br><span>corresponding to the time-varying of EEG.<br>Nest, </span><span>in order to</span> <span>generate</span> <span>the eigenvalues of </span><span>the</span> <span>AR</span> <span>matrix</span> <span>I used</span> <span>ARMODE</span> <span>algorithm.</span><br><br><span>I need</span> <span>your help to</span> <span>generate</span> differently <span>the eigenvalues</span> <span>for</span> <span>the</span> <span>AR</span> <span>parameters</span> <span>of</span> <span>each</span> <span>signal (AR vector) </span><span>and</span> <span>all of </span><span>signal</span> (AR matrix)<span></span></span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4">.<br><br>Thank you for your help, Ibtissem<br></font><br><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><font style="font-size:10pt;background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">Ibtissem KHOUAJA BENFRADJ</font><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">PhD in computer science</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Speciality: Biomedical Signal Processing</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LTIM, University of Monastir, Tunisia</font></span></div><div><a href="http://www.labtim.org/accueil.php" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://www.labtim.org/accueil.php</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LIGM, Univerisity of Paris-East, France </font></span></div><div><a href="http://ligm.u-pem.fr/" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://ligm.u-pem.fr/</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2"><br></font></span></div><div><span style="font-size:12pt"><br></span></div> </div></div>
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<br><br>---------- Forwarded message ----------<br>From: leo budinich <<a href="mailto:leo.budinich@edu.unito.it">leo.budinich@edu.unito.it</a>><br>To: <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Cc: <br>Date: Fri, 13 May 2016 14:33:36 +0200<br>Subject: [Eeglablist] Fwd: How to high-pass filter eeg data containing NaNs?<br><div dir="ltr">Thank you Carolina, the method you suggest is really simple (and that's super for a matlab neophyte as me), I am going to try it!<br><div class="gmail_quote"><div dir="ltr"><div>Ciao,<br></div><div>Leo<br></div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2016-05-12 18:17 GMT+02:00 Carolina Migliorelli <span dir="ltr"><<a href="mailto:carolina.migliorelli@upc.edu" target="_blank">carolina.migliorelli@upc.edu</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi Leo, You are right, filters doesn't work with NaN signals. You can try to replace NaNs by zeros. The easiest way to do this is to use the matlab function "isnan". If you do something like:<div><br></div><div>signal(isnan(signal))=0;</div><div><br></div><div>This will replace all the Nans by zeros. If you don't want the nans be part of the signal you can try</div><div><br></div><div>signal(isnan(signal))=[];</div><div><br></div><div>This will delete the NaNs of the whole signal.</div><div><br></div><div>Hope this works!</div><div><br></div><div>Best,</div><div><br></div><div>Carolina</div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div>2016-05-11 15:38 GMT+02:00 leo budinich <span dir="ltr"><<a href="mailto:leo.budinich@edu.unito.it" target="_blank">leo.budinich@edu.unito.it</a>></span>:<br></div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex"><div><div><div dir="ltr"><div><div>Hi Eeglablist,<br><br></div>I'm writing here because I'm a new user of EEGLAB and I am facing a problem with high-pass filtering some data.<br><br></div><div>I am trying to apply on a freely downloadable dataset some of the same processing steps that have been used by the researchers that provided the data (see <a href="http://www.stefanfrank.info/pubs/BL2015.pdf" target="_blank">http://www.stefanfrank.info/pubs/BL2015.pdf</a>).<br>The set I want to filter has 32 channels and has already been been band-pass filtered at 0.05Hz - 25Hz, recalibrated and re-referenced to the mastoids.<br><br></div><div>An important detail is that the EEG recordings concern the reading of two hundred english sentences; the data recorded between the presentations of two sentences have been "set to NaN" by the researchers who provided the dataset, so the channel data of, e.g. subject01, presents this aspect when you inspect it:<br><br></div><div><-------------sentence_1-------------><---------wait----------><---------sentence_2---------><br></div><div><br>signal-signal-signal-signal-signal-NaN-NaN-NaN-NaN-signal-signal-signal-signal <br></div><div><br><br>What I would like to do is high-pass filter the entire set to 0.50Hz, as a way to "mitigate the baseline problem by reducing the correlation between the baselines and amplitudes by applying an additional high-pass filter with a sufficiently high cut-off frequency" (see the reference, pp.4). <br><br></div><div>Unfortunately, when I apply a FIR filter with a 0.50Hz lower-edge I obtain a lot of NaNs on the areas that previously were 'signal', but not everywhere (and, curiously, the processing is really fast), so that it gets this kind of aspect:<br><br><br><div><-------------sentence_1-------------><---------wait----------><---------sentence_2---------><br></div><br>NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-signal-NaN-NaN</div><div><br><br>Here's the eeglab output:<br><font face="Liberation Mono, monospace"><font style="font-size:10pt" size="2"><br>pop_eegfiltnew()
- performing 1651 point highpass filtering.</font></font><font face="Liberation Mono, monospace"><font style="font-size:10pt" size="2"><br>pop_eegfiltnew()
- transition band width: 0.5 Hz</font></font><font face="Liberation Mono, monospace"><font style="font-size:10pt" size="2"><br>pop_eegfiltnew()
- passband edge(s): 0.5 Hz</font></font><font face="Liberation Mono, monospace"><font style="font-size:10pt" size="2"><br>pop_eegfiltnew()
- cutoff frequency(ies) (-6 dB): 0.25 Hz</font></font><font face="Liberation Mono, monospace"><font style="font-size:10pt" size="2"><br>pop_eegfiltnew()
- filtering the data (zero-phase)</font></font><font face="Liberation Mono, monospace"><font style="font-size:10pt" size="2"><br>firfilt():
|====================| 100%, ETE 00:00</font></font><font size="2"><font style="font-size:10pt" size="2"><br>Done.<br></font><font face="arial,helvetica,sans-serif"><br><br>I imagined that the filter function doesn't produce the right output because of the NaNs present between the sentences</font></font>, but as my comprehension of the functioning of filters and of eeglab in general is extremely limited at the moment, it turns out to be just a speculation. <br><br></div><div>Could you help me understanding what's wrong with my filtering?<br></div><div><br>And, if my hypothesis is correct, i.e. the filter cannot be applied to data containing NaNs, how would you apply a high-pass filter to data structured as I indicated above (i.e., containing NaNs in some parts)?<br><br></div><div>Thank you!<br></div><div>Leo<br></div></div>
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<div style="font-size:1.3em">------------------------</div><span style="font-size:small"><div><img src="http://www.mail.unito.it/img/logounito.png"></div><div><span style="font-size:small"><br></span></div>Indirizzo istituzionale di posta elettronica degli studenti e dei laureati dell'Università degli Studi di Torino</span><div><font size="2">Official University of Turin email address for students and graduates </font></div><br><br>---------- Forwarded message ----------<br>From: leo budinich <<a href="mailto:leo.budinich@edu.unito.it">leo.budinich@edu.unito.it</a>><br>To: <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Cc: <br>Date: Fri, 13 May 2016 14:33:00 +0200<br>Subject: [Eeglablist] Fwd: How to high-pass filter eeg data containing NaNs?<br><div dir="ltr"><br><div class="gmail_quote">---------- Forwarded message ----------<br>From: <b class="gmail_sendername">leo budinich</b> <span dir="ltr"><<a href="mailto:leo.budinich@edu.unito.it" target="_blank">leo.budinich@edu.unito.it</a>></span><br>Date: 2016-05-13 10:56 GMT+02:00<br>Subject: Re: [Eeglablist] How to high-pass filter eeg data containing NaNs?<br>To: Andreas Widmann <<a href="mailto:widmann@uni-leipzig.de" target="_blank">widmann@uni-leipzig.de</a>><br><br><br><div dir="ltr"><div><div><div><div>Thanks Andreas! Your answer has been really helpful! <br>Now I understand a little better how filtering works...<br></div>I'll make a try with cutting away the NaN parts and then applying the high-pass filter, keeping in mind that it's highly possible to incur in edge-effects. <br>I think also that I am going make a try with a more traditional approach to baseline correction (subtracting the mean amplitude of the 100ms preceding the target stimulus).<br></div><br>I would like also to try what Carolina proposed (transforming NaNs in zeros and then filtering the set): do you think it could work?<br><br></div>Thanks,<br></div>Leo <br></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2016-05-12 16:25 GMT+02:00 Andreas Widmann <span dir="ltr"><<a href="mailto:widmann@uni-leipzig.de" target="_blank">widmann@uni-leipzig.de</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex">Hi Leo,<br>
<br>
filtering is the convolution of two signals-the data and the filter’s impulse response. The impulse response has a duration, 1651 samples or 6.6 seconds in your case. If any sample of both signals is NaN the output is also NaN. That’s why your filter output mainly consists of NaNs. Unfortunately, there’s is not so much one could do. Thus, I would consider the decision NaNing large parts of the continuous data as, hm, suboptimal.<br>
<br>
Pragmatically, the only thing you could do is cutting away the NaN parts (the data are effectively kind of „epoched“ already anyway).<br>
<br>
tmp = any( isnan( EEG.data ), 1 );<br>
onsetArray = find( diff( [ 0 tmp ] ) == 1 );<br>
offsetArray = find( diff( [ tmp 0 ] ) == -1 );<br>
EEG = eeg_eegrej( EEG, [ onsetArray; offsetArray ]' );<br>
[ ALLEEG, EEG, CURRENTSET ] = pop_newset( ALLEEG, EEG, CURRENTSET );<br>
eeglab redraw<br>
<br>
Now, at least there is signal in the filter output where there was signal in the filter input.<br>
<br>
I would, however, recommend being careful with the further analysis of the data and interpretation of the results. High-pass filtering should always be done on the continuous data. You have to expect edge-effects up to the duration of the impulse response at the beginning and end of each epoch (actually all data regions which were signal in the filter input and NaN in the filter output before; essentially the observed NaNs reflect some kind of edge-effect themselves). Type, size, and duration of the edge-effects depend on impulse response length and whether and how data are padded during filtering but there is nothing you could do against edge-effects in general. Given the minimal required durations of the impulse responses for the filter cutoffs suggested in the paper I would consider the final sentence of footnote 3 as incorrect. This does also not depend on filter type (unfortunately not described in detail in the paper).<br>
<br>
Finally, you have to be careful with high-pass filtering with higher cutoffs in ERP analysis in general. Recent relevant literature on this issue you may find at the end of this page:<br>
<a href="http://sccn.ucsd.edu/wiki/Firfilt_FAQ" rel="noreferrer" target="_blank">http://sccn.ucsd.edu/wiki/Firfilt_FAQ</a><br>
<br>
Hope this helps!<br>
Andreas<br>
<div><div><br>
> Am 11.05.2016 um 15:38 schrieb leo budinich <<a href="mailto:leo.budinich@edu.unito.it" target="_blank">leo.budinich@edu.unito.it</a>>:<br>
><br>
> Hi Eeglablist,<br>
><br>
> I'm writing here because I'm a new user of EEGLAB and I am facing a problem with high-pass filtering some data.<br>
><br>
> I am trying to apply on a freely downloadable dataset some of the same processing steps that have been used by the researchers that provided the data (see <a href="http://www.stefanfrank.info/pubs/BL2015.pdf" rel="noreferrer" target="_blank">http://www.stefanfrank.info/pubs/BL2015.pdf</a>).<br>
> The set I want to filter has 32 channels and has already been been band-pass filtered at 0.05Hz - 25Hz, recalibrated and re-referenced to the mastoids.<br>
><br>
> An important detail is that the EEG recordings concern the reading of two hundred english sentences; the data recorded between the presentations of two sentences have been "set to NaN" by the researchers who provided the dataset, so the channel data of, e.g. subject01, presents this aspect when you inspect it:<br>
><br>
> <-------------sentence_1-------------><---------wait----------><---------sentence_2---------><br>
><br>
> signal-signal-signal-signal-signal-NaN-NaN-NaN-NaN-signal-signal-signal-signal<br>
><br>
><br>
> What I would like to do is high-pass filter the entire set to 0.50Hz, as a way to "mitigate the baseline problem by reducing the correlation between the baselines and amplitudes by applying an additional high-pass filter with a sufficiently high cut-off frequency" (see the reference, pp.4).<br>
><br>
> Unfortunately, when I apply a FIR filter with a 0.50Hz lower-edge I obtain a lot of NaNs on the areas that previously were 'signal', but not everywhere (and, curiously, the processing is really fast), so that it gets this kind of aspect:<br>
><br>
><br>
> <-------------sentence_1-------------><---------wait----------><---------sentence_2---------><br>
><br>
> NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-signal-NaN-NaN<br>
><br>
><br>
> Here's the eeglab output:<br>
><br>
> pop_eegfiltnew() - performing 1651 point highpass filtering.<br>
> pop_eegfiltnew() - transition band width: 0.5 Hz<br>
> pop_eegfiltnew() - passband edge(s): 0.5 Hz<br>
> pop_eegfiltnew() - cutoff frequency(ies) (-6 dB): 0.25 Hz<br>
> pop_eegfiltnew() - filtering the data (zero-phase)<br>
> firfilt(): |====================| 100%, ETE 00:00<br>
> Done.<br>
><br>
><br>
> I imagined that the filter function doesn't produce the right output because of the NaNs present between the sentences, but as my comprehension of the functioning of filters and of eeglab in general is extremely limited at the moment, it turns out to be just a speculation.<br>
><br>
> Could you help me understanding what's wrong with my filtering?<br>
><br>
> And, if my hypothesis is correct, i.e. the filter cannot be applied to data containing NaNs, how would you apply a high-pass filter to data structured as I indicated above (i.e., containing NaNs in some parts)?<br>
><br>
> Thank you!<br>
> Leo<br>
><br>
</div></div>> ------------------------<br>
><br>
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<br>
<div style="font-size:1.3em">------------------------</div><span style="font-size:small"><div><img src="http://www.mail.unito.it/img/logounito.png"></div><div><span style="font-size:small"><br></span></div>Indirizzo istituzionale di posta elettronica degli studenti e dei laureati dell'Università degli Studi di Torino</span><div><font size="2">Official University of Turin email address for students and graduates </font></div><br><br>---------- Forwarded message ----------<br>From: Matt Gerhold <<a href="mailto:matt.gerhold@gmail.com">matt.gerhold@gmail.com</a>><br>To: Ibtissem KHOUAJA BENFRADJ <<a href="mailto:ibtissem.khouaja@live.fr">ibtissem.khouaja@live.fr</a>><br>Cc: "<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>" <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Date: Fri, 13 May 2016 15:15:25 +0200<br>Subject: Re: [Eeglablist] Eigenvalues generation<br><div dir="ltr">
<p class="MsoNormal">Hi Ibtissem,</p>
<p class="MsoNormal">ARFit is a toolbox for fitting vector autoregressive
timeseries models. It uses QR factorisation to solve for a least-squares fit.
One can use the QR factors to compute the covariance matrix for progressively
lower model orders, thus enabling a step-wise least-square approach. It has
computational advantages over OLS and Yule-Walker fitting algorithms.</p>
<p class="MsoNormal">The ARFit provides a (chan X chan*order) matrix of
coefficients in the instance of a higher order model. For a higher-order model
one would compute the companion form matrix denoted, F.</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">% fit model to a bivariate timeseries data in variable ts</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">>>[w, A, C, sbc, fpe, th]=arfit(ts', 4, 4, 'sbc', 'zero');</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal"> % build companion form matrix
</p><p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">>> rw_2 = [eye(2) zeros(2,2) zeros(2,2) zeros(2,2)];</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">>> rw_3 = [zeros(2,2) eye(2) zeros(2,2) zeros(2,2)];</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">>> rw_4 = [zeros(2,2) zeros(2,2) eye(2) zeros(2,2)];</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">>> F = [A; rw_2; rw_3; rw_4]; % final companion form matrix</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal"> % check stability
</p><p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">>> eig_vals = abs(diag(jordan(F))) % jordan canonical form of A<br>
>> max(eig_vals) < 1 % should give a 1 if the system is stable</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">That’s pretty much it.
</p><p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">Rgds,
</p><p class="MsoNormal" style="margin-bottom:0.0001pt;line-height:normal">Matthew
</p></div><div class="gmail_extra"><br><div class="gmail_quote">On Fri, May 13, 2016 at 12:57 PM, Ibtissem KHOUAJA BENFRADJ <span dir="ltr"><<a href="mailto:ibtissem.khouaja@live.fr" target="_blank">ibtissem.khouaja@live.fr</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex">
<div><div dir="ltr"><font face="Times New Roman,sans-serif" color="#000000"><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>Hi Matt, </span></span></font><br></font><br><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>I </span><span>thank you</span> so much <span>for your reply.</span><br><br><span>Really, you have</span> <span>undersatand</span> <span>very well my goal.</span> <span><br>In fact, I</span>'<span>d like to know</span> <span>if you have applied</span> <span>a specific</span> <span>algorithm</span> <span>for the generation of</span> <span>the</span> <span>AR</span> </span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>matrix </span></span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>(A).</span> <br><span>In my work</span><span>, I applied</span> <span>the algorithm</span> <span>'</span><span>arfit</span><span>'</span><span>,</span> <span>and it gives me</span> <span>a</span></span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"> <span>vector </span><span></span> <span>of</span> p <span>AR</span> <span>parameters for each</span> <span>signal.</span><br><br>I<span>n this case,</span> <span>how can</span> I <span>present my</span> <span>matrix</span> <span>to extract</span> <span>the eigenvalues?<br><br>Thank's a lot for your reply, Ibtissem<br><br></span></span></font><span><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><font style="font-size:10pt;background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">Ibtissem KHOUAJA BENFRADJ</font><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">PhD in computer science</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Speciality: Biomedical Signal Processing</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LTIM, University of Monastir, Tunisia</font></span></div><div><a href="http://www.labtim.org/accueil.php" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://www.labtim.org/accueil.php</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LIGM, Univerisity of Paris-East, France </font></span></div><div><a href="http://ligm.u-pem.fr/" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://ligm.u-pem.fr/</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2"><br></font></span></div><div><span style="font-size:12pt"><br></span></div><br><br></span><div><hr>Date: Tue, 3 May 2016 21:09:01 +0200<br>Subject: Re: [Eeglablist] Eigenvalues generation<br>From: <a href="mailto:matt.gerhold@gmail.com" target="_blank">matt.gerhold@gmail.com</a><br>To: <a href="mailto:ibtissem.khouaja@live.fr" target="_blank">ibtissem.khouaja@live.fr</a><br>CC: <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><div><div><br><br><div dir="ltr">Hi,<br><br>One would compute the eigenvalues for the autoregressive parameter matrix--your estimated beta coefficients. This would be to assess the stability of the system. One only needs to compute these eigenvalues for this matrix; nothing more nothing less... Then take the modulus of the eigenvalues, they should all be less than one. Following Lutkepohl (2005), a simple way to do this is: <br><br>if A = autoregressive parameters (K x K), where K is number of data-channels: <br><br>>> eig_vals = abs(diag(jordan(A))) % jordan canonical form of A<br>>> eig_vals < 1 % should give a vector of 1's if the system is stable<br><br>Regards,<br><br>Matthew<br><br></div><div><br><div>On Mon, May 2, 2016 at 6:20 PM, Ibtissem KHOUAJA BENFRADJ <span dir="ltr"><<a href="mailto:ibtissem.khouaja@live.fr" target="_blank">ibtissem.khouaja@live.fr</a>></span> wrote:<br><blockquote style="border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex">
<div><div dir="ltr"><font face="Times New Roman,sans-serif" color="#000000"><font style="font-size:16pt" size="4">Dear list, </font><br><br></font><font face="Times New Roman,sans-serif"><br></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4"><span lang="en"><span>I applied</span> <span>the ARFIT</span> <span>algorithm</span> <span>in the generation of</span> <span>autoregressive</span> <span>parameters</span><br><span>corresponding to the time-varying of EEG.<br>Nest, </span><span>in order to</span> <span>generate</span> <span>the eigenvalues of </span><span>the</span> <span>AR</span> <span>matrix</span> <span>I used</span> <span>ARMODE</span> <span>algorithm.</span><br><br><span>I need</span> <span>your help to</span> <span>generate</span> differently <span>the eigenvalues</span> <span>for</span> <span>the</span> <span>AR</span> <span>parameters</span> <span>of</span> <span>each</span> <span>signal (AR vector) </span><span>and</span> <span>all of </span><span>signal</span> (AR matrix)<span></span></span></font><font style="font-size:16pt" face="Times New Roman,sans-serif" size="4">.<br><br>Thank you for your help, Ibtissem<br></font><br><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><div><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">----------------------------------------------------------</font></div><font style="font-size:10pt;background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">Ibtissem KHOUAJA BENFRADJ</font><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">PhD in computer science</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Speciality: Biomedical Signal Processing</font></span></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LTIM, University of Monastir, Tunisia</font></span></div><div><a href="http://www.labtim.org/accueil.php" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://www.labtim.org/accueil.php</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2">Laboratory: LIGM, Univerisity of Paris-East, France </font></span></div><div><a href="http://ligm.u-pem.fr/" target="_blank"><font style="background-color:rgb(255,255,255)" face="Comic Sans MS" color="#666666" size="2">http://ligm.u-pem.fr/</font></a></div><div><span style="background-color:rgb(255,255,255)"><font face="Comic Sans MS" color="#666666" size="2"><br></font></span></div><div><span style="font-size:12pt"><br></span></div> </div></div>
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</blockquote></div><br></div>
<br><br>---------- Forwarded message ----------<br>From: Andreas Widmann <<a href="mailto:widmann@uni-leipzig.de">widmann@uni-leipzig.de</a>><br>To: EEGLAB List <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Cc: <br>Date: Mon, 16 May 2016 11:49:48 +0200<br>Subject: Re: [Eeglablist] How to high-pass filter eeg data containing NaNs?<br>Hi Leo,<br>
<br>
filtering is the convolution of two signals-the data and the filter’s impulse response. The impulse response has a duration, 1651 samples or 6.6 seconds in your case. If any sample of both signals is NaN the output is also NaN. That’s why your filter output mainly consists of NaNs. Unfortunately, there’s is not so much one could do. Thus, I would consider the decision NaNing large parts of the continuous data as, hm, suboptimal.<br>
<br>
Pragmatically, the only thing you could do is cutting away the NaN parts (the data are effectively kind of „epoched“ already anyway).<br>
<br>
tmp = any( isnan( EEG.data ), 1 );<br>
onsetArray = find( diff( [ 0 tmp ] ) == 1 );<br>
offsetArray = find( diff( [ tmp 0 ] ) == -1 );<br>
EEG = eeg_eegrej( EEG, [ onsetArray; offsetArray ]' );<br>
[ ALLEEG, EEG, CURRENTSET ] = pop_newset( ALLEEG, EEG, CURRENTSET );<br>
eeglab redraw<br>
<br>
Now, at least there is signal in the filter output where there was signal in the filter input.<br>
<br>
I would, however, recommend being careful with the further analysis of the data and interpretation of the results. High-pass filtering should always be done on the continuous data. You have to expect edge-effects up to the duration of the impulse response at the beginning and end of each epoch (actually all data regions which were signal in the filter input and NaN in the filter output before; essentially the observed NaNs reflect some kind of edge-effect themselves). Type, size, and duration of the edge-effects depend on impulse response length and whether and how data are padded during filtering but there is nothing you could do against edge-effects in general. Given the minimal required durations of the impulse responses for the filter cutoffs suggested in the paper I would consider the final sentence of footnote 3 as incorrect. This does also not depend on filter type (unfortunately not described in detail in the paper).<br>
<br>
Finally, you have to be careful with high-pass filtering with higher cutoffs in ERP analysis in general. Recent relevant literature on this issue you may find at the end of this page:<br>
<a href="http://sccn.ucsd.edu/wiki/Firfilt_FAQ" rel="noreferrer" target="_blank">http://sccn.ucsd.edu/wiki/Firfilt_FAQ</a><br>
<br>
Hope this helps!<br>
Andreas<br>
<br>
> Am 11.05.2016 um 15:38 schrieb leo budinich <<a href="mailto:leo.budinich@edu.unito.it">leo.budinich@edu.unito.it</a>>:<br>
><br>
> Hi Eeglablist,<br>
><br>
> I'm writing here because I'm a new user of EEGLAB and I am facing a problem with high-pass filtering some data.<br>
><br>
> I am trying to apply on a freely downloadable dataset some of the same processing steps that have been used by the researchers that provided the data (see <a href="http://www.stefanfrank.info/pubs/BL2015.pdf" rel="noreferrer" target="_blank">http://www.stefanfrank.info/pubs/BL2015.pdf</a>).<br>
> The set I want to filter has 32 channels and has already been been band-pass filtered at 0.05Hz - 25Hz, recalibrated and re-referenced to the mastoids.<br>
><br>
> An important detail is that the EEG recordings concern the reading of two hundred english sentences; the data recorded between the presentations of two sentences have been "set to NaN" by the researchers who provided the dataset, so the channel data of, e.g. subject01, presents this aspect when you inspect it:<br>
><br>
> <-------------sentence_1-------------><---------wait----------><---------sentence_2---------><br>
><br>
> signal-signal-signal-signal-signal-NaN-NaN-NaN-NaN-signal-signal-signal-signal<br>
><br>
><br>
> What I would like to do is high-pass filter the entire set to 0.50Hz, as a way to "mitigate the baseline problem by reducing the correlation between the baselines and amplitudes by applying an additional high-pass filter with a sufficiently high cut-off frequency" (see the reference, pp.4).<br>
><br>
> Unfortunately, when I apply a FIR filter with a 0.50Hz lower-edge I obtain a lot of NaNs on the areas that previously were 'signal', but not everywhere (and, curiously, the processing is really fast), so that it gets this kind of aspect:<br>
><br>
><br>
> <-------------sentence_1-------------><---------wait----------><---------sentence_2---------><br>
><br>
> NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-NaN-signal-NaN-NaN<br>
><br>
><br>
> Here's the eeglab output:<br>
><br>
> pop_eegfiltnew() - performing 1651 point highpass filtering.<br>
> pop_eegfiltnew() - transition band width: 0.5 Hz<br>
> pop_eegfiltnew() - passband edge(s): 0.5 Hz<br>
> pop_eegfiltnew() - cutoff frequency(ies) (-6 dB): 0.25 Hz<br>
> pop_eegfiltnew() - filtering the data (zero-phase)<br>
> firfilt(): |====================| 100%, ETE 00:00<br>
> Done.<br>
><br>
><br>
> I imagined that the filter function doesn't produce the right output because of the NaNs present between the sentences, but as my comprehension of the functioning of filters and of eeglab in general is extremely limited at the moment, it turns out to be just a speculation.<br>
><br>
> Could you help me understanding what's wrong with my filtering?<br>
><br>
> And, if my hypothesis is correct, i.e. the filter cannot be applied to data containing NaNs, how would you apply a high-pass filter to data structured as I indicated above (i.e., containing NaNs in some parts)?<br>
><br>
> Thank you!<br>
> Leo<br>
><br>
> ------------------------<br>
><br>
><br>
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<br><br>---------- Forwarded message ----------<br>From: "Huy Hoàng Trần" <<a href="mailto:hoangtran355@gmail.com">hoangtran355@gmail.com</a>><br>To: <<a href="mailto:tarikbelbahar@gmail.com">tarikbelbahar@gmail.com</a>><br>Cc: eeglablist <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Date: Mon, 16 May 2016 04:19:22 +0000<br>Subject: Re: [Eeglablist] Question about ASR (Artifact Subspace Reconstruction)<br><div dir="ltr">Hello, <div>Thanks for your response.</div><div>I can use ASR in my data and it works very well, and now I want to modify the code. <div>I don't really understand the part of the code that performs PCA. Which variance represent the Principal Components in asr_process.m file?</div></div><div><br></div><div><div style="font-family:'helvetica neue',helvetica,arial,sans-serif"><br></div><div style="font-family:'helvetica neue',helvetica,arial,sans-serif"><br></div></div></div><br><div class="gmail_quote"><div dir="ltr">Vào Th 6, 13 thg 5, 2016 vào lúc 01:43 Tarik S Bel-Bahar <<a href="mailto:tarikbelbahar@gmail.com" target="_blank">tarikbelbahar@gmail.com</a>> đã viết:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div style="color:rgb(51,51,153)">Hello, I have a related question, can we set the specific channels used in ASR without hacking into the functions ?</div><div style="color:rgb(51,51,153)">Huy, you may benefit from using the new published PREP pipeline from Kothe and colleagues, which I believe also implements ASR and various other functioins for automatic pre-processing.</div><div style="color:rgb(51,51,153)">Best wishes!</div></div><div class="gmail_extra"><br><div class="gmail_quote"></div></div><div class="gmail_extra"><div class="gmail_quote">On Thu, May 12, 2016 at 12:27 AM, Huy Hoàng Trần <span dir="ltr"><<a href="mailto:hoangtran355@gmail.com" target="_blank">hoangtran355@gmail.com</a>></span> wrote:<br></div></div><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div>Hello </div><div>I am working with ASR and I wonder if it would be possible to replace PCA step in ASR with another algorithm?</div><div><br></div><div>Thank you for any responses!</div></div><div><br></div><div>Hoang</div><div><div><br></div><div>Department of Automation Technology,</div><div>Institute of Information Tech., Vietnam Academy of Science and Tech.</div></div></div>
<br></blockquote></div></div><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex">_______________________________________________<br>
Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" rel="noreferrer" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>
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<br><br>---------- Forwarded message ----------<br>From: Simon De Deyne <<a href="mailto:simon.dedeyne@adelaide.edu.au">simon.dedeyne@adelaide.edu.au</a>><br>To: "<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>" <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>><br>Cc: <br>Date: Mon, 16 May 2016 09:51:31 +0000<br>Subject: [Eeglablist] sending TTL triggers over parallel port<br><br>
<br>
My apologies if this is slightly off topic, but does anyone know how to send TTL triggers<br>
with matlab on Linux? I use an ubuntu 14.04 machine with psychtoolbox 3 and would like to communicate with Neuroscan Synamps2.<br>
So far I found some outdated information online, but nothing seems to work so far and I'm afraid I'm not that experienced with EEG studies as well.<br>
Any ideas on how to test to see if everything works?<br>
<br>
Thanks,<br>
Simon<br>
<br>
Simon De Deyne<br>
School of Psychology<br>
The University of Adelaide, AUSTRALIA 5005<br>
Level 5 Hughes Building<br>
Ph : <a href="tel:%2B61%208%208313%206785" value="+61883136785">+61 8 8313 6785</a><br>
e-mail: <a href="mailto:simon.dedeyne@adelaide.edu.au">simon.dedeyne@adelaide.edu.au</a><br>
<br>_______________________________________________<br>
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Eeglablist page: <a href="http://www.sccn.ucsd.edu/eeglab/eeglabmail.html" rel="noreferrer" target="_blank">http://www.sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>
To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsub@sccn.ucsd.edu">eeglablist-unsub@sccn.ucsd.edu</a><br>
To switch to non-digest mode, send an empty email to <a href="mailto:eeglablist-nodigest@sccn.ucsd.edu">eeglablist-nodigest@sccn.ucsd.edu</a><br></blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature"><div dir="ltr"><div>James Jones-Rounds</div>Laboratory Manager<br>Human Development EEG and Psychophysiology (HEP) Laboratory,<div>Department of Human Development,<br>--------------------------------------------<br>Cornell University | Ithaca, NY<br></div><div>607-255-9883</div><div><a href="mailto:eeg@cornell.edu" target="_blank">eeg@cornell.edu</a></div></div></div>
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