[Eeglablist] Cluster-based permutation tests for 3 conditions
Clayton Hickey
cmhickey at gmail.com
Wed Sep 13 01:25:00 PDT 2017
Hi Angel,
I’ve not used the clustering implemented in EEGLAB itself, but I’ve been very pleased with the spatiotemporal clustering and permutation tests implemented in the CosmoMVPA toolbox. This clusters across both sensors and time, which is what I think you want, and uses low-assumption approaches like TFCE. There is basic support for the EEGLAB ‘set’ data format.
cosmomvpa.org <http://cosmomvpa.org/>
this function in particular:
http://www.cosmomvpa.org/matlab/cosmo_montecarlo_cluster_stat.html#cosmo-montecarlo-cluster-stat <http://www.cosmomvpa.org/matlab/cosmo_montecarlo_cluster_stat.html#cosmo-montecarlo-cluster-stat>
The package is developed and maintained by Nick Oosterhof and Andrew Connolly, and I know Nick spent a lot of time and thought on exactly your issue (if I correctly understand what you mean).
best, clayton
> From: Angel Caputi <caputiangel at gmail.com <mailto:caputiangel at gmail.com>>
> Subject: Re: [Eeglablist] Cluster-based permutation tests for 3 conditions
> Date: September 11, 2017 at 8:56:14 PM GMT+2
> To: Stephen Politzer-Ahles <politzerahless at gmail.com <mailto:politzerahless at gmail.com>>
> Cc: "eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>" <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
>
>
> I wonder What kind of post-hoc test can be used for testing that the cluster site (i.e. combination of times and channels) we need to use for spatio-temporal localization. Would not be the use a sencond test on the cluster region a kind of double dipping? What would the correct procedure for testing where the spatio-temporal pattern differ?
> Sincerely
> Angel
>
>
> 2017-09-09 9:49 GMT-03:00 Stephen Politzer-Ahles <politzerahless at gmail.com <mailto:politzerahless at gmail.com>>:
> Cluster-based permutation tests are not necessarily between two conditions. They operate on any test statistic; this test statistic can be an F test from an ANOVA with three conditions. Just like with a two-condition permutation test, if the test comes out significant this will tell you that the three conditions differ in this dataset (whatever selection of channels, times, and frequency bands you looked at), and the cluster extent can give you an idea of what channel/time/frequency cluster is driving that difference. With an F-test, you would need to do follow-up comparisons to see which particular conditions are driving the difference.
>
>
> ---
> Stephen Politzer-Ahles
> The Hong Kong Polytechnic University
> Department of Chinese and Bilingual Studies
> http://www.mypolyuweb.hk/~sjpolit/ <http://www.mypolyuweb.hk/~sjpolit/> <http://www.nyu.edu/projects/politzer-ahles/>
>
> On Thu, Sep 7, 2017 at 9:43 AM, 時本真吾 <tokimoto at mejiro.ac.jp <mailto:tokimoto at mejiro.ac.jp>> wrote:
> Dear EEGLAB users,
>
> I usually perform cluster-based permutation tests for my EEG analyses. I understand permutation tests are tests between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the image file below. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.
>
> http://tokimoto.o.oo7.jp/ERSP_sample.jpg <http://tokimoto.o.oo7.jp/ERSP_sample.jpg>
>
> ******************************************
> Shingo Tokimoto, Ph.D.
> in Linguistics and Psychology
> Department of Foreign Languages
> Mejiro University
> 4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
> 161-8539, Japan
> tokimoto at mejiro.ac.jp <mailto:tokimoto at mejiro.ac.jp>
> ******************************************
>
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html <http://sccn.ucsd.edu/eeglab/eeglabmail.html>
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>
> From: Baris Demiral <demiral.007 at gmail.com <mailto:demiral.007 at gmail.com>>
> Subject: [Eeglablist] (no subject)
> Date: September 11, 2017 at 9:28:11 PM GMT+2
> To: eeglablist <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
>
>
> Statistician is needed for Sleep studies. Please see below:
>
> https://www.linkedin.com/jobs/view/423167080/ <https://www.linkedin.com/jobs/view/423167080/>
>
> Best,
> Baris
>
>
>
>
> From: lmm226 <laura.morett at yale.edu <mailto:laura.morett at yale.edu>>
> Subject: [Eeglablist] Open source data acquisition software for ANT Neuro
> Date: September 12, 2017 at 3:50:17 AM GMT+2
> To: "eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>" <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
>
>
> Dear colleagues:
> I'm writing to inquire into whether anyone knows of open source data acquisition software that works with ANT Neuro's hardware. I have an amplifier manufactured by TMSi that was purchased in 2015 and appears to have been manufactured in 2014. It came with ANT Neuro's proprietary data acquisition software, but it requires a dongle to use, and the group that I acquired it from needs their dongle for another system that they're currently using to collect data. I did find one open source option called OpenVIBE, but it isn't clear whether it works with this system. Does anyone have any experience using this or any other open source acquisition software, particularly with ANT Neuro's hardware? If so, I would be interested in hearing about your experience using it.
>
> Best wishes,
> Laura Morett
>
>
> ********************************************************
> Laura M. Morett
> Hilibrand Postdoctoral Fellow
>
> Yale Child Study Center
> 320 S. Frontage Rd.
> New Haven, CT 06520
>
> Email: laura.morett at yale.edu <mailto:laura.morett at yale.edu>
> Phone: (203) 737-4586
> Web: http://lauramorettphd.wix.com/home <http://lauramorettphd.wix.com/home>
>
>
>
> From: Stephen Politzer-Ahles <politzerahless at gmail.com <mailto:politzerahless at gmail.com>>
> Subject: Re: [Eeglablist] Cluster-based permutation tests for 3 conditions
> Date: September 12, 2017 at 4:32:09 AM GMT+2
> To: Angel Caputi <caputiangel at gmail.com <mailto:caputiangel at gmail.com>>
> Cc: "eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>" <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
>
>
> As far as I understand, double-dipping (in the form of non-independent analysis, e.g. Baker, Hutchison, & Kanwisher 2007) is when you use the same test statistic to identify an analysis region and to test it. e.g., if you do a cluster-based permutation t-test, find a p=.2 cluster, and then do a regular t-test on the average activation within that cluster and get p=.00001, that's double-dipping. Using a different analysis to identify a cluster than what you look at within the cluster is not double-dipping --- e.g., you can use some control manipulation to identify a cluster, and then look at the manipulation of interest just within this cluster. The issue described above is I guess kind of halfway in between, so I'm not totally sure. Using an omnibus F-test to find a cluster and then testing the pairwise comparisons in it is not using the exact same test twice; on the other hand, though, the F-test is not totally independent of the pairwise comparisons within it. My intuition is that it should be ok to look at these pairwise comparisons as long as your interpretation of them is "there was a significant omnibus ANOVA effect and it was driven by a difference between these conditions", rather than saying directly "there was a difference between these conditions" (because the latter is technically not what was tested in the cluster-based permutation test). I have done this before and gotten away with it (although that doesn't mean it's right; all kinds of bad stats stuff gets published, maybe my analysis was more of that). I'd be interested learn what others think.
>
>
> ---
> Stephen Politzer-Ahles
> The Hong Kong Polytechnic University
> Department of Chinese and Bilingual Studies
> http://www.mypolyuweb.hk/~sjpolit/ <http://www.mypolyuweb.hk/~sjpolit/> <http://www.nyu.edu/projects/politzer-ahles/>
> On Tue, Sep 12, 2017 at 2:56 AM, Angel Caputi <caputiangel at gmail.com <mailto:caputiangel at gmail.com>> wrote:
> I wonder What kind of post-hoc test can be used for testing that the cluster site (i.e. combination of times and channels) we need to use for spatio-temporal localization. Would not be the use a sencond test on the cluster region a kind of double dipping? What would the correct procedure for testing where the spatio-temporal pattern differ?
> Sincerely
> Angel
>
>
> 2017-09-09 9:49 GMT-03:00 Stephen Politzer-Ahles <politzerahless at gmail.com <mailto:politzerahless at gmail.com>>:
> Cluster-based permutation tests are not necessarily between two conditions. They operate on any test statistic; this test statistic can be an F test from an ANOVA with three conditions. Just like with a two-condition permutation test, if the test comes out significant this will tell you that the three conditions differ in this dataset (whatever selection of channels, times, and frequency bands you looked at), and the cluster extent can give you an idea of what channel/time/frequency cluster is driving that difference. With an F-test, you would need to do follow-up comparisons to see which particular conditions are driving the difference.
>
>
> ---
> Stephen Politzer-Ahles
> The Hong Kong Polytechnic University
> Department of Chinese and Bilingual Studies
> http://www.mypolyuweb.hk/~sjpolit/ <http://www.mypolyuweb.hk/~sjpolit/> <http://www.nyu.edu/projects/politzer-ahles/>
>
> On Thu, Sep 7, 2017 at 9:43 AM, 時本真吾 <tokimoto at mejiro.ac.jp <mailto:tokimoto at mejiro.ac.jp>> wrote:
> Dear EEGLAB users,
>
> I usually perform cluster-based permutation tests for my EEG analyses. I understand permutation tests are tests between two conditions. However, I have realized that the test results can be presented for the comparison of three conditions, as is shown by the image file below. I usually perform the test from the GUI of EEGLAB. Could anyone tell me how I should understand the test results? Thank you in advance.
>
> http://tokimoto.o.oo7.jp/ERSP_sample.jpg <http://tokimoto.o.oo7.jp/ERSP_sample.jpg>
>
> ******************************************
> Shingo Tokimoto, Ph.D.
> in Linguistics and Psychology
> Department of Foreign Languages
> Mejiro University
> 4-31-1, Naka-Ochiai, Shinjuku, Tokyo,
> 161-8539, Japan
> tokimoto at mejiro.ac.jp <mailto:tokimoto at mejiro.ac.jp>
> ******************************************
>
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html <http://sccn.ucsd.edu/eeglab/eeglabmail.html>
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu <mailto:eeglablist-unsubscribe at sccn.ucsd.edu>
> For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu <mailto:eeglablist-request at sccn.ucsd.edu>
>
>
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html <http://sccn.ucsd.edu/eeglab/eeglabmail.html>
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu <mailto:eeglablist-unsubscribe at sccn.ucsd.edu>
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>
>
>
>
>
> From: Samran <samranasghar at gmail.com <mailto:samranasghar at gmail.com>>
> Subject: Re: [Eeglablist] ICA pipeline questions
> Date: September 12, 2017 at 9:14:40 AM GMT+2
> To: tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>
> Cc: eeglablist <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
>
>
> Hi Tarik,
>
> Thanks a lot for the detailed response. Really helpful. :)
>
> *The data is not PREPed again, it's the same data loaded in step1 of the pipelines. It was not clear earlier.
>
> *It seems reasonable to move downsampling at step2. It will likely reduce the filtering time as well.
>
> *You have suggested to drop a channel, instead of using PCA. Can you please recommend the criteria for selection of channel to be dropped, or it can be any channel? As per Makoto's pipeline, it is reasonable to drop the reference channel, but the data I have is not referenced. Although I can reference it to a particular channel offline in EEGLAB, but I would like to go with average reference, as given by PREP pipeline.
>
> *Do you have any suggestions of using ICA with PREP pipeline (because of the interpolation step)? Here's what I have come up with: Use PREP pipeline to clean_line and identifying the bad channels. For ICA, use the clean_lined data having only the good channels (chans_G) referenced to the average of chans_G. After removing bad ICs (and rejecting any leftover bad epochs), interpolate and average reference the data. However, I think the data will not be referenced to the 'true' average now, one of the targets of PREP, since good channels were already referenced to average of chans_G. Is there a better way of doing ICA with PREP?
>
> Thanks.
>
> On Tue, Sep 12, 2017 at 7:22 AM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>> wrote:
> Some followup points here for your Samran:
>
> 1. many researchers use and trust near fully automatic or fully automatic pipelines. See for example TAPEEG. It really depends on their biases (e.g., being programmers/engineers versus EEG researchers) and their needs (e.g., having dozens versus thousands of files). Further, automated pipeleines can be tweaked to your satisfaction, and can also provide "quality metrics" to know when there are issues in the pipeline.
>
> 2. Your "Drop artifactual ICs" step can be complex. Some researchers only keep neural ICs, some researchers only remove artifactual ICs, some researchers fully trust the results of ICA classifcation plugins such as Adjust, MARA, IC-MARA, and SASICA. If you haven't had a chance to use/test the results form those plugins, please be sure to do so.
>
> 3. If you haven't had a chance to yet, make sure to avail yourself of the excellent IC classification site from Luca at the following link. I usually recommend to students and beginners to do at least 500 classifications there.
> http://reaching.ucsd.edu:8000/tutorial/overview <http://reaching.ucsd.edu:8000/tutorial/overview>
>
>
>
>
>
>
>
>
>
>
>
>
> On Mon, Sep 11, 2017 at 11:55 AM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>> wrote:
> Hello Samran, here's notes for you below, good luck on your eeg adventures!
>
>
> ******NOTES FOR SAMRAN********
>
> *you need to understand/know exactly what PREP is doing, and if you want comments on that, you should list the steps you think PREP is doing.
>
> *your pipeline 1 seems okay. One can drop channels instead of PCA.
>
> *Note It's generally not recommended to interpolate channels before ICA.
> *Not sure why you are running PREP again (I guess it's okay if it does exactly the same thing as earlier.
> *Reject epochs at step 13 after reviewing the data, unless for some reason you trust that A) you have removed all artifactual ICs and B) there are no remaining artifactual periods in the epoched data
>
> *In your Pipeline 2, it's up to users whether or not they run a second ICA after pruning the data of Bad ICs. I would not recommend that, but you can look in past eeglablist answers, and in Makoto's processing suggestions, and in publications using ICA for EEG.
>
> *Double check that you don't need to move the resampling to the be first or second step.
>
>
> Your question #1
> ***ICA in eeglab does not care if there are discontinuities in the data, so it does not matter if you give it continuous data with breaks, or epoched data. It mixes up the time points and focuses on spatial patterns (not temporal patterns).
>
> your question #2
> I've specified above that the Data Analyst (you) needs to be sure there is no dirty data going into your averages and metrics. That is regardless of whether or not you already pruned your data by rejecting ICs. Be careful to NOT DO everything automatically until you have checked the results of your pipelines (at the epoch level and averaging level), and your are sure really sure that you don't need to go extra cleaning after bad IC rejection.
> In short, there may still be dirt in the data after rejecting artifactual ICs. You need to personally check whether there is or is not remaining dirt in the data, and you need to be careful not to "assume" that things are working, but rather "check fully" that things are working.
>
> Your question #3b
> The PCA correction is correct. Personally I've had better success with "dropping a channel before ICA to account fix average referencing's drop in rank" instead of the PCA flag in runica. In other words, after average referecing, and before ICA, I drop 1 channel rather than use the PCA reduction.
>
> Your Question $3b
> Don't interpolate before ICA.
>
>
>
>
>
>
>
> --
> Regards,
>
>
> M. Samran Navid.
>
>
>
> From: Tarik S Bel-Bahar <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>>
> Subject: Re: [Eeglablist] ICA pipeline questions
> Date: September 11, 2017 at 8:55:02 PM GMT+2
> To: Samran <samranasghar at gmail.com <mailto:samranasghar at gmail.com>>
> Cc: eeglablist <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
> Reply-To: tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>
>
>
> Hello Samran, here's notes for you below, good luck on your eeg adventures!
>
>
> ******NOTES FOR SAMRAN********
>
> *you need to understand/know exactly what PREP is doing, and if you want comments on that, you should list the steps you think PREP is doing.
>
> *your pipeline 1 seems okay. One can drop channels instead of PCA.
>
> *Note It's generally not recommended to interpolate channels before ICA.
> *Not sure why you are running PREP again (I guess it's okay if it does exactly the same thing as earlier.
> *Reject epochs at step 13 after reviewing the data, unless for some reason you trust that A) you have removed all artifactual ICs and B) there are no remaining artifactual periods in the epoched data
>
> *In your Pipeline 2, it's up to users whether or not they run a second ICA after pruning the data of Bad ICs. I would not recommend that, but you can look in past eeglablist answers, and in Makoto's processing suggestions, and in publications using ICA for EEG.
>
> *Double check that you don't need to move the resampling to the be first or second step.
>
>
> Your question #1
> ***ICA in eeglab does not care if there are discontinuities in the data, so it does not matter if you give it continuous data with breaks, or epoched data. It mixes up the time points and focuses on spatial patterns (not temporal patterns).
>
> your question #2
> I've specified above that the Data Analyst (you) needs to be sure there is no dirty data going into your averages and metrics. That is regardless of whether or not you already pruned your data by rejecting ICs. Be careful to NOT DO everything automatically until you have checked the results of your pipelines (at the epoch level and averaging level), and your are sure really sure that you don't need to go extra cleaning after bad IC rejection.
> In short, there may still be dirt in the data after rejecting artifactual ICs. You need to personally check whether there is or is not remaining dirt in the data, and you need to be careful not to "assume" that things are working, but rather "check fully" that things are working.
>
> Your question #3b
> The PCA correction is correct. Personally I've had better success with "dropping a channel before ICA to account fix average referencing's drop in rank" instead of the PCA flag in runica. In other words, after average referecing, and before ICA, I drop 1 channel rather than use the PCA reduction.
>
> Your Question $3b
> Don't interpolate before ICA.
>
>
>
>
>
>
> From: Tarik S Bel-Bahar <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>>
> Subject: Re: [Eeglablist] ICA pipeline questions
> Date: September 11, 2017 at 9:22:17 PM GMT+2
> To: Samran <samranasghar at gmail.com <mailto:samranasghar at gmail.com>>
> Cc: eeglablist <eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>>
> Reply-To: tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>
>
>
> Some followup points here for your Samran:
>
> 1. many researchers use and trust near fully automatic or fully automatic pipelines. See for example TAPEEG. It really depends on their biases (e.g., being programmers/engineers versus EEG researchers) and their needs (e.g., having dozens versus thousands of files). Further, automated pipeleines can be tweaked to your satisfaction, and can also provide "quality metrics" to know when there are issues in the pipeline.
>
> 2. Your "Drop artifactual ICs" step can be complex. Some researchers only keep neural ICs, some researchers only remove artifactual ICs, some researchers fully trust the results of ICA classifcation plugins such as Adjust, MARA, IC-MARA, and SASICA. If you haven't had a chance to use/test the results form those plugins, please be sure to do so.
>
> 3. If you haven't had a chance to yet, make sure to avail yourself of the excellent IC classification site from Luca at the following link. I usually recommend to students and beginners to do at least 500 classifications there.
> http://reaching.ucsd.edu:8000/tutorial/overview <http://reaching.ucsd.edu:8000/tutorial/overview>
>
>
>
>
>
>
>
>
>
>
>
>
> On Mon, Sep 11, 2017 at 11:55 AM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>> wrote:
> Hello Samran, here's notes for you below, good luck on your eeg adventures!
>
>
> ******NOTES FOR SAMRAN********
>
> *you need to understand/know exactly what PREP is doing, and if you want comments on that, you should list the steps you think PREP is doing.
>
> *your pipeline 1 seems okay. One can drop channels instead of PCA.
>
> *Note It's generally not recommended to interpolate channels before ICA.
> *Not sure why you are running PREP again (I guess it's okay if it does exactly the same thing as earlier.
> *Reject epochs at step 13 after reviewing the data, unless for some reason you trust that A) you have removed all artifactual ICs and B) there are no remaining artifactual periods in the epoched data
>
> *In your Pipeline 2, it's up to users whether or not they run a second ICA after pruning the data of Bad ICs. I would not recommend that, but you can look in past eeglablist answers, and in Makoto's processing suggestions, and in publications using ICA for EEG.
>
> *Double check that you don't need to move the resampling to the be first or second step.
>
>
> Your question #1
> ***ICA in eeglab does not care if there are discontinuities in the data, so it does not matter if you give it continuous data with breaks, or epoched data. It mixes up the time points and focuses on spatial patterns (not temporal patterns).
>
> your question #2
> I've specified above that the Data Analyst (you) needs to be sure there is no dirty data going into your averages and metrics. That is regardless of whether or not you already pruned your data by rejecting ICs. Be careful to NOT DO everything automatically until you have checked the results of your pipelines (at the epoch level and averaging level), and your are sure really sure that you don't need to go extra cleaning after bad IC rejection.
> In short, there may still be dirt in the data after rejecting artifactual ICs. You need to personally check whether there is or is not remaining dirt in the data, and you need to be careful not to "assume" that things are working, but rather "check fully" that things are working.
>
> Your question #3b
> The PCA correction is correct. Personally I've had better success with "dropping a channel before ICA to account fix average referencing's drop in rank" instead of the PCA flag in runica. In other words, after average referecing, and before ICA, I drop 1 channel rather than use the PCA reduction.
>
> Your Question $3b
> Don't interpolate before ICA.
>
>
>
>
>
>
> _______________________________________________
> eeglablist mailing list eeglablist at sccn.ucsd.edu <mailto:eeglablist at sccn.ucsd.edu>
> Eeglablist page: http://www.sccn.ucsd.edu/eeglab/eeglabmail.html <http://www.sccn.ucsd.edu/eeglab/eeglabmail.html>
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