<div dir="ltr"><div>Hi Eelke,<br></div><div>Thank you for looking into this!<br></div><div>The piece of code I commented out is indeed [790:836] (changing line 784 accordingly or alternatively adding a copy of 785-789 after 790).<br>
</div><div>I did try running ft_timelockanalysis with keeptrials='no' for all 3 calls and that resulted in the same behaviour as before for LCMV (ie not the "correct" one). I also tried it setting keeptrials='no' in ft_sourceanalysis at the same time (ie keeptrials='no' for both ft_timelockanalysis and ft_sourceanalysis) which had the same outcome (I actually had run these tests before posting but wanted to 2ble check). <br>
</div><div>I may however be confused here because there are some other possible combinations with keeptrials since for each subject there is a total of 6 calls to functions that accept it as an input and the filter is estimated with only 2 of them.<br>
</div><div>Do let me know if there is another combination I should run and I will do that.<br></div><div>Best,<br></div><div>Haris <br><br>Charidimos [Haris] Tzagarakis MD, PhD, MRCPsych
<br>University of Minnesota Dept of Neuroscience and Brain Sciences Center<br></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On 24 October 2013 03:08, Eelke Spaak <span dir="ltr"><<a href="mailto:eelke.spaak@donders.ru.nl" target="_blank">eelke.spaak@donders.ru.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Charidimos,<br>
<br>
Thanks for your elaborate e-mail. We discussed this issue in<br>
yesterday's FieldTrip meeting. It is very reasonable that you expect<br>
the three methods to produce the same results, and the code/interface<br>
indeed suggests that this would be the case. In other words, we need<br>
to clean up this part of the code to produce consistent results;<br>
however, this might take a while.<br>
<br>
We believe the discrepancy is caused by the fact that you compute the<br>
covariance while specifying cfg.keeptrials = 'yes', which is<br>
unnecessary while computing the filter. This results in a covariance<br>
matrix with dimensions trial X channel X channel, which then is<br>
subsequently averaged over trials during ft_sourceanalysis. Likely the<br>
filter is still in fact computed on the averaged covariance, as it<br>
should be. However, the subsequent projection of data through the<br>
filter will not first average the covariance, and will in essence only<br>
project the first trial through the filter. (This is because the dip{}<br>
fields will be struct arrays of dimension nTrial X 1, and in an<br>
assignment a(1).b = 2; a(2).b = 3; x = a; then x will be 2, so<br>
everything but the first element of a struct array is ignored in an<br>
assignment statement.)<br>
<br>
Could you try to compute the covariance with cfg.keeptrials = 'no' and<br>
then run LCMV again? This will still compute the covariance on the raw<br>
traces, not on the average. If our hunch is right, you should get the<br>
correct results then.<br>
<br>
As said, this is indeed messy and unclear, our apologies for that.<br>
<br>
Finally, could you tell us what part of the code you commented out to<br>
make the results consistent? We suspect it will be between lines 790<br>
and 835, but it would be helpful to know for sure.<br>
<br>
Best,<br>
Eelke<br>
<div class="HOEnZb"><div class="h5"><br>
<br>
On 23 October 2013 21:43, Charidimos Tzagarakis <<a href="mailto:haristz@gmail.com">haristz@gmail.com</a>> wrote:<br>
> Hi Don,<br>
> Thanks for your reply. I do agree that some of these beamformers<br>
> are probably better suited than others to study beta desynchronisation, or<br>
> generally questions in the frequency domain. I in fact started from DICS.<br>
> The reason I looked at LCMV and SAM is to be sure that I get consistent<br>
> results (and also because my experiment can also address some additional<br>
> time domain questions, so I wanted to see what happens when my data goes<br>
> from one to the other). I therefore tried to set them up so I that the<br>
> results from all 3 are similar. So you are correct in pointing out that my<br>
> question is why SAM and LCMV produce different results. Based on their<br>
> description within the fieldtrip website and mailing list (if I have<br>
> interpreted these correctly) they should be treating the covariance matrix<br>
> in the same way (to get a true evoked response covariance matrix you would<br>
> normally need the extra call to ft_timelockanalysis that I show at the last<br>
> piece of code in my post). Also , if the covariance matrix is indeed the<br>
> issue, it seems that the change in how it is treated in the<br>
> ft_sourceanalysis code (based on the "hack" I described) is unique to LCMV<br>
> (none of the other beamformer options share that piece of code) and is<br>
> active only when LCMV is given a precomputed filter which is (I think)<br>
> unusual.<br>
> Hence my question!<br>
> Best,<br>
> Haris<br>
><br>
> Charidimos [Haris] Tzagarakis MD, PhD, MRCPsych<br>
> University of Minnesota Dept of Neuroscience and Brain Sciences Center<br>
><br>
><br>
><br>
> On 23 October 2013 13:31, Rojas, Don <<a href="mailto:Don.Rojas@ucdenver.edu">Don.Rojas@ucdenver.edu</a>> wrote:<br>
>><br>
>> Haris,<br>
>><br>
>> Sorry - In my last post, I mistakenly put DICS and the Fieldtrip<br>
>> implementation of SAM into the same frequency domain category. In Fieldtrip,<br>
>> SAM is a time-domain technique and is not the same as the implementation of<br>
>> SAM that has been used in the published literature for beta ERD. So, are you<br>
>> then wondering why the two time-domain approaches produce differing results?<br>
>> That probably does depend on how the covariance matrix is calculated.<br>
>> Although I still think it is a bad idea to use a time-domain beamformer on<br>
>> motor beta ERD/ERS.<br>
>><br>
>> Best,<br>
>><br>
>> Don<br>
>><br>
>><br>
>><br>
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