[Eeglablist] std_envtopo (plotchans and pvaf)

[Scott Makeig]

Makoto -   Cluster envelopes can indeed exceed the scalp ERP bounds -- IF
the clusters contribute activity with different polarities at maximal
projection channels.... whose sum is therefore smaller than the strongest
cluster contribution, not larger...

Scott


On Wed, Apr 17, 2013 at 6:09 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:

> Dear Ana,
>
> Sorry for slow response.
>
> Thanks Makoto. This is very helpful. I'd still love to be able to choose
>> some channels though; My P300 comes along with an effect of an opposite
>> (negative) signal in the frontal electrodes (I think this happens because I
>> use the average as a reference), and I believe that choosing just some
>> centro-parietal electrodes would give me a better estimate. I'll see if I
>> can try something myself.
>>
>
> Maybe you want to try my toolbox to backproject cluster ERPs to channels.
> I'll send it to you in the separate mail.
>
> It seems to me that the line is pointing at 559 and what I'm talking about
>> says "pvaf: 550.46. I had limited the latency anyway (from 200 to 600ms).
>> Another example is the cluster close to it, which shows "pvaf: -111.61".
>> This one is also pointing at latency ~559ms. I've attached an example of
>> the output I get from std_envtopo.
>>
>
> I saw it. It is quite strange indeed. I want to diagnose the situation.
> At least I can tell that your 'outermost envelope' (shown in thick black)
> is surpassed by other envelopes, which is not normal. That is like saying
> some of your clusters have larger values than the sum of all (it's not
> exactly like this but pretty much about it). Can you think of any reason
> why you see this?
>
> If you can't find anything wrong by yourself, I need to ask you to either
> 1. transfer all of your data to our server or 2. video conference by
> sharing your desktop. You can choose whichever you want. Sorry for
> inconvenience.
>
> Makoto
>
>
> 2013/4/10 Ana Navarro Cebrian <anavarrocebrian at gmail.com>
>
>> >Currently this is not supported. I see how to do it though.
>> I would suggest that you exclude clusters of non-interest, or specify the
>> clusters to use, so that your outermost envelope is consist of only
>> necessary ones.
>>
>> Thanks Makoto. This is very helpful. I'd still love to be able to choose
>> some channels though; My P300 comes along with an effect of an opposite
>> (negative) signal in the frontal electrodes (I think this happens because I
>> use the average as a reference), and I believe that choosing just some
>> centro-parietal electrodes would give me a better estimate. I'll see if I
>> can try something myself.
>>
>> >-550.46 should be a number of latency. Where does the line (extending
>> from the scalp topos) pointing? Isn't it -550.46 ms? If so, you should
>> limit the latency window to evaluate contribution.
>>
>> It seems to me that the line is pointing at 559 and what I'm talking
>> about says "pvaf: 550.46. I had limited the latency anyway (from 200 to
>> 600ms). Another example is the cluster close to it, which shows "pvaf:
>> -111.61". This one is also pointing at latency ~559ms. I've attached an
>> example of the output I get from std_envtopo.
>>
>> >No, actually pvaf never sums to 100% if you add up each clusters. That
>> means, pvaf(Cls1+Cls2) ~= pvaf(Cls1)+pvaf(Cls2). By the way the default
>> 100% is pvaf(Cls1+Cls2+...ClsN) if you have N number of clusters.
>> The measure pvaf is always superadditive i.e. exceeds 100% if summed
>> separately.
>>
>> Here I was talking about the individual pvaf (for each individual
>> cluster) shown under the cluster topoplot, and not the total pvaf. I'm not
>> sure if the response is still the same.
>> For example, for those two clusters that I'm talking about above, with
>> pvaf -550.46% and -111.61%. Would it be possible to get positive values
>> like those for individual clusters? i.e. pvaf: 550.46?
>>
>> Thank you again for your help, Makoto.
>>
>>
>>
>>
>> 2013/4/10 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>>
>>> Dear Ana,
>>>
>>> >First, I was wondering wether I could use something like the
>>> 'plotchans' (available in envtopo) to compute the contributions in just one
>>> or a few channels of interest. The default function computes the
>>> contributions for the grand ERPs which gives me a lot of variability that
>>> I'm not interested in.
>>>
>>> Currently this is not supported. I see how to do it though.
>>> I would suggest that you exclude clusters of non-interest, or specify
>>> the clusters to use, so that your outermost envelope is consist of only
>>> necessary ones.
>>>
>>> >Also, I'm still confuse about the pvaf for the individual clusters.
>>> For example, I'm looking for the clusters that explain the P300
>>> variability and there is a cluster (out of 7 clusters) that seems to me
>>> (based on the cluster's ERP) that explains most of the variability of the
>>> P300.
>>> When I run std_envtopo, I get a pvaf of -550.46 for that cluster.
>>> First, I understand that this is not talking about the P300 activity alone,
>>> but the grand average, and this implicates a lot of variance from many
>>> areas that I'm not interested in (and this is the reason why I'm trying to
>>> use just a few channels of interest). Therefore, because what I think is
>>> the 'P300 cluster' may have a different signal than all the other
>>> activities (that I'm not interested in), then I get a negative value for
>>> this cluster pvaf (-550.46). Am I getting something wrong so far?
>>>
>>> -550.46 should be a number of latency. Where does the line (extending
>>> from the scalp topos) pointing? Isn't it -550.46 ms? If so, you should
>>> limit the latency window to evaluate contribution.
>>>
>>> >Also, I imagine that, because this is the percent variance accounted
>>> for, for positive numbers, 100 should be the maximum possible value?
>>>
>>> No, actually pvaf never sums to 100% if you add up each clusters. That
>>> means, pvaf(Cls1+Cls2) ~= pvaf(Cls1)+pvaf(Cls2). By the way the default
>>> 100% is pvaf(Cls1+Cls2+...ClsN) if you have N number of clusters.
>>>
>>> The measure pvaf is always superadditive i.e. exceeds 100% if summed
>>> separately.
>>>
>>> Makoto
>>>
>>>  2013/4/10 Ana Navarro Cebrian <anavarrocebrian at gmail.com>
>>>
>>>> Hi,
>>>>  I have two questions about the function std_envtopo.m
>>>>
>>>> First, I was wondering wether I could use something like the
>>>> 'plotchans' (available in envtopo) to compute the contributions in just one
>>>> or a few channels of interest. The default function computes the
>>>> contributions for the grand ERPs which gives me a lot of variability that
>>>> I'm not interested in.
>>>>
>>>> Also, I'm still confuse about the pvaf for the individual clusters.
>>>> For example, I'm looking for the clusters that explain the P300
>>>> variability and there is a cluster (out of 7 clusters) that seems to me
>>>> (based on the cluster's ERP) that explains most of the variability of the
>>>> P300.
>>>> When I run std_envtopo, I get a pvaf of -550.46 for that cluster.
>>>> First, I understand that this is not talking about the P300 activity alone,
>>>> but the grand average, and this implicates a lot of variance from many
>>>> areas that I'm not interested in (and this is the reason why I'm trying to
>>>> use just a few channels of interest). Therefore, because what I think is
>>>> the 'P300 cluster' may have a different signal than all the other
>>>> activities (that I'm not interested in), then I get a negative value for
>>>> this cluster pvaf (-550.46). Am I getting something wrong so far?
>>>>
>>>> Also, I imagine that, because this is the percent variance accounted
>>>> for, for positive numbers, 100 should be the maximum possible value?
>>>>
>>>> I hope that makes sense. Thanks in advance for your help.
>>>>  Ana
>>>>
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>>>
>>>
>>>
>>> --
>>> Makoto Miyakoshi
>>> Swartz Center for Computational Neuroscience
>>> Institute for Neural Computation, University of California San Diego
>>>
>>
>>
>>
>> --
>> Ana Navarro-Cebrian
>> Postdoctoral Fellow. UCSF
>>
>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott
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