[FieldTrip] clustering, spatial neighbors and minnbchan

["Jörn M. Horschig"]

Dear Clara,

First of all, defining neighbours based on a sensor layout may not be 
the best approach. If you have 3D positions of your sensors available, I 
would suggest that you use these for neighbour selection (note that for 
EEG sensors, this is much less an issue than for MEG sensors, but you 
didn't mention what system you are using)

Second, for minnbchan, I think you are right. Of course sensors at the 
outer rim have, by definition, less neighbouring sensors than other 
sensors. As far as I know, minnbchan is defined as the number of 
neighbouring significant sensors. So, if you specify it to be e.g. 10, 
than of course all sensors with less than 10 neighbours can never be 
part of a cluster. Those with more than 10 need to have also at least 10 
neighbouring sensors that are significant as well. Of course, sensors in 
the center have more neighbouring sensors, thus higher minnbchan will 
have a less tremendous effect there. So as far as I can see, there are 
four ways out for you:

1) If you didn't do so already, you can choose a finer time-scale, 
hoping that this will separate the different neural effects (if they are 
different)

2) Do the same on source level - maybe this will reduce spatial leakage.

3) Define a wider range of neighbours for sensors at the outer rim, 
thereby forcing a minimum number of neighbouring sensors. Instead (or 
additionally), you could try to use cfg.method = 'triangulation' or 
'template' for neighbour selection. For more information, see here: 
http://fieldtrip.fcdonders.nl/faq/how_does_ft_prepare_neighbours_work
I would suggest that you use cfg.feedback = 'yes' and choose the 
method/parameters that seem most optimal to you (there is not *the* best 
way, unfortunately)

4) Live with it, as there are technical limitations with our recording 
equipment, and of course physiological limitations. As Eric pointed out 
in the old mails you referred to:
"

In fact, it is looking for isolated blobs that one would like to interpret as separate physiological entities.
This contrast with the nature of the EEG/MEG data, which has poor spatial resolution (due to volume conduction and common pick-up),
often poor spectral resolution (due to the short time windows of our time-resolved spectral analysis), and often poor time resolution
(due to latency jitter over trials).
"



Hope this helps.
Best,
Jörn



On 3/11/2012 10:52 PM, Clara A. Scholl wrote:
> Hi,
>
> I have a question about how to identify appropriate parameters for
> neighbor distance in ft_neighbourselection and how to set minnbchan
> for space-time cluster analysis.
>
> I identified a neighbor distance based on our sensor layout, so that
> spatially adjacent channels were defined as neighbors, and spatially
> non-adjacent channels were not defined as neighbors.  I then ran
> space-time cluster analysis and identified a cluster with a large
> spatial and temporal extent which appears to be the continuation of
> several effects lumped together.  I subsequently increased minnbchan,
> so that this cluster gets separated into separate clusters following
> the logic in this discussion thread:
> http://mailman.science.ru.nl/pipermail/fieldtrip/2011-February/003501.html
>
> However now a new problem appears: it seems that channels near the
> periphery of the sensor layout, which have few adjacent spatial
> neighbors, are not included in the clusters when minnbchan has a value
> greater than the number of neighbors the channels have according to
> the neighbor distance used in ft_neighbourselection.  Is this correct?
>   Put differently, increasing minnbchan appears to bias clusters toward
> including only channels at the center of the sensor layout, with many
> adjacent other channels (and channels at the periphery of the layout,
> which have fewer spatial neighbors, are excluded from the identified
> clusters).
>
> Obviously I want a principled, data-independent selection of cluster
> identification parameters so I want to ask what the "correct" way to
> identify these parameters (neighbor distance, and minnbchan) is.
> Should minnbchan be used differently for channel-time cluster analysis
> (what I'm doing) compared to channel-time-frequency cluster analysis?
>
> Thanks,
> Clara
>
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> fieldtrip at donders.ru.nl
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-- 
Jörn M. Horschig
PhD Student
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
Radboud University Nijmegen
Neuronal Oscillations Group

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