[Mne_analysis] spatio-temporal cluster on sensor level?

[Denis-Alexander Engemann]

Dear Elisabeth,

with regard to data organization, you can proceed exactly as with the
source space examples, just replace vertices with channels.
The critical difference amounts to how to define the neighboring spatiall
features, a.k.a. connectivity.
To compute this information you should use the FieldTrip neigbour
definitions. You can read those using this function:
http://martinos.org/mne/stable/generated/mne.read_ch_connectivity.html#mne.read_ch_connectivity


for exmple like this for a 4D/BTi 248 channels magnetometer system:

neighbor_file = 'fieldtrip/template/neighbours/bti248_neighb.mat'

connectivity = mne.channels.read_ch_connectivity(neighbor_file)

In case you have missing channels, you can define those to return the
connectivity matrix of the subset of remaining channels:

connectivity = mne.channels.read_ch_connectivity(neighbor_file, picks=picks)

The rest is the same, make sure your data matrices match the contrast type
(F vs T test) and make sure that the last dimension carries the spatial
information.

I hope that helps (we'll soon add a corresponding example).

Best,
Denis





On Fri, Aug 15, 2014 at 2:52 PM, Elisabeth Fonteneau <ef309 at cam.ac.uk>
wrote:

> Hello MNE (python) user,
>
>
>
> I have another question related to mne_python v.08
> I was wondering if there is a script for running Permutation t-test with
> spatio-temporal clustering on sensor data? I found one for source level
> (which looks brilliant), however for sensor level, I found “only” temporal
> clustering on 1 specific sensor. Thanks for your helpElisabeth
>
>
>
>
>
>
>
> __________________________________________________________
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>
>
> Dr Elisabeth Fonteneau
>
> Neurolex Group
>
> Department of Psychology
>
> University of Cambridge
>
> Downing Street, Cambridge CB2 3EB, UK
>
> Phone: +44 1223 333 548
>
> Email: ef309 at cam.ac.uk
>
> Web: www.neurolex.psychol.cam.ac.uk/directory/ef309 at cam.ac.uk
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