# [FieldTrip] Combineplanars and timelockstatistics

Thomas Hartmann thomas.hartmann at th-ht.de
Wed Nov 4 11:19:45 CET 2015

```dear sara,

Am 2015-11-04 um 09:51 schrieb Sara Aurtenetxe:
> When doing timelockstatistics (ERFs) at the gradiometers level:
>
> - Do the gradiometers need to be combined (ft_combineplanar) before the stats?
>    And/or do they need to be combined for visualization of effects?
>
> - Which is the (mathematical) explanation for the answer/s?
let me answer both questions at the same time: the two planar
gradiometers that make up the set of two that you find at each sensor
location point into orthogonal directions. you can imagine one pointing
along the x-axis, the other pointing along the y-axis of a 2d coordinate
system. so, the gradiometer pointing along the x-axis would pick up 100%
percent of an activity that increases or decreases in that direction. if
the activity increases or decreases along the y-axis, the x-axis
would pick up the whole energy.
if there is activity that increases or decreases in an angle 45° to both
gradiometers, both would pick up half the energy. this works accordingly
for any orientation of the underlying source. i.e. the two gradiometers
pick up the x and the y part of the signal. this means, that the
activity at the x-gradiometer is meaningless without the activity at the
with respect to the head are arbitrary, it does not make sense to
at another one. mathematically speaking: instead of looking at the
coordinates of your vector, you want the length of it.

so, in order to get that, you need to do: sqrt(x^2 + y^2).

so, long story short: yes, you need to to ft_combineplanar before sensor
level stats.

> - Does apply the same when doing source analysis with 'lcmv'?
no, this is a different story. it is quite the opposite: the activity at
a voxel (or grid point) is the linear combination (i.e. a weighed sum)
of the activity at all the sensors. the weighing coefficients are what
you calculate when you first do your forward and the then the inverse
model (e.g., bem modelling as forward model, LCMV for the inverse
solution). the forward modelling takes into account the orientation of
the sensors and also knows, where the head is, i.e. how the brain is
oriented with respect to the sensor.

so, no need to do ft_combineplanar here.

best,
thomas
>
> Thanks a lot in advance,
> All the best,
>
> Sara
>
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--
Dr. Thomas Hartmann

Centre for Cognitive Neuroscience
FB Psychologie
Universität Salzburg
Hellbrunnerstraße 34/II
5020 Salzburg

Tel: +43 662 8044 5109
Email:thomas.hartmann at th-ht.de

"I am a brain, Watson. The rest of me is a mere appendix. " (Arthur Conan Doyle)

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