[FieldTrip] head motion regression with ft_regressconfound on a continuous MEG data

[Arjen Stolk]

Hi Inna,
Ft_regressconfound will remove trial by trial variability of signal
amplitude or frequency power that can be explained by variability in head
position over those trials. The input, whether sensor or source level,
should contain a trial field (keep trials in the previous step - eg at
timelockanalysis). Also variance due to head position can only be reliably
estimated when the neural signal is consistent over trials (as with an
event related modulation or potential). I would not know how to segment a
resting state into trials (for further analysis, given that those analyses
typically involve connectivity measures over the resting period) but if you
an idea on how to do this, you could give ft_regressconfound a try. Note
that the output will also be in the trials format. Hope this has made its
use a bit more clear. Yours, arjen

Op 13 nov. 2013 11:50 schreef "McGowin, Inna" <mcgoiv0 at wfu.edu> het
volgende:
>
> Hello everybody,
>
> I would like to use the  ft_regressconfound function to remove/reduce the
head motion in a continuous MEG data.
> It's a resting state data and no averaging can be applied. Is there a way
to apply the correction without using the
>
> ft_timelockanalysis(cfg, data) function?
>
> I understand that the ft_regressconfound function has limits and caveats
but I still would like to see the results of its correction.
>
>
> Another way to ask this question is:
> what is the format/structure of the second input (timelock) in the
> ft_regressconfound(cfg, timelock) function and how it can be created
without ft_timelockanalysis?
>
>
> Thanks,
>
> Inna
>
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