[FieldTrip] Reject trials based on standard deviation

[Stephen Whitmarsh]

Hi Heidi,

I guess you could just e.g. zscore your data before the (semi)automatic
artefact *detection*, and apply those artefact definitions on your original
data in the artefact *rejection*.

Cheers,
Stephen

On 22 January 2018 at 14:15, Heidi Solberg Okland <
Heidi.SolbergOkland at mrc-cbu.cam.ac.uk> wrote:

> Dear Fieldtrippers,
>
>
>
> I’m currently working on an MEG dataset and have so far done automatic
> rejection of bad channels using Maxfilter and then done ICA-based artefact
> reduction (eyeblinks and heartbeat) in MNE Python. I now have my data in
> Fieldtrip trial structures. What is left is to remove trials that are
> outliers/noisy because the subject moved/coughed/etc. I have tried to use
> ft_visinspect with variance as metric, but the issue I have is that the
> variance is displayed in actual units (e.g. 1e10^23) rather than in terms
> of standard deviations. This is problematic, as my trial rejection will be
> based on my subjective decisions that some trials look like outliers and
> not others. What I would like instead is to just do something like “discard
> trials where the amplitude is more than X standard deviations above or
> below the mean”, which is reproducible. Is this way of rejecting trials
> implemented in FT? I have seen the tutorial on automatic artefact
> rejection, but it looks more like something that would be useful for
> removing e.g. eyeblinks.
>
>
>
> Best wishes,
>
> Heidi
>
>
>
>
>
> ----------------------------------------------------------
>
>
>
> Heidi Solberg Økland
>
> PhD candidate, Language group
>
>
>
> Tel: 01223 273721
>
> MRC Cognition & Brain Sciences Unit
>
> University of Cambridge
> 15 Chaucer Road
> Cambridge
>
> CB2 7EF
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>
>
> Web: http://www.mrc-cbu.cam.ac.uk
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