[FieldTrip] automatic artifact detection and ft_denoise_pca

"Jörn M. Horschig" jm.horschig at donders.ru.nl
Wed Feb 6 15:02:03 CET 2013

Hi Robin,

I think the steps you suggested sound reasonable, but I do not see how 
you are avoiding the filter artifact issue there, you just postpone it 
to a later stage. Instead it might be a smart way to 'pad' your trials 
when defining them with 1s pre- and post (so cut out more than you 
need); that way all filter artifacts will be in that 1s that you are not 
interested in anyway. Then, you can define cfg.xxx.trlpadding =-1 prior 
to calling ft_artifact_xxx (thereby ignoring that 1s of  'padded' data). 
If I understand your question correctly, that solves your problem, 
doesn't it?


On 2/6/2013 12:54 PM, Robin wrote:
> Hi all,
> I am a new fieldtrip user getting started preprocessing a large MEG
> data set (I am in Glasgow and the data was collected at CCNi).
> I think I am slowly getting to grips with all the steps necessary, but
> I have a question about the artifact rejection.
> My undersanding is that the denoise procedure helps correct external
> sources of noise, so having the signal cleaned in this way should help
> detect the biological artifacts which are valid magnetic signal at the
> scalp. But I can't see an easy way to do this since the ft_artifact_*
> functions want to load the raw continuous data from disk. I can get
> them to act on the in memory trials data if I set the padding options
> to 0, but then I get an unacceptable amount of rejections (I guess
> because of the filter artifacts the padding usually prevents).
> Is it possible to run ft_artifact_muscle, ft_artifact_eog etc. on the
> denoised signal from ft_denoise_pca and if so how?
> At the moment I am performing the following steps:
> Load each run
> Detect jumps with ft_artifact_jump.
> Concatenate the jump-free trials from all runs together for this block.
> Visually inspect the reference channels and remove high variance
> trials (across the whole block).
> Compute denoise PCA weights using only good reference data (and no MEG
> jump) trials across the whole block.
> I would now like to apply the denoise PCA weights, perform other
> automatic artifact removal on the cleaned data, before further visual
> inspection and the next steps of ICA etc.
> Is there any problems with this strategy?
> Thanks in advance for any advice,
> Robin
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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
FieldTrip Development Team

P.O. Box 9101
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The Netherlands

E-Mail: jm.horschig at donders.ru.nl
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