[FieldTrip] adapting leadfields after PCA/ICA
jan.schoffelen at donders.ru.nl
Mon Feb 27 11:14:48 CET 2012
I imagine that you use ft_componentanalysis and ft_rejectcomponent sequentially in order to remove the eye blink topographies.
If you use the data.grad in the output to ft_rejectcomponent for the computation of the leadfields you should be fine. The balancing matrix grad.tra will contain the information that will be used for the correction of the leadfields.
On Feb 27, 2012, at 10:33 AM, Tessa van Leeuwen wrote:
> Dear FieldTrip users,
> To save trials I plan on using ICA to remove eog artifacts from my MEG dataset. Because of the large number of channels (271) and limited trial-time (about 300 trials of 1.1 s in each run), we will most likely limit the number of possible components using PCA.
> The combined PCA/ICA data-cleaning step will have consequences for source reconstruction approaches that we would like to apply to the data later on, e.g. beamforming. When ICA components representing eyeblink artifacts have been removed from the data, how can the leadfield for source reconstruction be adapted to account for the alterations to the data?
> If anyone has more information about how to do this or can refer me to papers implementing such a pipeline, that would be highly appreciated.
> Thank you very much in advance for any tips.
> Best wishes,
> Tessa van Leeuwen
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
Jan-Mathijs Schoffelen, MD PhD
Donders Institute for Brain, Cognition and Behaviour,
Centre for Cognitive Neuroimaging,
Radboud University Nijmegen, The Netherlands
Max Planck Institute for Psycholinguistics,
Nijmegen, The Netherlands
J.Schoffelen at donders.ru.nl
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