Differences

This shows you the differences between two versions of the page.

Link to this comparison view

reference:ft_denoise_pca [2018/08/23 14:43] (current)
Line 1: Line 1:
 +=====  FT_DENOISE_PCA =====
 +
 +Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_denoise_pca"​.
 +
 +<​html><​pre>​
 +  <a href=/​reference/​ft_denoise_pca><​font color=green>​FT_DENOISE_PCA</​font></​a>​ performs a principal component analysis (PCA) on specified reference
 +  channels and subtracts the projection of the data of interest onto this orthogonal
 +  basis from the data of interest. This is the algorithm which is applied by 4D to
 +  compute noise cancellation weights on a dataset of interest. This function has been
 +  designed for 4D MEG data, but can also be applied to data from other MEG systems.
 + 
 +  Use as
 +    [dataout] = ft_denoise_pca(cfg,​ data)
 +  or as
 +    [dataout] = ft_denoise_pca(cfg,​ data, refdata)
 +  where "​data"​ is a raw data structure that was obtained with <a href=/​reference/​ft_preprocessing><​font color=green>​FT_PREPROCESSING</​font></​a>​. If
 +  you specify the additional input "​refdata",​ the specified reference channels for
 +  the regression will be taken from this second data structure. This can be useful
 +  when reference-channel specific preprocessing needs to be done (e.g. low-pass
 +  filtering).
 + 
 +  The output structure dataout contains the denoised data in a format that is
 +  consistent with the output of <a href=/​reference/​ft_preprocessing><​font color=green>​FT_PREPROCESSING</​font></​a>​.
 + 
 +  The configuration should contain
 +    cfg.refchannel = the channels used as reference signal (default = '​MEGREF'​)
 +    cfg.channel ​   = the channels to be denoised (default = '​MEG'​)
 +    cfg.truncate ​  = optional truncation of the singular value spectrum (default = '​no'​)
 +    cfg.zscore ​    = standardise reference data prior to PCA (default = '​no'​)
 +    cfg.pertrial ​  = '​no'​ (default) or '​yes'​. Regress out the references on a per trial basis
 +    cfg.trials ​    = list of trials that are used (default = '​all'​)
 +    cfg.updatesens = '​no'​ or '​yes'​ (default = '​yes'​)
 + 
 +  if cfg.truncate is integer n &gt; 1, n will be the number of singular values kept.
 +  if 0 &lt; cfg.truncate &lt; 1, the singular value spectrum will be thresholded at the
 +  fraction cfg.truncate of the largest singular value.
 + 
 +  See also <a href=/​reference/​ft_preprocessing><​font color=green>​FT_PREPROCESSING</​font></​a>,​ <a href=/​reference/​ft_denoise_synthetic><​font color=green>​FT_DENOISE_SYNTHETIC</​font></​a>​
 +</​pre></​html>​