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development:minimum_norm_estimate_light [2013/12/20 15:15]
lilla [Exercise 1]
development:minimum_norm_estimate_light [2017/08/17 11:21] (current)
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 We will also use MRI images which belong to the same subject, a [[template:​anatomy|template MRI]] and a [[template:​sourcemodel|template cortical sheet]].  ​ We will also use MRI images which belong to the same subject, a [[template:​anatomy|template MRI]] and a [[template:​sourcemodel|template cortical sheet]].  ​
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-FIXME it does not make sense that the "​template MRI" redirects to the template headmodel page. It needs to be reorganized there. 
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-FIXME: Use  fieldtrip/​template/​anatomy/​single_subj_T1_1mm instead standard_mri.mat 
  
 We will repeat code to select the trials and preprocess the data as described [[tutorial:​eventrelatedaveraging|here]]. We assume that preprocessing and event-related averaging is already clear for the reader. This tutorial will *not* show how to do group-averaging or statistics. ​ We will repeat code to select the trials and preprocess the data as described [[tutorial:​eventrelatedaveraging|here]]. We assume that preprocessing and event-related averaging is already clear for the reader. This tutorial will *not* show how to do group-averaging or statistics. ​
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 ==== Processing of the subject'​s mri ==== ==== Processing of the subject'​s mri ====
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-In the following, we will use the anatomical MRI belonging to Subject01. The file can be obtained from [[ftp://​ftp.fcdonders.nl/​pub/​fieldtrip/​tutorial/​Subject01.zip]].+In the following, we will use the anatomical MRI belonging to Subject01. The file can be obtained from [[ftp://​ftp.fieldtriptoolbox.org/​pub/​fieldtrip/​tutorial/​Subject01.zip]].
 We read in the subject'​s MRI as follows: We read in the subject'​s MRI as follows:
 <​code>​ <​code>​
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 <note important>​ <note important>​
-Note that the segmentation can be time consuming (~15 mins) and if you want, you can load the pre-computed result and skip ahead to the next step. The segmented MRI of this tutorial can be downloaded from the [[ftp://​ftp.fcdonders.nl/​pub/​fieldtrip/​tutorial/​mne|ftp server]] (template_seg.mat). ​+Note that the segmentation can be time consuming (~15 mins) and if you want, you can load the pre-computed result and skip ahead to the next step. The segmented MRI of this tutorial can be downloaded from the [[ftp://​ftp.fieldtriptoolbox.org/​pub/​fieldtrip/​tutorial/​mne|ftp server]] (template_seg.mat). ​
 </​note>​ </​note>​
  
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 As the final step of the anatomical processing pipeline it is important to check the alignment and the transformation of all geometrical data. We will plot the sensors together with the source and head model to check whether they are aligned to each other and have the right proportions. ​ As the final step of the anatomical processing pipeline it is important to check the alignment and the transformation of all geometrical data. We will plot the sensors together with the source and head model to check whether they are aligned to each other and have the right proportions. ​
  
-The location of the MEG channels are defined in the .ds file of the tutorial data. First, we need to get this information using the **[[reference:​ft_read_sens|ft_read_sens]]** function. (The .zip file that can be downloaded from the [[ftp://​ftp.fcdonders.nl/​pub/​fieldtrip/​tutorial/​Subject01.zip|FieldTrip ftp server]] also contains the .ds file.) Then, we will plot the sensors (MEG channels) with the **[[reference:​ft_plot_sens|ft_plot_sens]]** function. Second, we will plot the head model and the source model in the same figure with the sensors using **[[reference:​ft_plot_vol|ft_plot_vol]]** and **[[reference:​ft_plot_mesh|ft_plot_mesh]]**,​ respectively. ​+The location of the MEG channels are defined in the .ds file of the tutorial data. First, we need to get this information using the **[[reference:​ft_read_sens|ft_read_sens]]** function. (The .zip file that can be downloaded from the [[ftp://​ftp.fieldtriptoolbox.org/​pub/​fieldtrip/​tutorial/​Subject01.zip|FieldTrip ftp server]] also contains the .ds file.) Then, we will plot the sensors (MEG channels) with the **[[reference:​ft_plot_sens|ft_plot_sens]]** function. Second, we will plot the head model and the source model in the same figure with the sensors using **[[reference:​ft_plot_vol|ft_plot_vol]]** and **[[reference:​ft_plot_mesh|ft_plot_mesh]]**,​ respectively. ​
  
 <​code>​ <​code>​
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 ===== Processing of functional data ===== ===== Processing of functional data =====
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-In the following section, we will use the MEG data belonging to Subject01. The raw data can be obtained from [[ftp://​ftp.fcdonders.nl/​pub/​fieldtrip/​tutorial/​Subject01.zip]].+In the following section, we will use the MEG data belonging to Subject01. The raw data can be obtained from [[ftp://​ftp.fieldtriptoolbox.org/​pub/​fieldtrip/​tutorial/​Subject01.zip]].
 For both preprocessing and averaging, we will follow the steps outlined in the [[tutorial:​eventrelatedaveraging|Event related averaging]] tutorial. We will use the trials belonging to the FC and FIC conditions. ​ For both preprocessing and averaging, we will follow the steps outlined in the [[tutorial:​eventrelatedaveraging|Event related averaging]] tutorial. We will use the trials belonging to the FC and FIC conditions. ​