[Mne_analysis] MNE-Python and R compatibility

[Denis A. Engemann]

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Hi Bianca,

Hi Bianca,

Did you have a look at  MNE-R? https://mne.tools/mne-r/index.html
It is a small library that facilitates calling MNE-Python through R and making data frames from fif-compatible data structures.

For what concerns your question, the fif file is not meant to handle data from multiple subjects.
You would use other formats for that.
In Python we usually  do things in memory, making big matrices from multiple subjects.
For getting data for all subjects, you would need to write separate files and combine them in R or make a big data frame.

I  hope that helps.
Denis


> On Aug 6, 2019, at 9:00 PM, Bianca Islas <biancaisla1 at gmail.com> wrote:
> 
>         External Email - Use Caution        
> 
> 
> MNE Analysis Team,
>  
> Let me first begin by stating what our lab is primarily interested in, and currently doing. We do psychophys studies directly related to startle-blink response and postauricular response.  We also work with skin conductance, corrugator, zygomatic (EMG), EOG, ECG, and EEG.  Currently, we run Neuroscan, and use the resulting CNT files to do statistical analysis on all study subjects with SPSS and R.  We have been in works this summer to complete a script through Jupyter notebooks that will process our raw CNT files into processed FIF files, and this is where the questions begin. 
>  
> How large can a FIF file be?  If a FIF file has a limitation on its size, how do we run statistical analysis on multiple files for the same participant?  Furthermore, how do we run analysis on multiple subjects and multiple files? Will a FIF file be compatible with statistical analysis?  The real issue that our lab sees is how will be able to create component scores that can be output to other programs for statistical analysis, primarily R.  There's a hint about how to do this at the start of the scripts on this page after the from import statements:
> https://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_envelope_correlation.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-envelope-correlation-py
>  
> However, maybe we require further explanation as we are not interested necessarily in one subject at a time rather ALL subjects at a time.
>  
> Thank you in advance for any insight that you may be able to provide on these matters and of course your time.
>  
> Best,
> UNLV PEPLab
> Bianca Islas
> Research Assistant
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