[Mne_analysis] MNE-Python and R compatibility

[Phillip Alday]

Depending on what you're doing, several of us have little auxiliary
packages that might help.  For e.g. extracting single-trial mean voltage
within a given time window, I have a utility function in my philistine
package:

https://philistine.readthedocs.io/en/latest/api/philistine.mne.retrieve.html#philistine.mne.retrieve

Phillip

On 6/8/19 10:46 pm, Dan McCloy wrote:
>         External Email - Use Caution        
> 
> Hi Bianca,
> To expand on Denis's answer a little:
> 
> Many MNE-Python objects (Raw, Epochs, Evoked, SourceEstimate) have a
> to_data_frame() method that will create a Pandas DataFrame in memory,
> which you can then save to many formats including CSV.  From what you've
> told us, that might be an easier way than using FIF as an intermediate
> format. Looping over subjects in Python, you could write a separate CSV
> for each subject and the combine them in R, or you can combine the
> pandas DataFrames within Python before writing one big CSV. Or (as Denis
> says) you can write the loop within R and use MNE-R to do whatever
> preprocessing steps you need, and then in theory you don't even need to
> write intermediate files (though you might want to anyway).
> -- dan
> 
> Daniel McCloy
> http://dan.mccloy.info/
> Research Engineer
> Institute for Learning and Brain Sciences
> University of Washington
> 
> 
> 
> On Tue, Aug 6, 2019 at 12:11 PM Denis A. Engemann
> <denis-alexander.engemann at inria.fr
> <mailto:denis-alexander.engemann at inria.fr>> wrote:
> 
>             External Email - Use Caution       
> 
>     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
>     <mailto: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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