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75 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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SFB Pipeline

Run scripts in this sequence:

convert.py

Grabs Brainvision raw data from a directory, converts to MNE-Python -raw.fif and saves in another directory

convert_sham.py

Grabs Brainvision raw sham data from a directory, converts to MNE-Python -raw.fif. Specifically, these sham files have their sham stimulation periods already marked by hand. Results are saved directly as afsham-raw.fif

do_gssc.py

Grabs -raw.fif files, filters to 0.3-30Hz, runs GSSC and saves results either as a CSV file or MNE-Python annotation file.

dofilter.py

Grabs -raw.fif files, filters, and saves as f*-raw.fif files

mark_stimulation.py

Grabs f*-raw.fif files, automatically identifies the stimulation type, and marks stimulations with mne.Annotations. Saves as af*-raw.fif. Instead of having T# (Tag) in the resulting filename, they will now have the identified condition (e.g. Sham, eig30s, fix5m, etc).

cutout_raw.py

Grabs af*-raw.fif files and cut away everything except the desired periods of time before and/or after stimulation, save as caf*-raw.fif

channel_organise.py

Grabs caf*-raw.fif files and makes sure channel and channel-types are properly configured, save them as scaf*-raw.fif

mark_badchans.py

Grabs scaf*-raw.fif files and runs an algorithmic bad channel detector, saves as bscaf*-raw.fif

doica.py

Grabs bscaf*-raw.fif, does ICA, and saves the solution under bscaf*-ica.fif

icaclean.py

Grabs bscaf*-raw.fif and bscaf*-ica.fif, identifies and removes bad components, saves as ibscaf*-raw.fif

mark_osc.py

Grabs ibscaf*-raw.fif files and marks SO and DOs by the standard methodology. Results are saved in two ways. The marked raw files are saved under aibscaf*-raw.fif. Epoching is also done with the down state trough as the 0 point. These epochs are saved in one file, grand_CHANNEL-epo.fif, where CHANNEL is the name of the ROI (typically "central"). This epoch file also has an extensive metadata, which gives all sorts of information about each epoch.

epo_drop.py

Grabs the epo files output by mark_osc.py and drops noisy epochs. Outputs with d_*-epo.fif

epo_cat.py

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