Function Reference

clean_eog_ecg.py - Script from Alex, Nanditta & Sheraz that is supposed to remove ECG

cmpEve.py - Part of artifact rejection procedure, see documentation for pipeline option --preProc_reject

compute_cluster_f_test.py - Uses clustering method to compute statistics for each electrode and each time point and correct for multiple comparisons

compute_cluster_src_stats.py - Uses clustering method to compute statistics in source space

eve2accuracy.py - Computes accuracy at behavioral task

fif2rej.m - This is the workhorse of the artifact rejection. Currently uses VEOG and HEOG and all EEG channels as rejection criteria

fixTriggers.py - This recodes triggers more meaningfully and fixes timing offset, outputs ModEve files.

makeAveFiles.py - This makes the .ave parameter file needed for processing every single raw file in MNE

makeCovFiles.py - This makes the .cov parameter file needed for processing every raw file contributing to covariance estimate.

makeInv.sh - This makes the forward and inverse solutions

makeSTC.sh - This outputs a bunch of stored MNE inverse solution movies (stc files)

meg_py.py - This is collection of mne functions rewritten in python, not yet incorporated into our current workflow

plot_label_source_activations.py - This is a script modified from Alex Gramfort that takes in a label and an stc file and plots the mean of the vertices across time.

plot_sensor_waveform.py - This allows you to make cuter plots of single EEG or MEG waveforms than mne_browse_raw.

preAnat.sh - This does all the preprocessing of the freesurfer anatomy required by MNE

preProc_avg.sh - This does the averaging for each subject, each run, and creates averages across runs

preProc_cov.sh - This creates the covariance files needed for inverse solution computation

preProc_reject.sh - This calls the artifact rejection scripts

preProc_setup.sh - This does the setup on raw files, creating eve files, marking bad channels, renaming channels, etc.

readInput.py - Little function for reading in tables from text

README.md - Readme text for github

rej2eve.py - Makes the new eve files incorporating the artifact rejection

rejTable.py - Computes table of rejected trials across subjects

sensor_ - Matlab scripts for subsequent sensor analyses and visualization

sensor_allfif2mat.m - For a given paradigm, this reads in all the average fif files (for each subject) and saves each struct to a big Matlab cell array. Any subsequent Matlab script that needs all the data from the fif files can load this .mat file.

sensor_avgAcrossSubjs.m - This reads in the stored mat file with all subject data and outputs a grand-average across sensors. Actually outputs two versions: one with the bad channels removed for each subject and one not.

sensor_avgFieldmapinTime.py - This makes an -ave.fif file that averages across the selected time-window and saves this average to the samples in this time-window. Why is this useful? Because if you load it in mne_analyze and click on one of those samples, you can view a field map that reflects the average across a time-window, like we often do for ERP data using the topoplot function in EEGLAB.

sensor_visualizeT.m - This reads in the stored mat file with all subject data and the grand-average fif file created by previous script. Then it computes a t-test on each sensor individually, only including the good channels, and outputs a grand-average fif file that whites out the waveforms for all sensors that are not significant.

source_ - Matlab scripts for subsequent source space analyses and visualization

source_avgSTCAcrossSubjs - This reads in a morphed-to-fsaverage stc file for each subject and then averages them for visualization. You specify whether you want to average spm maps or mne maps. The result is saved into /MEG/results/ga_stc/single_condition/

source_avgSTCDiffAcrossSubjs - This reads in two morphed-to-fsaverage stc files for each subject and subtracts the first from the second. The result is saved to an stc file in the subject's stc directory, and the average difference across subjects is saved into /MEG/results/ga_stc/diff

source_avgSTCinTime - This just reads in an STC file and averages it across a given time-window, then outputs this file to the same directory as the source.

source_avgSTCDiffinTime - This reads in for each subject the morphed-to-fsaverage diff stc file created by source_avgSTCDiffAcrossSubjs.m and averages the activity in a given time-window using source_avgSTCinTime.

source_statSTC - This runs stats over a set of stc difference maps (cond2-cond1) and outputs a -log(p) map in source space.

source_statSTCTime - This runs stats over a set of stc difference maps that have already been averaged in time with source_avgSTCDiffinTime.m. So note that you have to have already computed these for the time window of interest.

visualizeCommands.m - convenience list of calls to Matlab visualization functions

writeOutput.py - Little function for writing out tables to text files