First you will need to update the config.py file. This file is meant to contain study specific parameters:

• study_path : path to the directory that contains your data.
• subjects_dir : path pointing to the antomical files for all subjects
• meg_dir : path pointing to the MEG files for all subjects
• subjects : a list of the subject names
• exclude_subjects : the list of subjects to exclude from the above
• study_name : string defining what the files in your study are named. (e.g. study_name = 's_audvis')
• set_channel_types : set channel type of extra channels that were recorded (e.g. EOG, ECG etc.) Example: set type for EEG062 as EOG.
• rename_channels : rename channels. Example: rename channel EEG062 to EOG062.
• bads : dictionary containing he list of bad channels for each subject
• h_freq : the high-frequency cut-off in the lowpass filtering step. Keep it None if no lowpass filtering should be applied.
• l_freq : the low-frequency cut-off in the highpass filtering step. Keep it None if no highpass filtering should be applied.
• ctc : for maxfiltering, path to the cross talk file on this machine.
• cal : for maxiltering, path to the calibration file on this machine.
• mf_reference_run : specify which run to use for HPI recalibration, all other runs will have head position adjusted to this one.
• mf_st_duration: a float that specifies the buffer duration in seconds, default = 10 s, meaning it acts like a 0.1 Hz highpass filter. If None, no temporal spatial filtering is applied during MaxFilter.
• mf_head_origin: The origin of the head used for maxwell filtering.
• resample_sfreq : a float that specifies at which sampling frequency the data should be resampled. If None then no resampling will be done.
• decim : integer that says how much to decimate data at the epochs level. It is typically an alternative to the resample_sfreq parameter.
• reject : the default rejection limits to make some epochs as bads. This allows to remove strong transient artifacts. Note: these numbers tend to vary between subjects.
• tmin: float that gives the start time before event of an epoch.
• tmax : float that gives the end time after event of an epochs.
• baseline : tuple that specifies how to baseline the epochs. If None, then no baseline applied.
• event_id : python dictionary that maps events (trigger/marker values) to conditions. E.g. event_id = {'Auditory/Left': 1, 'Auditory/Right': 2}
• runica : boolean that says if ICA should be used or not.

Advanced:

• l_trans_bandwidth : float that specifies the transition bandwidth of the highpass filter. By default it's 'auto' and uses default mne parameters.

• h_trans_bandwidth : float that specifies the transition bandwidth of the lowpass filter. By default it's 'auto' and uses default mne parameters.

• N_JOBS : an integer that specifies how many subjects you want to run in parallel.

• decoding_conditions: list of tuples of strings that contain the conditions to compare. For example: [('Auditory/Left', 'Auditory/Right'), ('Auditory', 'Visual')]

• decoding_metric : the scikit-learn scoring metric to use. For example: 'roc_auc' or 'accuracy'

• decoding_n_splits : the number of splits to use in the cross-validation. For example 5 that will mean 5-folds cross-validation.

• add visualization options
• description of how to debug/ run script in interactive mode
• work on artifact rejection scripts (04_)
• document the scripts

First, you need to make sure you have mne-python installed and working on your system. See installation instructions and once is done you should be able to run in a terminal this:

$ python -c "import mne; mne.sys_info()"

Once you have mne-python installed on your machine you need the analysis script that you'll need to adjust to your need. You can download the current version of these script, or get them through git:

$ git clone https://github.com/mne-tools/mne-study-template.git

For source analysis you'll also need freesurfer, follow the instructions on their website.

• Mainak Jas, Telecom ParisTech, Université Paris-Saclay
• Eric Larson, University of Washington ILABS
• Denis Engemann, Neurospin, CEA/INSERM, UNICOG Team
• Jaakko Leppäkangas, Telecom ParisTech, Université Paris-Saclay
• Samu Taulu, University of Washington, ILABS
• Matti Hämäläinen, Martinos Center, MGH, Harvard Medical School
• Alexandre Gramfort, INRIA, Université Paris-Saclay