Initial processing

UML diagrams presenting the flow of the analysis for each module are available here: https://github.com/ANCPLabOldenburg/MEG-QC-code/tree/main/diagrams

meg_qc.calculation.initial_meg_qc.Epoch_meg(epoching_params, data: Raw)[source]

Epoch MEG data based on the parameters provided in the config file.

Parameters:

  • epoching_params (dict) – Dictionary with parameters for epoching.

  • data (mne.io.Raw) – MEG data to be epoch.

Returns:

dict_epochs_mg – Dictionary with epochs for each channel type: mag, grad.

Return type:

dict

meg_qc.calculation.initial_meg_qc.get_all_config_params(config_file_name: str)[source]

Parse all the parameters from config and put into a python dictionary divided by sections. Parsing approach can be changed here, which will not affect working of other fucntions.

Parameters:

config_file_name (str) – The name of the config file.

Returns:

all_qc_params – A dictionary with all the parameters from the config file.

Return type:

dict

meg_qc.calculation.initial_meg_qc.get_internal_config_params(config_file_name: str)[source]

Parse all the parameters from config and put into a python dictionary divided by sections. Parsing approach can be changed here, which will not affect working of other fucntions. These are interanl parameters, NOT to be changed by the user.

Parameters:

config_file_name (str) – The name of the config file.

Returns:

internal_qc_params – A dictionary with all the parameters.

Return type:

dict

meg_qc.calculation.initial_meg_qc.initial_processing(default_settings: dict, filtering_settings: dict, epoching_params: dict, data_file: str)[source]

Here all the initial actions needed to analyse MEG data are done:

  • read fif file,

  • separate mags and grads names into 2 lists,

  • crop the data if needed,

  • filter and downsample the data,

  • epoch the data.

Parameters:

  • default_settings (dict) – Dictionary with default settings for MEG QC.

  • filtering_settings (dict) – Dictionary with parameters for filtering.

  • epoching_params (dict) – Dictionary with parameters for epoching.

  • data_file (str) – Path to the fif file with MEG data.

Returns:

  • dict_epochs_mg (dict) – Dictionary with epochs for each channel type: mag, grad.

  • chs_by_lobe (dict) – Dictionary with channel objects for each channel type: mag, grad. And by lobe. Each obj hold info about the channel name, lobe area and color code, locations and (in the future) pther info, like: if it has noise of any sort.

  • raw_crop_filtered (mne.io.Raw) – Filtered and cropped MEG data.

  • raw_crop_filtered_resampled (mne.io.Raw) – Filtered, cropped and resampled MEG data.

  • raw_cropped (mne.io.Raw) – Cropped MEG data.

  • raw (mne.io.Raw) – MEG data.

  • active_shielding_used (bool) – True if active shielding was used during recording.

  • epoching_str (str) – String with information about epoching.

meg_qc.calculation.initial_meg_qc.sanity_check(m_or_g_chosen, channels_objs)[source]

Check if the channels which the user gave in config file to analize actually present in the data set.

Parameters:

  • m_or_g_chosen (list) – List with channel types to analize: mag, grad. These are theones the user chose.

  • channels_objs (dict) – Dictionary with channel names for each channel type: mag, grad. These are the ones present in the data set.

Returns:

  • channels_objs (dict) – Dictionary with channel objects for each channel type: mag, grad.

  • m_or_g_chosen (list) – List with channel types to analize: mag, grad.

  • m_or_g_skipped_str (str) – String with information about which channel types were skipped.