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 epoched.

Returns:

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

Return type:

dict

meg_qc.calculation.initial_meg_qc.add_3d_ch_locations(raw, channels_objs)[source]

Add channel locations to the MEG channels objects.

Parameters:

  • raw (mne.io.Raw) – MEG data.

  • channels_objs (dict) – Dictionary with MEG channels.

Returns:

channels_objs – Dictionary with MEG channels with added locations.

Return type:

dict

meg_qc.calculation.initial_meg_qc.assign_channels_properties(channels_short: dict, meg_system: str)[source]

Assign lobe area to each channel according to the lobe area dictionary + the color for plotting + channel location.

Can later try to make this function a method of the MEG_channels class. At the moment not possible because it needs to know the total number of channels to figure which meg system to use for locations. And MEG_channels class is created for each channel separately.

Parameters:

  • channels (dict) – dict with channels names like: {‘mag’: […], ‘grad’: […]}

  • meg_system (str) – CTF, Triux, None…

Returns:

  • channels_objs (dict) – Dictionary with channel names for each channel type: mag, grad. Each channel has assigned lobe area and color for plotting + channel location.

  • lobes_color_coding_str (str) – A string with information about the color coding of the lobes.

meg_qc.calculation.initial_meg_qc.change_ch_type_CTF(raw, channels)[source]

For CTF data channels types and units need to be chnaged from mag to grad.

Parameters:

channels (dict) – dict with ch names separated by mag and grad

Returns:

channels – dict with ch names separated by mag and grad UPDATED

Return type:

dict

meg_qc.calculation.initial_meg_qc.check_chosen_ch_types(m_or_g_chosen: List, channels_objs: dict)[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:

  • 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.

meg_qc.calculation.initial_meg_qc.choose_channels(raw: Raw)[source]

Separate channels by ‘mag’ and ‘grad’. Done this way, because pick() or pick_types() sometimes gets wrong results, especialy for CTF data.

Parameters:

raw (mne.io.Raw) – MEG data

Returns:

channels – dict with ch names separated by mag and grad

Return type:

dict

meg_qc.calculation.initial_meg_qc.chs_dict_to_csv(chs_by_lobe: dict, file_name_prefix: str)[source]

Convert dictionary with channels objects to a data frame and save it as a csv file.

Parameters:

  • 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.

  • file_name_prefix (str) – Prefix for the file name. Example: ‘Sensors’ will result in file name ‘Sensors.csv’.

Returns:

df_deriv – List with data frames with sensors info.

Return type:

list

meg_qc.calculation.initial_meg_qc.get_all_config_params(config_file_path: 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_path (str) – The path to 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, file_path: 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.

  • file_path (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.

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

  • 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.

  • info_derivs (list) – List with QC_derivative objects with MNE info object.

  • shielding_str (str) – String with information about active shielding.

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

  • sensors_derivs (list) – List with data frames with sensors info.

  • 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.

  • lobes_color_coding_str (str) – String with information about color coding for lobes.

  • resample_str (str) – String with information about resampling.

meg_qc.calculation.initial_meg_qc.load_data(file_path)[source]

Load MEG data from a file. It can be a CTF data or a FIF file.

Parameters:

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

Returns:

  • raw (mne.io.Raw) – MEG data.

  • shielding_str (str) – String with information about active shielding.

meg_qc.calculation.initial_meg_qc.sort_channels_by_lobe(channels_objs: dict)[source]

Sorts channels by lobes.

Parameters:

channels_objs (dict) – A dictionary of channel objects.

Returns:

chs_by_lobe – A dictionary of channels sorted by ch type and lobe.

Return type:

dict

meg_qc.calculation.initial_meg_qc.stim_data_to_df(raw: Raw)[source]

Extract stimulus data from MEG data and put it into a pandas DataFrame.

Parameters:

raw (mne.io.Raw) – MEG data.

Returns:

stim_deriv – List with QC_derivative object with stimulus data.

Return type:

list