timeflux.helpers.mne


MNE helpers

mne

timeflux.helpers.mne.logger
timeflux.helpers.mne.xarray_to_mne(data, meta, context_key, event_id, reporting='warn', ch_types='eeg', **kwargs)[source]

Convert DataArray and meta into mne Epochs object

Parameters
  • data (DataArray) – Array of dimensions (‘epoch’, ‘time’, ‘space’)

  • meta (dict) – Dictionary with keys ‘epochs_context’, ‘rate’, ‘epochs_onset’

  • context_key (str|None) – key to select the context label.

  • the context is a string, context_key should be set to None. (If) –

  • event_id (dict) – Associates context label to an event_id that should be an int. (eg. dict(auditory=1, visual=3))

  • reporting ('warn'|'error'| None) – How this function handles epochs with invalid context: - ‘error’ will raise a TimefluxException - ‘warn’ will print a warning with warnings.warn() and skip the corrupted epochs - None will skip the corrupted epochs

  • ch_types (list|str) – Channel type to

Returns

mne object with the converted data.

Return type

epochs (mne.Epochs)

timeflux.helpers.mne.mne_to_xarray(epochs, context_key, event_id, output='dataarray')[source]

Convert mne Epochs object into DataArray along with meta.

Parameters
  • epochs (mne.Epochs) – mne object with the converted data.

  • context_key (str|None) – key to select the context label.

  • the context is a string, context_key should be set to None. (If) –

  • event_id (dict) – Associates context label to an event_id that should be an int. (eg. dict(auditory=1, visual=3))

  • output (str) – type of the expected output (DataArray or Dataset)

Returns

Array of dimensions (‘epoch’, ‘time’, ‘space’) meta (dict): Dictionary with keys ‘epochs_context’, ‘rate’, ‘epochs_onset’

Return type

data (DataArray|Dataset)