braindecode.preprocessing.Anonymize#

class braindecode.preprocessing.Anonymize(daysback=None, keep_his=False, verbose=None)[source]#

Braindecode preprocessor wrapper for anonymize().

Anonymize measurement information in place.

Parameters:
daysbackint | None

Number of days to subtract from all dates. If None (default), the acquisition date, info['meas_date'], will be set to January 1ˢᵗ, 2000. This parameter is ignored if info['meas_date'] is None (i.e., no acquisition date has been set).

keep_hisbool

If True, his_id of subject_info will not be overwritten. Defaults to False.

Warning

This could mean that info is not fully anonymized. Use with caution.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
instinstance of Raw | Epochs | Evoked

The modified instance.

Notes

Removes potentially identifying information if it exists in info. Specifically for each of the following we use:

  • meas_date, file_id, meas_id

    A default value, or as specified by daysback.

  • subject_info

    Default values, except for ‘birthday’ which is adjusted to maintain the subject age.

  • experimenter, proj_name, description

    Default strings.

  • utc_offset

    None.

  • proj_id

    Zeros.

  • proc_history

    Dates use the meas_date logic, and experimenter a default string.

  • helium_info, device_info

    Dates use the meas_date logic, meta info uses defaults.

If info['meas_date'] is None, it will remain None during processing the above fields.

Operates in place.

Added in version 0.13.0.

Examples using braindecode.preprocessing.Anonymize#

Comprehensive Preprocessing with MNE-based Classes

Comprehensive Preprocessing with MNE-based Classes