mne.datasets.eegbci.load_data#

mne.datasets.eegbci.load_data(subject, runs, path=None, force_update=False, update_path=None, base_url='https://physionet.org/files/eegmmidb/1.0.0/', verbose=None)[source]#

Get paths to local copies of EEGBCI dataset files.

This will fetch data for the EEGBCI dataset [1], which is also available at PhysioNet [2].

Parameters:

subjectint: The subject to use. Can be in the range of 1-109 (inclusive).
runsint | list of int: The runs to use. See Notes for details.
pathNone | str: Location of where to look for the EEGBCI data storing location. If None, the environment variable or config parameter MNE_DATASETS_EEGBCI_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the EEGBCI dataset is not found under the given path, the data will be automatically downloaded to the specified folder.
force_updatebool: Force update of the dataset even if a local copy exists.
update_pathbool | None: If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python config to the given path. If None, the user is prompted.
base_urlstr: The URL root for the data.
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:

pathslist: List of local data paths of the given type.

Notes

The run numbers correspond to:

run	task
1	Baseline, eyes open
2	Baseline, eyes closed
3, 7, 11	Motor execution: left vs right hand
4, 8, 12	Motor imagery: left vs right hand
5, 9, 13	Motor execution: hands vs feet
6, 10, 14	Motor imagery: hands vs feet

For example, one could do:

>>> from mne.datasets import eegbci
>>> eegbci.load_data(1, [4, 10, 14], os.getenv('HOME') + '/datasets') 

This would download runs 4, 10, and 14 (hand/foot motor imagery) runs from subject 1 in the EEGBCI dataset to the ‘datasets’ folder, and prompt the user to save the ‘datasets’ path to the mne-python config, if it isn’t there already.

References