mne.datasets.eegbci.load_data(subject, runs, path=None, force_update=False, update_path=None, base_url='', 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].


The subject to use. Can be in the range of 1-109 (inclusive).

runsint | list of int

The runs to use. The runs correspond to:




Baseline, eyes open


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

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 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.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.


List of local data paths of the given type.


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.



Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE TBME 51(6):1034-1043


Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220