mne.datasets.eegbci.load_data

mne.datasets.eegbci.load_data(subject, runs, path=None, force_update=False, update_path=None, base_url=’http://www.physionet.org/physiobank/database/eegmmidb/’, verbose=None)[source]

Get paths to local copies of EEGBCI dataset files.

This will fetch data for the EEGBCI dataset [R30], which is also available at PhysioNet [R31].

Parameters:

subject : int

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

runs : int | list of int

The runs to use. The runs 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

path : None | 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 : bool

Force update of the dataset even if a local copy exists.

update_path : bool | None

If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python config to the given path. If None, the user is prompted.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).
Returns:

paths : list

List of local data paths of the given type.

Notes

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

[R30](1, 2) 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
[R31](1, 2) 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