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

Examples using `mne.datasets.eegbci.load_data`#

Repairing artifacts with ICA

EEG forward operator with a template MRI

Identify EEG Electrodes Bridged by too much Gel

Removing muscle ICA components

Compute and visualize ERDS maps

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

mne.datasets.eegbci.load_data#

Examples using mne.datasets.eegbci.load_data#

Examples using `mne.datasets.eegbci.load_data`#