mne.datasets.kiloword.data_path#

mne.datasets.kiloword.data_path(path=None, force_update=False, update_path=True, download=True, *, verbose=None)[source]#

Get path to local copy of the kiloword dataset.

This is the dataset from [1].

Parameters:
pathNone | str

Location of where to look for the kiloword data storing location. If None, the environment variable or config parameter MNE_DATASETS_KILOWORD_PATH is used. If it doesn’t exist, the “mne-python/examples” directory is used. If the kiloword dataset is not found under the given path (e.g., as “mne-python/examples/MNE-kiloword-data”), 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_KILOWORD_PATH in mne-python config to the given path. If None, the user is prompted.

downloadbool

If False and the kiloword dataset has not been downloaded yet, it will not be downloaded and the path will be returned as ‘’ (empty string). This is mostly used for debugging purposes and can be safely ignored by most users.

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:
pathlist of pathlib.Path

Local path to the given data file. This path is contained inside a list of length one, for compatibility.

References

Examples using mne.datasets.kiloword.data_path#

The Raw data structure: continuous data

The Raw data structure: continuous data

The Raw data structure: continuous data
Working with Epoch metadata

Working with Epoch metadata

Working with Epoch metadata
Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data
Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space