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.
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.

Returns

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

References

1: Dufau, S., Grainger, J., Midgley, KJ., Holcomb, PJ. A thousand words are worth a picture: Snapshots of printed-word processing in an event-related potential megastudy. Psychological science, 2015

Examples using `mne.datasets.kiloword.data_path`¶

The Raw data structure: continuous data — The Raw data structure: continuous data¶

Working with Epoch metadata — Working with Epoch metadata¶

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¶