moabb.datasets.PhysionetMI

class moabb.datasets.PhysionetMI(imagined=True, executed=False)[source][source]

Physionet Motor Imagery dataset.

Physionet MI dataset: https://physionet.org/pn4/eegmmidb/

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2].

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org) [1]. Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:

  1. A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.

  2. A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.

  3. A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

  4. A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

Parameters
  • imagined (bool (default True)) – if True, return runs corresponding to motor imagination.

  • executed (bool (default False)) – if True, return runs corresponding to motor execution.

References

1

Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering, 51(6), pp.1034-1043.

2

Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. and PhysioBank, P., PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 Volume 101 Issue 23 pp. E215–E220.

Methods

data_path(subject[, path, force_update, …])

Get path to local copy of a subject data.

download([subject_list, path, force_update, …])

Download all data from the dataset.

get_data([subjects])

Return the data correspoonding to a list of subjects.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source][source]

Get path to local copy of a subject data.

Parameters
  • subject (int) – Number of subject to use

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the 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 **Deprecated**) – If True, set the MNE_DATASETS_(dataset)_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()).

Returns

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

Return type

list of str