moabb.datasets.Ofner2017#

class moabb.datasets.Ofner2017(imagined=True, executed=False)[source]#

Motor Imagery ataset from Ofner et al 2017.

Dataset summary

Name

#Subj

#Chan

#Classes

#Trials / class

Trials len

Sampling rate

#Sessions

Ofner2017

15

61

7

60

3s

512Hz

1

Upper limb Motor imagery dataset from the paper [1].

Dataset description

We recruited 15 healthy subjects aged between 22 and 40 years with a mean age of 27 years (standard deviation 5 years). Nine subjects were female, and all the subjects except s1 were right-handed.

We measured each subject in two sessions on two different days, which were not separated by more than one week. In the first session the subjects performed ME, and MI in the second session. The subjects performed six movement types which were the same in both sessions and comprised of elbow flexion/extension, forearm supination/pronation and hand open/close; all with the right upper limb. All movements started at a neutral position: the hand half open, the lower arm extended to 120 degree and in a neutral rotation, i.e. thumb on the inner side. Additionally to the movement classes, a rest class was recorded in which subjects were instructed to avoid any movement and to stay in the starting position. In the ME session, we instructed subjects to execute sustained movements. In the MI session, we asked subjects to perform kinesthetic MI of the movements done in the ME session (subjects performed one ME run immediately before the MI session to support kinesthetic MI).

The paradigm was trial-based and cues were displayed on a computer screen in front of the subjects, Fig 2 shows the sequence of the paradigm. At second 0, a beep sounded and a cross popped up on the computer screen (subjects were instructed to fixate their gaze on the cross). Afterwards, at second 2, a cue was presented on the computer screen, indicating the required task (one out of six movements or rest) to the subjects. At the end of the trial, subjects moved back to the starting position. In every session, we recorded 10 runs with 42 trials per run. We presented 6 movement classes and a rest class and recorded 60 trials per class in a session.

References

[1]

Ofner, P., Schwarz, A., Pereira, J. and Müller-Putz, G.R., 2017. Upper limb movements can be decoded from the time-domain of low-frequency EEG. PloS one, 12(8), p.e0182578. https://doi.org/10.1371/journal.pone.0182578

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