moabb.datasets.Weibo2014#

class moabb.datasets.Weibo2014[source]#

Motor Imagery dataset from Weibo et al 2014.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/weibo2014-moabb

Dataset summary

#Subj

#Chan

#Classes

#Trials

Trial length

Freq

#Session

#Runs

Total_trials

10

60

7

80

4s

200Hz

1

1

5600

Dataset from the article Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery [1].

It contains data recorded on 10 subjects, with 60 electrodes.

This dataset was used to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery. Seven kinds of mental tasks have been designed, involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery combining hand with hand/foot (both hands, left hand combined with right foot, right hand combined with left foot) and rest state.

At the beginning of each trial (8 seconds), a white circle appeared at the center of the monitor. After 2 seconds, a red circle (preparation cue) appeared for 1 second to remind the subjects of paying attention to the character indication next. Then red circle disappeared and character indication (‘Left Hand’, ‘Left Hand & Right Foot’, et al) was presented on the screen for 4 seconds, during which the participants were asked to perform kinesthetic motor imagery rather than a visual type of imagery while avoiding any muscle movement. After 7 seconds, ‘Rest’ was presented for 1 second before next trial (Fig. 1(a)). The experiments were divided into 9 sections, involving 8 sections consisting of 60 trials each for six kinds of MI tasks (10 trials for each MI task in one section) and one section consisting of 80 trials for rest state. The sequence of six MI tasks was randomized. Intersection break was about 5 to 10 minutes.

References

1

Yi, Weibo, et al. “Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery.” PloS one 9.12 (2014). https://doi.org/10.1371/journal.pone.0114853

data_path(subject, path=None, force_update=False, update_path=None, verbose=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