moabb.datasets.Lee2019_MI

class moabb.datasets.Lee2019_MI(train_run=True, test_run=None, resting_state=False, sessions=(1, 2))[source][source]

BMI/OpenBMI dataset for MI.

Dataset from Lee et al 2019 [1].

Dataset Description

EEG signals were recorded with a sampling rate of 1,000 Hz and collected with 62 Ag/AgCl electrodes. The EEG amplifier used in the experiment was a BrainAmp (Brain Products; Munich, Germany). The channels were nasion-referenced and grounded to electrode AFz. Additionally, an EMG electrode recorded from each flexor digitorum profundus muscle with the olecranon used as reference. The EEG/EMG channel configuration and indexing numbers are described in Fig. 1. The impedances of the EEG electrodes were maintained below 10 k during the entire experiment.

MI paradigm The MI paradigm was designed following a well-established system protocol. For all blocks, the first 3 s of each trial began with a black fixation cross that appeared at the center of the monitor to prepare subjects for the MI task. Afterwards, the subject performed the imagery task of grasping with the appropriate hand for 4 s when the right or left arrow appeared as a visual cue. After each task, the screen remained blank for 6 s (± 1.5 s). The experiment consisted of training and test phases; each phase had 100 trials with balanced right and left hand imagery tasks. During the online test phase, the fixation cross appeared at the center of the monitor and moved right or left, according to the real-time classifier output of the EEG signal.

Parameters
  • train_run (bool (default True)) – if True, return runs corresponding to the training/offline phase (see paper).

  • test_run (bool (default: False for MI and SSVEP paradigms, True for ERP)) – if True, return runs corresponding to the test/online phase (see paper). Beware that test_run for MI and SSVEP do not have labels associated with trials: these runs could not be used in classification tasks.

  • resting_state (bool (default False)) – if True, return runs corresponding to the resting phases before and after recordings (see paper).

  • sessions (list of int (default [1,2])) – the list of the sessions to load (2 available).

References

1

Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E., Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience, 8(5), 1–16. https://doi.org/10.1093/gigascience/giz002

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.