moabb.datasets.Lee2019_SSVEP#

class moabb.datasets.Lee2019_SSVEP(train_run=True, test_run=None, resting_state=False, sessions=(1, 2))[source]#

BMI/OpenBMI dataset for SSVEP.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/lee2019-ssvep-moabb-1

Dataset summary

#Subj

#Chan

#Classes

#Trials / class

Trials length

Sampling rate

#Sessions

54

62

4

50

4s

1000Hz

2

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.

SSVEP paradigm Four target SSVEP stimuli were designed to flicker at 5.45, 6.67, 8.57, and 12 Hz and were presented in four positions (down, right, left, and up, respectively) on a monitor. The designed paradigm followed the conventional types of SSVEP-based BCI systems that require four-direction movements. Partici- pants were asked to fixate the center of a black screen and then to gaze in the direction where the target stimulus was high- lighted in a different color. Each SSVEP stimulus was presented for 4 s with an ISI of 6 s. Each target frequency was presented 25 times. Therefore, the corrected EEG data had 100 trials (4 classes x 25 trials) in the offline training phase and another 100 trials in the online test phase. Visual feedback was presented in the test phase; the estimated target frequency was highlighted for 1 s with a red border at the end of each trial.

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