moabb.datasets.Beetl2021_B#

class moabb.datasets.Beetl2021_B(phase='final')[source]#

Motor Imagery dataset from BEETL Competition - Dataset B.

Dataset summary

#Subj

#Chan

#Classes

Trial length(s)

Freq(Hz)

#Session

#Runs

Total_trials

2

32

4

4

200

1

1

1590

Dataset description

Dataset B contains data from subjects with 200 Hz sampling rate and 32 EEG channels. In the leaderboard phase, this includes subjects 3-5, while in the final phase it includes subjects 4-5.

Note: for the BEETL competition, there was a leaderboard phase and a final phase. Both phases contained data from two datasets, A and B. However, during leaderboard phase, dataset A contained data from subjects 1-2, while dataset B contained data from subjects 3-5. During the final phase, dataset A contained data from subjects 1-3, while dataset B contained data from subjects 4-5.

Note: for the competition the data is cut into 4 second trials, here the data is concatenated into one session! In order to get the data as provided in the competition, the data has to be cut into 4 second trials.

For the leaderboard phase, the dataset contains only training data, while for the final phase it includes both training and testing data. To learn more about the datasets in detail see [1]. To learn more about the competition see [2].

For benchmarking the BEETL competition use phase “final”, train on training data benchmark on testing data.

The data was filtered using a highpass filter with a cutoff frequency of 1 Hz and a lowpass filter with a cutoff frequency of 100 Hz.

Motor imagery tasks include: - Left hand (label 0) - Right hand (label 1) - Feet (label 2) - Rest (label 3)

phase#

Either “leaderboard” or “final”

Type:

str

References

[1]

Wei, X., Faisal, A. A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S., Jayaram, V., … & Tempczyk, P. (2022, July). 2021 BEETL competition: Advancing transfer learning for subject independence and heterogeneous EEG data sets. In NeurIPS 2021 Competitions and Demonstrations Track (pp. 205-219). PMLR.

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

Return path to the data files.