class moabb.datasets.BNCI2015001[source][source]

BNCI 2015-001 Motor Imagery dataset.

Dataset from [1].

Dataset description

We acquired the EEG from three Laplacian derivations, 3.5 cm (center-to- center) around the electrode positions (according to International 10-20 System of Electrode Placement) C3 (FC3, C5, CP3 and C1), Cz (FCz, C1, CPz and C2) and C4 (FC4, C2, CP4 and C6). The acquisition hardware was a g.GAMMAsys active electrode system along with a g.USBamp amplifier (g.tec, Guger Tech- nologies OEG, Graz, Austria). The system sampled at 512 Hz, with a bandpass filter between 0.5 and 100 Hz and a notch filter at 50 Hz. The order of the channels in the data is FC3, FCz, FC4, C5, C3, C1, Cz, C2, C4, C6, CP3, CPz, CP4.

The task for the user was to perform sustained right hand versus both feet movement imagery starting from the cue (second 3) to the end of the cross period (sec- ond 8). A trial started with 3 s of reference period, followed by a brisk audible cue and a visual cue (arrow right for right hand, arrow down for both feet) from second 3 to 4.25. The activity period, where the users received feedback, lasted from second 4 to 8. There was a random 2 to 3 s pause between the trials.



J. Faller, C. Vidaurre, T. Solis-Escalante, C. Neuper and R. Scherer (2012). Autocalibration and recurrent adaptation: Towards a plug and play online ERD- BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3), 313-319.


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.


Return the data correspoonding to a list of subjects.