moabb.datasets.BNCI2014002

class moabb.datasets.BNCI2014002[source][source]

BNCI 2014-002 Motor Imagery dataset.

Motor Imagery Dataset from [1].

Dataset description

The session consisted of eight runs, five of them for training and three with feedback for validation. One run was composed of 20 trials. Taken together, we recorded 50 trials per class for training and 30 trials per class for validation. Participants had the task of performing sustained (5 seconds) kinaesthetic motor imagery (MI) of the right hand and of the feet each as instructed by the cue. At 0 s, a white colored cross appeared on screen, 2 s later a beep sounded to catch the participant’s attention. The cue was displayed from 3 s to 4 s. Participants were instructed to start with MI as soon as they recognized the cue and to perform the indicated MI until the cross disappeared at 8 s. A rest period with a random length between 2 s and 3 s was presented between trials. Participants did not receive feedback during training. Feedback was presented in form of a white coloured bar-graph. The length of the bar-graph reflected the amount of correct classifications over the last second. EEG was measured with a biosignal amplifier and active Ag/AgCl electrodes (g.USBamp, g.LADYbird, Guger Technologies OG, Schiedlberg, Austria) at a sampling rate of 512 Hz. The electrodes placement was designed for obtaining three Laplacian derivations. Center electrodes at positions C3, Cz, and C4 and four additional electrodes around each center electrode with a distance of 2.5 cm, 15 electrodes total. The reference electrode was mounted on the left mastoid and the ground electrode on the right mastoid. The 13 participants were aged between 20 and 30 years, 8 naive to the task, and had no known medical or neurological diseases.

References

1

Steyrl, D., Scherer, R., Faller, J. and Müller-Putz, G.R., 2016. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomedical Engineering/Biomedizinische Technik, 61(1), pp.77-86.

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.