moabb.datasets.BNCI2014_004#

class moabb.datasets.BNCI2014_004[source]#

BNCI 2014-004 Motor Imagery dataset.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/bnci2014-004-moabb-1

Dataset summary

#Subj

#Chan

#Classes

#Trials

Trial length

Freq

#Session

#Runs

Total_trials

9

3

2

360

4.5s

250Hz

5

1

32400

Dataset B from BCI Competition 2008.

Dataset description

This data set consists of EEG data from 9 subjects of a study published in [1]. The subjects were right-handed, had normal or corrected-to-normal vision and were paid for participating in the experiments. All volunteers were sitting in an armchair, watching a flat screen monitor placed approximately 1 m away at eye level. For each subject 5 sessions are provided, whereby the first two sessions contain training data without feedback (screening), and the last three sessions were recorded with feedback.

Three bipolar recordings (C3, Cz, and C4) were recorded with a sampling frequency of 250 Hz.They were bandpass- filtered between 0.5 Hz and 100 Hz, and a notch filter at 50 Hz was enabled. The placement of the three bipolar recordings (large or small distances, more anterior or posterior) were slightly different for each subject (for more details see [1]). The electrode position Fz served as EEG ground. In addition to the EEG channels, the electrooculogram (EOG) was recorded with three monopolar electrodes.

The cue-based screening paradigm consisted of two classes, namely the motor imagery (MI) of left hand (class 1) and right hand (class 2). Each subject participated in two screening sessions without feedback recorded on two different days within two weeks. Each session consisted of six runs with ten trials each and two classes of imagery. This resulted in 20 trials per run and 120 trials per session. Data of 120 repetitions of each MI class were available for each person in total. Prior to the first motor im- agery training the subject executed and imagined different movements for each body part and selected the one which they could imagine best (e. g., squeezing a ball or pulling a brake).

Each trial started with a fixation cross and an additional short acoustic warning tone (1 kHz, 70 ms). Some seconds later a visual cue was presented for 1.25 seconds. Afterwards the subjects had to imagine the corresponding hand movement over a period of 4 seconds. Each trial was followed by a short break of at least 1.5 seconds. A randomized time of up to 1 second was added to the break to avoid adaptation

For the three online feedback sessions four runs with smiley feedback were recorded, whereby each run consisted of twenty trials for each type of motor imagery. At the beginning of each trial (second 0) the feedback (a gray smiley) was centered on the screen. At second 2, a short warning beep (1 kHz, 70 ms) was given. The cue was presented from second 3 to 7.5. At second 7.5 the screen went blank and a random interval between 1.0 and 2.0 seconds was added to the trial.

References

1

R. Leeb, F. Lee, C. Keinrath, R. Scherer, H. Bischof, G. Pfurtscheller. Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 473–482, 2007

Notes

Note

BNCI2014_004 was previously named BNCI2014004. BNCI2014004 will be removed in version 1.1.

Examples using moabb.datasets.BNCI2014_004#

Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures

Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures

Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures