moabb.datasets.BNCI2015_001

class moabb.datasets.BNCI2015_001(subjects=None, sessions=None)

BNCI 2015-001 Motor Imagery dataset.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/bnci2015-001-moabb-1

Dataset summary

#Subj	12
#Chan	13
#Classes	2
#Trials / class	200
Trials length	5 s
Freq	512 Hz
#Sessions	3
#Runs	1
Total_trials	14400

Participants

• Population: healthy

• Age: 24.8 years

• Handedness: all right-handed

• BCI experience: naive

Equipment

• Amplifier: g.tec

• Electrodes: active electrode

• Montage: 10-20

• Reference: Car

Preprocessing

• Data state: filtered

• Bandpass filter: 0.5-100 Hz

• Steps: bandpass filter, notch filter

• Re-reference: car

Data Access

• DOI: 10.1109/tnsre.2012.2189584

• Repository: BNCI Horizon

Experimental Protocol

• Paradigm: imagery

• Feedback: visual

• Stimulus: cursor_feedback

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Dataset from [1].

Dataset Description

This dataset contains EEG data from 12 subjects performing two-class motor imagery tasks (right hand vs feet). Each subject participated in multiple sessions, with some subjects having three sessions.

Participants

• 12 healthy subjects

• Gender: not specified

• Age: not specified

Recording Details

• Channels: 13 EEG electrodes

• Sampling rate: 512 Hz

• Reference: not specified

References

[1]

Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (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. https://doi.org/10.1109/tnsre.2012.2189584

Notes

Note

BNCI2015_001 was previously named BNCI2015001. BNCI2015001 will be removed in version 1.1.

Added in version 0.4.0.