moabb.datasets.BNCI2003_004#

class moabb.datasets.BNCI2003_004[source]#

BNCI2003_IVa Motor Imagery dataset.

Dataset summary

#Subj	5
#Chan	118
#Classes	2
#Trials / class	84
Trials length	3.5 s
Freq	100 Hz
#Sessions	1
#Runs	1
Total_trials	1400

Participants

Population: healthy

Equipment

Amplifier: multichannel EEG amplifiers
Montage: standard_1005
Reference: Car

Preprocessing

Data state: downsampled to 100 Hz for offline analysis
Bandpass filter: 0.05-200 Hz
Steps: downsampling, baseline correction, spatial Laplacian filtering, bandpass filtering
Re-reference: car

Experimental Protocol

Paradigm: imagery
Stimulus: cursor_feedback

Dataset IVa from BCI Competition III [1].

Dataset Description

This data set was recorded from five healthy subjects. Subjects sat in a comfortable chair with arms resting on armrests. This data set contains only data from the 4 initial sessions without feedback. Visual cues indicated for 3.5 s which of the following 3 motor imageries the subject should perform: (L) left hand, (R) right hand, (F) right foot. The presentation of target cues were intermitted by periods of random length, 1.75 to 2.25 s, in which the subject could relax.

There were two types of visual stimulation: (1) where targets were indicated by letters appearing behind a fixation cross (which might nevertheless induce little target-correlated eye movements), and (2) where a randomly moving object indicated targets (inducing target- uncorrelated eye movements). From subjects al and aw 2 sessions of both types were recorded, while from the other subjects 3 sessions of type (2) and 1 session of type (1) were recorded.

References

[1]

Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Muller. Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Trans. Biomed. Eng., 51(6):993-1002, June 2004.

Notes

Added in version 0.4.0.

This is one of the earliest and most influential motor imagery BCI datasets, used extensively for benchmarking classification algorithms. The dataset was part of BCI Competition III and has been cited in hundreds of papers.