moabb.datasets.BNCI2014_001#

class moabb.datasets.BNCI2014_001[source]#

BNCI 2014-001 Motor Imagery dataset.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/bnci2014-001-moabb-1

Dataset summary

#Subj

#Chan

#Classes

#Trials / class

Trial length

Freq

#Session

#Runs

Total_trials

9

22

4

144

4s

250Hz

2

6

62208

Dataset IIa from BCI Competition 4 [1].

Dataset Description

This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four different motor imagery tasks, namely the imag- ination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4). Two sessions on different days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session.

The subjects were sitting in a comfortable armchair in front of a computer screen. At the beginning of a trial ( t = 0 s), a fixation cross appeared on the black screen. In addition, a short acoustic warning tone was presented. After two seconds ( t = 2 s), a cue in the form of an arrow pointing either to the left, right, down or up (corresponding to one of the four classes left hand, right hand, foot or tongue) appeared and stayed on the screen for 1.25 s. This prompted the subjects to perform the desired motor imagery task. No feedback was provided. The subjects were ask to carry out the motor imagery task until the fixation cross disappeared from the screen at t = 6 s.

Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm) were used to record the EEG; the montage is shown in Figure 3 left. All signals were recorded monopolarly with the left mastoid serving as reference and the right mastoid as ground. The signals were sampled with. 250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of the amplifier was set to 100 μV . An additional 50 Hz notch filter was enabled to suppress line noise

References

1

Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G. and Nolte, G., 2012. Review of the BCI competition IV. Frontiers in neuroscience, 6, p.55.

Notes

Note

BNCI2014_001 was previously named BNCI2014001. BNCI2014001 will be removed in version 1.1.

Examples using moabb.datasets.BNCI2014_001#

Benchmarking with MOABB showing the CO2 footprint

Benchmarking with MOABB showing the CO2 footprint

Benchmarking with MOABB showing the CO2 footprint
Benchmarking on MOABB with Tensorflow deep net architectures

Benchmarking on MOABB with Tensorflow deep net architectures

Benchmarking on MOABB with Tensorflow deep net architectures
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
Cross-session motor imagery with deep learning EEGNet v4 model

Cross-session motor imagery with deep learning EEGNet v4 model

Cross-session motor imagery with deep learning EEGNet v4 model
Cross-Session Motor Imagery

Cross-Session Motor Imagery

Cross-Session Motor Imagery
Cross-Session on Multiple Datasets

Cross-Session on Multiple Datasets

Cross-Session on Multiple Datasets
Explore Paradigm Object

Explore Paradigm Object

Explore Paradigm Object
FilterBank CSP versus CSP

FilterBank CSP versus CSP

FilterBank CSP versus CSP
GridSearch within a session

GridSearch within a session

GridSearch within a session
Select Electrodes and Resampling

Select Electrodes and Resampling

Select Electrodes and Resampling
Statistical Analysis

Statistical Analysis

Statistical Analysis
Within Session Motor Imagery with Learning Curve

Within Session Motor Imagery with Learning Curve

Within Session Motor Imagery with Learning Curve
Tutorial 0: Getting Started

Tutorial 0: Getting Started

Tutorial 0: Getting Started
Tutorial 1: Simple Motor Imagery

Tutorial 1: Simple Motor Imagery

Tutorial 1: Simple Motor Imagery
Tutorial 2: Using multiple datasets

Tutorial 2: Using multiple datasets

Tutorial 2: Using multiple datasets
Tutorial 3: Benchmarking multiple pipelines

Tutorial 3: Benchmarking multiple pipelines

Tutorial 3: Benchmarking multiple pipelines