moabb.datasets.BNCI2014_004#

class moabb.datasets.BNCI2014_004(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2014_004

BCI Competition 2008 - Graz data set B: Two-class motor imagery dataset (left/right hand) with screening sessions (no feedback) and smiley feedback sessions. 9 subjects, 3 bipolar EEG channels (C3, Cz, C4) + 3 EOG channels, 250 Hz.

Motor Imagery, 2 classes (left_hand vs right_hand)

AuthorsR. Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof

🇦🇹 Graz University of Technology, AT·2007
Motor Imagery Code: BNCI2014-004 9 subjects 5 sessions 3 ch 250 Hz 2 classes 7.5 s trials CC BY-ND 4.0

Class Labels: left_hand, right_hand

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Overview

BNCI 2014-004 Motor Imagery dataset.

BCI Competition IV Dataset 2b

Dataset Description

This dataset consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of two different motor imagery tasks, namely the imagination of movement of the left hand (class 1) and the right hand (class 2). 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 20 trials (10 for each of the two possible classes), yielding a total of 120 trials per session.

The subjects were sitting in a comfortable chair 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 or to the right 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.

Three bipolar channels (C3, Cz, C4) and three EOG channels were recorded. The signals were sampled at 250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The reference was the left mastoid and the ground was the right mastoid. The electrode montage is a reduced version of the 10-20 system.

Open in Overview tab →

Benchmark Context

WithinSession

Included in 1 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 9 subjects × 5 sessions

  • MI left vs right 19 pipelinesMax 82.67 · Median 79.27 · Mean 76.35 · Std 5.28

Citation & Impact

  • Paper DOI10.1109/TNSRE.2007.906956
  • CitationsLoading…
  • Public APICrossref | OpenAlex
  • MOABB tables1 (WithinSession)
  • Page Views
    30d: 85 · all-time: 1,357
    #7 of 151 · Top 5% most viewed
    Updated: 2026-03-21 UTC
Stimulus Protocol
../_images/BNCI2014_004.svg

7.5s task window per trial · 2-class motor imagery paradigm · 1 runs/session across 5 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
2
Sensory-event
2
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action

HED tree view

Tree · left_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Leftward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand
Tree · right_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Rightward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand
Channel Summary
Total channels3
EEG3 (EEG)
EOG3
Montagestandard_1020
Sampling250 Hz
Referenceleft mastoid
Filter0.5-100 Hz bandpass, 50 Hz notch
Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

BNCI 2014-004 Motor Imagery dataset.

BCI Competition IV Dataset 2b [1].

Dataset Description

This dataset consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of two different motor imagery tasks, namely the imagination of movement of the left hand (class 1) and the right hand (class 2). 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 20 trials (10 for each of the two possible classes), yielding a total of 120 trials per session.

The subjects were sitting in a comfortable chair 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 or to the right 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.

Three bipolar channels (C3, Cz, C4) and three EOG channels were recorded. The signals were sampled at 250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The reference was the left mastoid and the ground was the right mastoid. The electrode montage is a reduced version of the 10-20 system.

References

[1]

Tangermann, M., Muller, 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.

from moabb.datasets import BNCI2014_004
dataset = BNCI2014_004()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

9

#Chan

3

#Classes

2

#Trials / class

360

Trials length

4.5 s

Freq

250 Hz

#Sessions

5

#Runs

1

Total_trials

32400

Participants

  • Population: healthy

  • Age: 24.7 years

  • Handedness: right

  • BCI experience: naive

Equipment

  • Amplifier: g.tec

  • Electrodes: EEG

  • Montage: standard_1020

  • Reference: left mastoid

Preprocessing

  • Data state: raw with online filtering

  • Bandpass filter: 0.5-100 Hz

  • Steps: bandpass filtering, notch filtering

  • Notes: Online bandpass (0.5-100 Hz) and notch (50 Hz) filters applied during recording. Artifact trials marked with event type 1023. EOG channels provided for user-applied artifact correction.

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Task type: motor_imagery

  • Tasks: left_hand_imagery, right_hand_imagery

  • Feedback: visual

  • Stimulus: arrow_cue

Notes

Note

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

Added in version 0.4.0.

This dataset is commonly referred to as “BCI Competition IV Dataset 2b”. It is widely used for binary motor imagery classification tasks.

See also

BNCI2014_001

4-class motor imagery (Dataset 2a)

BNCI2014_002

2-class motor imagery with Laplacian derivations

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Initialize function for the BaseDataset.

property all_subjects#

Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None, generate_figures=False)[source]#

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

  • path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).

  • subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.

  • overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

  • format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".

  • verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

  • generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

Notes

Use CacheConfig to configure caching for get_data(). Use moabb.datasets.bids_interface.get_bids_root to get the BIDS root path.

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#

Get path to local copy of a subject data.

Parameters:

  • subject (int) – Number of subject to use

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns:

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type:

list of str

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#

Download all data from the dataset.

This function is only useful to download all the dataset at once.

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • accept (bool) – Accept licence term to download the data, if any. Default: False

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str)[source]#

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

  • subject (str) – The identifier for the subject.

  • session (str) – The identifier for the session.

  • run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pandas.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

See also

get_data

Parameters:

  • subjects (List of int) – List of subject number

  • block_list (List of int) – List of block number

  • repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)[source]#

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {'subject_id' :
            {'session_id':
                {'run_id': run}
            }
        }

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

  • subjects (List of int) – List of subject number

  • cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

  • process_pipeline (sklearn.pipeline.Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray. This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata[source]#

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:

The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.

Return type:

DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0