moabb.datasets.BNCI2014_001#

class moabb.datasets.BNCI2014_001(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2014_001

Review of the BCI competition IV - Data set 1: Asynchronous Motor Imagery

Motor Imagery, 4 classes (left_hand vs right_hand vs feet vs tongue)

AuthorsMichael Tangermann, Klaus-Robert Müller, Ad Aertsen, Niels Birbaumer, Christoph Braun, Clemens Brunner, Robert Leeb, Carsten Mehring, Kai J. Miller, Gernot R. Müller-Putz, Guido Nolte, Gert Pfurtscheller, Hubert Preissl, Gerwin Schalk, Alois Schlögl, Carmen Vidaurre, Stephan Waldert, Benjamin Blankertz

🇦🇹 Berlin Institute of Technology, AT·2012·michael.tangermann@tu-berlin.de
Motor Imagery Code: BNCI2014-001 9 subjects 2 sessions 25 ch (22 EEG) 250 Hz 4 classes 4.0 s trials CC BY-ND 4.0

Class Labels: left_hand, right_hand, feet, tongue

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Overview

BNCI 2014-001 Motor Imagery dataset.

Dataset IIa from BCI Competition 4

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 uV . An additional 50 Hz notch filter was enabled to suppress line noise

Open in Overview tab →

Benchmark Context

WithinSession

Included in 3 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 9 subjects × 2 sessions

  • MI left vs right 19 pipelinesMax 91.71 · Median 82.34 · Mean 81.10 · Std 6.58
  • MI all classes 16 pipelinesMax 77.82 · Median 66.42 · Mean 61.34 · Std 13.86
  • MI right hand vs feet 16 pipelinesMax 97.32 · Median 91.53 · Mean 88.49 · Std 8.91

Citation & Impact

  • Paper DOI10.3389/fnins.2012.00055
  • CitationsLoading…
  • Public APICrossref | OpenAlex
  • MOABB tables3 (WithinSession)
  • Page Views
    30d: 310 · all-time: 5,946
    #1 of 151 · Top 1% most viewed
    Updated: 2026-03-18 UTC
Stimulus Protocol
../_images/BNCI2014_001.svg

4s task window per trial · 4-class motor imagery paradigm · 6 runs/session across 2 sessions

HED Event Tags
HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
4
Sensory-event
4
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action
feet
Sensory-eventAgent-action
tongue
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
Tree · feet
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Downward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot
Tree · tongue
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Upward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Tongue
Channel Summary
Total channels25
EEG22 (Ag/AgCl)
EOG3
Montagecustom
Sampling250 Hz
Referenceleft mastoid
Filterbandpass 0.05-200 Hz
Notch / line50 Hz

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

BNCI 2014-001 Motor Imagery dataset.

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 uV . An additional 50 Hz notch filter was enabled to suppress line noise

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_001
dataset = BNCI2014_001()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

9

#Chan

22

#Classes

4

#Trials / class

144

Trials length

4 s

Freq

250 Hz

#Sessions

2

#Runs

6

Total_trials

62208

Participants

  • Population: healthy

Equipment

  • Amplifier: BrainAmp MR plus

  • Electrodes: Ag/AgCl

  • Montage: custom

  • Reference: none

Preprocessing

  • Data state: minimally preprocessed (bandpass and notch filtered)

  • Bandpass filter: 0.05-200 Hz

  • Steps: bandpass filtering

  • Re-reference: none

  • Notes: Data provided in two versions: original at 1000 Hz and downsampled to 100 Hz (with Chebyshev Type II filter order 10, stop band ripple 50 dB, stop band edge 49 Hz)

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: none

  • Stimulus: arrow_cue

Notes

Note

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

Added in version 0.4.0.

This is one of the most widely used motor imagery datasets in BCI research, commonly referred to as “BCI Competition IV Dataset 2a”. It serves as a standard benchmark for 4-class motor imagery classification algorithms.

The dataset is particularly useful for:

  • Multi-class motor imagery classification (4 classes)

  • Transfer learning studies (9 subjects, 2 sessions each)

  • Cross-session variability analysis

See also

BNCI2014_004

BCI Competition 2008 2-class motor imagery (Dataset B)

BNCI2003_004

BCI Competition III 2-class motor imagery

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.subject_list
[1, 2, 3, 4, 5, 6, 7, 8, 9]
__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])

See also

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

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) → None | DataFrame[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 | pd.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

BaseDataset.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 (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.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: DatasetMetadata | None[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

Examples using moabb.datasets.BNCI2014_001#

Tutorial 0: Getting Started

Tutorial 0: Getting Started

Tutorial 1: Simple Motor Imagery

Tutorial 1: Simple Motor Imagery

Tutorial 2: Using multiple datasets

Tutorial 2: Using multiple datasets

Tutorial 3: Benchmarking multiple pipelines

Tutorial 3: Benchmarking multiple pipelines

Cross-Session Motor Imagery

Cross-Session Motor Imagery

Cross-Session on Multiple Datasets

Cross-Session on Multiple Datasets

Explore Paradigm Object

Explore Paradigm Object

Benchmarking with MOABB showing the CO2 footprint

Benchmarking with MOABB showing the CO2 footprint

Tutorial: Within-Session Splitting on Real MI Dataset

Tutorial: Within-Session Splitting on Real MI Dataset

Pipelines using the mne-features library

Pipelines using the mne-features library

Dataset bubble plot

Dataset bubble plot

FilterBank CSP versus CSP

FilterBank CSP versus CSP

GridSearch within a session

GridSearch within a session

Playing with the pre-processing steps

Playing with the pre-processing steps

Pseudo-Online Motor Imagery with Sliding Window

Pseudo-Online Motor Imagery with Sliding Window

Select Electrodes and Resampling

Select Electrodes and Resampling

Time-Resolved Decoding with SlidingEstimator

Time-Resolved Decoding with SlidingEstimator

Statistical Analysis and Chance Level Assessment

Statistical Analysis and Chance Level Assessment

Within Session Motor Imagery with Learning Curve

Within Session Motor Imagery with Learning Curve