moabb.datasets.BNCI2003_004#

class moabb.datasets.BNCI2003_004(subjects=None, sessions=None)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2003_004

Motor Imagery, 2 classes (right_hand vs feet)

AuthorsGuido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller

🇩🇪 Fraunhofer FIRST (IDA); Charité University Medicine Berlin, DE·2004·benjamin.blankertz@tu-berlin.de

Motor Imagery Code: BNCI2003-004 5 subjects 1 session 118 ch 100 Hz 2 classes 3.5 s trials CC BY 4.0

Class Labels: right_hand, feet

Read Paper Compare Similar Report Issue

Overview

BNCI2003_IVa Motor Imagery dataset.

Dataset IVa from BCI Competition III

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.

Open in Overview tab →

Citation & Impact

Paper DOI10.1109/TBME.2004.827088
CitationsLoading…
Public APICrossref | OpenAlex
Page Views
30d: 50 · all-time: 59
#57 of 151 · Top 38% most viewed
Updated: 2026-03-20 UTC

Stimulus Protocol

3.5s task window per trial · 2-class motor imagery paradigm · 1 runs/session across 1 sessions

HED Event Tags

HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action

2

Sensory-event

2

right_hand

Sensory-eventAgent-action

feet

Sensory-eventAgent-action

HED tree view

Tree · right_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand

Tree · feet

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot

Channel Summary

Total channels118

EEG118 (EEG)

Montage10-05

Sampling100 Hz

Filter{'bandpass': [0.05, 200]}

Notch / line50 Hz

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

Overview

BNCI2003_IVa Motor Imagery dataset.

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.

Code Examples

from moabb.datasets import BNCI2003_004
dataset = BNCI2003_004()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

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: BrainAmp
Electrodes: EEG
Montage: standard_1005

Preprocessing

Data state: downsampled to 100 Hz for offline analysis
Bandpass filter: 0.05-200 Hz
Steps: bandpass filtering, downsampling
Notes: Band-pass filtered 0.05-200 Hz during acquisition at 1000 Hz with 16-bit (0.1 uV) accuracy, then downsampled to 100 Hz by picking each 10th sample. Original experiment also recorded EMG and EOG but these are not in the shared data files.

Data Access

DOI: 10.1109/TBME.2004.827088
Repository: BBCI

Experimental Protocol

Paradigm: imagery
Stimulus: visual cue

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.

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