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

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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.

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Citation & Impact

Stimulus Protocol
../_images/BNCI2003_004.svg

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.

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.

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

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

BNCI2014_001

BCI Competition IV 4-class motor imagery dataset

BNCI2014_004

BCI Competition 2008 2-class motor imagery dataset

__init__(subjects=None, sessions=None)[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