moabb.datasets.BNCI2015_004#

class moabb.datasets.BNCI2015_004(subjects=None, sessions=None)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2015_004

Motor Imagery, 5 classes (math vs letter vs rotation vs count vs baseline)

AuthorsReinhold Scherer, Josef Faller, Elisabeth V. C. Friedrich, Eloy Opisso, Ursula Costa, Andrea Kübler, Gernot R. Müller-Putz

🇪🇸 Institut Guttmann, Spain·2015·reinhold.scherer@tugraz.at
Motor Imagery Code: BNCI2015-004 9 subjects 2 sessions 30 ch 256 Hz 5 classes 11.0 s trials CC BY-NC-ND 4.0

Class Labels: math, letter, rotation, count, baseline

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Overview

BNCI 2015-004 Mental tasks dataset.

Dataset from

Dataset Description

This dataset contains EEG data from 9 subjects performing five different mental tasks: mental multiplication, mental letter composing, mental rotation, mental counting, and a baseline task.

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 × 2 sessions

  • MI right hand vs feet 16 pipelinesMax 62.60 · Median 54.11 · Mean 55.46 · Std 4.29

Citation & Impact

Stimulus Protocol
../_images/BNCI2015_004.svg

11s task window per trial · 5-class motor imagery paradigm · 1 runs/session across 2 sessions

HED Event Tags
HED tags5/5 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
5
Agent-action
4
Experimental-stimulus
1
Rest
1
Visual-presentation
1
math
Sensory-eventAgent-action
letter
Sensory-eventAgent-action
rotation
Sensory-eventAgent-action
count
Sensory-eventAgent-action
baseline
Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · math
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Think
      └─ Label
Tree · letter
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Think
      └─ Label
Tree · rotation
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Think
      └─ Label
Tree · count
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      └─ Count
Tree · baseline
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest
Channel Summary
Total channels30
EEG30 (active electrode)
Montage10-20
Sampling256 Hz
Referenceleft and right 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 2015-004 Mental tasks dataset.

Dataset from [1].

Dataset Description

This dataset contains EEG data from 9 subjects performing five different mental tasks: mental multiplication, mental letter composing, mental rotation, mental counting, and a baseline task.

References

[1]

Zhang, X., Yao, L., Zhang, Q., Kanhere, S., Sheng, M., & Liu, Y. (2017). A survey on deep learning based brain computer interface: Recent advances and new frontiers. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 145-163.

from moabb.datasets import BNCI2015_004
dataset = BNCI2015_004()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

9

#Chan

30

#Classes

5

#Trials / class

80

Trials length

7 s

Freq

256 Hz

#Sessions

2

#Runs

1

Total_trials

7200

Participants

  • Population: CNS tissue damage

  • Clinical population: stroke and spinal cord injury

  • Age: 38 (range: 20-57) years

  • Handedness: not specified

  • BCI experience: naive

Equipment

  • Amplifier: g.tec

  • Electrodes: active electrode

  • Montage: 10-20

  • Reference: left and right mastoid

Preprocessing

  • Data state: filtered

  • Bandpass filter: 0.5-100 Hz

  • Steps: bandpass filter, notch filter, artifact rejection

  • Re-reference: left and right mastoid

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Tasks: word_association, mental_subtraction, spatial_navigation, right_hand_imagery, feet_imagery

  • Feedback: none

  • Stimulus: visual cue

Notes

Note

BNCI2015_004 was previously named BNCI2015004. BNCI2015004 will be removed in version 1.1.

Added in version 0.4.0.

__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