moabb.datasets.BNCI2014_002#

class moabb.datasets.BNCI2014_002(subjects=None, sessions=None)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2014_002

Motor Imagery, 2 classes (right_hand vs feet)

AuthorsDavid Steyrl, Reinhold Scherer, Oswin Förstner, Gernot R. Müller-Putz

🇦🇹 Graz University of Technology, Austria·2014·david.steyrl@tugraz.at
Motor Imagery Code: BNCI2014-002 14 subjects 1 session 15 ch 512 Hz 2 classes 5.0 s trials CC BY-ND 4.0

Class Labels: right_hand, feet

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Overview

BNCI 2014-002 Motor Imagery dataset.

Motor Imagery Dataset from

Dataset description

The session consisted of eight runs, five of them for training and three with feedback for validation. One run was composed of 20 trials. Taken together, we recorded 50 trials per class for training and 30 trials per class for validation. Participants had the task of performing sustained (5 seconds) kinaesthetic motor imagery (MI) of the right hand and of the feet each as instructed by the cue. At 0 s, a white colored cross appeared on screen, 2 s later a beep sounded to catch the participant's attention. The cue was displayed from 3 s to 4 s. Participants were instructed to start with MI as soon as they recognized the cue and to perform the indicated MI until the cross disappeared at 8 s. A rest period with a random length between 2 s and 3 s was presented between trials. Participants did not receive feedback during training. Feedback was presented in form of a white coloured bar-graph. The length of the bar-graph reflected the amount of correct classifications over the last second. EEG was measured with a biosignal amplifier and active Ag/AgCl electrodes (g.USBamp, g.LADYbird, Guger Technologies OG, Schiedlberg, Austria) at a sampling rate of 512 Hz. The electrodes placement was designed for obtaining three Laplacian derivations. Center electrodes at positions C3, Cz, and C4 and four additional electrodes around each center electrode with a distance of 2.5 cm, 15 electrodes total. The reference electrode was mounted on the left mastoid and the ground electrode on the right mastoid. The 13 participants were aged between 20 and 30 years, 8 naive to the task, and had no known disabilities.

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Benchmark Context

WithinSession

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

Sample frame: 14 subjects × 1 sessions

  • MI right hand vs feet 16 pipelinesMax 88.60 · Median 82.57 · Mean 81.61 · Std 5.91

Citation & Impact

Stimulus Protocol
../_images/BNCI2014_002.svg

5s task window per trial · 2-class motor imagery paradigm · 8 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 channels15
EEG15 (Ag/AgCl)
MontageLaplacian
Sampling512 Hz
Referenceleft mastoid
Filter8th order Butterworth band-pass filters
Notch / line50 Hz

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

BNCI 2014-002 Motor Imagery dataset.

Motor Imagery Dataset from [1].

Dataset description

The session consisted of eight runs, five of them for training and three with feedback for validation. One run was composed of 20 trials. Taken together, we recorded 50 trials per class for training and 30 trials per class for validation. Participants had the task of performing sustained (5 seconds) kinaesthetic motor imagery (MI) of the right hand and of the feet each as instructed by the cue. At 0 s, a white colored cross appeared on screen, 2 s later a beep sounded to catch the participant’s attention. The cue was displayed from 3 s to 4 s. Participants were instructed to start with MI as soon as they recognized the cue and to perform the indicated MI until the cross disappeared at 8 s. A rest period with a random length between 2 s and 3 s was presented between trials. Participants did not receive feedback during training. Feedback was presented in form of a white coloured bar-graph. The length of the bar-graph reflected the amount of correct classifications over the last second. EEG was measured with a biosignal amplifier and active Ag/AgCl electrodes (g.USBamp, g.LADYbird, Guger Technologies OG, Schiedlberg, Austria) at a sampling rate of 512 Hz. The electrodes placement was designed for obtaining three Laplacian derivations. Center electrodes at positions C3, Cz, and C4 and four additional electrodes around each center electrode with a distance of 2.5 cm, 15 electrodes total. The reference electrode was mounted on the left mastoid and the ground electrode on the right mastoid. The 13 participants were aged between 20 and 30 years, 8 naive to the task, and had no known disabilities.

References

[1]

Scherer, R., Faller, J., Balderas, D., Friedrich, E. V., & Müller-Putz, G. (2015). Brain-computer interfacing: more than the sum of its parts. Soft Computing, 19(11), 3173-3186. https://doi.org/10.1007/s00500-012-0895-4

from moabb.datasets import BNCI2014_002
dataset = BNCI2014_002()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

14

#Chan

15

#Classes

2

#Trials / class

80

Trials length

5 s

Freq

512 Hz

#Sessions

1

#Runs

8

Total_trials

17920

Participants

  • Population: healthy

  • BCI experience: mixed

Equipment

  • Amplifier: g.USBamp

  • Electrodes: Ag/AgCl

  • Montage: Laplacian

  • Reference: left mastoid

Preprocessing

  • Data state: minimally preprocessed (online filtered)

  • Steps: bandpass filtering

Data Access

  • DOI: 10.1515/bmt-2014-0117

  • Repository: BNCI Horizon

Experimental Protocol

  • Paradigm: imagery

  • Feedback: continuous

  • Stimulus: bar_graph

Notes

Note

BNCI2014_002 was previously named BNCI2014002. BNCI2014002 will be removed in version 1.1.

Added in version 0.4.0.

See also

BNCI2014_001

4-class motor imagery (BCI Competition IV Dataset 2a)

BNCI2014_004

2-class motor imagery (Dataset B)

__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])

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