moabb.datasets.BNCI2015_007#

class moabb.datasets.BNCI2015_007(subjects=None, sessions=None)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2015_007

Exploring motion VEPs for gaze-independent communication

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsSulamith Schaeff, Matthias Sebastian Treder, Bastian Venthur, Benjamin Blankertz

πŸ‡©πŸ‡ͺ Berlin Institute of Technology, GermanyΒ·2012Β·benjamin.blankertz@tu-berlin.de
P300 / ERP Code: BNCI2015-007 16 subjects 1 session 63 ch 100 Hz 2 classes 30.0 s trials CC BY-NC-ND 4.0

Class Labels: Target, NonTarget

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Overview

BNCI 2015-007 Motion VEP (mVEP) Speller dataset.

Dataset from

Dataset Description

This dataset implements a motion-onset visual evoked potential (mVEP) based brain-computer interface for gaze-independent spelling. Unlike conventional flash-based P300 spellers that use luminance changes, this paradigm uses motion onset (moving bar stimuli) to elicit visual evoked potentials, specifically the N200 component. This approach has advantages including lower visual fatigue, reduced luminance and contrast requirements, and potential for use in bright environments.

The motion VEP (mVEP) speller operates by presenting moving bar stimuli at different positions in a matrix layout. When the user attends to a target position, the motion onset at that location elicits a characteristic N200 response that can be detected to determine the user's intended selection.

Participants

  • - 16 healthy subjects
  • Gender: Not specified in metadata
  • Age: Not specified in metadata
  • BCI experience: Not specified
  • Health status: Healthy volunteers
  • Location: Neurotechnology Group, Technische Universitat Berlin, Germany

Recording Details

  • - Equipment: BrainProducts actiCap active electrode system
  • Channels: 63 EEG electrodes (standard 10-10 system)
  • Sampling rate: 100 Hz (downsampled from original recording)
  • Reference: Nose reference
  • Montage: standard_1005
  • Filters: Bandpass filtered during preprocessing
  • Units: uV (converted to V during loading)

Experimental Procedure

  • - 6x6 matrix speller layout (36 possible targets)
  • Motion onset stimulation (moving bars)
  • 6 stimulus positions per row/column
  • Overt attention paradigm (gaze-dependent) and covert attention modes
  • One recording session per subject with multiple runs (typically 2)
  • Each run contains multiple spelling sequences

Data Organization

  • - Subject codes: fat, gdf, gdg, iac, iba, ibe, ibq, ibs, ibt, ibu, ibv, ibw, ibx, iby, ice, icv
  • Data URL: http://doc.ml.tu-berlin.de/bbci/BNCIHorizon2020-MVEP/

Event Codes

  • - Target (1): Target stimulus presented (attended)
  • NonTarget (2): Non-target stimulus presented (not attended)
Open in Overview tab β†’

Citation & Impact

Stimulus Protocol
../_images/BNCI2015_007.svg

30s task window per trial Β· 2-class p300 / erp paradigm Β· 2 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Sensory-event
2
Visual-presentation
2
Non-target
1
Target
1
Target
Sensory-eventExperimental-stimulusVisual-presentationTarget
NonTarget
Sensory-eventExperimental-stimulusVisual-presentationNon-target

HED tree view

Tree Β· Target
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Target
Tree Β· NonTarget
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Non-target
Channel Summary
Total channels63
EEG63 (active electrode)
Montage10-10
Sampling100 Hz
Referencelinked mastoids
Filterhardware bandpass filter 0.016–250 Hz
Notch / line50 Hz

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

BNCI 2015-007 Motion VEP (mVEP) Speller dataset.

Dataset from [1].

Dataset Description

This dataset implements a motion-onset visual evoked potential (mVEP) based brain-computer interface for gaze-independent spelling. Unlike conventional flash-based P300 spellers that use luminance changes, this paradigm uses motion onset (moving bar stimuli) to elicit visual evoked potentials, specifically the N200 component. This approach has advantages including lower visual fatigue, reduced luminance and contrast requirements, and potential for use in bright environments.

The motion VEP (mVEP) speller operates by presenting moving bar stimuli at different positions in a matrix layout. When the user attends to a target position, the motion onset at that location elicits a characteristic N200 response that can be detected to determine the user’s intended selection.

Participants

  • 16 healthy subjects

  • Gender: Not specified in metadata

  • Age: Not specified in metadata

  • BCI experience: Not specified

  • Health status: Healthy volunteers

  • Location: Neurotechnology Group, Technische Universitat Berlin, Germany

Recording Details

  • Equipment: BrainProducts actiCap active electrode system

  • Channels: 63 EEG electrodes (standard 10-10 system)

  • Sampling rate: 100 Hz (downsampled from original recording)

  • Reference: Nose reference

  • Montage: standard_1005

  • Filters: Bandpass filtered during preprocessing

  • Units: uV (converted to V during loading)

Experimental Procedure

  • 6x6 matrix speller layout (36 possible targets)

  • Motion onset stimulation (moving bars)

  • 6 stimulus positions per row/column

  • Overt attention paradigm (gaze-dependent) and covert attention modes

  • One recording session per subject with multiple runs (typically 2)

  • Each run contains multiple spelling sequences

Data Organization

Event Codes

  • Target (1): Target stimulus presented (attended)

  • NonTarget (2): Non-target stimulus presented (not attended)

References

[1]

Treder, M. S., Purwins, H., Miklody, D., Sturm, I., & Blankertz, B. (2012). Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. Journal of Neural Engineering, 11(2), 026009. https://doi.org/10.1088/1741-2560/11/2/026009

from moabb.datasets import BNCI2015_007
dataset = BNCI2015_007()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

16

#Chan

63

#Trials / class

varies NT / T

Trials length

0.7 s

Freq

100 Hz

#Sessions

1

Participants

  • Population: Healthy

  • Age: 23.8 (range: 21-30) years

  • Handedness: normal or corrected-to-normal vision

  • BCI experience: naive

Equipment

  • Amplifier: BrainAmp EEG amplifier

  • Electrodes: active electrode

  • Montage: 10-10

  • Reference: linked mastoids

Preprocessing

  • Data state: filtered

  • Bandpass filter: 0.016-250 Hz

  • Steps: downsampling, low-pass filter, baseline correction, artifact rejection

  • Re-reference: linked mastoids

  • Notes: For offline analysis: downsampled to 200 Hz, low-pass filtered (42 Hz passband, 49 Hz stopband). For online: downsampled to 100 Hz. Artifact rejection: min-max β‰₯70 ΞΌV. Nontarget epochs filtered to avoid overlap with targets (3 preceding and 4 following stimuli must be nontargets).

Data Access

  • DOI: 10.1088/1741-2560/9/4/045006

  • Repository: BNCI Horizon

Experimental Protocol

  • Paradigm: p300

  • Task type: visual_speller

  • Feedback: visual

  • Stimulus: motion VEP (mVEP)

Dataset summary

Name

#Subj

#Chan

#Trials/class

Trials length

Sampling Rate

#Sessions

BNCI2015_007

16

63

~1800 NT / ~360 T

0.7s

100Hz

1

Notes

Added in version 1.2.0.

See also

BNCI2015_008

Center Speller P300 dataset (gaze-independent)

BNCI2015_009

AMUSE auditory spatial P300 dataset

BNCI2015_010

RSVP visual speller (gaze-independent visual paradigm)

__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 β€œ~/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 β€œ~/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 β€œfixed” 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