moabb.datasets.BNCI2015_009#

class moabb.datasets.BNCI2015_009(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: MNEBNCI

[source]

Dataset Snapshot

BNCI2015_009

A new auditory multi-class brain-computer interface paradigm using spatial hearing as an informative cue

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsMartijn Schreuder, Benjamin Blankertz, Michael Tangermann

🇩🇪 Berlin Institute of Technology, Germany·2010·martijn@cs.tu-berlin.de
P300 / ERP Code: BNCI2015-009 21 subjects 1 session 60 ch 250 Hz 2 classes 0.8 s trials CC BY-NC-ND 4.0

Class Labels: Target, NonTarget

Read Paper Compare Similar Report Issue

Overview

BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset.

Dataset from

Dataset Description

This dataset contains EEG recordings from 21 subjects performing an auditory spatial attention task for brain-computer interface (BCI) control. The AMUSE (Auditory Multi-class Spatial ERP) paradigm uses auditory stimuli from different spatial locations to elicit P300-like event-related potentials.

Subjects were presented with auditory stimuli (75 ms bandpass filtered white noise, 150-8000 Hz) from 8 loudspeakers arranged at ear height in a circle around the subject, with 45 degree spacing at approximately 1 meter distance. By attending to stimuli from a specific spatial location, subjects could select one of multiple targets, enabling multi-class BCI control without relying on visual stimulation.

Participants

  • - 21 healthy subjects
  • Location: Berlin Institute of Technology, Germany

Recording Details

  • - Equipment: 128-channel Brain Products amplifier
  • Channels: 60 EEG + 2 EOG (62 total)
  • Electrode type: Ag/AgCl electrodes
  • Sampling rate: 1000 Hz (downsampled to 100 Hz for analysis in original paper)
  • Auditory stimuli: 75 ms bandpass filtered white noise (150-8000 Hz), 58 dB
  • Speaker setup: 8 speakers at ear height, 45 degree spacing, ~1 meter distance

Data Organization

  • - Subject codes: fce, kw, faz, fcj, fcg, far, faw, fax, fcc, fcm, fas, fch, fcd, fca, fcb, fau, fci, fav, fat, fcl, fck
  • Data URL: http://doc.ml.tu-berlin.de/bbci/BNCIHorizon2020-AMUSE/

Event Codes

  • - Target (1): Attended stimulus
  • NonTarget (2): Unattended stimulus
Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/BNCI2015_009.svg

0.8s task window per trial · 2-class p300 / erp paradigm · 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 · Target
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target
Tree · NonTarget
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target
Channel Summary
Total channels60
EEG60 (Ag/AgCl electrodes)
EOG2
Montage10-20
Sampling250 Hz
Referencenose
Filter0.1-250 Hz analog bandpass
Notch / line50 Hz

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

BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset.

Dataset from [1].

Dataset Description

This dataset contains EEG recordings from 21 subjects performing an auditory spatial attention task for brain-computer interface (BCI) control. The AMUSE (Auditory Multi-class Spatial ERP) paradigm uses auditory stimuli from different spatial locations to elicit P300-like event-related potentials.

Subjects were presented with auditory stimuli (75 ms bandpass filtered white noise, 150-8000 Hz) from 8 loudspeakers arranged at ear height in a circle around the subject, with 45 degree spacing at approximately 1 meter distance. By attending to stimuli from a specific spatial location, subjects could select one of multiple targets, enabling multi-class BCI control without relying on visual stimulation.

Participants

  • 21 healthy subjects

  • Location: Berlin Institute of Technology, Germany

Recording Details

  • Equipment: 128-channel Brain Products amplifier

  • Channels: 60 EEG + 2 EOG (62 total)

  • Electrode type: Ag/AgCl electrodes

  • Sampling rate: 1000 Hz (downsampled to 100 Hz for analysis in original paper)

  • Auditory stimuli: 75 ms bandpass filtered white noise (150-8000 Hz), 58 dB

  • Speaker setup: 8 speakers at ear height, 45 degree spacing, ~1 meter distance

Data Organization

Event Codes

  • Target (1): Attended stimulus

  • NonTarget (2): Unattended stimulus

References

[1]

Schreuder, M., Rost, T., & Tangermann, M. (2011). Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Frontiers in neuroscience, 5, 112. https://doi.org/10.3389/fnins.2011.00112

from moabb.datasets import BNCI2015_009
dataset = BNCI2015_009()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

21

#Chan

62

#Trials / class

10071 NT / 2014 T

Trials length

0.8 s

Freq

1000 Hz

#Sessions

2

Participants

  • Population: Healthy

  • Age: 30.3 (range: 22-55) years

  • Handedness: unknown

  • BCI experience: mixed

Equipment

  • Amplifier: Brain Products 128-channel amplifier

  • Electrodes: Ag/AgCl electrodes

  • Montage: 10-20

  • Reference: nose

Preprocessing

  • Data state: filtered

  • Bandpass filter: 0.1-250 Hz

  • Steps: bandpass filter, notch filter, downsampling, artifact rejection

  • Re-reference: nose

  • Notes: Raw data acquired at 1000 Hz. For visual inspection: low-pass filtered with order 8 Chebyshev II filter (30 Hz pass, 42 Hz stop, 50 dB damping) applied forward and backward to minimize phase shifts, then downsampled to 100 Hz. For classification: same filter applied causally (forward only) for online portability. Artifact rejection used simple threshold method: subtrials with deflection >70 µV over ocular channels compared to baseline were rejected.

Data Access

  • DOI: 10.1371/journal.pone.0009813

  • Repository: BNCI Horizon

Experimental Protocol

  • Paradigm: p300

  • Task type: oddball

  • Tasks: spatial_auditory_oddball

  • Feedback: none

  • Stimulus: auditory_spatial

Dataset summary

Name

#Subj

#Chan

#Trials/class

Trials length

Sampling Rate

#Sessions

BNCI2015_009

21

62

Variable T/NT

0.8s

1000Hz

varies

Notes

Added in version 1.2.0.

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[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