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

Listen, You are Writing! Speeding up Online Spelling with a Dynamic Auditory BCI (AMUSE dataset)

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsMartijn Schreuder, Thomas Rost, Michael Tangermann

🇩🇪 Berlin Institute of Technology, DE·2011
P300 / ERP Code: BNCI2015-009 21 subjects 1 session 62 ch (60 EEG) 1000 Hz 2 classes 0.8 s trials CC BY-NC-ND 4.0

Class Labels: Target, NonTarget

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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 surrounded by six speakers at ear height, evenly distributed in a circle with 60 degree distance between them (radius ~65 cm, ~58 dB). The stimulus of each direction was a complex of a tone (base frequency and harmonics) and noise. By attending to stimuli from a specific spatial location, subjects could select one of six directions of a two-step auditory speller, enabling multi-class BCI control without relying on visual stimulation. All 21 subjects performed a first session (calibration of 48 trials, 8 per direction, followed by online copy spelling); those who could write the sentence were invited to a second session with dynamic stopping.

Participants

  • - 21 healthy, BCI-naive subjects, aged 20 to 57 (mean 34.1, SD 11.4)
  • Location: Berlin Institute of Technology, Germany

Recording Details

  • - Equipment: BrainAmp amplifiers (Brain Products, Munich, Germany)
  • Channels: 60 EEG + 2 EOG in the released files (the paper reports a fixed set of 56 Ag/AgCl electrodes plus two bipolar EOG channels)
  • Reference: nose; impedances below 15 kOhm
  • Sampling rate: 1000 Hz, hardware analog band-pass 0.1-250 Hz (the paper low-pass filtered below 40 Hz and down sampled to 100 Hz for online use)
  • Sessions: up to two per subject in the original study; the BNCI release is loaded as one session with the recordings as runs

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
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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 channels62
EEG60 (Ag/AgCl electrodes)
EOG2
Montage10-05
Sampling1000 Hz
Referencenose
Filterhardware analog band-pass filter between 0.1 and 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 2015-009 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 surrounded by six speakers at ear height, evenly distributed in a circle with 60 degree distance between them (radius ~65 cm, ~58 dB). The stimulus of each direction was a complex of a tone (base frequency and harmonics) and noise. By attending to stimuli from a specific spatial location, subjects could select one of six directions of a two-step auditory speller, enabling multi-class BCI control without relying on visual stimulation. All 21 subjects performed a first session (calibration of 48 trials, 8 per direction, followed by online copy spelling); those who could write the sentence were invited to a second session with dynamic stopping.

Participants

  • 21 healthy, BCI-naive subjects, aged 20 to 57 (mean 34.1, SD 11.4)

  • Location: Berlin Institute of Technology, Germany

Recording Details

  • Equipment: BrainAmp amplifiers (Brain Products, Munich, Germany)

  • Channels: 60 EEG + 2 EOG in the released files (the paper reports a fixed set of 56 Ag/AgCl electrodes plus two bipolar EOG channels)

  • Reference: nose; impedances below 15 kOhm

  • Sampling rate: 1000 Hz, hardware analog band-pass 0.1-250 Hz (the paper low-pass filtered below 40 Hz and down sampled to 100 Hz for online use)

  • Sessions: up to two per subject in the original study; the BNCI release is loaded as one session with the recordings as runs

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

[2]

Schreuder, M., Blankertz, B., & Tangermann, M. (2010). A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS ONE, 5(4), e9813. https://doi.org/10.1371/journal.pone.0009813 (introduces the AMUSE paradigm in an offline study with 10 subjects; not this dataset)

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

1

Participants

  • Population: Healthy

  • Age: 34.1 (range: 20-57) years

  • BCI experience: naive

Equipment

  • Amplifier: BrainAmp (Brain Products, Munich, Germany)

  • Electrodes: Ag/AgCl electrodes

  • Montage: standard_1005

  • Reference: nose

Preprocessing

  • Data state: raw

  • Bandpass filter: 0.1-250 Hz

  • Re-reference: nose

  • Notes: Signals were sampled at 1 kHz and filtered by a hardware analog band-pass filter between 0.1 and 250 Hz. In the paper, the online signal was low-pass filtered below 40 Hz, down sampled to 100 Hz and baselined with the 150 ms pre-stimulus interval.

Data Access

  • DOI: 10.3389/fnins.2011.00112

  • Repository: BNCI Horizon

Experimental Protocol

  • Paradigm: p300

  • Task type: auditory_oddball

  • Tasks: spelling, auditory_attention

  • Feedback: auditory

  • Stimulus: spatial_auditory

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.

When the dataset declares a nemar_id and the download provider is not "upstream", the files come from NEMAR’s sourcedata/ – the original pre-BIDS distribution, byte-identical to what the upstream host serves. On any NEMAR failure this falls back to the dataset’s own downloader with a warning (unless the provider is pinned to "nemar"). See sourcedata_path() and moabb.set_download_provider().

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded. On the NEMAR path each subject is resolved through the deposit’s sourcedata_provenance.json; deposits enriched before that manifest recorded subjects fetch the whole tree.

  • 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. Not used on the NEMAR path.

  • accept (bool) – Accept licence term to download the data, if any. Default: False. Only relevant to the dataset’s own downloader; NEMAR mirrors are already public.

  • 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, n_jobs=1)[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.

  • n_jobs (int) – Number of jobs to run in parallel over subjects (passed to joblib.Parallel). Default 1 (sequential). Per-subject processing (reading, filtering, resampling, epoching) is independent, so this gives a near-linear speedup for datasets with many subjects.

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
sourcedata_path(subject=None, path=None, force_update=False, verbose=None)[source]#

Get the dataset’s original pre-BIDS distribution from NEMAR.

Where data_path() fetches the original files from the upstream host, this fetches the copy NEMAR mirrors under sourcedata/. The files keep their upstream names, so the two are interchangeable in content – but NEMAR stays reachable when the upstream host is slow, rate-limited, behind a bot gate, or retired.

Parameters:

  • subject (int | str | None) – Restrict the download to one subject, resolved through the deposit’s sourcedata_provenance.json. Deposits enriched before that manifest recorded subjects fall back to the whole tree with a warning. When None the whole tree is fetched.

  • path (None | str) – Base path where MOABB stores datasets.

  • force_update (bool) – Re-fetch even when a local copy is present.

  • verbose (bool, str, int, or None) – If not None, override default verbose level.

Returns:

Local path to the sourcedata directory.

Return type:

str

Raises:

  • ValueError – If the dataset declares no nemar_id.

  • moabb.datasets.download.NemarDownloadError – If the download fails or the deposit publishes no sourcedata/.