EEGdash›NeMAR›NM000234

Iss. 234 · 21 subjects · 42 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset

NM000234: eeg dataset, 21 subjects#

Name: BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset
Published: 2011-01-01
License: CC-BY-NC-ND-4.0

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

Citation: Martijn Schreuder, Benjamin Blankertz, Michael Tangermann (2011). BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset.

21-participant EEG dataset — BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset.

Data & curation Martijn Schreuder · Benjamin Blankertz · Michael Tangermann
Year 2011 · Distributed via NeMAR
Funding European ICT Programme Project FP7-224631 · European ICT Programme Project FP7-216886 · + 4 more

EEG · 60 ch250 HzBIDS 1.9.0Task · p300HealthyAuditoryAttention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000234

dataset = NM000234(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000234(cache_dir="./data", subject="01")

Advanced query

dataset = NM000234(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000234,
  title = {BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset},
  author = {Martijn Schreuder and Benjamin Blankertz and Michael Tangermann},
}

§ 02Study · The README

About This Dataset#

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

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

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

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

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

Target

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Target

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Paradigm-Specific Parameters

Detected paradigm: p300
Number of targets: 5
Number of repetitions: 15
Inter-stimulus interval: 300.0 ms

Data Structure

Trials: varied by condition
Blocks per session: 50
Trials context: BCI experiments: C300 (50 trials × 75 subtrials = 3750 subtrials), C175 (40 trials × 75 subtrials = 3000 subtrials), C300s (20 trials × 75 subtrials = 1500 subtrials). Physiological experiments: C1000 (32 trials × 80 subtrials = 2560 subtrials), Cr (576-768 subtrials)

Preprocessing

Data state: filtered
Preprocessing applied: True
Steps: bandpass filter, notch filter, downsampling, artifact rejection
Highpass filter: 0.1 Hz
Lowpass filter: 250.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 250.0}
Notch filter: [50] Hz
Filter type: Chebyshev II order 8 (for visual inspection: 30 Hz pass, 42 Hz stop, 50 dB damping)
Artifact methods: threshold-based artifact rejection
Re-reference: nose
Downsampled to: 100.0 Hz
Epoch window: [-0.15, 0.8]
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.

Signal Processing

Classifiers: LDA
Feature extraction: ROC-separability-index
Frequency bands: analyzed=[0.1, 250.0] Hz

Cross-Validation

Method: cross-validation
Evaluation type: offline

Performance (Original Study)

Accuracy: 90.0%
Itr: 17.39 bits/min
Best Subject Itr: 25.2
Best Subject Accuracy: 100.0
C300S Accuracy: 70.0

BCI Application

Applications: speller, communication
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Auditory
Type: P300

Documentation

Description: A new auditory multi-class brain-computer interface paradigm using spatial hearing as an informative cue
DOI: 10.1371/journal.pone.0009813
Associated paper DOI: 10.3389/fnins.2011.00112
License: CC-BY-NC-ND-4.0
Investigators: Martijn Schreuder, Benjamin Blankertz, Michael Tangermann
Senior author: Michael Tangermann
Contact: martijn@cs.tu-berlin.de
Institution: Berlin Institute of Technology
Department: Machine Learning Department
Address: Berlin, Germany
Country: Germany
Repository: BNCI Horizon
Publication year: 2010
Funding: European ICT Programme Project FP7-224631; European ICT Programme Project FP7-216886; Deutsche Forschungsgemeinschaft (DFG) MU 987/3-1; Bundesministerium für Bildung und Forschung (BMBF) FKZ 01IB001A; Bundesministerium für Bildung und Forschung (BMBF) FKZ 01GQ0850; FP7-ICT PASCAL2 Network of Excellence ICT-216886
Ethics approval: Ethics Committee of the Charité University Hospital (number EA4/073/09)
Keywords: auditory BCI, P300, spatial hearing, multi-class, oddball paradigm

References

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 Notes .. versionadded:: 1.2.0 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=21, range 30–30 yr, mean 30.0 yr)

30

Other · 21

Channel counts: 60 ch (n=42 recordings)

Sampling frequencies: 250.0 Hz (n=42 recordings)

Total recording duration: 30 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 60 ch · EEG · 250 Hz · 21 subjects, 42 recordings

Live trace viewer — sub-13 · ses-0 · task-p300 · run-0

Showing one representative recording out of 21 subjects and 42 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 58 sensors — 58 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000234(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Schreuder2015_ERP

Canonical—

Importable asNM000234 · Schreuder2015_ERP

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000234(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

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

Study:: nm000234 (NeMAR)
Author (year):: Schreuder2015_ERP
Canonical:: —

Also importable as: NM000234, Schreuder2015_ERP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000234 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000234

Examples

>>> from eegdash.dataset import NM000234
>>> dataset = NM000234(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000234.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000234 to reproduce the tutorial on this dataset.

Citation

Martijn Schreuder, Benjamin Blankertz, Michael Tangermann (2011). BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset.

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

Related & sibling datasets

NM000190EEG · 10 subj NM000192EEG · 11 subj NM000189EEG · 10 subjSame authors NM000193EEG · 11 subj NM000207EEG · 15 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-ND-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000234`
Title	BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset
Author (year)	`Schreuder2015_ERP`
Canonical	—
Importable as	`NM000234`, `Schreuder2015_ERP`
Year	2011
Authors	Martijn Schreuder, Benjamin Blankertz, Michael Tangermann
License	CC-BY-NC-ND-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000234: eeg dataset, 21 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#