NM000234: eeg dataset, 21 subjects

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

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 21 Recordings: 42 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

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},
}

About This Dataset

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

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

Dataset Overview

• Code: BNCI2015-009

• Paradigm: p300

• DOI: 10.3389/fnins.2011.00112

View full README

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

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

Dataset Overview

• Code: BNCI2015-009

• Paradigm: p300

• DOI: 10.3389/fnins.2011.00112

• Subjects: 21

• Sessions per subject: 1

• Events: Target=1, NonTarget=2

• Trial interval: [0, 0.8] s

• Runs per session: 2

• File format: gdf

• Data preprocessed: True

Acquisition

• Sampling rate: 250.0 Hz

• Number of channels: 60

• Channel types: eeg=60, eog=2

• Montage: 10-20

• Hardware: Brain Products 128-channel amplifier

• Software: Matlab

• Reference: nose

• Sensor type: Ag/AgCl electrodes

• Line frequency: 50.0 Hz

• Online filters: 0.1-250 Hz analog bandpass

• Auxiliary channels: EOG (2 ch, bipolar)

Participants

• Number of subjects: 21

• Health status: patients

• Clinical population: Healthy

• Age: mean=30.3, min=22, max=55

• Gender distribution: male=6, female=4

• Handedness: unknown

• BCI experience: mixed

• Species: human

Experimental Protocol

• Paradigm: p300

• Task type: oddball

• Number of classes: 2

• Class labels: Target, NonTarget

• Trial duration: 0.8 s

• Tasks: spatial_auditory_oddball

• Study design: Offline auditory oddball task using spatial location of auditory stimuli as discriminating cue. Frontal five speakers used (speakers 1,2,3,7,8) with 45 degree spacing. Three conditions tested: C300 (300ms ISI), C175 (175ms ISI), C300s (300ms ISI, single speaker). Each stimulus was unique 40ms complex sound from bandpass filtered white noise with tone overlay.

• Study domain: BCI

• Feedback type: none

• Stimulus type: auditory_spatial

• Stimulus modalities: auditory

• Primary modality: auditory

• Synchronicity: synchronous

• Mode: offline

• Training/test split: False

• Instructions: Subjects asked to mentally count target stimulations or respond by keypress (condition Cr). Minimize eye movements and muscle contractions. Target direction indicated prior to each block visually and by presenting stimulus from that location.

HED Event Annotations

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

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) https://github.com/NeuroTechX/moabb

Dataset Information

Dataset ID	NM000234
Title	BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset
Author (year)	Schreuder2015_ERP
Canonical	BNCI2015_ERP
Importable as	NM000234, Schreuder2015_ERP, BNCI2015_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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details

Subjects & recordings

• Subjects: 21

• Recordings: 42

• Tasks: 1

Channels & sampling rate

• Channels: 60

• Sampling rate (Hz): 250.0

• Duration (hours): 30.17912

Tags

• Pathology: Healthy

• Modality: Auditory

• Type: Attention

Files & format

• Size on disk: 4.6 GB

• File count: 42

• Format: BIDS

License & citation

• License: CC-BY-NC-ND-4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000234

• NeMAR: nm000234

API Reference

Use the NM000234 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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

Study:

nm000234 (NeMAR)

Author (year):

Schreuder2015_ERP

Canonical:

BNCI2015_ERP

Also importable as: NM000234, Schreuder2015_ERP, BNCI2015_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)

save(path, overwrite=False)

Save the dataset to disk.

Parameters:

• path (str or Path) – Destination file path.

• overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971