EEGdash›NeMAR›NM000189

Iss. 189 · 10 subjects · 20 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-003 P300 dataset

NM000189: eeg dataset, 10 subjects#

Name: BNCI 2015-003 P300 dataset
Published: 2011-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-003 P300 dataset

Citation: Martijn Schreuder, Thomas Rost, Michael Tangermann (2011). BNCI 2015-003 P300 dataset. 10.82901/nemar.nm000189

10-participant EEG dataset — BNCI 2015-003 P300 dataset.

Data & curation Martijn Schreuder · Thomas Rost · Michael Tangermann
Year 2011 · Distributed via NeMAR
Funding European ICT Programme Project FP7-224631 · European ICT Programme Project FP7-216886 · + 3 more

EEG · 8 ch256 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 NM000189

dataset = NM000189(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 = NM000189(cache_dir="./data", subject="01")

Advanced query

dataset = NM000189(
    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{nm000189,
  title = {BNCI 2015-003 P300 dataset},
  author = {Martijn Schreuder and Thomas Rost and Michael Tangermann},
  doi = {10.82901/nemar.nm000189},
  url = {https://doi.org/10.82901/nemar.nm000189},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-003 P300 dataset.

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

DOI

BNCI 2015-003 P300 dataset

Target

View full README

DOI

BNCI 2015-003 P300 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: 6
Stimulus onset asynchrony: 175.0 ms

Data Structure

Trials: 48
Trials per class: calibration_per_direction=8
Trials context: calibration_phase

Preprocessing

Data state: filtered
Preprocessing applied: True
Steps: low-pass filter, downsampling, baselining
Highpass filter: 0.1 Hz
Lowpass filter: 40.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 40.0}
Filter type: analog hardware filter for acquisition; low-pass for online
Artifact methods: variance criterium, peak-to-peak difference criterium
Re-reference: nose
Downsampled to: 100.0 Hz
Epoch window: [-0.15, None]
Notes: For online use signal was low-pass filtered below 40 Hz and downsampled to 100 Hz. Data baselined using 150 ms pre-stimulus data as reference.

Signal Processing

Classifiers: LDA, linear binary classifier
Feature extraction: spatio-temporal features, r2 coefficient, interval averaging
Spatial filters: shrinkage regularization (Ledoit-Wolf)

Cross-Validation

Method: online
Evaluation type: online

Performance (Original Study)

Accuracy: 77.4%
Itr: 2.84 bits/min
Char Per Min Session1: 0.59
Char Per Min Session2 Max: 1.41
Char Per Min Session2 Avg: 0.94
Itr Session2 Avg: 5.26
Itr Session2 Max: 7.55
Success Rate Session1: 76.0

BCI Application

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

Tags

Pathology: Healthy
Modality: Auditory
Type: ERP, P300

Documentation

Description: Auditory BCI speller using spatial cues (AMUSE paradigm) allowing purely auditory communication interface
DOI: 10.1016/j.neulet.2009.06.045
Associated paper DOI: 10.3389/fnins.2011.00112
License: CC-BY-NC-ND-4.0
Investigators: Martijn Schreuder, Thomas Rost, Michael Tangermann
Senior author: Michael Tangermann
Contact: schreuder@tu-berlin.de
Institution: Berlin Institute of Technology
Department: Machine Learning Laboratory
Address: Machine Learning Laboratory, Berlin Institute of Technology, FR6-9, Franklinstraße 28/29, 10587 Berlin, Germany
Country: Germany
Repository: BNCI Horizon
Publication year: 2011
Funding: European ICT Programme Project FP7-224631; European ICT Programme Project FP7-216886; Deutsche Forschungsgemeinschaft (DFG MU 987/3-2); Bundesministerium fur Bildung und Forschung (BMBF FKZ 01IB001A, 01GQ0850); FP7-ICT PASCAL2 Network of Excellence ICT-216886
Ethics approval: Ethics Committee of the Charité University Hospital
Acknowledgements: Thomas Denck, David List and Larissa Queda for help with experiments. Klaus-Robert Müller and Benjamin Blankertz for fruitful discussions.
Keywords: brain-computer interface, directional hearing, auditory event-related potentials, P300, N200, dynamic subtrials

External Links

Source: http://www.frontiersin.org/neuroprosthetics/10.3389/fnins.2011.00112/abstract

Abstract

This online study introduces an auditory spelling interface that eliminates the necessity for visual representation. In up to two sessions, a group of healthy subjects (N=21) was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multi-class Spatial ERP). The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 char/min (7.55 bits/min) could be reached during the second session (average: 0.94 char/min, 5.26 bits/min).

Methodology

Participants surrounded by six speakers at ear height in circle (60° spacing, 65 cm radius). Each direction associated with unique combination of tone (base frequency + harmonics) and band-pass filtered noise. Two-step hex-o-spell interface for character selection. Session 1: calibration (48 trials, 8 per direction, 15 iterations each) followed by online spelling with 15 fixed iterations. Session 2: calibration followed by online spelling with dynamic stopping method (4-15 iterations). Spatio-temporal feature extraction using r2 coefficient and interval selection (2-4 intervals for early and late components, 112-224 features total). Linear binary classifier with shrinkage regularization (Ledoit-Wolf). Decision making based on median classifier scores across iterations.

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 .. note:: BNCI2015_003 was previously named BNCI2015003. BNCI2015003 will be removed in version 1.1. .. versionadded:: 0.4.0 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=10, range 34–34 yr, mean 34.0 yr)

30

Other · 10

Channel counts: 8 ch (n=40 recordings)

Sampling frequencies: 256.0 Hz (n=40 recordings)

Total recording duration: 1 h 52 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 8 ch · EEG · 256 Hz · 10 subjects, 20 recordings

Live trace viewer — sub-6 · ses-0 · task-p300 · run-0

Showing one representative recording out of 10 subjects and 20 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 · 8 sensors — 8 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.NM000189(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Schreuder2015_P300

Canonical—

Importable asNM000189 · Schreuder2015_P300

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

class eegdash.dataset.NM000189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2015-003 P300 dataset

Study:: nm000189 (NeMAR)
Author (year):: Schreuder2015_P300
Canonical:: —

Also importable as: NM000189, Schreuder2015_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 20; 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/nm000189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000189 DOI: https://doi.org/10.82901/nemar.nm000189

Examples

>>> from eegdash.dataset import NM000189
>>> dataset = NM000189(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 — NM000189.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 nm000189 to reproduce the tutorial on this dataset.

Citation

Martijn Schreuder, Thomas Rost, Michael Tangermann (2011). BNCI 2015-003 P300 dataset. 10.82901/nemar.nm000189

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000189.

Related & sibling datasets

NM000190EEG · 10 subj NM000192EEG · 11 subj NM000234EEG · 21 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 · 10.82901/nemar.nm000189

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000189`
Title	BNCI 2015-003 P300 dataset
Author (year)	`Schreuder2015_P300`
Canonical	—
Importable as	`NM000189`, `Schreuder2015_P300`
Year	2011
Authors	Martijn Schreuder, Thomas Rost, Michael Tangermann
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000189
Source links	OpenNeuro \| NeMAR \| Source URL

NM000189: eeg dataset, 10 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#