EEGdash›NeMAR›NM000190

Iss. 190 · 10 subjects · 20 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-012 PASS2D P300 dataset

NM000190: eeg dataset, 10 subjects#

Name: BNCI 2015-012 PASS2D P300 dataset
Published: 2011-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-012 PASS2D P300 dataset

Citation: Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann (2011). BNCI 2015-012 PASS2D P300 dataset. 10.82901/nemar.nm000190

10-participant EEG dataset — BNCI 2015-012 PASS2D P300 dataset.

Data & curation Johannes Höhne · Martijn Schreuder · Benjamin Blankertz · Michael Tangermann
Year 2011 · Distributed via NeMAR

EEG · 63 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 NM000190

dataset = NM000190(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 = NM000190(cache_dir="./data", subject="01")

Advanced query

dataset = NM000190(
    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{nm000190,
  title = {BNCI 2015-012 PASS2D P300 dataset},
  author = {Johannes Höhne and Martijn Schreuder and Benjamin Blankertz and Michael Tangermann},
  doi = {10.82901/nemar.nm000190},
  url = {https://doi.org/10.82901/nemar.nm000190},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-012 PASS2D P300 dataset.

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

DOI

BNCI 2015-012 PASS2D P300 dataset

Target

View full README

DOI

BNCI 2015-012 PASS2D 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
Stimulus frequencies: [708.0, 524.0, 380.0] Hz
Number of targets: 9
Number of repetitions: 15
Inter-stimulus interval: 125.0 ms
Stimulus onset asynchrony: 225.0 ms

Data Structure

Trials: 27
Trials context: total across all calibration runs (3 runs × 9 trials per run)

Preprocessing

Data state: filtered and downsampled
Preprocessing applied: True
Steps: analog bandpass filter, lowpass filter, downsampling, artifact rejection
Highpass filter: 0.1 Hz
Lowpass filter: 40.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 250.0}
Filter type: analog bandpass then digital lowpass
Artifact methods: threshold rejection
Re-reference: nose
Downsampled to: 100.0 Hz
Epoch window: [-0.15, 0.8]
Notes: Epochs with peak-to-peak voltage difference exceeding 100 μV in any channel were rejected during calibration. No artifact correction applied in online runs.

Signal Processing

Classifiers: FDA, Fisher discriminant analysis
Feature extraction: mean amplitude in discriminative intervals
Spatial filters: shrinkage regularization

Cross-Validation

Method: cross-validation
Evaluation type: within_session

Performance (Original Study)

Accuracy: 72.5%
Itr: 3.4 bits/min
Characters Per Minute: 0.8
Spelling Speed Chars Per Min: 0.8

BCI Application

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

Tags

Pathology: Healthy
Modality: Auditory
Type: ERP, P300

Documentation

Description: A novel 9-class auditory ERP paradigm driving a predictive text entry system
DOI: 10.3389/fnins.2011.00099
Associated paper DOI: 10.3389/fnins.2011.00112
License: CC-BY-NC-ND-4.0
Investigators: Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann
Senior author: Michael Tangermann
Contact: j.hoehne@tu-berlin.de
Institution: Berlin Institute of Technology
Department: Machine Learning Laboratory
Address: Franklinstr. 28/19, 10587 Berlin, Germany
Country: Germany
Repository: BNCI Horizon
Publication year: 2011
Keywords: brain–computer interface, BCI, auditory ERP, P300, N200, spatial auditory stimuli, T9, user-centered design

Abstract

Brain–computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user’s gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.

Methodology

Participants performed a single session lasting 3-4 hours consisting of calibration phase and online spelling task. Calibration: 3 runs (plus 1 practice run), each with 9 trials covering all 9 stimuli as targets. Each trial had 13-14 pseudo-random sequences of all 9 auditory stimuli (108 subtrials total, 12 target + 96 non-target). Online spelling: 2 runs spelling German sentences using T9-style predictive text system with 9-class decisions. Each trial consisted of 135 subtrials (15 iterations of 9 stimuli). Binary classification using linear FDA with shrinkage regularization on 2-4 amplitude values per channel from discriminative intervals (N200 at 230-300ms and P300 at 350+ ms). Multiclass decision based on one-sided t-test with unequal variances across 15 classifier outputs per key.

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=10, range 21–34 yr, mean 24.8 yr)

202530

Male · 8Other · 2

Sex composition

Male
8

Channel counts: 63 ch (n=20 recordings)

Sampling frequencies: 250.0 Hz (n=20 recordings)

Total recording duration: 13 h 34 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 ch · EEG · 250 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 · 63 sensors — 63 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.NM000190(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Hohne2015

Canonical—

Importable asNM000190 · Hohne2015

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

class eegdash.dataset.NM000190(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2015-012 PASS2D P300 dataset

Study:: nm000190 (NeMAR)
Author (year):: Hohne2015
Canonical:: —

Also importable as: NM000190, Hohne2015.

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/nm000190 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000190 DOI: https://doi.org/10.82901/nemar.nm000190

Examples

>>> from eegdash.dataset import NM000190
>>> dataset = NM000190(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 — NM000190.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 nm000190 to reproduce the tutorial on this dataset.

Citation

Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann (2011). BNCI 2015-012 PASS2D P300 dataset. 10.82901/nemar.nm000190

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000190.

Related & sibling datasets

NM000192EEG · 11 subj NM000189EEG · 10 subj NM000234EEG · 21 subj 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.nm000190

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000190`
Title	BNCI 2015-012 PASS2D P300 dataset
Author (year)	`Hohne2015`
Canonical	—
Importable as	`NM000190`, `Hohne2015`
Year	2011
Authors	Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000190
Source links	OpenNeuro \| NeMAR \| Source URL

NM000190: 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#