EEGdash›NeMAR›NM000243

Iss. 243 · 15 subjects · 15 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2016-002 Emergency Braking during Simulated Driving dataset

NM000243: eeg dataset, 15 subjects#

Name: BNCI 2016-002 Emergency Braking during Simulated Driving dataset
Published: 2011-01-01
License: CC-BY-NC-ND-4.0

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Citation: Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz (2011). BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

15-participant EEG dataset — BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

Data & curation Stefan Haufe · Matthias S Treder · Manfred F Gugler · Max Sagebaum · Gabriel Curio · Benjamin Blankertz
Year 2011 · Distributed via NeMAR
Funding DFG grant · BMBF grant · + 1 more

EEG · 59 ch200 HzBIDS 1.9.0Task · p300HealthyVisualMotor

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 NM000243

dataset = NM000243(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 = NM000243(cache_dir="./data", subject="01")

Advanced query

dataset = NM000243(
    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{nm000243,
  title = {BNCI 2016-002 Emergency Braking during Simulated Driving dataset},
  author = {Stefan Haufe and Matthias S Treder and Manfred F Gugler and Max Sagebaum and Gabriel Curio and Benjamin Blankertz},
}

§ 02Study · The README

About This Dataset#

BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

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

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Target

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

NonTarget

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

Paradigm-Specific Parameters

Detected paradigm: p300

Data Structure

Trials: ~99 emergency braking events per subject (test set)
Blocks per session: 3
Block duration: 2700.0 s
Trials context: Emergency braking events with 20-40s inter-stimulus-interval, total ~225 events across 3 blocks per subject

Preprocessing

Data state: preprocessed
Preprocessing applied: True
Steps: lowpass filtering, bandpass filtering, notch filtering, rectification, downsampling/upsampling, baseline correction, synchronization
Highpass filter: 0.1 Hz
Lowpass filter: 45.0 Hz
Bandpass filter: [15.0, 90.0]
Notch filter: 50.0 Hz
Filter type: Chebychev type II (EEG lowpass), Elliptic (EMG bandpass), digital (notch)
Filter order: tenth-order (EEG), sixth-order (EMG), second-order (notch)
Re-reference: nose
Downsampled to: 200.0 Hz
Epoch window: [-0.3, 1.2]
Notes: EEG lowpass filtered at 45 Hz (causal). EMG bandpass filtered 15-90 Hz with 50 Hz notch and rectified. All signals synchronized and resampled to 200 Hz. Baseline correction using first 100 ms.

Signal Processing

Classifiers: RLDA, Regularized Linear Discriminant Analysis, Shrinkage LDA
Feature extraction: Event-Related Potentials, Spatio-temporal features, Bi-serial correlation, Area Under Curve
Spatial filters: Artifact rejection based on spectral power

Cross-Validation

Method: sequential temporal split
Evaluation type: temporal_validation

Performance (Original Study)

Auc: 0.5
Braking Time Reduction Ms: 130
Braking Distance Reduction M: 3.66

BCI Application

Applications: driving_assistance, emergency_braking_detection, neuroergonomics
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Visual, Multisensory
Type: Driving, Neuroergonomics

Documentation

Description: Emergency braking detection during simulated driving using EEG and EMG to predict driver’s braking intention before behavioral response.
DOI: 10.1088/1741-2560/8/5/056001
Associated paper DOI: 10.1088/1741-2560/8/5/056001
License: CC-BY-NC-ND-4.0
Investigators: Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz
Senior author: Benjamin Blankertz
Contact: stefan.haufe@tu-berlin.de
Institution: Berlin Institute of Technology
Department: Machine Learning Group, Department of Computer Science
Address: Franklinstraße 28/29, D-10587 Berlin, Germany
Country: Germany
Repository: BNCI Horizon
Publication year: 2011
Funding: DFG grant; BMBF grant; Bernstein Focus Neurotechnology, Berlin
Ethics approval: IRB of Charité University Medicine, Berlin; Declaration of Helsinki; Written informed consent from all participants
Keywords: emergency braking, driving simulation, EEG, EMG, brain-computer interface, neuroergonomics, event-related potentials, machine learning, driver assistance

References

Haufe, S., Treder, M. S., Gugler, M. F., Sagebaum, M., Curio, G., & Blankertz, B. (2011). EEG potentials predict upcoming emergency brakings during simulated driving. Journal of Neural Engineering, 8(5), 056001. https://doi.org/10.1088/1741-2560/8/5/056001 Notes .. versionadded:: 1.3.0 This dataset is valuable for research on: - Predictive braking assistance systems - Neuroergonomics and driving safety - Real-time detection of emergency intentions - Multimodal biosignal integration (EEG + EMG + vehicle dynamics)

The paradigm represents a unique blend of ERP (event-related potential) analysis with ecological validity in a naturalistic driving context. Data Availability: Currently 15 of 18 subjects are available. Files are hosted at the BBCI (Berlin Brain-Computer Interface) archive.

License: Creative Commons Attribution Non-Commercial No Derivatives (CC BY-NC-ND 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=15, range 31–31 yr, mean 30.0 yr)

30

Other · 15

Channel counts: 59 ch (n=15 recordings)

Sampling frequencies: 200.0 Hz (n=15 recordings)

Total recording duration: 33 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 59 ch · EEG · 200 Hz · 15 subjects, 15 recordings

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

Showing one representative recording out of 15 subjects and 15 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 · 59 sensors — 59 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.NM000243(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Haufe2016

Canonical—

Importable asNM000243 · Haufe2016

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

class eegdash.dataset.NM000243(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Study:: nm000243 (NeMAR)
Author (year):: Haufe2016
Canonical:: —

Also importable as: NM000243, Haufe2016.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 15; recordings: 15; 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/nm000243 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000243

Examples

>>> from eegdash.dataset import NM000243
>>> dataset = NM000243(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 — NM000243.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 nm000243 to reproduce the tutorial on this dataset.

Citation

Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, … (2011). BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000146EEG · 10 subj NM000137EEG · 7 subj NM000162EEG · 20 subj NM000161EEG · 20 subj NM000143EEG · 5 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	`NM000243`
Title	BNCI 2016-002 Emergency Braking during Simulated Driving dataset
Author (year)	`Haufe2016`
Canonical	—
Importable as	`NM000243`, `Haufe2016`
Year	2011
Authors	Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz
License	CC-BY-NC-ND-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000243: eeg dataset, 15 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#