NM000243: eeg dataset, 15 subjects#

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Access recordings and metadata through EEGDash.

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.

Modality: eeg Subjects: 15 Recordings: 15 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

Dataset Overview

  • Code: BNCI2016-002

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/8/5/056001

View full README

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

Dataset Overview

  • Code: BNCI2016-002

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/8/5/056001

  • Subjects: 15

  • Sessions per subject: 1

  • Events: Target=1, NonTarget=2

  • Trial interval: [-0.5, 1.0] s

  • File format: .mat

  • Data preprocessed: True

  • Contributing labs: Machine Learning Group, Berlin Institute of Technology, Bernstein Focus Neurotechnology, Berlin, Neurophysics Group, Charité University Medicine Berlin, Intelligent Data Analysis Group, Fraunhofer Institute FIRST

Acquisition

  • Sampling rate: 200.0 Hz

  • Number of channels: 59

  • Channel types: eeg=59, emg=1, eog=2, misc=7

  • Channel names: AF3, AF4, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EMGf, EOGh, EOGv, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8, brake, dist_to_lead, gas, lead_brake, lead_gas, wheel_X, wheel_Y

  • Montage: extended 10-20

  • Hardware: BrainAmp

  • Software: TORCS

  • Reference: nose

  • Sensor type: Ag/AgCl

  • Line frequency: 50.0 Hz

  • Online filters: {‘highpass_hz’: 0.1, ‘lowpass_hz’: 250}

  • Impedance threshold: {‘eeg’: 20, ‘emg’: 50} kOhm

  • Cap manufacturer: Easycap

  • Cap model: Easycap

  • Auxiliary channels: EOG (2 ch, vertical, horizontal), EMG (1 ch), technical_markers

Participants

  • Number of subjects: 15

  • Health status: healthy

  • Age: mean=30.6, std=5.4

  • Gender distribution: male=14, female=4

  • Handedness: right-handed

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: driving_simulation

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 3.0 s

  • Study design: Participants drove a virtual racing car using steering wheel and gas/brake pedals, tightly following a computer-controlled lead vehicle at 100 km/h. The lead vehicle occasionally decelerated abruptly (20-40s inter-stimulus-interval) to 60-80 km/h, requiring immediate emergency braking. Three blocks of 45 min each with 10-15 min rest between blocks.

  • Feedback type: visual (colored circle indicating distance: green <20m, yellow otherwise; brakelight flashing)

  • Stimulus type: emergency_braking_scenario

  • Stimulus modalities: visual, multisensory

  • Primary modality: visual

  • Synchronicity: asynchronous

  • Mode: online

  • Training/test split: True

  • Instructions: Drive a virtual racing car using steering wheel and gas/brake pedals, tightly follow the lead vehicle within 20m at 100 km/h. Perform immediate emergency braking when the lead vehicle decelerates abruptly to avoid a crash.

  • Stimulus presentation: isi_range=20-40 seconds, deceleration_range=60-80 km/h, brakelight=flashing, oncoming_traffic=present, sharp_curves=present

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

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

Dataset Information#

Dataset ID

NM000243

Title

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Author (year)

Haufe2016

Canonical

BNCI2016, BNCI2016002

Importable as

NM000243, Haufe2016, BNCI2016, BNCI2016002

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

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: 15

  • Recordings: 15

  • Tasks: 1

Channels & sampling rate

  • Channels: 59

  • Sampling rate (Hz): 200.0

  • Duration (hours): 33.74497916666667

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 4.0 GB

  • File count: 15

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000243 class to access this dataset programmatically.

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

Bases: EEGDashDataset

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Study:

nm000243 (NeMAR)

Author (year):

Haufe2016

Canonical:

BNCI2016, BNCI2016002

Also importable as: NM000243, Haufe2016, BNCI2016, BNCI2016002.

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, overwrite=False)[source]#

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#