moabb.datasets.BNCI2016_002#

class moabb.datasets.BNCI2016_002(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BNCIBaseDataset

[source]

Dataset Snapshot

BNCI2016_002

Emergency braking detection during simulated driving using EEG and EMG to predict driver's braking intention before behavioral response.

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsStefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz

🇩🇪 Berlin Institute of Technology, Germany·2011·stefan.haufe@tu-berlin.de
P300 / ERP Code: BNCI2016-002 15 subjects 1 session 59 ch 200 Hz 2 classes 3.0 s trials CC BY-NC-ND 4.0

Class Labels: Target, NonTarget

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Overview

BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

Dataset from

Dataset Description

This dataset contains EEG and physiological signals recorded during emergency braking maneuvers in a driving simulator. The study demonstrated that drivers' intentions to perform emergency braking can be detected from brain and muscle activity prior to the behavioral response, enabling predictive braking assistance systems.

Participants drove in a realistic driving simulator, maintaining distance from a lead vehicle while navigating curves and traffic. When the lead vehicle unexpectedly braked (emergency situation), subjects had to brake as quickly as possible. The dataset captures the neural and physiological signatures preceding emergency braking actions.

Participants

  • - 18 subjects (14 males, 4 females)
  • currently 15 subjects available
  • Age: 30.6 +/- 5.4 years
  • All healthy with valid driver's licenses
  • Location: Berlin Institute of Technology (TU Berlin), Germany

Recording Details

  • - Equipment: BrainProducts actiCap system with BrainAmp amplifiers
  • Channels: 59 EEG + 2 EOG + 1 EMG + 7 driving-related signals = 69 total
  • Sampling rate: 200 Hz (downsampled from 1000 Hz)
  • Reference: Common average reference
  • EEG electrode montage: Extended 10-20 system

Additional Channels

  • - EOGv, EOGh: Vertical and horizontal electrooculogram
  • EMGf: Electromyogram (right foot, tibialis anterior muscle)
  • lead_gas, lead_brake: Lead vehicle gas/brake pedal positions
  • dist_to_lead: Distance to lead vehicle
  • wheel_X, wheel_Y: Steering wheel position
  • gas, brake: Subject's gas/brake pedal positions

Experimental Procedure

  • - Three 45-minute driving blocks per subject (135 minutes total)
  • Driving task: Follow a lead vehicle, maintain safe distance
  • Emergency situations: Lead vehicle brakes unexpectedly
  • Subject response: Emergency braking required
  • Inter-trial interval: Variable (realistic driving conditions)

Event Codes

For P300 paradigm compatibility, events are mapped to Target/NonTarget:

  • - Target: Lead car starts braking (emergency situation onset, originally car_brake)
  • NonTarget: Lead car driving normally (originally car_normal)

Additional events (not used for P300 classification):

  • - car_hold: Lead car holding/stopped
  • car_collision: Collision occurred (subject failed to brake in time)
  • react_emg: Subject's EMG reaction detected (braking initiated)

Key Findings

The study found that combining EEG and EMG signals enables detection of emergency braking intention 130 ms earlier than pedal-based systems alone. At 100 km/h, this corresponds to a 3.66 m reduction in braking distance.

The EEG analysis revealed a characteristic event-related potential signature comprising three components:

1. Sensory registration of critical traffic situations 2. Mental evaluation of the sensory information 3. Motor preparation

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/BNCI2016_002.svg

3s task window per trial · 2-class p300 / erp paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Sensory-event
2
Visual-presentation
2
Non-target
1
Target
1
Target
Sensory-eventExperimental-stimulusVisual-presentationTarget
NonTarget
Sensory-eventExperimental-stimulusVisual-presentationNon-target

HED tree view

Tree · Target
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target
Tree · NonTarget
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target
Channel Summary
Total channels59
EEG59 (Ag/AgCl)
MISC7
EOG2
EMG1
Montageextended 10-20
Sampling200 Hz
Referencenose
Filter{'highpass_hz': 0.1, 'lowpass_hz': 250}
Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

BNCI 2016-002 Emergency Braking during Simulated Driving dataset.

Dataset from [1].

Dataset Description

This dataset contains EEG and physiological signals recorded during emergency braking maneuvers in a driving simulator. The study demonstrated that drivers’ intentions to perform emergency braking can be detected from brain and muscle activity prior to the behavioral response, enabling predictive braking assistance systems.

Participants drove in a realistic driving simulator, maintaining distance from a lead vehicle while navigating curves and traffic. When the lead vehicle unexpectedly braked (emergency situation), subjects had to brake as quickly as possible. The dataset captures the neural and physiological signatures preceding emergency braking actions.

Participants

  • 18 subjects (14 males, 4 females) - currently 15 subjects available

  • Age: 30.6 +/- 5.4 years

  • All healthy with valid driver’s licenses

  • Location: Berlin Institute of Technology (TU Berlin), Germany

Recording Details

  • Equipment: BrainProducts actiCap system with BrainAmp amplifiers

  • Channels: 59 EEG + 2 EOG + 1 EMG + 7 driving-related signals = 69 total

  • Sampling rate: 200 Hz (downsampled from 1000 Hz)

  • Reference: Common average reference

  • EEG electrode montage: Extended 10-20 system

Additional Channels

  • EOGv, EOGh: Vertical and horizontal electrooculogram

  • EMGf: Electromyogram (right foot, tibialis anterior muscle)

  • lead_gas, lead_brake: Lead vehicle gas/brake pedal positions

  • dist_to_lead: Distance to lead vehicle

  • wheel_X, wheel_Y: Steering wheel position

  • gas, brake: Subject’s gas/brake pedal positions

Experimental Procedure

  • Three 45-minute driving blocks per subject (135 minutes total)

  • Driving task: Follow a lead vehicle, maintain safe distance

  • Emergency situations: Lead vehicle brakes unexpectedly

  • Subject response: Emergency braking required

  • Inter-trial interval: Variable (realistic driving conditions)

Event Codes

For P300 paradigm compatibility, events are mapped to Target/NonTarget:

  • Target: Lead car starts braking (emergency situation onset, originally car_brake)

  • NonTarget: Lead car driving normally (originally car_normal)

Additional events (not used for P300 classification):

  • car_hold: Lead car holding/stopped

  • car_collision: Collision occurred (subject failed to brake in time)

  • react_emg: Subject’s EMG reaction detected (braking initiated)

Key Findings

The study found that combining EEG and EMG signals enables detection of emergency braking intention 130 ms earlier than pedal-based systems alone. At 100 km/h, this corresponds to a 3.66 m reduction in braking distance.

The EEG analysis revealed a characteristic event-related potential signature comprising three components:

  1. Sensory registration of critical traffic situations

  2. Mental evaluation of the sensory information

  3. Motor preparation

References

[1]

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

from moabb.datasets import BNCI2016_002
dataset = BNCI2016_002()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

15

#Chan

69

#Trials / class

varies brake / EMG

Trials length

1.5 s

Freq

200 Hz

#Sessions

1

Participants

  • Population: healthy

  • Age: 30.6 years

  • Handedness: right-handed

  • BCI experience: naive

Equipment

  • Amplifier: BrainAmp

  • Electrodes: Ag/AgCl

  • Montage: extended 10-20

  • Reference: nose

Preprocessing

  • Data state: preprocessed

  • Bandpass filter: 15-90 Hz

  • Steps: lowpass filtering, bandpass filtering, notch filtering, rectification, downsampling/upsampling, baseline correction, synchronization

  • Re-reference: nose

  • 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.

Data Access

  • DOI: 10.1088/1741-2560/8/5/056001

  • Repository: BNCI Horizon

Experimental Protocol

  • Paradigm: emergency_braking

  • Task type: driving_simulation

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

  • Stimulus: emergency_braking_scenario

Dataset summary

Name

#Subj

#Chan

#Trials/class

Trials length

Sampling Rate

#Sessions

BNCI2016_002

15

69

~200 brake events

1.0s

200Hz

1

Notes

Added in version 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)

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Initialize function for the BaseDataset.

property all_subjects#

Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None, generate_figures=False)[source]#

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

  • path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).

  • subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.

  • overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

  • format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".

  • verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

  • generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

See also

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#

Return paths to data files for a single subject.

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#

Download all data from the dataset.

This function is only useful to download all the dataset at once.

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • accept (bool) – Accept licence term to download the data, if any. Default: False

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str) → None | DataFrame[source]#

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

  • subject (str) – The identifier for the subject.

  • session (str) – The identifier for the session.

  • run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pd.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

See also

BaseDataset.get_data

Parameters:

  • subjects (List of int) – List of subject number

  • block_list (List of int) – List of block number

  • repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)[source]#

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {'subject_id' :
            {'session_id':
                {'run_id': run}
            }
        }

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

  • subjects (List of int) – List of subject number

  • cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

  • process_pipeline (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray(). This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata: DatasetMetadata | None[source]#

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:

The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.

Return type:

DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0