{"dataset":{"id":"276","dataset_id":"nm000243","name":"BNCI 2016-002 Emergency Braking during Simulated Driving dataset","description":"This dataset comprises EEG and EMG recordings from 15 healthy participants performing emergency braking tasks during simulated driving at 200 Hz sampling rate. Participants drove a virtual racing car following a lead vehicle, with occasional abrupt decelerations requiring immediate emergency braking responses. The study demonstrates the feasibility of predicting driver braking intentions from neural and muscular signals before behavioral response, with applications to neuroergonomics and driving assistance systems.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000243","concept_doi":"10.82901/nemar.nm000243","latest_version_doi":"10.82901/nemar.nm000243.v1.0.2","created_at":"2026-03-25 21:50:09","updated_at":"2026-08-18 21:21:13","zenodo_concept_id":"20520990","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2016-002 Emergency Braking during Simulated Driving dataset\",\n  \"description\": \"This dataset comprises EEG and EMG recordings from 15 healthy participants performing emergency braking tasks during simulated driving at 200 Hz sampling rate. Participants drove a virtual racing car following a lead vehicle, with occasional abrupt decelerations requiring immediate emergency braking responses. The study demonstrates the feasibility of predicting driver braking intentions from neural and muscular signals before behavioral response, with applications to neuroergonomics and driving assistance systems.\",\n  \"methods_description\": \"Data were acquired using a 59-channel EEG system (extended 10-20 montage, Ag/AgCl electrodes, BrainAmp hardware) plus EMG, EOG, and vehicle dynamics channels at 200 Hz. Participants completed three 45-minute driving blocks with 10-15 minute rest intervals in a TORCS driving simulator. Online filters applied: 0.1 Hz highpass, 250 Hz lowpass. Preprocessing included lowpass filtering at 45 Hz (EEG), bandpass filtering 15-90 Hz with 50 Hz notch (EMG), rectification, baseline correction, and resampling to 200 Hz. Epochs were extracted from -0.3 to 1.2 seconds relative to emergency braking events.\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Stefan Haufe\": {},\n    \"Matthias S Treder\": {},\n    \"Manfred F Gugler\": {},\n    \"Max Sagebaum\": {},\n    \"Gabriel Curio\": {},\n    \"Benjamin Blankertz\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"EMG\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"driving simulation\"\n    },\n    {\n      \"term\": \"neuroergonomics\"\n    },\n    {\n      \"term\": \"emergency braking detection\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1088/1741-2560/8/5/056001\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000243\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000243\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"DFG grant\"\n    },\n    {\n      \"funder_name\": \"BMBF grant\"\n    },\n    {\n      \"funder_name\": \"Bernstein Focus Neurotechnology, Berlin\"\n    },\n    {\n      \"funder_name\": \"DFG\"\n    },\n    {\n      \"funder_name\": \"BMBF\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"15.3 GB (31 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"30249579d64b97fd7530b22840b654801b48e54f76dca316e38e85a52de877cd\"\n}","last_activity_at":"2026-08-16 13:36:44","source":null,"source_id":null,"subject_count":15,"modalities":"eeg","age_min":30.6,"age_max":30.6,"file_size":15268803598,"total_files":191,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz","license":"CC-BY-NC-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000243-blue)](https://doi.org/10.82901/nemar.nm000243)\n\n# BNCI 2016-002 Emergency Braking during Simulated Driving dataset\n\nBNCI 2016-002 Emergency Braking during Simulated Driving dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2016-002\n- **Paradigm**: p300\n- **DOI**: 10.1088/1741-2560/8/5/056001\n- **Subjects**: 15\n- **Sessions per subject**: 1\n- **Events**: Target=1, NonTarget=2\n- **Trial interval**: [-0.5, 1.0] s\n- **File format**: .mat\n- **Data preprocessed**: True\n- **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\n\n## Acquisition\n\n- **Sampling rate**: 200.0 Hz\n- **Number of channels**: 59\n- **Channel types**: eeg=59, emg=1, eog=2, misc=7\n- **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\n- **Montage**: extended 10-20\n- **Hardware**: BrainAmp\n- **Software**: TORCS\n- **Reference**: nose\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {'highpass_hz': 0.1, 'lowpass_hz': 250}\n- **Impedance threshold**: {'eeg': 20, 'emg': 50} kOhm\n- **Cap manufacturer**: Easycap\n- **Cap model**: Easycap\n- **Auxiliary channels**: EOG (2 ch, vertical, horizontal), EMG (1 ch), technical_markers\n\n## Participants\n\n- **Number of subjects**: 15\n- **Health status**: healthy\n- **Age**: mean=30.6, std=5.4\n- **Gender distribution**: male=14, female=4\n- **Handedness**: right-handed\n- **BCI experience**: naive\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Task type**: driving_simulation\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 3.0 s\n- **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.\n- **Feedback type**: visual (colored circle indicating distance: green <20m, yellow otherwise; brakelight flashing)\n- **Stimulus type**: emergency_braking_scenario\n- **Stimulus modalities**: visual, multisensory\n- **Primary modality**: visual\n- **Synchronicity**: asynchronous\n- **Mode**: online\n- **Training/test split**: True\n- **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.\n- **Stimulus presentation**: isi_range=20-40 seconds, deceleration_range=60-80 km/h, brakelight=flashing, oncoming_traffic=present, sharp_curves=present\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: p300\n\n## Data Structure\n\n- **Trials**: ~99 emergency braking events per subject (test set)\n- **Blocks per session**: 3\n- **Block duration**: 2700.0 s\n- **Trials context**: Emergency braking events with 20-40s inter-stimulus-interval, total ~225 events across 3 blocks per subject\n\n## Preprocessing\n\n- **Data state**: preprocessed\n- **Preprocessing applied**: True\n- **Steps**: lowpass filtering, bandpass filtering, notch filtering, rectification, downsampling/upsampling, baseline correction, synchronization\n- **Highpass filter**: 0.1 Hz\n- **Lowpass filter**: 45.0 Hz\n- **Bandpass filter**: [15.0, 90.0]\n- **Notch filter**: 50.0 Hz\n- **Filter type**: Chebychev type II (EEG lowpass), Elliptic (EMG bandpass), digital (notch)\n- **Filter order**: tenth-order (EEG), sixth-order (EMG), second-order (notch)\n- **Re-reference**: nose\n- **Downsampled to**: 200.0 Hz\n- **Epoch window**: [-0.3, 1.2]\n- **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.\n\n## Signal Processing\n\n- **Classifiers**: RLDA, Regularized Linear Discriminant Analysis, Shrinkage LDA\n- **Feature extraction**: Event-Related Potentials, Spatio-temporal features, Bi-serial correlation, Area Under Curve\n- **Spatial filters**: Artifact rejection based on spectral power\n\n## Cross-Validation\n\n- **Method**: sequential temporal split\n- **Evaluation type**: temporal_validation\n\n## Performance (Original Study)\n\n- **Auc**: 0.5\n- **Braking Time Reduction Ms**: 130\n- **Braking Distance Reduction M**: 3.66\n\n## BCI Application\n\n- **Applications**: driving_assistance, emergency_braking_detection, neuroergonomics\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Visual, Multisensory\n- **Type**: Driving, Neuroergonomics\n\n## Documentation\n\n- **Description**: Emergency braking detection during simulated driving using EEG and EMG to predict driver's braking intention before behavioral response.\n- **DOI**: 10.1088/1741-2560/8/5/056001\n- **Associated paper DOI**: 10.1088/1741-2560/8/5/056001\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz\n- **Senior author**: Benjamin Blankertz\n- **Contact**: stefan.haufe@tu-berlin.de\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Group, Department of Computer Science\n- **Address**: Franklinstraße 28/29, D-10587 Berlin, Germany\n- **Country**: Germany\n- **Repository**: BNCI Horizon\n- **Publication year**: 2011\n- **Funding**: DFG grant; BMBF grant; Bernstein Focus Neurotechnology, Berlin\n- **Ethics approval**: IRB of Charité University Medicine, Berlin; Declaration of Helsinki; Written informed consent from all participants\n- **Keywords**: emergency braking, driving simulation, EEG, EMG, brain-computer interface, neuroergonomics, event-related potentials, machine learning, driver assistance\n\n## References\n\nHaufe, 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\n\nNotes\n\n.. versionadded:: 1.3.0\n\nThis dataset is valuable for research on:\n\n- Predictive braking assistance systems - Neuroergonomics and driving safety - Real-time detection of emergency intentions - Multimodal biosignal integration (EEG + EMG + vehicle dynamics)\n\nThe paradigm represents a unique blend of ERP (event-related potential) analysis with ecological validity in a naturalistic driving context.\n\n**Data Availability**: Currently 15 of 18 subjects are available. Files are hosted at the BBCI (Berlin Brain-Computer Interface) archive.\n\nLicense: Creative Commons Attribution Non-Commercial No Derivatives (CC BY-NC-ND 4.0)\nAppelhoff, 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\n\nPernet, 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\n\n---\nG","bids_version":"1.9.0","sessions_count":1,"publish_date":"2026-03-25 21:50:09","embedding_dirty":0,"license_tier":"noderiv","zarr_status":"ready","zarr_converted_at":"2026-09-04 09:30:43","zarr_store_count":15,"zarr_index_etag":"14c33f350745f06441424b231a76b3cc","zarr_source_commit":"ec66267237f92ffb1ea439b9fc73956f0cba9910","archive_status":"ready","archive_size":14707309827,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":81,"n_channels":59,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":15268383920,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":191,"zarr_pool_breaks":0,"total_recording_duration":121482,"recording_duration_min":8098,"recording_duration_max":8100,"recording_count":15,"recordings_unavailable":0,"recordings_measured":15,"channel_count_min":59,"channel_count_max":59,"sampling_frequency":200,"power_line_frequency":50,"eeg_reference":"nose","placement_scheme":"extended 10-20","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:14:50\",\"metadata_updated_at\":\"2026-08-18 21:21:11\",\"archive_checked_at\":\"2026-08-18 21:30:20\",\"zarr_checked_at\":\"2026-06-07 17:58:35\",\"records_checked_at\":\"2026-08-18 21:22:17\",\"citations_updated_at\":\"2026-09-08 03:00:47\",\"channel_montage_checked_at\":\"2026-06-28 23:00:07\",\"hed_checked_at\":\"2026-06-30 07:32:13\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-22 03:00:47\",\"signal_defaults_at\":\"2026-09-02 11:48:16\",\"recording_stats_at\":\"2026-09-05 03:01:48\"}","participants":15,"num_citations":81,"latest_version":"v1.0.2","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"14.22 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000243/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}