{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cbe","dataset_id":"nm000243","associated_paper_doi":null,"authors":["Stefan Haufe","Matthias S Treder","Manfred F Gugler","Max Sagebaum","Gabriel Curio","Benjamin Blankertz"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":15,"ages":[30,30,30,30,30,30,30,30,30,30,30,30,30,30,30],"age_min":30,"age_max":30,"age_mean":30.0,"species":null,"sex_distribution":null,"handedness_distribution":{"r":15}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000243","osf_url":null,"github_url":null,"paper_url":null},"funding":["DFG grant","BMBF grant","Bernstein Focus Neurotechnology, Berlin"],"ingestion_fingerprint":"563fff8e26a47ddbc4bec5c9b37f85d1af2e3e9346e97aee1a32d4e0461431c6","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"BNCI 2016-002 Emergency Braking during Simulated Driving dataset","readme":"# BNCI 2016-002 Emergency Braking during Simulated Driving dataset\nBNCI 2016-002 Emergency Braking during Simulated Driving dataset.\n## Dataset Overview\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## Acquisition\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## Participants\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## Experimental Protocol\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## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: p300\n## Data Structure\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## Preprocessing\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## Signal Processing\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## Cross-Validation\n- **Method**: sequential temporal split\n- **Evaluation type**: temporal_validation\n## Performance (Original Study)\n- **Auc**: 0.5\n- **Braking Time Reduction Ms**: 130\n- **Braking Distance Reduction M**: 3.66\n## BCI Application\n- **Applications**: driving_assistance, emergency_braking_detection, neuroergonomics\n- **Environment**: laboratory\n- **Online feedback**: True\n## Tags\n- **Pathology**: Healthy\n- **Modality**: Visual, Multisensory\n- **Type**: Driving, Neuroergonomics\n## Documentation\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## References\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\nNotes\n.. versionadded:: 1.3.0\nThis dataset is valuable for research on:\n- Predictive braking assistance systems - Neuroergonomics and driving safety - Real-time detection of emergency intentions - Multimodal biosignal integration (EEG + EMG + vehicle dynamics)\nThe paradigm represents a unique blend of ERP (event-related potential) analysis with ecological validity in a naturalistic driving context.\n**Data Availability**: Currently 15 of 18 subjects are available. Files are hosted at the BBCI (Berlin Brain-Computer Interface) archive.\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\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---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0"],"size_bytes":4305262556,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000243","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:36.612850+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-25T21:52:16Z"},"total_files":15,"computed_title":"BNCI 2016-002 Emergency Braking during Simulated Driving dataset","nchans_counts":[{"val":59,"count":15}],"sfreq_counts":[{"val":200.0,"count":15}],"stats_computed_at":"2026-05-01T13:49:34.646019+00:00","total_duration_s":121481.925,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"fc72221e057edc80","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Motor"],"confidence":{"pathology":0.9,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot by purpose is the \"EEG Motor Movement/Imagery Dataset\" (Healthy, Visual, Motor): it uses visually presented cues with the scientific focus on movement execution/imagery, which maps to Type=Motor by convention. This BNCI dataset similarly uses visual driving stimuli while the stated objective is to detect/predict an upcoming braking action (motor intention) from EEG/ERP features. A secondary similarity is to oddball/P300-style datasets (e.g., the Parkinson's cross-modal oddball example) in that it is labeled as a P300 paradigm with Target/NonTarget events; those conventions suggest an Attention-like framing, but here the application focus is explicitly motor/braking intention.","metadata_analysis":"Key participant facts: \"Health status: healthy\" and in Tags \"Pathology: Healthy\".\nKey stimulus/modality facts: \"Primary modality: visual\", \"Feedback type: visual (colored circle...; brakelight flashing)\", and HED tags show \"Visual-presentation\" for both Target and NonTarget.\nKey purpose/type facts: \"Instructions: ... Perform immediate emergency braking when the lead vehicle decelerates abruptly\" and \"Description: Emergency braking detection during simulated driving using EEG and EMG to predict driver's braking intention before behavioral response.\" Also: \"Paradigm: p300\" with \"Events: Target=1, NonTarget=2\" indicates a target/non-target ERP setting embedded in the driving task.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\" (and Tags: \"Pathology: Healthy\"). Few-shot patterns for non-clinical volunteer studies map to Healthy. ALIGN.\nModality: Metadata says \"Primary modality: visual\" and HED annotations include \"Visual-presentation\". Few-shot conventions label the modality by stimulus channel; despite \"Stimulus modalities: visual, multisensory\", the dominant/primary stimulus channel is explicitly visual. ALIGN (with minor ambiguity due to multisensory tag).\nType: Metadata says the goal is \"predict driver's braking intention\" and participants must \"Perform immediate emergency braking\". Few-shot conventions: movement execution/imagery focus -> Type=Motor (as in the Motor Movement/Imagery example). The P300/Target-vs-NonTarget structure could suggest Attention, but the dataset’s stated research/application purpose centers on motor intention/braking. PARTIAL ALIGN (Motor convention fits the stated purpose; P300 structure suggests Attention as runner-up).","decision_summary":"Top-2 candidates:\n- Pathology: (1) Healthy vs (2) Other. Healthy wins because metadata explicitly states \"Health status: healthy\" and Tags \"Pathology: Healthy\".\n- Modality: (1) Visual vs (2) Multisensory. Visual wins because metadata explicitly states \"Primary modality: visual\", visual feedback is described (\"Feedback type: visual\"), and HED labels events as \"Visual-presentation\".\n- Type: (1) Motor vs (2) Attention. Motor wins because the explicit purpose is emergency braking prediction (\"predict driver's braking intention\") and the task requires an overt braking action (\"Perform immediate emergency braking\"); Attention remains plausible because it is a \"Paradigm: p300\" with \"Target\"/\"NonTarget\" events.\nConfidence justification:\n- Pathology 0.9: supported by 2 explicit metadata statements (\"Health status: healthy\"; \"Pathology: Healthy\") plus consistent non-clinical framing.\n- Modality 0.8: supported by 3 explicit cues (\"Primary modality: visual\"; \"Feedback type: visual\"; HED \"Visual-presentation\"), with remaining ambiguity due to the additional \"multisensory\" mention.\n- Type 0.8: supported by 2 explicit purpose/task quotes (\"predict driver's braking intention\"; \"Perform immediate emergency braking\"), with a clear runner-up (Attention) due to \"Paradigm: p300\" Target/NonTarget structure."}},"canonical_name":null,"name_confidence":0.86,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Haufe2016"}}