{"dataset":{"id":"221","dataset_id":"nm000189","name":"BNCI 2015-003 P300 dataset","description":"An auditory brain-computer interface dataset implementing the AMUSE (Auditory Multi-class Spatial ERP) paradigm for spelling applications. The dataset comprises EEG recordings from 10 healthy subjects performing an auditory oddball task with spatial cues from six speaker locations, achieving online spelling performance of up to 1.41 characters per minute. Data includes preprocessed EEG signals with P300 and N200 event-related potentials, suitable for BCI research and benchmarking.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000189","concept_doi":"10.82901/nemar.nm000189","latest_version_doi":"10.82901/nemar.nm000189.v1.0.3","created_at":"2026-03-23 23:03:38","updated_at":"2026-08-18 18:16:03","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2015-003 P300 dataset\",\n  \"description\": \"An auditory brain-computer interface dataset implementing the AMUSE (Auditory Multi-class Spatial ERP) paradigm for spelling applications. The dataset comprises EEG recordings from 10 healthy subjects performing an auditory oddball task with spatial cues from six speaker locations, achieving online spelling performance of up to 1.41 characters per minute. Data includes preprocessed EEG signals with P300 and N200 event-related potentials, suitable for BCI research and benchmarking.\",\n  \"methods_description\": \"EEG data acquired at 256 Hz from 8 channels (Fz, Cz, P3, Pz, P4, PO7, Oz, PO8) using BrainAmp hardware with Ag/AgCl electrodes referenced to nose. Subjects were positioned within six speakers arranged in a circle (60° spacing, 65 cm radius) and performed an auditory oddball task with spatial cues. Two sessions per subject included calibration (48 trials, 8 per direction) followed by online spelling. Data preprocessed with bandpass filtering (0.1-40 Hz), downsampling to 100 Hz, and baselining using 150 ms pre-stimulus data. Spatio-temporal features extracted using r2 coefficient with interval selection, classified using linear discriminant analysis with Ledoit-Wolf shrinkage regularization.\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Martijn Schreuder\": {},\n    \"Thomas Rost\": {},\n    \"Michael Tangermann\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"P300\"\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\": \"auditory oddball\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"spatial attention\"\n    },\n    {\n      \"term\": \"speller interface\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1016/j.neulet.2009.06.045\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.3389/fnins.2011.00112\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000189\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000189\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"FP7-ICT PASCAL2 Network of Excellence ICT-216886\"\n    },\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"FP7-224631\"\n    },\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"FP7-216886\"\n    },\n    {\n      \"funder_name\": \"DFG\",\n      \"award_number\": \"MU 987/3-2\"\n    },\n    {\n      \"funder_name\": \"BMBF\",\n      \"award_number\": \"01IB001A\"\n    },\n    {\n      \"funder_name\": \"BMBF\",\n      \"award_number\": \"01GQ0850\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"77.1 MB (31 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"6c394b972654e51681516109031d2d288f4f41062447a6e74d70e0e8c58bf868\"\n}","last_activity_at":"2026-08-16 13:28:03","source":null,"source_id":null,"subject_count":10,"modalities":"eeg","age_min":34.1,"age_max":34.1,"file_size":77973004,"total_files":191,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Martijn Schreuder, Thomas Rost, Michael Tangermann","license":"CC-BY-NC-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000189-blue)](https://doi.org/10.82901/nemar.nm000189)\n\n# BNCI 2015-003 P300 dataset\n\nBNCI 2015-003 P300 dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2015-003\n- **Paradigm**: p300\n- **DOI**: 10.1016/j.neulet.2009.06.045\n- **Subjects**: 10\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 0.8] s\n- **Runs per session**: 2\n- **Session IDs**: Session 1, Session 2\n- **File format**: gdf\n- **Data preprocessed**: True\n- **Number of contributing labs**: 1\n\n## Acquisition\n\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 8\n- **Channel types**: eeg=8\n- **Channel names**: Fz, Cz, P3, Pz, P4, PO7, Oz, PO8\n- **Montage**: standard_1005\n- **Hardware**: BrainAmp\n- **Software**: Matlab\n- **Reference**: nose\n- **Sensor type**: Ag/AgCl electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: hardware analog band-pass filter between 0.1 and 250 Hz\n- **Impedance threshold**: 15.0 kOhm\n- **Cap manufacturer**: Brain Products\n- **Electrode type**: Ag/AgCl\n- **Electrode material**: silver/silver chloride\n- **Auxiliary channels**: EOG (2 ch, bipolar)\n\n## Participants\n\n- **Number of subjects**: 10\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=34.1, std=11.4, min=20, max=57\n- **BCI experience**: naive\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Task type**: auditory_oddball\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Tasks**: spelling, auditory_attention\n- **Study design**: Auditory Multi-class Spatial ERP (AMUSE) paradigm using spatial auditory cues from six speaker locations in azimuth plane. Two-step hex-o-spell like interface for character selection. Subjects mentally count target stimuli from one of six spatial directions.\n- **Study domain**: communication\n- **Feedback type**: auditory\n- **Stimulus type**: spatial_auditory\n- **Stimulus modalities**: auditory\n- **Primary modality**: auditory\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: True\n- **Instructions**: Focus attention to one target direction and mentally count the number of appearances\n- **Stimulus presentation**: soa_ms=175, stimulus_duration_ms=40, stimulus_intensity_db=58, speaker_arrangement=6 speakers at ear height, evenly distributed in circle with 60° distance, radius 65 cm\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- **Number of targets**: 6\n- **Stimulus onset asynchrony**: 175.0 ms\n\n## Data Structure\n\n- **Trials**: 48\n- **Trials per class**: calibration_per_direction=8\n- **Trials context**: calibration_phase\n\n## Preprocessing\n\n- **Data state**: filtered\n- **Preprocessing applied**: True\n- **Steps**: low-pass filter, downsampling, baselining\n- **Highpass filter**: 0.1 Hz\n- **Lowpass filter**: 40.0 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.1, 'high_cutoff_hz': 40.0}\n- **Filter type**: analog hardware filter for acquisition; low-pass for online\n- **Artifact methods**: variance criterium, peak-to-peak difference criterium\n- **Re-reference**: nose\n- **Downsampled to**: 100.0 Hz\n- **Epoch window**: [-0.15, None]\n- **Notes**: For online use signal was low-pass filtered below 40 Hz and downsampled to 100 Hz. Data baselined using 150 ms pre-stimulus data as reference.\n\n## Signal Processing\n\n- **Classifiers**: LDA, linear binary classifier\n- **Feature extraction**: spatio-temporal features, r2 coefficient, interval averaging\n- **Spatial filters**: shrinkage regularization (Ledoit-Wolf)\n\n## Cross-Validation\n\n- **Method**: online\n- **Evaluation type**: online\n\n## Performance (Original Study)\n\n- **Accuracy**: 77.4%\n- **Itr**: 2.84 bits/min\n- **Char Per Min Session1**: 0.59\n- **Char Per Min Session2 Max**: 1.41\n- **Char Per Min Session2 Avg**: 0.94\n- **Itr Session2 Avg**: 5.26\n- **Itr Session2 Max**: 7.55\n- **Success Rate Session1**: 76.0\n\n## BCI Application\n\n- **Applications**: speller, communication\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Auditory\n- **Type**: ERP, P300\n\n## Documentation\n\n- **Description**: Auditory BCI speller using spatial cues (AMUSE paradigm) allowing purely auditory communication interface\n- **DOI**: 10.1016/j.neulet.2009.06.045\n- **Associated paper DOI**: 10.3389/fnins.2011.00112\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Martijn Schreuder, Thomas Rost, Michael Tangermann\n- **Senior author**: Michael Tangermann\n- **Contact**: schreuder@tu-berlin.de\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Laboratory\n- **Address**: Machine Learning Laboratory, Berlin Institute of Technology, FR6-9, Franklinstraße 28/29, 10587 Berlin, Germany\n- **Country**: Germany\n- **Repository**: BNCI Horizon\n- **Publication year**: 2011\n- **Funding**: European ICT Programme Project FP7-224631; European ICT Programme Project FP7-216886; Deutsche Forschungsgemeinschaft (DFG MU 987/3-2); Bundesministerium fur Bildung und Forschung (BMBF FKZ 01IB001A, 01GQ0850); FP7-ICT PASCAL2 Network of Excellence ICT-216886\n- **Ethics approval**: Ethics Committee of the Charité University Hospital\n- **Acknowledgements**: Thomas Denck, David List and Larissa Queda for help with experiments. Klaus-Robert Müller and Benjamin Blankertz for fruitful discussions.\n- **Keywords**: brain-computer interface, directional hearing, auditory event-related potentials, P300, N200, dynamic subtrials\n\n## External Links\n\n- **Source**: http://www.frontiersin.org/neuroprosthetics/10.3389/fnins.2011.00112/abstract\n\n## Abstract\n\nThis online study introduces an auditory spelling interface that eliminates the necessity for visual representation. In up to two sessions, a group of healthy subjects (N=21) was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multi-class Spatial ERP). The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 char/min (7.55 bits/min) could be reached during the second session (average: 0.94 char/min, 5.26 bits/min).\n\n## Methodology\n\nParticipants surrounded by six speakers at ear height in circle (60° spacing, 65 cm radius). Each direction associated with unique combination of tone (base frequency + harmonics) and band-pass filtered noise. Two-step hex-o-spell interface for character selection. Session 1: calibration (48 trials, 8 per direction, 15 iterations each) followed by online spelling with 15 fixed iterations. Session 2: calibration followed by online spelling with dynamic stopping method (4-15 iterations). Spatio-temporal feature extraction using r2 coefficient and interval selection (2-4 intervals for early and late components, 112-224 features total). Linear binary classifier with shrinkage regularization (Ledoit-Wolf). Decision making based on median classifier scores across iterations.\n\n## References\n\nSchreuder, M., Rost, T., & Tangermann, M. (2011). Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Frontiers in neuroscience, 5, 112. https://doi.org/10.3389/fnins.2011.00112\n\nNotes\n\n.. note::\n\n``BNCI2015_003`` was previously named ``BNCI2015003``. ``BNCI2015003`` will be removed in version 1.1.\n\n.. versionadded:: 0.4.0\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. 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