{"dataset":{"id":"267","dataset_id":"nm000234","name":"BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset","description":"The BNCI 2015-009 AMUSE dataset comprises EEG recordings from 21 healthy subjects performing an auditory oddball task using spatial hearing cues for brain-computer interface applications. Participants discriminated target and non-target stimuli presented from five spatially distributed speakers with varying inter-stimulus intervals, yielding preprocessed data suitable for P300-based BCI research and offline classification studies.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000234","concept_doi":"10.82901/nemar.nm000234","latest_version_doi":"10.82901/nemar.nm000234.v1.0.2","created_at":"2026-03-25 18:12:26","updated_at":"2026-08-18 21:19:11","zenodo_concept_id":"20520039","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset\",\n  \"description\": \"The BNCI 2015-009 AMUSE dataset comprises EEG recordings from 21 healthy subjects performing an auditory oddball task using spatial hearing cues for brain-computer interface applications. Participants discriminated target and non-target stimuli presented from five spatially distributed speakers with varying inter-stimulus intervals, yielding preprocessed data suitable for P300-based BCI research and offline classification studies.\",\n  \"methods_description\": \"EEG data were acquired at 250 Hz using a 128-channel Brain Products amplifier with Ag/AgCl electrodes in 10-20 montage, referenced to nose. Stimuli consisted of 40ms complex sounds from bandpass-filtered white noise presented from five speakers with 45-degree spacing. Three experimental conditions were tested: C300 (300ms ISI), C175 (175ms ISI), and C300s (300ms ISI, single speaker). Preprocessing included bandpass filtering (0.1-250 Hz), 50 Hz notch filtering, downsampling to 100 Hz, and threshold-based artifact rejection (>70 µV on ocular channels).\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Martijn Schreuder\": {},\n    \"Benjamin Blankertz\": {},\n    \"Michael Tangermann\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"P300\"\n    },\n    {\n      \"term\": \"auditory BCI\"\n    },\n    {\n      \"term\": \"spatial hearing\"\n    },\n    {\n      \"term\": \"oddball paradigm\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"BNCI\"\n    },\n    {\n      \"term\": \"BCI benchmark\"\n    },\n    {\n      \"term\": \"offline classification\"\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 Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D001931\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"Auditory Perception\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D001307\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2011.00112\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000234\",\n      \"identifier_type\": 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{\n      \"funder_name\": \"Deutsche Forschungsgemeinschaft\",\n      \"award_number\": \"MU 987/3-1\"\n    },\n    {\n      \"funder_name\": \"Bundesministerium für Bildung und Forschung\",\n      \"award_number\": \"01IB001A\"\n    },\n    {\n      \"funder_name\": \"Bundesministerium für Bildung und Forschung\",\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    \"17.7 GB (64 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"787e9a0b7afa451b447e15351199ad86feb737aa0596aa8e647ad7028c88aead\"\n}","last_activity_at":"2026-08-16 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channels**: 60\n- **Channel types**: eeg=60, eog=2\n- **Montage**: 10-20\n- **Hardware**: Brain Products 128-channel amplifier\n- **Software**: Matlab\n- **Reference**: nose\n- **Sensor type**: Ag/AgCl electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 0.1-250 Hz analog bandpass\n- **Auxiliary channels**: EOG (2 ch, bipolar)\n\n## Participants\n\n- **Number of subjects**: 21\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=30.3, min=22, max=55\n- **Gender distribution**: male=6, female=4\n- **Handedness**: unknown\n- **BCI experience**: mixed\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Task type**: oddball\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 0.8 s\n- **Tasks**: spatial_auditory_oddball\n- **Study design**: Offline auditory oddball task using spatial location of auditory stimuli as discriminating cue. Frontal five speakers used (speakers 1,2,3,7,8) with 45 degree spacing. Three conditions tested: C300 (300ms ISI), C175 (175ms ISI), C300s (300ms ISI, single speaker). Each stimulus was unique 40ms complex sound from bandpass filtered white noise with tone overlay.\n- **Study domain**: BCI\n- **Feedback type**: none\n- **Stimulus type**: auditory_spatial\n- **Stimulus modalities**: auditory\n- **Primary modality**: auditory\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: False\n- **Instructions**: Subjects asked to mentally count target stimulations or respond by keypress (condition Cr). Minimize eye movements and muscle contractions. Target direction indicated prior to each block visually and by presenting stimulus from that location.\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**: 5\n- **Number of repetitions**: 15\n- **Inter-stimulus interval**: 300.0 ms\n\n## Data Structure\n\n- **Trials**: varied by condition\n- **Blocks per session**: 50\n- **Trials context**: BCI experiments: C300 (50 trials × 75 subtrials = 3750 subtrials), C175 (40 trials × 75 subtrials = 3000 subtrials), C300s (20 trials × 75 subtrials = 1500 subtrials). Physiological experiments: C1000 (32 trials × 80 subtrials = 2560 subtrials), Cr (576-768 subtrials)\n\n## Preprocessing\n\n- **Data state**: filtered\n- **Preprocessing applied**: True\n- **Steps**: bandpass filter, notch filter, downsampling, artifact rejection\n- **Highpass filter**: 0.1 Hz\n- **Lowpass filter**: 250.0 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.1, 'high_cutoff_hz': 250.0}\n- **Notch filter**: [50] Hz\n- **Filter type**: Chebyshev II order 8 (for visual inspection: 30 Hz pass, 42 Hz stop, 50 dB damping)\n- **Artifact methods**: threshold-based artifact rejection\n- **Re-reference**: nose\n- **Downsampled to**: 100.0 Hz\n- **Epoch window**: [-0.15, 0.8]\n- **Notes**: Raw data acquired at 1000 Hz. For visual inspection: low-pass filtered with order 8 Chebyshev II filter (30 Hz pass, 42 Hz stop, 50 dB damping) applied forward and backward to minimize phase shifts, then downsampled to 100 Hz. For classification: same filter applied causally (forward only) for online portability. Artifact rejection used simple threshold method: subtrials with deflection >70 µV over ocular channels compared to baseline were rejected.\n\n## Signal Processing\n\n- **Classifiers**: LDA\n- **Feature extraction**: ROC-separability-index\n- **Frequency bands**: analyzed=[0.1, 250.0] Hz\n\n## Cross-Validation\n\n- **Method**: cross-validation\n- **Evaluation type**: offline\n\n## Performance (Original Study)\n\n- **Accuracy**: 90.0%\n- **Itr**: 17.39 bits/min\n- **Best Subject Itr**: 25.2\n- **Best Subject Accuracy**: 100.0\n- **C300S Accuracy**: 70.0\n\n## BCI Application\n\n- **Applications**: speller, communication\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Auditory\n- **Type**: P300\n\n## Documentation\n\n- **Description**: A new auditory multi-class brain-computer interface paradigm using spatial hearing as an informative cue\n- **DOI**: 10.1371/journal.pone.0009813\n- **Associated paper DOI**: 10.3389/fnins.2011.00112\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Martijn Schreuder, Benjamin Blankertz, Michael Tangermann\n- **Senior author**: Michael Tangermann\n- **Contact**: martijn@cs.tu-berlin.de\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Department\n- **Address**: Berlin, Germany\n- **Country**: Germany\n- **Repository**: BNCI Horizon\n- **Publication year**: 2010\n- **Funding**: European ICT Programme Project FP7-224631; European ICT Programme Project FP7-216886; Deutsche Forschungsgemeinschaft (DFG) MU 987/3-1; Bundesministerium für Bildung und Forschung (BMBF) FKZ 01IB001A; Bundesministerium für Bildung und Forschung (BMBF) FKZ 01GQ0850; FP7-ICT PASCAL2 Network of Excellence ICT-216886\n- **Ethics approval**: Ethics Committee of the Charité University Hospital (number EA4/073/09)\n- **Keywords**: auditory BCI, P300, spatial hearing, multi-class, oddball paradigm\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.. versionadded:: 1.2.0\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. 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