{"dataset":{"id":"222","dataset_id":"nm000190","name":"BNCI 2015-012 PASS2D P300 dataset","description":"This dataset comprises preprocessed EEG recordings from a novel 9-class auditory event-related potential (ERP) paradigm designed for brain-computer interface (BCI) applications. Ten healthy participants performed a spelling task using a predictive text entry system (PASS2D) that exploited spatial auditory stimuli varying in pitch and direction. The study demonstrates competitive performance for auditory ERP-based BCIs, achieving 0.8 characters per minute and 3.4 bits/min information transfer rate, with potential applications for communication in patients with severe motor impairment.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000190","concept_doi":"10.82901/nemar.nm000190","latest_version_doi":"10.82901/nemar.nm000190.v1.0.2","created_at":"2026-03-23 23:16:36","updated_at":"2026-08-18 18:15:37","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 2015-012 PASS2D P300 dataset\",\n  \"description\": \"This dataset comprises preprocessed EEG recordings from a novel 9-class auditory event-related potential (ERP) paradigm designed for brain-computer interface (BCI) applications. Ten healthy participants performed a spelling task using a predictive text entry system (PASS2D) that exploited spatial auditory stimuli varying in pitch and direction. The study demonstrates competitive performance for auditory ERP-based BCIs, achieving 0.8 characters per minute and 3.4 bits/min information transfer rate, with potential applications for communication in patients with severe motor impairment.\",\n  \"methods_description\": \"Data were acquired at 250 Hz from 63 EEG channels using Brain Products hardware with wet Ag/AgCl electrodes in a 10-20 montage. Preprocessing included analog bandpass filtering (0.1-250 Hz), digital lowpass filtering (40 Hz), downsampling to 100 Hz, and artifact rejection (peak-to-peak voltage >100 μV). Participants completed a single session with calibration (3 runs of 9 trials each) and online spelling (2 runs). Classification used linear Fisher discriminant analysis with shrinkage regularization on mean amplitudes from discriminative intervals (N200: 230-300 ms, P300: 350+ ms).\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Johannes Höhne\": {},\n    \"Martijn Schreuder\": {},\n    \"Benjamin Blankertz\": {},\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\": \"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\": \"P300\"\n    },\n    {\n      \"term\": \"auditory ERP\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"speller paradigm\"\n    },\n    {\n      \"term\": \"spatial auditory stimuli\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2011.00099\",\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/nm000190\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000190\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"8.2 GB (31 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"dab8604a5c0e04d9220cfa9d2448303bd7ab5b231ab6481957909ade4fd207ee\"\n}","last_activity_at":"2026-08-16 13:28:29","source":null,"source_id":null,"subject_count":10,"modalities":"eeg","age_min":21,"age_max":34,"file_size":8183746330,"total_files":191,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann","license":"CC-BY-NC-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000190-blue)](https://doi.org/10.82901/nemar.nm000190)\n\n# BNCI 2015-012 PASS2D P300 dataset\n\nBNCI 2015-012 PASS2D P300 dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2015-012\n- **Paradigm**: p300\n- **DOI**: 10.3389/fnins.2011.00099\n- **Subjects**: 10\n- **Sessions per subject**: 1\n- **Events**: Target=1, NonTarget=2\n- **Trial interval**: [0, 0.8] s\n- **Runs per session**: 2\n- **Session IDs**: session_1\n- **File format**: gdf\n- **Data preprocessed**: True\n- **Contributing labs**: Berlin Institute of Technology, Fraunhofer FIRST\n\n## Acquisition\n\n- **Sampling rate**: 250.0 Hz\n- **Number of channels**: 63\n- **Channel types**: eeg=63\n- **Channel names**: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F10, F2, F3, F4, F5, F6, F7, F8, F9, 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\n- **Montage**: 10-20\n- **Hardware**: Brain Products\n- **Software**: Matlab\n- **Reference**: nose\n- **Sensor type**: wet Ag/AgCl electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 0.1-250 Hz analog bandpass, then 40 Hz lowpass\n- **Cap manufacturer**: EasyCap GmbH\n- **Cap model**: Fast'n Easy Cap\n- **Electrode type**: wet Ag/AgCl electrodes\n- **Electrode material**: Ag/AgCl\n- **Auxiliary channels**: EOG (1 ch)\n\n## Participants\n\n- **Number of subjects**: 10\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=25.1, min=21, max=34\n- **Gender distribution**: male=9, female=3\n- **BCI experience**: mostly naive\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Task type**: auditory ERP speller\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Tasks**: text spelling, counting task\n- **Study design**: Nine-class auditory ERP paradigm with predictive text entry system (PASS2D). Users focus attention on two-dimensional auditory stimuli varying in pitch (high/medium/low) and direction (left/middle/right) presented via headphones.\n- **Study domain**: communication\n- **Feedback type**: visual\n- **Stimulus type**: auditory tones\n- **Stimulus modalities**: auditory, visual\n- **Primary modality**: auditory\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: True\n- **Instructions**: Focus on target stimuli while ignoring all non-target stimuli. Minimize eye movements and muscle artifacts. Count targets during calibration. Spell sentences during online phase.\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- **Stimulus frequencies**: [708.0, 524.0, 380.0] Hz\n- **Number of targets**: 9\n- **Number of repetitions**: 15\n- **Inter-stimulus interval**: 125.0 ms\n- **Stimulus onset asynchrony**: 225.0 ms\n\n## Data Structure\n\n- **Trials**: 27\n- **Trials context**: total across all calibration runs (3 runs × 9 trials per run)\n\n## Preprocessing\n\n- **Data state**: filtered and downsampled\n- **Preprocessing applied**: True\n- **Steps**: analog bandpass filter, lowpass filter, downsampling, artifact rejection\n- **Highpass filter**: 0.1 Hz\n- **Lowpass filter**: 40.0 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.1, 'high_cutoff_hz': 250.0}\n- **Filter type**: analog bandpass then digital lowpass\n- **Artifact methods**: threshold rejection\n- **Re-reference**: nose\n- **Downsampled to**: 100.0 Hz\n- **Epoch window**: [-0.15, 0.8]\n- **Notes**: Epochs with peak-to-peak voltage difference exceeding 100 μV in any channel were rejected during calibration. No artifact correction applied in online runs.\n\n## Signal Processing\n\n- **Classifiers**: FDA, Fisher discriminant analysis\n- **Feature extraction**: mean amplitude in discriminative intervals\n- **Spatial filters**: shrinkage regularization\n\n## Cross-Validation\n\n- **Method**: cross-validation\n- **Evaluation type**: within_session\n\n## Performance (Original Study)\n\n- **Accuracy**: 72.5%\n- **Itr**: 3.4 bits/min\n- **Characters Per Minute**: 0.8\n- **Spelling Speed Chars Per Min**: 0.8\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**: A novel 9-class auditory ERP paradigm driving a predictive text entry system\n- **DOI**: 10.3389/fnins.2011.00099\n- **Associated paper DOI**: 10.3389/fnins.2011.00112\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann\n- **Senior author**: Michael Tangermann\n- **Contact**: j.hoehne@tu-berlin.de\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Laboratory\n- **Address**: Franklinstr. 28/19, 10587 Berlin, Germany\n- **Country**: Germany\n- **Repository**: BNCI Horizon\n- **Publication year**: 2011\n- **Keywords**: brain–computer interface, BCI, auditory ERP, P300, N200, spatial auditory stimuli, T9, user-centered design\n\n## Abstract\n\nBrain–computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user's gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.\n\n## Methodology\n\nParticipants performed a single session lasting 3-4 hours consisting of calibration phase and online spelling task. Calibration: 3 runs (plus 1 practice run), each with 9 trials covering all 9 stimuli as targets. Each trial had 13-14 pseudo-random sequences of all 9 auditory stimuli (108 subtrials total, 12 target + 96 non-target). Online spelling: 2 runs spelling German sentences using T9-style predictive text system with 9-class decisions. Each trial consisted of 135 subtrials (15 iterations of 9 stimuli). Binary classification using linear FDA with shrinkage regularization on 2-4 amplitude values per channel from discriminative intervals (N200 at 230-300ms and P300 at 350+ ms). Multiclass decision based on one-sided t-test with unequal variances across 15 classifier outputs per key.\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). 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