{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4c93","dataset_id":"nm000190","associated_paper_doi":null,"authors":["Johannes Höhne","Martijn Schreuder","Benjamin Blankertz","Michael Tangermann"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":10,"ages":[26,25,21,25,23,34,23,23,24,24],"age_min":21,"age_max":34,"age_mean":24.8,"species":null,"sex_distribution":{"m":8},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000190","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"81f8bd1f0271323cb8474d511fd75bf1e893ec9b778d4752b9896bcea438a131","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"BNCI 2015-012 PASS2D P300 dataset","readme":"# BNCI 2015-012 PASS2D P300 dataset\nBNCI 2015-012 PASS2D P300 dataset.\n## Dataset Overview\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## Acquisition\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## Participants\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## Experimental Protocol\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## 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- **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## Data Structure\n- **Trials**: 27\n- **Trials context**: total across all calibration runs (3 runs × 9 trials per run)\n## Preprocessing\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## Signal Processing\n- **Classifiers**: FDA, Fisher discriminant analysis\n- **Feature extraction**: mean amplitude in discriminative intervals\n- **Spatial filters**: shrinkage regularization\n## Cross-Validation\n- **Method**: cross-validation\n- **Evaluation type**: within_session\n## Performance (Original Study)\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## BCI Application\n- **Applications**: speller, communication\n- **Environment**: laboratory\n- **Online feedback**: True\n## Tags\n- **Pathology**: Healthy\n- **Modality**: Auditory\n- **Type**: ERP, P300\n## Documentation\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## Abstract\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## Methodology\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## References\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\nNotes\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\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":2321801479,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000190","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:08:53.175694+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-02T21:18:46Z"},"total_files":20,"computed_title":"BNCI 2015-012 PASS2D P300 dataset","nchans_counts":[{"val":63,"count":20}],"sfreq_counts":[{"val":250.0,"count":20}],"stats_computed_at":"2026-05-01T13:49:34.645469+00:00","total_duration_s":48871.06,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"f03909418bb50adb","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Attention"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the oddball/target-vs-nontarget ERP style datasets (e.g., the 'Cross-modal Oddball Task' and 'Three-Stim Auditory Oddball' examples). Those examples show that when the paradigm is an oddball-like target detection task evoking ERPs, the Modality is set by the stimulus channel (auditory/visual/multisensory) and the Type tends to reflect the cognitive construct of attending to targets among non-targets (often categorized as Attention in EEGDash unless the primary aim is explicitly clinical/intervention). This dataset is a P300 speller (a classic ERP/oddball BCI target-detection paradigm), so we follow the same convention: Auditory modality (auditory tones) and an attention-focused Type rather than motor.","metadata_analysis":"Key population facts: (1) \"Clinical population: Healthy\"; (2) \"The present work proposes ... This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants.\"; (3) \"Tags - Pathology: Healthy\".\n\nKey task/modality facts: (1) \"Paradigm: p300\" and \"Task type: auditory ERP speller\"; (2) \"Stimulus type: auditory tones\" and \"Users focus attention on two-dimensional auditory stimuli ... presented via headphones\"; (3) \"Primary modality: auditory\" (while also noting \"Feedback type: visual\").\n\nKey construct/type facts: (1) \"Events: Target=1, NonTarget=2\"; (2) \"Instructions: Focus on target stimuli while ignoring all non-target stimuli\"; (3) described as an \"auditory ERP paradigm\" / \"auditory evoked potentials\" for a speller BCI.","paper_abstract_analysis":"No useful paper information beyond what is already included in the dataset README/abstract text (the README contains the study abstract).","evidence_alignment_check":"Pathology: Metadata says healthy participants (e.g., \"Clinical population: Healthy\", and \"online study with 12 healthy participants\"). Few-shot pattern would also label non-clinical BCI/ERP studies as Healthy when no disorder-specific recruitment is stated. ALIGN.\n\nModality: Metadata explicitly states auditory stimulation dominance (\"Task type: auditory ERP speller\", \"Stimulus type: auditory tones\", \"Primary modality: auditory\"), with visual feedback secondary (\"Feedback type: visual\"). Few-shot convention assigns Modality by stimulus channel; thus Auditory is consistent. ALIGN.\n\nType: Metadata describes a P300 target-vs-nontarget ERP speller requiring focus on targets (\"Focus on target stimuli while ignoring all non-target stimuli\", \"Target=1, NonTarget=2\"). Few-shot oddball-like examples support treating this as an attention/target-detection ERP construct unless the dataset is primarily clinical/intervention. No clinical recruitment focus here, so Attention fits better than alternatives. ALIGN.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Healthy (WINNER) — supported by: \"Clinical population: Healthy\"; \"online study with 12 healthy participants\"; \"Tags - Pathology: Healthy\".\n2) Unknown (runner-up) — only because one line says \"Health status: patients\" which conflicts with the explicit \"Clinical population: Healthy\" and multiple other healthy-participant statements; treated as a metadata inconsistency.\nAlignment: Mostly aligned; minor internal inconsistency resolved in favor of explicit clinical population statements.\nConfidence justification: 3 explicit supporting quotes/fields for Healthy.\n\nModality:\n1) Auditory (WINNER) — supported by: \"Task type: auditory ERP speller\"; \"Stimulus type: auditory tones\"; \"Primary modality: auditory\"; plus headphone auditory presentation description.\n2) Multisensory (runner-up) — because \"Stimulus modalities: auditory, visual\" and \"Feedback type: visual\" appear, but visual is feedback/secondary.\nAlignment: Aligned with few-shot convention to use dominant stimulus channel.\nConfidence justification: 3 explicit modality statements naming auditory/primary auditory.\n\nType:\n1) Attention (WINNER) — supported by: \"Events: Target=1, NonTarget=2\" and \"Focus on target stimuli while ignoring all non-target stimuli\" in a P300 ERP speller context.\n2) Perception (runner-up) — plausible because it is also a sensory discrimination/detection (target vs non-target tones), but the P300/oddball construct here is primarily selective attention to targets for BCI control.\nAlignment: Aligned with few-shot oddball/ERP conventions (target detection → Attention unless explicitly framed as pure perception).\nConfidence justification: 2+ explicit attention/targeting-related quotes plus strong paradigm match (P300/oddball-like ERP)."}},"canonical_name":null,"name_confidence":0.99,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Hohne2015"}}