{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4c97","dataset_id":"nm000194","associated_paper_doi":null,"authors":["Laura Acqualagna","Benjamin Blankertz"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":12,"ages":[29,29,29,29,29,29,29,29,29,29,29,29],"age_min":29,"age_max":29,"age_mean":29.0,"species":null,"sex_distribution":null,"handedness_distribution":{"r":12}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000194","osf_url":null,"github_url":null,"paper_url":null},"funding":["BMBF Grant","Grant Nos s","Grant No. MU MU","DFG Grant"],"ingestion_fingerprint":"7cf6bd0604eda31939758e450a007be404bcd8c13e943e5288e675ffc52535dd","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"BNCI 2015-010 RSVP P300 dataset","readme":"# BNCI 2015-010 RSVP P300 dataset\nBNCI 2015-010 RSVP P300 dataset.\n## Dataset Overview\n- **Code**: BNCI2015-010\n- **Paradigm**: p300\n- **DOI**: 10.1016/j.clinph.2012.12.050\n- **Subjects**: 12\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**: calibration, copy-spelling, free-spelling\n- **File format**: EEG\n- **Data preprocessed**: True\n## Acquisition\n- **Sampling rate**: 200.0 Hz\n- **Number of channels**: 63\n- **Channel types**: eeg=63\n- **Channel names**: Fp1, Fp2, AF3, AF4, Fz, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, POz, PO3, PO4, PO7, PO8, PO9, PO10, Oz, O1, O2\n- **Montage**: 10-20\n- **Hardware**: BrainAmp amplifiers\n- **Software**: Python with Pyff framework\n- **Reference**: left mastoid\n- **Sensor type**: active electrode\n- **Line frequency**: 50.0 Hz\n- **Online filters**: lowpass Chebyshev filter up to 40 Hz\n- **Impedance threshold**: 10.0 kOhm\n- **Cap manufacturer**: Brain Products\n- **Cap model**: actiCap\n- **Electrode type**: active electrode\n## Participants\n- **Number of subjects**: 12\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=29.17, std=8.4, min=24, max=55\n- **Gender distribution**: male=6, female=6\n- **Handedness**: all right-handed\n- **BCI experience**: mixed\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: p300\n- **Task type**: spelling\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 46.5 s\n- **Study design**: RSVP (Rapid Serial Visual Presentation) BCI speller where 30 symbols are presented one-by-one in random order at the center of the screen. Three conditions tested: NoColor 116ms SOA, Color 116ms SOA, and Color 83ms SOA. Colors used to facilitate discrimination.\n- **Feedback type**: visual\n- **Stimulus type**: RSVP letters\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: True\n- **Instructions**: Participants fixate center of screen, concentrate on target letter, silently count its occurrences. Avoid blinking during visual presentation.\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- **Number of targets**: 30\n- **Number of repetitions**: 10\n- **Stimulus onset asynchrony**: 116.0 ms\n## Data Structure\n- **Trials**: 10 sequences of 30 symbols\n- **Blocks per session**: 3\n- **Trials context**: per sequence\n## Preprocessing\n- **Data state**: filtered\n- **Preprocessing applied**: True\n- **Steps**: lowpass filter, downsampling, baseline correction, artifact rejection\n- **Lowpass filter**: 40.0 Hz\n- **Filter type**: Chebyshev\n- **Filter order**: passband up to 40 Hz, stopband starting at 49 Hz\n- **Artifact methods**: min-max criterion for eye movement rejection (75 µV on F9, Fz, F10, AF3, AF4), broadband power rejection (5-40 Hz)\n- **Re-reference**: linked mastoids (offline)\n- **Downsampled to**: 200.0 Hz\n- **Epoch window**: [-0.1, 1.2]\n- **Notes**: Baseline correction on pre-stimulus interval (116ms for 116ms SOA, 83/2ms for 83ms SOA). Non-target epochs excluded if 3 preceding or following symbols were targets.\n## Signal Processing\n- **Classifiers**: LDA with shrinkage\n- **Feature extraction**: spatio-temporal features, averaged voltages within time windows\n- **Frequency bands**: alpha=[7, 13] Hz\n- **Spatial filters**: 55 channels used for classification (all except Fp1,2, AF3,4, F9,10, FT7,8)\n## Cross-Validation\n- **Method**: calibration/test split\n- **Evaluation type**: within_session\n## Performance (Original Study)\n- **Accuracy**: 94.8%\n- **Mean Spelling Rate Symb Per Min**: 1.43\n- **Trial Duration 116Ms Soa S**: 46.5\n- **Trial Duration 83Ms Soa S**: 36.6\n## BCI Application\n- **Applications**: speller, communication\n- **Environment**: laboratory\n- **Online feedback**: True\n## Tags\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: ERP\n## Documentation\n- **DOI**: 10.1016/j.clinph.2012.12.050\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Laura Acqualagna, Benjamin Blankertz\n- **Senior author**: Benjamin Blankertz\n- **Contact**: laura.acqualagna@tu-berlin.de; benjamin.blankertz@tu-berlin.de\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Laboratory; Neurotechnology Group\n- **Country**: Germany\n- **Repository**: BNCI Horizon\n- **Publication year**: 2013\n- **Funding**: BMBF Grant; Grant Nos s; Grant No. MU MU; DFG Grant\n- **Ethics approval**: Study performed in accordance with the declaration of Helsinki\n- **Keywords**: Brain Computer Interfaces, RSVP, ERPs, Speller, P300, N2, gaze-independent\n## Abstract\nA Brain Computer Interface (BCI) speller using rapid serial visual presentation (RSVP) paradigm for gaze-independent mental typewriting. Twelve healthy participants successfully operated the RSVP speller with mean online spelling rate of 1.43 symb/min and mean symbol selection accuracy of 94.8%. The RSVP speller does not require gaze shifts and can be operated by non-spatial visual attention, making it suitable for patients with impaired oculo-motor control.\n## Methodology\nThree experimental conditions tested (NoColor 116ms, Color 116ms, Color 83ms SOA). Each condition included calibration, copy-spelling, and free-spelling phases. Vocabulary of 30 symbols presented one-by-one at screen center in pseudo-random order. EEG recorded at 1000 Hz with 63 channels, downsampled to 200 Hz for ERP analysis. Classification using LDA with shrinkage on spatio-temporal features from 5 individually selected time windows. Symbol selection based on averaged classifier output across 10 sequences.\n## References\nAcqualagna, L., & Blankertz, B. (2013). Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP). Clinical Neurophysiology, 124(5), 901-908. https://doi.org/10.1016/j.clinph.2012.12.050\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":2221721340,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000194","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:08:54.981285+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-24T00:37:18Z"},"total_files":24,"computed_title":"BNCI 2015-010 RSVP P300 dataset","nchans_counts":[{"val":63,"count":22},{"val":61,"count":2}],"sfreq_counts":[{"val":200.0,"count":24}],"stats_computed_at":"2026-05-01T13:49:34.645529+00:00","total_duration_s":58187.62,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"f438f5e7e84a10b1","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot paradigms are the oddball/target-vs-nontarget style datasets: (1) Cross-modal Oddball Task (Parkinson’s) and (2) Three-Stim Auditory Oddball and Rest in TBI. These examples establish the convention that datasets built around rare targets among frequent non-targets (oddball/P300-like) are typically typed by the attentional/cognitive-control construct rather than by the mechanics of button presses. Another relevant convention is that when the recruited cohort is explicitly clinical (e.g., Parkinson’s, TBI), Pathology follows that fact; here, the RSVP-P300 dataset explicitly states healthy participants, so it should be labeled Healthy despite being “suitable for patients” as an application.","metadata_analysis":"Pathology-related quotes: (1) \"Clinical population: Healthy\" (Participants section). (2) \"A Brain Computer Interface (BCI) speller... Twelve healthy participants successfully operated the RSVP speller\" (Abstract). (3) \"Tags - Pathology: Healthy\".\n\nModality-related quotes: (1) \"RSVP (Rapid Serial Visual Presentation) BCI speller where 30 symbols are presented one-by-one in random order at the center of the screen\" (Study design). (2) \"Stimulus type: RSVP letters\". (3) \"Stimulus modalities: visual\" and \"Primary modality: visual\".\n\nType-related quotes: (1) \"Paradigm: p300\" and \"Events: Target=1, NonTarget=2\" (Dataset overview). (2) \"Participants fixate center of screen, concentrate on target letter, silently count its occurrences\" (Instructions). (3) \"can be operated by non-spatial visual attention\" (Abstract).","paper_abstract_analysis":"The dataset README includes an abstract stating: \"Twelve healthy participants\" and emphasizes \"non-spatial visual attention\" as the operational principle of the RSVP speller, supporting Healthy pathology and an Attention-centric type.","evidence_alignment_check":"Pathology: Metadata SAYS \"Clinical population: Healthy\" and abstract says \"Twelve healthy participants\". Few-shot pattern SUGGESTS using the explicitly recruited cohort label (e.g., Parkinson’s dataset labeled Parkinson’s; TBI dataset labeled TBI). ALIGN.\n\nModality: Metadata SAYS \"Stimulus modalities: visual\" and describes letters presented on a screen. Few-shot pattern SUGGESTS labeling by stimulus channel (e.g., visual discrimination -> Visual; auditory ABR -> Auditory). ALIGN.\n\nType: Metadata SAYS \"Paradigm: p300\", Target/NonTarget events, and instructions to \"concentrate on target letter\" and \"silently count its occurrences\" plus \"non-spatial visual attention\". Few-shot pattern SUGGESTS oddball/target-detection paradigms map naturally to Attention (even when responses are made), unless the primary aim is explicitly something else (e.g., clinical biomarker/clinical intervention emphasis). ALIGN.","decision_summary":"Pathology top-2: (A) Healthy vs (B) Other/Unknown. Healthy supported by: \"Clinical population: Healthy\", \"Twelve healthy participants\", \"Tags - Pathology: Healthy\". No competing clinical recruitment described. Final: Healthy.\n\nModality top-2: (A) Visual vs (B) Multisensory. Visual supported by: \"RSVP... symbols... at the center of the screen\", \"Stimulus type: RSVP letters\", and \"Stimulus modalities: visual\"/\"Primary modality: visual\". No auditory/tactile channel described. Final: Visual.\n\nType top-2: (A) Attention vs (B) Perception. Attention supported by: \"Paradigm: p300\" with \"Target\" vs \"NonTarget\", instruction to \"concentrate on target letter\" and \"silently count its occurrences\", and abstract claim of operation via \"non-spatial visual attention\". Perception is less fitting because the core construct is target detection/attentional selection rather than sensory discrimination. Final: Attention.\n\nConfidence justification: Pathology and Modality have 3+ explicit supporting quotes each; Type has multiple direct quotes but fewer tightly-matched few-shot examples labeled specifically as Attention for P300 spellers (oddball examples exist but differ in study aim/population), so Type confidence is slightly lower."}},"canonical_name":null,"name_confidence":0.9,"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":"Acqualagna2015"}}