{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4c9b","dataset_id":"nm000198","associated_paper_doi":null,"authors":["M S Treder","N M Schmidt","B Blankertz"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":13,"ages":[27,27,27,27,27,27,27,27,27,27,27,27,27],"age_min":27,"age_max":27,"age_mean":27.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000198","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"bacf0e618b14efa4af9eec2317c0856dde4bdb043de76d494070902623140fd2","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"BNCI 2015-008 Center Speller P300 dataset","readme":"# BNCI 2015-008 Center Speller P300 dataset\nBNCI 2015-008 Center Speller P300 dataset.\n## Dataset Overview\n- **Code**: BNCI2015-008\n- **Paradigm**: p300\n- **DOI**: 10.1088/1741-2560/8/6/066003\n- **Subjects**: 13\n- **Sessions per subject**: 1\n- **Events**: Target=1, NonTarget=2\n- **Trial interval**: [0, 1.0] s\n- **Runs per session**: 2\n- **File format**: gdf\n- **Data preprocessed**: True\n## Acquisition\n- **Sampling rate**: 250.0 Hz\n- **Number of channels**: 63\n- **Channel types**: eeg=63\n- **Channel names**: Fp2, AF3, AF4, Fz, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, FCz, FC1, FC2, FC3, FC4, FC5, FC6, T7, T8, Cz, C1, C2, C3, C4, C5, C6, TP7, TP8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, Pz, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, POz, PO3, PO4, PO7, PO8, PO9, PO10, Oz, O1, O2, Iz, I1, I2\n- **Montage**: 10-10\n- **Hardware**: Brain Products actiCAP\n- **Reference**: left mastoid\n- **Ground**: forehead\n- **Sensor type**: active electrode\n- **Line frequency**: 50.0 Hz\n- **Online filters**: 0.016-250 Hz bandpass\n- **Impedance threshold**: 20.0 kOhm\n- **Cap manufacturer**: Brain Products\n## Participants\n- **Number of subjects**: 13\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=27.0, min=16.0, max=45.0\n- **Gender distribution**: male=8, female=5\n- **Handedness**: {'right': 12, 'left': 1}\n- **BCI experience**: naive\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 30.0 s\n- **Study design**: Two-stage visual speller using covert spatial attention and non-spatial feature attention (color and form). Three speller variants tested: Hex-o-Spell (6 discs with size enhancement and unique colors), Cake Speller (6 triangular faces with unique colors), Center Speller (sequential presentation of 6 geometric shapes with unique colors and forms).\n- **Feedback type**: none\n- **Stimulus type**: visual_flash\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: online\n- **Training/test split**: True\n- **Instructions**: Participants had to strictly fixate the center of the screen and covertly attend to the target symbol. They were instructed to silently count the number of intensifications of the target symbol.\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**: 200.0 ms\n## Data Structure\n- **Trials**: 60 intensifications per stage (10 sequences × 6 elements)\n- **Trials context**: per_stage\n## Preprocessing\n- **Data state**: filtered\n- **Preprocessing applied**: True\n- **Steps**: downsampling, lowpass filter, baseline correction\n- **Highpass filter**: 0.016 Hz\n- **Lowpass filter**: 49.0 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.016, 'high_cutoff_hz': 250.0}\n- **Filter type**: Chebyshev\n- **Re-reference**: linked mastoids\n- **Downsampled to**: 250.0 Hz\n- **Epoch window**: [-200.0, 800.0]\n- **Notes**: For offline ERP analysis: downsampled to 250 Hz, lowpass filtered below 49 Hz using Chebyshev filter (passbands/stopbands: 42/49 Hz). For online classification: downsampled to 100 Hz, no software filter applied. Baseline correction using -200 ms prestimulus interval.\n## Signal Processing\n- **Classifiers**: LDA, SLDA\n- **Feature extraction**: ERP components, P300, P3\n- **Spatial filters**: shrinkage covariance\n## Cross-Validation\n- **Method**: calibration-test split\n- **Evaluation type**: within_session\n## Performance (Original Study)\n- **Accuracy**: 92.0%\n- **Hex O Spell Accuracy**: 88.0\n- **Cake Speller Accuracy**: 90.0\n- **Center Speller Accuracy**: 97.0\n- **Communication Rate Symbols Per Min**: 2.3\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, P300\n## Documentation\n- **DOI**: 10.1088/1741-2560/8/6/066003\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: M S Treder, N M Schmidt, B Blankertz\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Laboratory\n- **Country**: Germany\n- **Repository**: GitHub\n- **Data URL**: https://github.com/bbci/bbci_public/blob/master/doc/index.markdown\n- **Publication year**: 2011\n- **Keywords**: P300, ERP, BCI, speller, covert attention, feature attention, gaze-independent\n## References\nTreder, M. S., Schmidt, N. M., & Blankertz, B. (2011). Gaze-independent brain-computer interfaces based on covert attention and feature attention. Journal of Neural Engineering, 8(6), 066003. https://doi.org/10.1088/1741-2560/8/6/066003\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":3308105910,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000198","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:08:59.518430+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-24T01:00:02Z"},"total_files":26,"computed_title":"BNCI 2015-008 Center Speller P300 dataset","nchans_counts":[{"val":63,"count":26}],"sfreq_counts":[{"val":250.0,"count":26}],"stats_computed_at":"2026-05-01T13:49:34.645584+00:00","total_duration_s":69734.856,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"53132d7ef0153905","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"No few-shot example is a direct P300-speller BCI dataset, but the conventions for mapping task aim to Type are illustrated by: (1) the visual cognitive-control/attention example (\"EEG: DPX Cog Ctl Task in Acute Mild TBI\") labeled as Type=Attention, where the key construct is attentional/executive control rather than stimulus modality or button presses; (2) the auditory oddball dataset (\"Three-Stim Auditory Oddball...\") demonstrates that oddball-like target vs nontarget paradigms are typically categorized by the cognitive construct (often attention/perception) rather than by 'ERP/P300' as a Type label (since Type must be chosen from the catalog list). This guides labeling the P300 speller as Attention (covert attention to a target among flashes) rather than Perception or Motor.","metadata_analysis":"Key participant/pathology facts: (a) \"Clinical population: Healthy\" (despite also listing \"Health status\": \"patients\"); (b) \"Tags - Pathology: Healthy\".\nKey modality facts: (a) \"Stimulus modalities: visual\"; (b) \"Primary modality: visual\"; (c) \"Stimulus type: visual_flash\".\nKey task/type facts: (a) \"Study design: Two-stage visual speller using covert spatial attention\"; (b) \"Participants had to strictly fixate the center of the screen and covertly attend to the target symbol\"; (c) \"They were instructed to silently count the number of intensifications of the target symbol.\"","paper_abstract_analysis":"No useful paper information. (Only a DOI/citation is provided; no abstract text included in the metadata payload.)","evidence_alignment_check":"Pathology: Metadata says \"Clinical population: Healthy\" and also \"Tags - Pathology: Healthy\" but contains a potentially confusing line \"Health status\": \"patients\". Few-shot convention: when an explicit diagnosis/clinical group is named, use it; when not, treat as Healthy. Here, metadata explicitly states Healthy, so it ALIGNs with few-shot conventions; the word \"patients\" is treated as a metadata inconsistency and does not override the explicit \"Clinical population: Healthy\" fact.\nModality: Metadata explicitly says \"Stimulus modalities: visual\" / \"Primary modality: visual\" / \"Stimulus type: visual_flash\". Few-shot convention: modality is based on stimulus channel. This ALIGNs strongly.\nType: Metadata emphasizes \"covert spatial attention\" and \"covertly attend to the target symbol\" (target vs non-target flashes). Few-shot convention: oddball/target-detection and covert-attention paradigms are categorized by the construct (Attention vs Perception). This mostly ALIGNs; the only ambiguity is whether to call it Perception (visual discrimination) vs Attention (covert target attention). The explicit mention of covert attention makes Attention the stronger match.","decision_summary":"Top-2 Pathology candidates: (1) Healthy — supported by \"Clinical population: Healthy\" and \"Tags - Pathology: Healthy\"; (2) Unknown/Other — suggested only by the inconsistent phrase \"Health status\": \"patients\". Winner: Healthy (explicit clinical population statement outweighs generic/possibly erroneous 'patients').\nTop-2 Modality candidates: (1) Visual — supported by \"Stimulus modalities: visual\", \"Primary modality: visual\", and \"Stimulus type: visual_flash\"; (2) Multisensory/Other — no supporting evidence. Winner: Visual.\nTop-2 Type candidates: (1) Attention — supported by \"covert spatial attention\" and \"covertly attend to the target symbol\" and the instruction to count target intensifications; (2) Perception — plausible because it is visual target vs non-target processing (P300/ERP), but less directly supported than attention. Winner: Attention.\nConfidence justification: Pathology=0.8 (2 explicit 'Healthy' quotes, but one conflicting line 'patients'); Modality=0.9 (3 explicit modality quotes); Type=0.8 (3 explicit attention-focused quotes + consistent few-shot convention mapping target-detection/covert-attention paradigms to Attention)."}},"canonical_name":null,"name_confidence":0.78,"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":"Treder2015_P300"}}