{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cae","dataset_id":"nm000217","associated_paper_doi":null,"authors":["Louis Korczowski","Martine Cederhout","Anton Andreev","Grégoire Cattan","Pedro Luiz Coelho Rodrigues","Violette Gautheret","Marco Congedo"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":44,"ages":[23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23],"age_min":23,"age_max":23,"age_mean":23.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000217","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"d5fc9812afa5b6d2d36b466a5518a91cb9ea44c0bfae201c2900afa9a7d68b9d","license":"CC-BY-4.0","n_contributing_labs":null,"name":"P300 dataset BI2015b from a \"Brain Invaders\" experiment","readme":"# P300 dataset BI2015b from a \"Brain Invaders\" experiment\nP300 dataset BI2015b from a \"Brain Invaders\" experiment.\n## Dataset Overview\n- **Code**: BrainInvaders2015b\n- **Paradigm**: p300\n- **DOI**: https://doi.org/10.5281/zenodo.3267307\n- **Subjects**: 44\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1] s\n- **Runs per session**: 4\n- **File format**: mat and csv\n- **Contributing labs**: GIPSA-lab\n## Acquisition\n- **Sampling rate**: 512.0 Hz\n- **Number of channels**: 32\n- **Channel types**: eeg=32\n- **Channel names**: AFz, C3, C4, CP1, CP2, CP5, CP6, Cz, F3, F4, F7, F8, FC1, FC2, FC5, FC6, Fp1, Fp2, O1, O2, Oz, P3, P4, P7, P8, PO10, PO7, PO8, PO9, Pz, T7, T8\n- **Montage**: 10-10\n- **Hardware**: g.USBamp (g.tec, Schiedlberg, Austria)\n- **Software**: OpenVibe\n- **Reference**: right earlobe\n- **Ground**: Fz\n- **Sensor type**: wet Silver/Silver Chloride electrodes\n- **Line frequency**: 50.0 Hz\n- **Online filters**: no digital filter applied\n- **Cap manufacturer**: g.tec\n- **Cap model**: g.GAMMAcap\n- **Electrode type**: wet\n- **Electrode material**: Silver/Silver Chloride\n## Participants\n- **Number of subjects**: 44\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=23.7, std=3.19\n- **Gender distribution**: male=36, female=14\n- **BCI experience**: mostly students and young researchers\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Study design**: Three game sessions with different flash durations (110ms, 80ms, 50ms), with resting state and eyes closed conditions recorded before and after. Subjects were instructed to limit eye blinks, head movements and face muscular contractions.\n- **Feedback type**: visual (game interface with reward screen)\n- **Stimulus type**: visual flash\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Mode**: online\n- **Training/test split**: False\n- **Instructions**: Players had up to eight attempts to destroy the target symbol per level. Target symbol identification using oddball paradigm with 36 aliens flashing in pseudo-random groups of six symbols.\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**: 1\n- **Number of repetitions**: 12\n## Data Structure\n- **Trials**: variable per subject (up to 8 attempts per level, 9 levels per session, 3 sessions)\n- **Blocks per session**: 9\n- **Trials context**: per session (9 levels per session, 3 sessions with different flash durations)\n## Preprocessing\n- **Data state**: raw EEG with synchronized hardware tagging via USB digital-to-analog converter (reduced jitter compared to software tagging)\n- **Preprocessing applied**: False\n- **Notes**: Data were stored with no digital filter applied. USB digital-to-analog converter connected to the g.USBamp trigger channel was used to synchronize experimental tags produced by Brain Invaders with EEG signals to reduce jitter.\n## Signal Processing\n- **Classifiers**: Riemannian Minimum Distance to Mean (RMDM), xDAWN, Riemannian MDM\n- **Feature extraction**: Covariance/Riemannian, xDAWN\n## Cross-Validation\n- **Evaluation type**: cross_session\n## Performance (Original Study)\n- **Note**: Real-time adaptive classifier used during experiment, performance variable per subject\n## BCI Application\n- **Applications**: gaming\n- **Environment**: small room with a surface of four meters square, containing a 24' screen\n- **Online feedback**: True\n## Tags\n- **Pathology**: Healthy\n- **Modality**: Visual\n- **Type**: Perception\n## Documentation\n- **Description**: EEG recordings of 50 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named Brain Invaders. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. Three conditions: flash duration 50ms, 80ms or 110ms.\n- **DOI**: 10.5281/zenodo.3266930\n- **Associated paper DOI**: hal-02172347\n- **License**: CC-BY-4.0\n- **Investigators**: Louis Korczowski, Martine Cederhout, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Violette Gautheret, Marco Congedo\n- **Senior author**: Marco Congedo\n- **Institution**: GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP\n- **Address**: GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402, France\n- **Country**: France\n- **Repository**: Zenodo\n- **Data URL**: https://doi.org/10.5281/zenodo.3266930\n- **Publication year**: 2019\n- **Ethics approval**: Ethical Committee of the University of Grenoble Alpes (Comité d'Ethique pour la Recherche Non-Interventionnelle)\n- **Keywords**: Electroencephalography (EEG), P300, Brain-Computer Interface, Experiment\n## Abstract\nWe describe the experimental procedures for an experiment dataset that we have made publicly available at https://doi.org/10.5281/zenodo.3266930 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 50 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named Brain Invaders. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes with three conditions: flash duration 50ms, 80ms or 110ms. The experiment took place at GIPSA-lab, Grenoble, France, in 2015.\n## Methodology\nThe experiment consisted of three game sessions of Brain Invaders of 9 levels each with different flash duration (110ms, 80ms, 50ms). Before and after the three game sessions, around one minute of resting state and eyes closed conditions were recorded. The interface is composed of 36 aliens. A repetition is composed of 12 flashes of pseudo-random groups of six symbols chosen in such a way that after each repetition each symbol has flashed exactly two times. The ratio of Target versus non-Target is one-to-five. During the experiment, the output of a real-time adaptive Riemannian Minimum Distance to Mean (RMDM) classifier was used for assessing the participants' command. This scheme allows a calibration-free classifier.\n## References\nKorczowski, L., Cederhout, M., Andreev, A., Cattan, G., Rodrigues, P. L. C., Gautheret, V., & Congedo, M. (2019). Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (BI2015b) https://hal.archives-ouvertes.fr/hal-02172347\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":4640665576,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000217","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:07.982153+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-24T05:59:13Z"},"total_files":176,"computed_title":"P300 dataset BI2015b from a \"Brain Invaders\" experiment","nchans_counts":[{"val":32,"count":176}],"sfreq_counts":[{"val":512.0,"count":176}],"stats_computed_at":"2026-05-01T13:49:34.645805+00:00","total_duration_s":93888.03125,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"e10be3d926910117","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":"Most similar few-shot paradigms are the oddball-style datasets: (1) “Cross-modal Oddball Task” (Parkinson’s) and (2) “Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI”. These examples establish the convention that oddball/target-vs-nontarget paradigms are labeled by (a) the recruited clinical population for Pathology (PD/TBI there; not applicable here) and (b) the stimulus channel for Modality (multisensory/auditory there; visual here). For Type, those few-shots are not clean ‘attention’ exemplars because they are framed around clinical biomarkers/decision/cognitive dysfunction; however, they confirm that oddball target detection is a distinct paradigm family (P300/oddball), where the construct is best captured as attentional target selection rather than pure sensory perception.","metadata_analysis":"Key population facts: (1) “Clinical population: Healthy” and (2) “BCI experience: mostly students and young researchers” (i.e., not a disorder-recruited cohort). There is also an internal inconsistency: “Health status: patients” conflicts with “Clinical population: Healthy”; per instruction, explicit clinical population recruitment wording is prioritized.\n\nKey modality/task facts: (1) “Stimulus type: visual flash” and (2) “Stimulus modalities: visual” / “Primary modality: visual”. Paradigm: “Paradigm: p300” and “Target symbol identification using oddball paradigm with 36 aliens flashing... to elicit the P300 response.”","paper_abstract_analysis":"The included abstract reiterates the paradigm and modality: “EEG recordings of 50 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame... symbols ... flashed pseudo-randomly to elicit the P300 response.” No additional clinical-population information beyond reinforcing a non-clinical BCI dataset.","evidence_alignment_check":"Pathology — metadata says: “Clinical population: Healthy” (also “mostly students and young researchers”). Few-shot pattern suggests: in oddball datasets, Pathology follows recruitment (e.g., PD/TBI when explicitly recruited). Alignment: ALIGNS (this dataset is not presented as a clinical cohort despite the stray “Health status: patients”).\n\nModality — metadata says: “Stimulus type: visual flash”, “Stimulus modalities: visual”, “Primary modality: visual”. Few-shot pattern suggests: modality tracks stimulus channel (auditory in auditory oddball; multisensory in cross-modal oddball). Alignment: ALIGNS → Visual.\n\nType — metadata says: “Target symbol identification using oddball paradigm... to elicit the P300 response” (target vs nontarget) implying attentional target selection. Few-shot pattern suggests: oddball paradigms are handled as a recognizable family; when not primarily clinical/intervention, the cognitive construct is typically attentional target detection rather than broad clinical focus. Alignment: generally ALIGNS with choosing Attention over Perception, because the core operation is selecting an infrequent target among frequent non-target flashes (P300/oddball attention).","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Healthy (selected) — Evidence: “Clinical population: Healthy”; “mostly students and young researchers”; abstract describes a BCI game dataset without disease recruitment.\n2) Unknown/Other (runner-up) — Evidence: conflicting line “Health status: patients” could imply miscoding, but no disorder is named.\nAlignment status: mostly aligned; conflict resolved in favor of explicit “Clinical population: Healthy”.\n\nModality:\n1) Visual (selected) — Evidence: “Stimulus type: visual flash”; “Stimulus modalities: visual”; “visual P300 Brain-Computer Interface”.\n2) Multisensory (runner-up) — Only weakly plausible because there is “visual (game interface with reward screen)” and no other sensory stimulus is described.\nAlignment status: aligned.\n\nType:\n1) Attention (selected) — Evidence: “Target symbol identification using oddball paradigm” and explicit Target vs NonTarget events (“Events: Target=2, NonTarget=1”) to elicit P300, which operationalizes attentional target detection.\n2) Perception (runner-up) — Visual discrimination is involved, but the defining construct of P300 oddball is attentional selection of rare targets rather than sensory encoding alone.\nAlignment status: aligned with oddball/P300 conventions; not primarily Clinical/Intervention because the cohort is not clinical."}},"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":"Korczowski2015_P300_BI2015b"}}