{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cca","dataset_id":"nm000266","associated_paper_doi":null,"authors":["Jan Sosulski","David Hübner","Aaron Klein","Michael Tangermann"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.48550/arXiv.2109.06011","datatypes":["eeg"],"demographics":{"subjects_count":13,"ages":[22,22,22,22,22,22,22,22,22,22,22,22,22],"age_min":22,"age_max":22,"age_mean":22.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/nm000266","osf_url":null,"github_url":null,"paper_url":null},"funding":["Cluster of Excellence BrainLinks-BrainTools funded by the German Research Foundation (DFG) [grant number EXC 1086]","DFG project SuitAble [grant number TA 1258/1-1]","state of Baden-Württemberg, Germany, through bwHPC and the German Research Foundation (DFG) [grant number INST 39/963-1 FUGG]"],"ingestion_fingerprint":"3632aa2b8083fdaa72d49e00a3dd0569e36ca12610145d43208fbd163119d74a","license":"CC-BY-SA-4.0","n_contributing_labs":null,"name":"Sosulski et al. 2019 — Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints","readme":"Sosulski2019\n============\nP300 dataset from initial spot study.\nDataset Overview\n----------------\n  Code: Sosulski2019\n  Paradigm: p300\n  DOI: 10.6094/UNIFR/154576\n  Subjects: 13\n  Sessions per subject: 80\n  Events: Target=21, NonTarget=1\n  Trial interval: [-0.2, 1] s\n  File format: brainvision\nAcquisition\n-----------\n  Sampling rate: 1000.0 Hz\n  Number of channels: 31\n  Channel types: eeg=31, eog=1, misc=5\n  Channel names: C3, C4, CP1, CP2, CP5, CP6, Cz, EOGvu, F10, F3, F4, F7, F8, F9, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, O1, O2, P10, P3, P4, P7, P8, P9, Pz, T7, T8, x_EMGl, x_GSR, x_Optic, x_Pulse, x_Respi\n  Montage: standard_1020\n  Hardware: BrainProducts BrainAmp DC\n  Reference: nose\n  Sensor type: passive Ag/AgCl\n  Line frequency: 50.0 Hz\n  Auxiliary channels: EOG (1 ch, vertical)\nParticipants\n------------\n  Number of subjects: 13\n  Health status: healthy\n  Age: mean=22.7, std=1.64, min=20, max=26\n  Gender distribution: male=5, female=8\n  Species: human\nExperimental Protocol\n---------------------\n  Paradigm: p300\n  Number of classes: 2\n  Class labels: Target, NonTarget\n  Study design: Subjects focused attention on target tones (1000 Hz) and ignored non-target tones (500 Hz) presented via speaker at 65 cm distance. One trial consisted of 15 target and 75 non-target stimuli in pseudo-random order with at least two non-target tones between target tones. The experiment was split into optimization and validation parts.\n  Stimulus type: oddball\n  Stimulus modalities: auditory\n  Primary modality: auditory\n  Synchronicity: synchronous\n  Mode: online\n  Instructions: Focus on the target tones (1000 Hz) and ignore the non-target tones (500 Hz). Refrain from blinking and movement as much as possible.\n  Stimulus presentation: target_tone_hz=1000, non_target_tone_hz=500, tone_duration_ms=40, distance_cm=65\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\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\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: p300\n  Number of targets: 1\nData Structure\n--------------\n  Trials: Variable: optimization part used time-limited trials (20 minutes per strategy), validation part used 20 trials per SOA\n  Trials per class: target=13 per trial (after preprocessing, originally 15), non_target=65 per trial (after preprocessing, originally 75)\n  Trials context: Each trial consisted of 90 stimuli (15 target, 75 non-target). After preprocessing (removing first and last 6 epochs), 78 data points available per trial: 13 target and 65 non-target epochs.\nSignal Processing\n-----------------\n  Classifiers: rLDA, Shrinkage LDA\n  Feature extraction: Mean amplitude in time intervals\n  Frequency bands: analyzed=[1.5, 40.0] Hz\nCross-Validation\n----------------\n  Method: 13-fold\n  Folds: 13\n  Evaluation type: within_session\nPerformance (Original Study)\n----------------------------\n  Auc: 0.701\n  Mean Auc Ucb: 0.701\n  Mean Auc Rand: 0.704\n  Mean Auc P300 Ucb: 0.67\n  Mean Auc P300 Rand: 0.681\n  Mean Auc Fixed60: 0.517\nBCI Application\n---------------\n  Applications: communication\n  Online feedback: False\nTags\n----\n  Pathology: Healthy\n  Modality: Auditory\n  Type: Research\nDocumentation\n-------------\n  Description: Auditory oddball ERP dataset from 13 healthy subjects. Two sinusoidal tones (target 1000 Hz, non-target 500 Hz) presented at various stimulus onset asynchronies (SOAs, 60-600 ms). 31-channel EEG recorded at 1000 Hz with BrainProducts BrainAmp DC. Raw BrainVision format data.\n  DOI: 10.48550/arXiv.2109.06011\n  License: CC-BY-SA-4.0\n  Investigators: Jan Sosulski, David Hübner, Aaron Klein, Michael Tangermann\n  Senior author: Michael Tangermann\n  Contact: jan.sosulski@blbt.uni-freiburg.de; davhuebn@gmail.com; kleinaa@cs.uni-freiburg.de; michael.tangermann@donders.ru.nl\n  Institution: University of Freiburg\n  Country: DE\n  Repository: FreiDok\n  Data URL: https://freidok.uni-freiburg.de/data/154576\n  Publication year: 2021\n  Funding: Cluster of Excellence BrainLinks-BrainTools funded by the German Research Foundation (DFG) [grant number EXC 1086]; DFG project SuitAble [grant number TA 1258/1-1]; state of Baden-Württemberg, Germany, through bwHPC and the German Research Foundation (DFG) [grant number INST 39/963-1 FUGG]\n  Ethics approval: Approved by the ethics committee of the university medical center of Freiburg\n  Acknowledgements: Experiments were performed according to the Declaration of Helsinki.\n  Keywords: Bayesian optimization, individual experimental parameters, brain-computer interfaces, learning from small data, auditory event-related potentials, closed-loop parameter optimization\nAbstract\n--------\nThe decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experimental settings of the BCI protocol typically are chosen according to the literature. The decoding performance directly depends on the choice of parameters, as they influence the elicited brain signals and optimal parameters are subject-dependent. Thus a fast and automated selection procedure for experimental parameters could greatly improve the usability of BCIs. We evaluate a standalone random search and a combined Bayesian optimization with random search into a closed-loop auditory event-related potential protocol. We aimed at finding the individually best stimulation speed—also known as stimulus onset asynchrony (SOA)—that maximizes the classification performance of a regularized linear discriminant analysis.\nMethodology\n-----------\nThe experiment was divided into two parts: (1) Optimization part: four strategies (AUC-ucb, AUC-rand, P300-ucb, P300-rand) each allocated 20 minutes to find optimal SOA. Strategies alternated to minimize non-stationarity effects. (2) Validation part: evaluated SOAs from each optimization strategy plus fixed 60ms SOA using 20 trials each (in blocks of 5 trials). Features were mean amplitudes in 5 time intervals ([100, 170], [171, 230], [231, 300], [301, 410], [411, 500] ms) across 31 channels (155 dimensions total). Classification used rLDA with automatic shrinkage regularization and 13-fold cross-validation on single trials.\nReferences\n----------\nSosulski, J., Tangermann, M.: Electroencephalogram signals recorded from 13 healthy subjects during an auditory oddball paradigm under different stimulus onset asynchrony conditions. Dataset. DOI: 10.6094/UNIFR/154576\nSosulski, J., Tangermann, M.: Spatial filters for auditory evoked potentials transfer between different experimental conditions. Graz BCI Conference. 2019.\nSosulski, J., Hübner, D., Klein, A., Tangermann, M.:  Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints. arXiv preprint. 2021.\nNotes\n.. versionadded:: 0.4.5\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":["0soa175","0soa235","0soa306","0soa359","0soa447","0soa506","0soa508","0soa513","0soa60","10soa177","10soa220","10soa226","10soa286","10soa418","10soa60","10soa600","11soa177","11soa193","11soa220","11soa226","11soa418","11soa60","11soa600","12soa177","12soa220","12soa226","12soa286","12soa418","12soa60","12soa600","13soa177","13soa220","13soa226","13soa286","13soa418","13soa60","13soa600","14soa177","14soa220","14soa226","14soa286","14soa418","14soa60","14soa600","15soa161","15soa180","15soa193","15soa194","15soa208","15soa256","15soa273","15soa286","15soa296","15soa402","15soa457","15soa60","15soa600","16soa161","16soa180","16soa193","16soa194","16soa208","16soa256","16soa273","16soa286","16soa296","16soa402","16soa457","16soa60","16soa600","17soa161","17soa180","17soa193","17soa194","17soa208","17soa256","17soa273","17soa296","17soa402","17soa457","17soa506","17soa60","17soa600","18soa161","18soa180","18soa193","18soa194","18soa208","18soa256","18soa273","18soa296","18soa402","18soa457","18soa506","18soa60","18soa600","19soa161","19soa180","19soa193","19soa194","19soa208","19soa256","19soa273","19soa296","19soa402","19soa457","19soa506","19soa60","19soa600","1soa175","1soa235","1soa306","1soa359","1soa447","1soa508","1soa513","1soa60","20soa175","20soa306","20soa359","20soa447","20soa506","20soa508","20soa513","20soa518","20soa60","21soa175","21soa286","21soa306","21soa359","21soa447","21soa508","21soa513","21soa518","21soa60","22soa175","22soa306","22soa359","22soa447","22soa506","22soa508","22soa513","22soa518","22soa60","23soa175","23soa306","23soa359","23soa447","23soa508","23soa513","23soa518","23soa60","24soa175","24soa306","24soa359","24soa447","24soa508","24soa513","24soa518","24soa60","25soa179","25soa200","25soa220","25soa226","25soa235","25soa325","25soa384","25soa400","25soa425","25soa446","25soa474","25soa60","25soa600","26soa179","26soa200","26soa220","26soa226","26soa235","26soa325","26soa384","26soa400","26soa425","26soa446","26soa474","26soa60","26soa600","27soa179","27soa200","27soa220","27soa226","27soa235","27soa325","27soa384","27soa400","27soa425","27soa446","27soa474","27soa60","27soa600","28soa179","28soa193","28soa200","28soa220","28soa226","28soa235","28soa325","28soa384","28soa400","28soa425","28soa446","28soa474","28soa600","29soa179","29soa193","29soa200","29soa220","29soa226","29soa235","29soa325","29soa384","29soa400","29soa425","29soa446","29soa474","29soa600","2soa175","2soa235","2soa306","2soa359","2soa447","2soa508","2soa513","2soa60","30soa177","30soa193","30soa220","30soa418","30soa596","30soa60","30soa600","31soa177","31soa193","31soa220","31soa418","31soa596","31soa60","31soa600","32soa177","32soa220","32soa286","32soa418","32soa596","32soa60","32soa600","33soa177","33soa193","33soa220","33soa418","33soa596","33soa60","33soa600","34soa177","34soa220","34soa286","34soa418","34soa596","34soa60","34soa600","35soa161","35soa180","35soa193","35soa194","35soa208","35soa226","35soa256","35soa273","35soa286","35soa296","35soa402","35soa457","35soa600","36soa161","36soa180","36soa193","36soa194","36soa208","36soa226","36soa256","36soa273","36soa286","36soa296","36soa402","36soa457","36soa600","37soa161","37soa180","37soa193","37soa194","37soa208","37soa226","37soa256","37soa273","37soa286","37soa296","37soa402","37soa457","37soa600","38soa161","38soa180","38soa193","38soa194","38soa208","38soa226","38soa256","38soa273","38soa286","38soa296","38soa402","38soa457","38soa600","39soa161","39soa180","39soa193","39soa194","39soa208","39soa226","39soa256","39soa273","39soa296","39soa402","39soa457","39soa506","39soa600","3soa175","3soa235","3soa306","3soa359","3soa447","3soa508","3soa513","3soa60","40soa175","40soa306","40soa359","40soa447","40soa506","40soa508","40soa513","40soa60","41soa175","41soa306","41soa359","41soa447","41soa506","41soa508","41soa513","41soa60","42soa175","42soa306","42soa359","42soa447","42soa506","42soa508","42soa513","42soa60","43soa175","43soa286","43soa306","43soa359","43soa447","43soa508","43soa513","43soa60","44soa175","44soa306","44soa359","44soa447","44soa506","44soa508","44soa513","44soa60","45soa179","45soa200","45soa220","45soa226","45soa325","45soa384","45soa400","45soa425","45soa446","45soa474","45soa518","45soa60","45soa600","46soa179","46soa200","46soa220","46soa226","46soa325","46soa384","46soa400","46soa425","46soa446","46soa474","46soa518","46soa60","46soa600","47soa179","47soa200","47soa220","47soa226","47soa325","47soa384","47soa400","47soa425","47soa446","47soa474","47soa518","47soa60","47soa600","48soa179","48soa200","48soa220","48soa226","48soa325","48soa384","48soa400","48soa425","48soa446","48soa474","48soa518","48soa60","48soa600","49soa179","49soa200","49soa220","49soa226","49soa325","49soa384","49soa400","49soa425","49soa446","49soa474","49soa518","49soa60","49soa600","4soa175","4soa235","4soa306","4soa359","4soa447","4soa508","4soa513","4soa60","50soa177","50soa193","50soa220","50soa235","50soa418","50soa60","50soa600","51soa177","51soa193","51soa220","51soa235","51soa418","51soa60","51soa600","52soa177","52soa193","52soa220","52soa235","52soa418","52soa60","52soa600","53soa177","53soa193","53soa220","53soa235","53soa418","53soa60","53soa600","54soa177","54soa220","54soa235","54soa286","54soa418","54soa60","54soa600","55soa161","55soa180","55soa193","55soa194","55soa208","55soa256","55soa273","55soa296","55soa402","55soa457","55soa596","55soa600","56soa161","56soa180","56soa193","56soa194","56soa208","56soa256","56soa273","56soa286","56soa296","56soa402","56soa457","56soa596","56soa600","57soa161","57soa180","57soa193","57soa194","57soa208","57soa256","57soa273","57soa286","57soa296","57soa402","57soa457","57soa596","57soa600","58soa161","58soa180","58soa193","58soa194","58soa208","58soa256","58soa273","58soa286","58soa296","58soa402","58soa457","58soa596","58soa600","59soa161","59soa180","59soa193","59soa194","59soa208","59soa256","59soa273","59soa286","59soa296","59soa402","59soa457","59soa596","59soa600","5soa179","5soa200","5soa220","5soa226","5soa325","5soa384","5soa400","5soa425","5soa446","5soa474","5soa596","5soa60","5soa600","60soa175","60soa226","60soa286","60soa306","60soa359","60soa447","60soa508","60soa513","60soa60","61soa175","61soa226","61soa306","61soa359","61soa447","61soa506","61soa508","61soa513","61soa60","62soa175","62soa226","62soa306","62soa359","62soa447","62soa506","62soa508","62soa513","62soa60","63soa175","63soa226","63soa306","63soa359","63soa447","63soa506","63soa508","63soa513","63soa60","64soa175","64soa226","64soa306","64soa359","64soa447","64soa506","64soa508","64soa513","64soa60","65soa179","65soa200","65soa220","65soa226","65soa325","65soa384","65soa400","65soa425","65soa446","65soa474","65soa506","65soa60","65soa600","66soa179","66soa200","66soa220","66soa226","66soa325","66soa384","66soa400","66soa425","66soa446","66soa474","66soa506","66soa60","66soa600","67soa179","67soa200","67soa220","67soa226","67soa325","67soa384","67soa400","67soa425","67soa446","67soa474","67soa60","67soa600","68soa179","68soa200","68soa220","68soa226","68soa325","68soa384","68soa400","68soa425","68soa446","68soa474","68soa60","68soa600","69soa179","69soa200","69soa220","69soa226","69soa325","69soa384","69soa400","69soa425","69soa446","69soa474","69soa60","69soa600","6soa179","6soa193","6soa200","6soa220","6soa226","6soa325","6soa384","6soa400","6soa425","6soa446","6soa474","6soa596","6soa600","70soa177","70soa220","70soa418","70soa518","70soa60","70soa600","71soa177","71soa220","71soa418","71soa518","71soa60","71soa600","72soa177","72soa193","72soa220","72soa418","72soa518","72soa60","72soa600","73soa177","73soa193","73soa220","73soa418","73soa518","73soa60","73soa600","74soa177","74soa193","74soa220","74soa418","74soa518","74soa60","74soa600","75soa161","75soa180","75soa193","75soa194","75soa208","75soa235","75soa256","75soa273","75soa296","75soa402","75soa457","75soa600","76soa161","76soa180","76soa193","76soa194","76soa208","76soa235","76soa256","76soa273","76soa296","76soa402","76soa457","76soa506","76soa600","77soa161","77soa180","77soa193","77soa194","77soa208","77soa235","77soa256","77soa273","77soa296","77soa402","77soa457","77soa600","78soa161","78soa180","78soa193","78soa194","78soa208","78soa235","78soa256","78soa273","78soa296","78soa402","78soa457","78soa506","78soa600","79soa161","79soa180","79soa193","79soa194","79soa208","79soa235","79soa256","79soa273","79soa296","79soa402","79soa457","79soa506","79soa600","7soa179","7soa193","7soa200","7soa220","7soa226","7soa325","7soa384","7soa400","7soa425","7soa446","7soa474","7soa596","7soa600","80soa596","81soa596","82soa596","83soa596","84soa596","85soa226","86soa226","87soa226","88soa226","89soa226","8soa179","8soa193","8soa200","8soa220","8soa226","8soa325","8soa384","8soa400","8soa425","8soa446","8soa474","8soa596","8soa600","90soa60","91soa60","92soa60","93soa60","94soa60","95soa518","96soa518","97soa518","98soa518","99soa518","9soa179","9soa193","9soa200","9soa220","9soa226","9soa325","9soa384","9soa400","9soa425","9soa446","9soa474","9soa596","9soa600"],"size_bytes":3944124637,"source":"nemar","storage":{"backend":"s3","base":"s3://openneuro.org/nm000266","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-22T12:52:17.807795+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":1060,"computed_title":"Sosulski et al. 2019 — Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints","nchans_counts":[{"val":37,"count":1060}],"sfreq_counts":[{"val":1000.0,"count":1060}],"stats_computed_at":"2026-04-22T23:16:00.314338+00:00","total_duration_s":35256.94,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"c5fc26a56fee95ae","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":0.75},"reasoning":{"few_shot_analysis":"Most similar few-shot paradigms are the oddball/P300-style datasets: (1) the Parkinson's “Cross-modal Oddball Task” example uses an oddball paradigm with pre-cues and a go cue; it maps oddball stimuli to the relevant stimulus modalities (visual+auditory) and sets Type to “Clinical/Intervention” because the recruited population is Parkinson’s. (2) the TBI “Three-Stim Auditory Oddball” example shows that an auditory oddball paradigm should be labeled with Modality “Auditory” (stimulus-driven), while Pathology reflects recruitment (TBI there, Healthy here). These examples guide the convention that oddball/P300 datasets are categorized by the stimulus channel (auditory tones) and by whether the primary aim is clinical vs cognitive. Since Sosulski2019 is explicitly healthy and is a classic target-vs-nontarget attention paradigm, Type should align more with an attention/perception construct rather than Clinical/Intervention.","metadata_analysis":"Key facts from the dataset metadata/readme:\n- Population: \"Health status: healthy\" and \"Auditory oddball ERP dataset from 13 healthy subjects.\"\n- Stimulus modality/paradigm: \"Subjects focused attention on target tones (1000 Hz) and ignored non-target tones (500 Hz)\" and \"Stimulus type: oddball\" with \"Stimulus modalities: auditory\" / \"Primary modality: auditory\".\n- Purpose/setting: \"P300 dataset\" and \"BCI Application ... Applications: communication\"; also described as an \"auditory event-related potential protocol\" optimizing SOA for classification performance.","paper_abstract_analysis":"The included abstract emphasizes optimizing stimulation speed (SOA) in an \"auditory event-related potential protocol\" to improve decoding performance for BCI usability, consistent with an auditory P300/oddball attention paradigm rather than a clinical study.","evidence_alignment_check":"Pathology:\n- Metadata says: \"Health status: healthy\" / \"13 healthy subjects\".\n- Few-shot pattern suggests: pathology should reflect recruited diagnosis (e.g., PD->Parkinson's; TBI->TBI). For healthy cohorts, label Healthy.\n- ALIGN: both metadata and few-shot conventions indicate “Healthy”.\n\nModality:\n- Metadata says: \"Stimulus modalities: auditory\" and target/non-target \"tones (1000 Hz)... (500 Hz)\".\n- Few-shot pattern suggests: oddball modality is determined by stimulus channel (e.g., TBI auditory oddball -> Auditory; cross-modal oddball -> Multisensory).\n- ALIGN: “Auditory” is directly supported.\n\nType:\n- Metadata says: \"Subjects focused attention on target tones ... and ignored non-target\" and \"Stimulus type: oddball\" / \"P300 dataset\".\n- Few-shot pattern suggests: oddball/P300 tasks can map to a cognitive construct label rather than mechanics; clinical cohorts may map to Clinical/Intervention when pathology is the main focus (PD example). For non-clinical oddball, the construct is typically selective attention to targets (and related ERP/BCI decoding).\n- ALIGN: dataset is not clinical; the strongest construct implied is selective attention. Some ambiguity remains with “Perception” (tone discrimination), but the instruction emphasis on focusing/ignoring supports “Attention” more strongly.","decision_summary":"Top-2 candidates per category with head-to-head choice:\n\nPathology:\n- Candidate 1: Healthy — Evidence: \"Health status: healthy\"; \"13 healthy subjects\".\n- Candidate 2: Unknown — would apply if recruitment health not stated, but it is explicit.\nWinner: Healthy. (Alignment: aligns with few-shot convention that pathology reflects recruitment.)\n\nModality:\n- Candidate 1: Auditory — Evidence: \"Stimulus modalities: auditory\"; target/non-target \"tones\" (1000/500 Hz).\n- Candidate 2: Multisensory — rejected because no concurrent visual/tactile stimulus is described; only tones via speaker.\nWinner: Auditory. (Alignment: aligns with few-shot oddball modality convention.)\n\nType:\n- Candidate 1: Attention — Evidence: \"focused attention on target tones\" and \"ignored non-target\" in an oddball/P300 paradigm.\n- Candidate 2: Perception — Evidence: tone target vs non-target could be construed as auditory detection/discrimination.\nWinner: Attention, because the task instruction explicitly frames selective attention (attend targets, ignore standards) as the central demand, typical of P300/oddball paradigms.\n\nConfidence justification:\n- Pathology high due to multiple explicit population quotes.\n- Modality high due to explicit \"Primary modality: auditory\" plus tone description.\n- Type moderate-high: supported by explicit attention instruction, but some plausible overlap with Perception."}},"canonical_name":null,"name_confidence":0.8,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Sosulski2019"}}