{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a337e","dataset_id":"ds004745","associated_paper_doi":null,"authors":["Velu Prabhakar Kumaravel","Victor Kartsch","Simone Benatti","Giorgio Vallortigara","Elisabetta Farella","Marco Buiatti"],"bids_version":"1.8.0","contact_info":["Velu Prabhakar Kumaravel","Velu Prabhakar Kumaravel","Velu Prabhakar Kumaravel"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004745.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":6,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004745","osf_url":null,"github_url":null,"paper_url":null},"funding":["NeuroSoNew ERC PoC Grant 842243"],"ingestion_fingerprint":"0df58a1d9d144bba2ea693fb48053d855346f931bf4a8d07a960a97b25564882","license":"CC0","n_contributing_labs":null,"name":"8-Channel SSVEP EEG Dataset with Artifact Trials","readme":"Dataset consists of 6 participants who performed SSVEP tasks. We designed stimulations at 3 different frequencies (2 Hz, 4 Hz, 8 Hz). Each participant attended to 3 trials for each frequency in which they remained static as much as possible to avoid artifacts. They attended to 3 trials for each frequency in which they made voluntary head/neck and eye movements. Please refer to Kumaravel et al., (IEEE EMBC 2021) for further details.","recording_modality":["eeg"],"senior_author":"Marco Buiatti","sessions":[],"size_bytes":253839277,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["unnamed"],"timestamps":{"digested_at":"2026-04-22T12:26:50.394363+00:00","dataset_created_at":"2023-09-08T14:10:18.127Z","dataset_modified_at":"2023-09-08T14:15:03.000Z"},"total_files":6,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004745","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-unnamed_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"69f1e052856a991a","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:46:38.631105+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Other"],"confidence":{"pathology":0.7,"modality":0.7,"type":0.6},"reasoning":{"few_shot_analysis":"Most similar few-shot example by study purpose is the 'Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts...' example, which is explicitly about generating/handling artifacts and is labeled with a non-cognitive-purpose Type (it uses an artifact/methods-oriented label rather than a classic cognitive task label). This guides the convention that when the dataset manipulation is primarily about artifact generation (motion/eye), Type can be set to a non-specific category (e.g., Other) rather than Perception/Attention. For stimulus modality, the schizophrenia visual discrimination and motor imagery examples show the convention that visual screen-based paradigms are labeled Visual for Modality; SSVEP paradigms are also visually driven (flicker/periodic stimulation).","metadata_analysis":"Key quoted facts from the provided README: (1) Task/stimulus structure: \"performed SSVEP tasks\" and \"stimulations at 3 different frequencies (2 Hz, 4 Hz, 8 Hz)\" which strongly implies visual periodic stimulation typical of SSVEP. (2) Primary manipulation appears artifact-related: \"remained static as much as possible to avoid artifacts\" versus \"made voluntary head/neck and eye movements.\" (3) No clinical recruitment is mentioned: \"Dataset consists of 6 participants\" with no diagnosis/condition stated.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says participants are simply \"6 participants\" with no disorder mentioned (supports Healthy). Few-shot patterns suggest Healthy when no clinical population is described. ALIGN.\nModality: Metadata says \"SSVEP\" and \"stimulations at ... frequencies\" (typical visual flicker stimulation). Few-shot patterns map visual stimulus tasks to Modality=Visual. ALIGN.\nType: Metadata emphasizes artifact conditions (\"avoid artifacts\"; \"voluntary head/neck and eye movements\"), suggesting a methods/artifact-focused dataset rather than primarily studying perception or attention. Few-shot convention from the artifact-focused phantom dataset supports using a non-cognitive-specific Type. ALIGN (though less explicit because the README does not state the research aim directly).","decision_summary":"Pathology top-2: (1) Healthy — supported by \"Dataset consists of 6 participants\" with no diagnosis stated; no patient/control groups mentioned. (2) Unknown — possible because health status is not explicitly stated. Winner: Healthy. Evidence alignment: aligned with few-shot convention for non-clinical samples. Confidence 0.7 due to lack of an explicit 'healthy' statement.\nModality top-2: (1) Visual — supported by \"performed SSVEP tasks\" and \"stimulations at 3 different frequencies (2 Hz, 4 Hz, 8 Hz)\" (SSVEP is classically visually evoked by flicker). (2) Unknown/Other — if SSVEP modality were not specified (but it is strongly implied). Winner: Visual. Confidence 0.7 (strong contextual inference from SSVEP + frequency-tagged stimulation, but not explicitly saying 'visual flicker').\nType top-2: (1) Other — supported by artifact/manipulation emphasis: \"remained static ... to avoid artifacts\" vs \"made voluntary head/neck and eye movements\" suggesting data for artifact characterization rather than a cognitive construct. (2) Perception/Attention — plausible because SSVEP tasks often involve attending to a stimulus. Winner: Other. Confidence 0.6 because the README does not explicitly state the scientific aim (artifact-methods vs attention), but the described conditions center on artifact generation/avoidance."}},"nemar_citation_count":0,"computed_title":"8-Channel SSVEP EEG Dataset with Artifact Trials","nchans_counts":[{"val":8,"count":6}],"sfreq_counts":[{"val":1000.0,"count":6}],"stats_computed_at":"2026-04-22T23:16:00.308231+00:00","total_duration_s":6320.378,"author_year":"Kumaravel2023","canonical_name":null}}