{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a345a","dataset_id":"ds006563","associated_paper_doi":null,"authors":["Klaus Gramann, Thomas Töllner, Hermann J. Müller"],"bids_version":"1.8.0","contact_info":["Carlos H. Mendoza-Cardenas","Austin Jay Brockmeier"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006563.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":12,"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/ds006563","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"f0e6d786c4388195a0de0d2ef72413a887f72077b74ec0f90c439921f2a9d81b","license":"CC0","n_contributing_labs":null,"name":"Dimension-based attention modulates early visual processing","readme":"## Overview\nThis dataset was originally published in\nGramann, K., Töllner, T. and Müller, H.J. (2010), Dimension-based attention modulates early visual processing. Psychophysiology, 47: 968-978. https://doi.org/10.1111/j.1469-8986.2010.00998.x\nIt was subsequently used to investigate automatic labeling of independent components in ICA and is referred to as the \"Cue\" dataset:\nFrølich, L., Andersen, T.S. and Mørup, M. (2015), Multi-class classification of ICS of EEG. Psychophysiol, 52: 32-45. https://doi.org/10.1111/psyp.12290\n\"64 scalp channels “referenced to Cz and re-referenced off-line to linked mastoids” from 12 subjects during a visual task (Gramann et al., 2010). ICA was performed with the implementation of the ICA infomax algorithm in the Brain Vision Analyzer software from Brain Products GmbH.2 The data sets we had access to were between 56 and 66 min long for the different subjects.\" After contacting the above authors, Laura Frølich provided a copy of the data. With Klaus Gramann's permission, this was converted to BIDS format by Austin J. Brockmeier and Carlos H. Mendoza-Cardenas.\nContinuous EEG from 12 with ICA weights.\n### Subjects\n\"Twelve observers took part in the Experiment (2 female; age range 21–25 years). All were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorder. Observers were either paid or received course credit for participating. All observers provided written informed consent, and the experimental procedure was approved by the ethics committee of the Department of Psychology, University of Munich, in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).\"\n## Expert-annotated Independent Components (ICs)\nThe expert-annotated labels of the ICs can be found in the field `expert_ica_labels`, and the class names in `ica_classes`. `expert_ica_labels(i)` is the Matlab-index of `ica_classes` for the i-th IC. ICs can be computed using the fields `data`, `icasphere`, and `icaweights` (e.g., `icaact = icaweights * icasphere * data`).\n## Details related to access to the data\nCC-BY\nContact persons:\nklaus.gramann@tu-berlin.de  https://orcid.org/0000-0003-2673-1832\najbrock@udel.edu https://orcid.org/0000-0002-7293-8140","recording_modality":["eeg"],"senior_author":"Klaus Gramann, Thomas Töllner, Hermann J. Müller","sessions":[],"size_bytes":6045373165,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DimensionDiscrimination"],"timestamps":{"digested_at":"2026-04-22T12:29:33.478150+00:00","dataset_created_at":"2025-08-15T06:04:34.622Z","dataset_modified_at":"2025-08-15T19:16:48.000Z"},"total_files":12,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006563","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-DimensionDiscrimination_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"0e444407a0a4829f","model":"openai/gpt-5.2","tagged_at":"2026-01-20T19:03:29.597192+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot convention matches: (1) The TBI DPX cognitive control dataset is labeled Type=Attention with a cue/probe paradigm and task-evoked responses; this guides mapping a cue-driven attentional control paradigm to Type=Attention (even though our dataset is Healthy, not TBI). (2) The schizophrenia visual discrimination example is labeled Modality=Visual and Type=Perception; it shows that visual stimulus discrimination maps to Visual/Perception, but our metadata emphasizes attention (dimension-based attention) as the primary construct, so we follow the convention that the primary construct (attention) determines Type when explicitly stated.","metadata_analysis":"Key facts from the README: (1) Population: \"Twelve observers took part in the Experiment (2 female; age range 21–25 years)... reported no history of neurological disorder.\" (2) Stimulus/task modality: \"12 subjects during a visual task\" and \"had normal or corrected-to-normal vision\". (3) Cognitive construct: the cited primary publication title is \"Dimension-based attention modulates early visual processing\" indicating an attention manipulation over visual processing.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says healthy participants (\"reported no history of neurological disorder\"), few-shot patterns suggest using the recruited clinical condition when present; they ALIGN (no clinical population stated) -> Healthy.\nModality: Metadata says \"visual task\" and normal vision; few-shot patterns label stimulus channel (not response) as Visual for visual paradigms; ALIGN -> Visual.\nType: Metadata (paper title) explicitly centers \"attention\" (\"Dimension-based attention...\"); few-shot patterns show some visual tasks map to Perception when framed as discrimination, but when attention is the primary construct, convention is Type=Attention; mostly ALIGN (visual task could suggest Perception, but explicit attention framing wins).","decision_summary":"Pathology top-2: (a) Healthy: supported by \"reported no history of neurological disorder\" and standard observer sample; (b) Unknown: only if no population info were present. Winner: Healthy. Alignment: aligned.\nModality top-2: (a) Visual: supported by \"visual task\" and vision-related inclusion criteria; (b) Other: only if modality were unspecified. Winner: Visual. Alignment: aligned.\nType top-2: (a) Attention: supported by \"Dimension-based attention modulates early visual processing\" (explicit construct) and cue-based attention framing; (b) Perception: plausible because it is a visual processing task, but attention is the stated manipulation/aim. Winner: Attention. Alignment: minor potential conflict with generic visual-perception mapping, resolved by explicit attention emphasis in metadata.\nConfidence justification: Pathology has an explicit health statement quote; Modality has explicit \"visual task\" quote; Type has explicit \"attention\" in the cited publication title."}},"computed_title":"Dimension-based attention modulates early visual processing","nchans_counts":[{"val":64,"count":12}],"sfreq_counts":[{"val":500.0,"count":12}],"stats_computed_at":"2026-04-22T23:16:00.311800+00:00","total_duration_s":45501.702,"author_year":"Gramann2025","canonical_name":null}}