{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33ef","dataset_id":"ds005505","associated_paper_doi":null,"authors":["Seyed Yahya Shirazi","Alexandre Franco","Maurício Scopel Hoffmann","Nathalia B. Esper","Dung Truong","Arnaud Delorme","Michael Milham","Scott Makeig"],"bids_version":"1.9.0","contact_info":["Seyed Yahya Shirazi"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005505.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":136,"ages":[11,7,10,10,12,6,13,8,8,11,9,12,6,6,6,5,12,9,7,9,8,9,7,13,10,9,15,16,11,7,5,12,7,16,10,6,12,15,19,7,14,8,5,19,11,14,21,11,9,15,6,7,9,13,9,6,8,15,14,12,6,7,11,6,8,9,10,11,6,8,9,14,9,5,13,15,13,6,15,9,6,13,11,8,9,16,7,9,16,5,9,8,7,12,9,7,10,7,16,9,14,13,14,8,5,10,10,6,9,11,9,5,13,9,5,10,8,6,8,7,15,8,6,8,14,13,6,9,11,7,7,10,14,18,14,5],"age_min":5,"age_max":21,"age_mean":9.897058823529411,"species":null,"sex_distribution":{"f":46,"m":90},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005505","osf_url":null,"github_url":null,"paper_url":null},"funding":["See https://childmind.org/science/global-open-science/healthy-brain-network/#donors","NIH/NIMH R01MH125934 for BIDS data preparation"],"ingestion_fingerprint":"bce44176e6427184ce619016140ddac6ac15e223daaacdd9b075e11effcdebdd","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 1","readme":"# The HBN-EEG Dataset\nThis is **Release 1** of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).\n## Data Description\nThis dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.\n### Tasks\nThe HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.\n#### Passive Tasks\n1. **Resting State**: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.\n2. **Surround Suppression**: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.\n3. **Movie Watching**: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.\n#### Active Tasks\n4. **Contrast Change Detection**: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.\n5. **Sequence Learning**: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.\n6. **Symbol Search**: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.\n### Contents\n* **EEG Data:** High-resolution EEG recordings capture a wide range of neural activity during various tasks.\n* **Behavioral Responses:** Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the `events.tsv` files.\n### Special Features\n* **Hierarchical Event Descriptors (HED):** Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.\n* **P-Factor, Attention, Internalization and Externalization:** Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.\n* **Data quality and availability:** We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the `participants.tsv` file.\n* **Future Releases:** We are committed to enhancing this dataset with additional, valuable features in its next stages, including:\n  * **Personalized EEG Electrode Locations:** To offer more detailed insights into individual neural activity patterns.\n  * **Personalized Lead Field Matrix:** Enabling better understanding and interpretation of EEG data.\n  * **Eye-Tracking Data:** Providing a window into the visual attention and processing mechanisms during EEG experiments.\n## Other HBN-EEG Datasets\nFor access all releases of the HBN-EEG dataset, follow this [link on NEMAR.org](https://nemar.org/dataexplorer/local?search=HBN-EEG). The links to the individual releases are below:\n#### **Release 1** | [DS005505](https://nemar.org/dataexplorer/detail?dataset_id=ds005505)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1`\n- **Total subjects:** 136\n#### **Release 2** | [DS005506](https://nemar.org/dataexplorer/detail?dataset_id=ds005506)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2`\n- **Total subjects:** 152\n#### **Release 3** | [DS005507](https://nemar.org/dataexplorer/detail?dataset_id=ds005507)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3`\n- **Total subjects:** 183\n#### **Release 4** | [DS005508](https://nemar.org/dataexplorer/detail?dataset_id=ds005508)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4`\n- **Total subjects:** 324\n#### **Release 5** | [DS005509](https://nemar.org/dataexplorer/detail?dataset_id=ds005509)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5`\n- **Total subjects:** 330\n#### **Release 6** | [DS05510](https://nemar.org/dataexplorer/detail?dataset_id=ds005510)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6`\n- **Total subjects:** 134\n#### **Release 7** | [DS005511](https://nemar.org/dataexplorer/detail?dataset_id=ds005511)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7`\n- **Total subjects:** 381\n#### **Release 8** | [DS005512](https://nemar.org/dataexplorer/detail?dataset_id=ds005512)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8`\n- **Total subjects:** 257\n#### **Release 9** | [DS005514](https://nemar.org/dataexplorer/detail?dataset_id=ds005514)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9`\n- **Total subjects:** 295\n#### **Release 10** | [DS005515](https://nemar.org/dataexplorer/detail?dataset_id=ds005515)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10`\n- **Total subjects:** 533\n#### **Release 11** | [DS005516](https://nemar.org/dataexplorer/detail?dataset_id=ds005516)\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11`\n- **Total subjects:** 430\n#### **Release NC** | *--NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.*\n- **S3 URI:** `s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC`\n- **Total subjects:** 458\n## Copyright and License\nThe HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).\n## Acknowledgments\nWe would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.","recording_modality":["eeg"],"senior_author":"Scott Makeig","sessions":[],"size_bytes":110708612638,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","seqLearning6target","seqLearning8target","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:27:47.751953+00:00","dataset_created_at":"2024-09-21T13:20:28.497Z","dataset_modified_at":"2025-03-07T21:58:22.000Z"},"total_files":1342,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005505","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-DespicableMe_eeg.json","task-DespicableMe_events.json","task-DiaryOfAWimpyKid_eeg.json","task-DiaryOfAWimpyKid_events.json","task-FunwithFractals_eeg.json","task-FunwithFractals_events.json","task-RestingState_eeg.json","task-RestingState_events.json","task-ThePresent_eeg.json","task-ThePresent_events.json","task-contrastChangeDetection_eeg.json","task-contrastChangeDetection_events.json","task-seqLearning6target_eeg.json","task-seqLearning6target_events.json","task-seqLearning8target_eeg.json","task-seqLearning8target_events.json","task-surroundSupp_eeg.json","task-surroundSupp_events.json","task-symbolSearch_eeg.json","task-symbolSearch_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"06fdad5f16425ccb","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:19:00.746159+00:00"},"tags":{"pathology":["Development"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.85,"type":0.75},"reasoning":{"few_shot_analysis":"Most similar few-shot conventions are the large clinical-cohort datasets where the primary goal is disease/clinical characterization rather than a single cognitive paradigm. For example, the Parkinson’s cross-modal oddball dataset is labeled Type=Clinical/Intervention because it explicitly recruits a clinical group and analyzes biomarkers of dysfunction, even though it uses an oddball task. Likewise, the Dementia resting-state dataset is labeled Type=Clinical/Intervention because the cohort is defined by a disorder and the purpose is clinical characterization. This guides labeling HBN-EEG as Clinical/Intervention (purpose) given its explicit focus on child/adolescent mental health/psychopathology across many tasks. For Modality, few-shot examples consistently map visual discrimination/movies/dot tasks to Modality=Visual; here most tasks described are visual, guiding Visual as the dominant stimulus channel despite inclusion of resting-state.","metadata_analysis":"Key facts from the provided README:\n1) Population/clinical framing: the dataset is part of an initiative \"to advance the understanding of child and adolescent mental health\" and includes psychopathology-derived measures: \"P-Factor, Attention, Internalization and Externalization... provide valuable insights into the psychopathology of the participants\".\n2) Developmental age range: \"participants (5-21 yo)\".\n3) Task/stimulus modality is predominantly visual: \"Participants viewed flashing peripheral disks\" (Surround Suppression); \"Participants watched four short movies\" (Movie Watching); \"Participants identified flickering disks\" (Contrast Change Detection); \"sequences of flashed circles on the screen\" (Sequence Learning); and \"performed a computerized symbol search task\" (Symbol Search). Resting state is also present: \"open or close their eyes and fixate on a central cross\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: focus on \"child and adolescent mental health\" with \"psychopathology\" factors and ages \"5-21 yo\".\n- Few-shot pattern suggests: pediatric/child mental health-focused cohorts map to Pathology=Development.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: multiple explicitly visual tasks (\"viewed flashing peripheral disks\", \"watched... movies\", \"flickering disks\", \"flashed circles on the screen\", \"symbol search\"), plus resting-state eye open/closed.\n- Few-shot pattern suggests: when the majority of experimental tasks are visual stimulation, choose Modality=Visual (even if some rest is included).\n- Alignment: ALIGN.\n\nType:\n- Metadata says: primary project aim is mental health/psychopathology characterization (\"advance the understanding of child and adolescent mental health\"; CBCL-derived \"psychopathology\" factors).\n- Few-shot pattern suggests: when pathology/clinical characterization is the main focus (large cohort with clinical metrics), choose Type=Clinical/Intervention even if tasks span domains.\n- Alignment: ALIGN.","decision_summary":"Pathology (top-2):\n1) Development (selected): Supported by \"child and adolescent mental health\" and age range \"5-21 yo\", plus explicit \"psychopathology\" factors from CBCL.\n2) Healthy (runner-up): The project name includes \"Healthy Brain Network\", but the metadata emphasizes mental health/psychopathology rather than a strictly normative cohort.\nAlignment: few-shot conventions align with metadata. Confidence justified by 2+ explicit quotes and clear developmental framing.\n\nModality (top-2):\n1) Visual (selected): Multiple task descriptions are explicitly visual: \"viewed flashing peripheral disks\", \"watched... movies\", \"flickering disks\", \"flashed circles on the screen\", \"symbol search\".\n2) Resting State (runner-up): Resting-state is included (\"open or close their eyes and fixate\"), but it is one of six tasks and not the dominant stimulus channel across the dataset.\nAlignment: few-shot visual-task mapping matches metadata. Confidence justified by 3+ explicit visual-stimulus quotes.\n\nType (top-2):\n1) Clinical/Intervention (selected): Overall purpose is mental-health/psychopathology characterization (\"advance... mental health\"; CBCL-derived \"psychopathology\" factors) across a very large cohort (>3000).\n2) Other (runner-up): The dataset spans multiple cognitive domains (resting, perception, learning, attention-like tasks), making a single cognitive-construct label less fitting.\nAlignment: few-shot convention (clinical cohort focus -> Clinical/Intervention) matches metadata. Confidence justified by 2 explicit clinical-purpose quotes, though the multi-task nature reduces certainty."}},"nemar_citation_count":1,"computed_title":"Healthy Brain Network (HBN) EEG - Release 1","nchans_counts":[{"val":129,"count":1342}],"sfreq_counts":[{"val":500.0,"count":1342}],"stats_computed_at":"2026-04-22T23:16:00.309712+00:00","total_duration_s":423140.054,"canonical_name":null,"name_confidence":0.98,"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":"Shirazi2024_R1"}}