{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33f6","dataset_id":"ds005514","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.ds005514.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":295,"ages":[14,9,9,7,12,7,9,18,5,6,9,13,5,8,15,9,6,8,15,8,10,8,10,11,9,8,11,8,8,8,9,9,8,9,9,8,8,5,7,10,7,7,10,12,17,7,6,9,10,14,9,9,8,6,12,13,8,7,6,7,11,8,5,9,16,8,8,9,10,6,8,19,12,8,6,10,5,9,7,13,11,7,6,8,12,16,10,12,7,9,12,8,10,11,7,7,6,10,13,12,6,7,10,13,8,6,12,10,6,7,6,8,8,9,12,15,6,6,9,12,8,10,9,8,15,5,12,12,10,15,13,13,14,16,9,10,9,9,5,9,6,9,8,10,14,13,13,10,10,6,9,7,6,8,7,12,9,17,6,8,10,8,10,8,10,8,9,10,9,13,7,6,8,9,11,7,9,9,7,7,16,6,15,11,6,9,6,11,8,10,15,5,17,6,8,11,5,7,13,8,8,11,7,10,11,10,14,5,6,10,5,8,7,11,8,14,12,7,7,11,9,9,9,11,10,11,6,10,7,10,8,14,10,9,9,6,9,10,6,10,13,9,8,8,10,6,12,9,10,6,12,13,16,8,8,13,8,10,12,6,5,9,6,15,10,10,10,7,17,11,11,9,12,8,7,8,10,7,13,6,6,17,12,13,10,15,14,8,15,10,13,6,13,6,8],"age_min":5,"age_max":19,"age_mean":9.464406779661017,"species":null,"sex_distribution":{"m":192,"f":103},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005514","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":"71a65e88b8d443602322eb2d6eb0cb4b590e643f799ace08f052cefba6bce46f","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 9","readme":"# The HBN-EEG Dataset\nThis is **Release 9** 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":198677619354,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","seqLearning6target","seqLearning8target","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:28:16.103200+00:00","dataset_created_at":"2024-09-21T13:48:01.282Z","dataset_modified_at":"2025-03-11T02:55:15.000Z"},"total_files":2885,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005514","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":"056ace60d9f6f7b4","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:23:20.783795+00:00"},"tags":{"pathology":["Development"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.75},"reasoning":{"few_shot_analysis":"Few-shot conventions used: (1) Large cohorts explicitly recruited for a clinical condition are labeled Type=Clinical/Intervention (e.g., the dementia resting-state dataset labeled Clinical/Intervention). Although HBN-EEG is not a single-diagnosis dataset, its stated purpose is to study child/adolescent mental health and psychopathology at scale, which aligns more with a clinical-cohort purpose than a single cognitive experiment. (2) For Modality, tasks dominated by screen-based viewing paradigms map to Visual, consistent with the schizophrenia visual discrimination example (Visual modality for dot-motion stimuli) and other visually driven task datasets. (3) For Pathology, the catalog rule states childhood/adolescence mental health cohorts should be labeled Development; this fits the HBN framing even without a single named diagnosis.","metadata_analysis":"Key metadata facts supporting labels:\n- Population/clinical framing: \"advance the understanding of child and adolescent mental health\" and \"participants (5-21 yo) involved in the HBN project\".\n- Psychopathology measures included: \"P-Factor, Attention, Internalization and Externalization... provide valuable insights into the psychopathology of the participants\".\n- Visual stimulus dominance across tasks: \"Participants viewed flashing peripheral disks\", \"Participants watched four short movies\", \"Participants identified flickering disks\", \"memorized and repeated sequences of flashed circles on the screen\", and \"performed a computerized symbol search task\".\n- Resting state is included but is only one of several tasks: \"Resting State... instructions to open or close their eyes and fixate on a central cross.\"","paper_abstract_analysis":"No useful paper information. (Only a citation link/DOI is mentioned in the README; no abstract text provided.)","evidence_alignment_check":"Pathology: Metadata SAYS \"child and adolescent mental health\" and includes \"psychopathology\" factors (CBCL-derived). Few-shot pattern SUGGESTS using explicit diagnoses when stated; here none are explicitly listed, but the catalog rule maps childhood/adolescent mental health cohorts to Development. ALIGN (metadata supports a developmental mental-health cohort framing).\n\nModality: Metadata SAYS multiple tasks are visual (\"viewed flashing...\", \"watched... movies\", \"flickering disks\", \"flashed circles on the screen\", \"symbol search\"). Few-shot pattern SUGGESTS Visual for screen-based discrimination/perception paradigms (e.g., visual discrimination example). ALIGN.\n\nType: Metadata SAYS the dataset is part of an initiative to understand \"child and adolescent mental health\" and includes derived \"psychopathology\" factors; it is a multi-task battery rather than a single construct experiment. Few-shot pattern SUGGESTS labeling large clinically oriented cohorts as Clinical/Intervention (e.g., dementia cohort). PARTIAL ALIGN: not an intervention and no single diagnosis is named, but the primary purpose is clinical/mental-health characterization at scale, so Clinical/Intervention is favored over a single cognitive type.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Development (SELECTED): Supported by \"child and adolescent mental health\", age range \"5-21 yo\", and inclusion of \"psychopathology\" factors.\n2) Healthy: Plausible due to the project name \"Healthy Brain Network\", but metadata emphasizes mental health/psychopathology rather than a purely normative sample.\nAlignment status: Align.\n\nModality:\n1) Visual (SELECTED): Supported by multiple explicit visual tasks: \"viewed flashing peripheral disks\", \"watched... movies\", \"flickering disks\", \"flashed circles on the screen\", \"computerized symbol search\".\n2) Resting State: Present (\"Resting State\" with eyes open/closed), but the dataset is not exclusively resting and is dominated by visual paradigms.\nAlignment status: Align.\n\nType:\n1) Clinical/Intervention (SELECTED): Best matches the stated overarching goal \"understanding of child and adolescent mental health\" and the inclusion of \"psychopathology\" factors, consistent with few-shot convention that clinically oriented cohorts map to Clinical/Intervention.\n2) Other: Plausible because the dataset spans multiple cognitive domains (resting-state, perception, learning, attention), making a single cognitive construct label difficult.\nAlignment status: Partial align (chosen as overall purpose label). Confidence reflects multi-purpose nature."}},"nemar_citation_count":1,"computed_title":"Healthy Brain Network (HBN) EEG - Release 9","nchans_counts":[{"val":129,"count":2885}],"sfreq_counts":[{"val":500.0,"count":2885}],"stats_computed_at":"2026-04-22T23:16:00.309801+00:00","total_duration_s":758849.62,"canonical_name":null,"name_confidence":0.93,"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_R9"}}