{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33f7","dataset_id":"ds005515","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.ds005515.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":533,"ages":[7,13,7,5,12,7,6,7,6,6,20,9,6,6,5,16,18,8,8,9,7,11,8,6,6,6,5,9,7,9,18,11,6,12,8,8,5,5,17,12,7,14,10,9,15,6,9,10,16,11,5,11,6,17,8,8,9,9,10,7,6,10,6,13,12,12,8,7,11,11,7,14,7,9,14,6,14,8,11,15,10,10,17,7,11,10,16,8,8,16,13,14,13,6,7,13,7,5,7,6,11,8,12,17,9,11,5,6,12,12,9,5,15,11,13,11,7,21,8,8,10,8,9,17,20,9,6,10,8,16,14,7,8,8,9,6,18,10,8,9,11,8,6,6,15,10,14,10,14,13,8,6,6,7,11,7,10,15,6,7,6,16,6,5,7,9,12,14,7,6,7,9,12,14,13,8,7,9,21,10,7,6,10,7,16,12,6,9,7,8,18,9,7,8,10,9,13,15,7,6,11,5,9,7,8,16,5,10,6,9,12,11,10,10,6,11,10,9,12,14,10,9,12,5,8,13,10,9,6,8,10,16,7,13,5,8,12,5,9,11,10,10,8,7,12,15,9,14,7,12,10,10,8,9,9,11,12,6,6,8,6,8,12,7,14,9,7,10,6,7,7,7,9,8,7,11,10,9,6,8,9,5,15,9,11,13,7,6,10,6,15,9,9,11,7,8,5,9,6,5,13,5,7,10,10,6,7,9,7,10,20,9,15,14,9,13,9,11,13,7,16,7,12,8,11,9,10,6,14,15,9,10,8,10,9,7,5,7,8,10,11,11,6,5,17,6,8,11,7,7,13,9,12,13,13,7,8,7,7,13,8,13,11,9,6,7,11,11,9,9,6,7,13,10,11,7,12,13,7,7,14,12,15,7,12,7,11,11,13,9,9,17,7,6,16,10,9,11,10,13,11,17,10,6,5,10,14,11,7,8,9,8,5,7,6,9,9,7,6,9,7,10,14,6,8,8,8,6,10,10,7,6,7,13,5,10,8,7,7,9,8,9,9,10,9,11,9,6,13,11,6,5,11,10,9,5,11,8,9,15,12,9,6,6,8,12,6,9,14,7,13,15,9,12,10,6,8,7,10,7,9,5,13,8,9,8,6,8,8,10,11,9,10,10,15,13,8,8,16,9,7,5,5,13,6,5,5,7,6,7,16,6,6,6,8,5,16,8,15,7,7,10,6,15,5,8,16,5,7,18,18,9,8],"age_min":5,"age_max":21,"age_mean":9.487804878048781,"species":null,"sex_distribution":{"m":342,"f":191},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005515","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":"e23cb9fcf7ab821a9823ff39b5251195cd22279d2a67041e9d6cab7ab5c4d35e","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 10","readme":"# The HBN-EEG Dataset\nThis is **Release 10** 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":172385562881,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:28:16.343522+00:00","dataset_created_at":"2024-09-21T13:49:57.271Z","dataset_modified_at":"2025-03-11T03:01:17.000Z"},"total_files":2516,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005515","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-surroundSupp_eeg.json","task-surroundSupp_events.json","task-symbolSearch_eeg.json","task-symbolSearch_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"3a66e0776e7d91e8","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:23:59.808734+00:00"},"tags":{"pathology":["Development"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.75},"reasoning":{"few_shot_analysis":"No few-shot example is an exact match (large pediatric mental-health cohort with a multi-task EEG battery), but several examples guide conventions: (1) Dementia resting-state and Parkinson’s oddball are labeled Type=Clinical/Intervention because they are large, clinically oriented cohorts/datasets; this supports using Clinical/Intervention when the dataset’s primary purpose is clinical characterization rather than a single cognitive paradigm. (2) The EEG Motor Movement/Imagery example is labeled Modality=Visual even though the focus is motor behavior; this demonstrates the convention that Modality follows stimulus/input channel (e.g., on-screen targets), not the response. This supports labeling HBN-EEG as Visual because its tasks are predominantly screen-based visual paradigms (movies, flashing disks, symbol search, etc.).","metadata_analysis":"Key population/aim facts: The README states the project’s purpose is mental-health focused: \"advance the understanding of child and adolescent mental health\" and that participants are \" >3000 participants (5-21 yo) involved in the HBN project.\" It also highlights psychopathology-relevant derived measures: \"P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire... provide valuable insights into the psychopathology of the participants\".\n\nKey task/stimulus facts (dominantly visual): \"Surround Suppression: Participants viewed flashing peripheral disks\", \"Movie Watching: Participants watched four short movies\", \"Contrast Change Detection: Participants identified flickering disks\", \"Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen\", and \"Symbol Search: Participants performed a computerized symbol search task\". Resting-state is included (eyes open/closed with fixation), but most non-rest tasks are explicitly visual/screen-based.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says the dataset is part of an initiative to study \"child and adolescent mental health\" and includes CBCL-derived \"psychopathology\" factors. Few-shot patterns do not provide a direct Development example, but they do show that clinically oriented cohorts map to non-Healthy pathologies and often Type=Clinical/Intervention. ALIGN (no conflict).\n\nModality: Metadata says tasks involve visual stimuli (\"viewed flashing peripheral disks\", \"watched four short movies\", \"flickering disks\", \"flashed circles on the screen\", \"computerized symbol search\"). Few-shot convention (e.g., motor-imagery dataset labeled Visual due to screen targets) suggests choosing Visual when stimuli are on-screen. ALIGN.\n\nType: Metadata emphasizes a broad clinical/developmental mental-health research purpose (\"advance... child and adolescent mental health\"; CBCL-derived psychopathology factors) rather than a single cognitive construct. Few-shot convention in large clinical cohorts (Parkinson’s, Dementia) maps to Type=Clinical/Intervention. ALIGN (no conflict).","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Development — Evidence: \"child and adolescent mental health\"; participants are \"5-21 yo\"; CBCL-derived \"psychopathology\" factors are highlighted.\n2) Healthy — Counterpoint: README does not explicitly state a single diagnosed clinical group (e.g., \"ADHD\", \"autism\"), so one could argue it is a general cohort.\nDecision: Development (dataset is explicitly framed around child/adolescent mental health and psychopathology).\nConfidence notes: supported by 2+ direct quotes about mental health focus and psychopathology + age range.\n\nModality:\n1) Visual — Evidence: \"viewed flashing peripheral disks\"; \"watched four short movies\"; \"flashed circles on the screen\"; \"computerized symbol search task\".\n2) Resting State — Counterpoint: includes a \"Resting State\" task with eyes open/closed fixation.\nDecision: Visual (majority of tasks are explicitly visual stimulus paradigms, with resting-state as one component).\nConfidence notes: 3+ direct task quotes + strong few-shot convention that modality follows stimulus channel.\n\nType:\n1) Clinical/Intervention — Evidence: explicit purpose to \"advance the understanding of child and adolescent mental health\"; inclusion of CBCL-derived \"psychopathology\" factors; very large cohort (>3000) typical of clinical characterization resources.\n2) Other — Counterpoint: multi-task battery spans resting-state, perception, learning, and attention, making a single cognitive construct label difficult.\nDecision: Clinical/Intervention (primary framing is mental-health/psychopathology resource rather than one cognitive construct).\nConfidence notes: 2 key quotes support clinical characterization intent, but not an intervention and not a single diagnosis, so confidence is moderate."}},"computed_title":"Healthy Brain Network (HBN) EEG - Release 10","nchans_counts":[{"val":129,"count":2516}],"sfreq_counts":[{"val":500.0,"count":2516}],"stats_computed_at":"2026-04-22T23:16:00.309815+00:00","total_duration_s":659244.966,"canonical_name":null,"name_confidence":0.97,"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_R10"}}