{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33f8","dataset_id":"ds005516","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.ds005516.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":430,"ages":[20,7,22,7,7,15,14,9,15,9,13,13,8,11,13,6,9,8,8,7,9,17,8,5,6,14,5,8,8,10,15,8,11,14,5,9,8,7,15,10,13,8,8,8,8,7,7,10,6,9,12,12,7,10,9,8,11,11,7,14,10,14,8,7,5,10,15,7,8,14,7,8,16,16,6,12,7,9,7,8,9,13,5,6,6,12,9,12,7,12,9,14,8,9,10,11,10,9,13,9,7,14,5,7,16,10,12,7,6,11,17,11,9,7,12,14,9,6,12,10,11,9,10,9,7,10,13,13,9,13,7,12,5,16,16,12,8,10,13,11,8,6,6,5,15,7,8,9,6,6,7,7,12,10,9,7,10,12,12,12,13,9,11,6,9,14,8,8,15,12,11,9,10,15,13,6,16,7,14,12,6,10,10,8,6,10,7,7,12,11,11,15,10,14,18,8,8,7,7,8,6,6,10,21,7,8,12,17,10,12,10,11,21,13,9,7,12,7,8,10,6,5,6,8,11,10,12,7,18,6,9,7,14,8,9,6,5,9,15,13,17,6,8,13,5,7,11,8,18,5,7,9,8,13,13,5,5,13,13,7,6,8,16,14,9,9,13,10,6,19,9,7,14,11,12,8,10,9,11,14,5,11,13,8,11,10,6,10,6,15,10,12,9,8,6,10,9,13,6,8,8,8,11,12,10,8,9,8,14,8,9,7,12,15,7,11,11,9,10,9,10,11,13,20,9,5,8,13,10,6,10,9,6,6,10,11,9,14,10,8,13,9,16,7,6,10,6,12,8,12,8,6,10,10,7,12,8,11,5,6,7,15,12,10,9,6,10,7,9,12,13,6,9,19,15,11,7,8,6,6,10,15,19,7,11,8,10,16,10,5,6,8,15,9,20,8,7,18,9,9,9,7,12,8,11,8,14,11,13,7,13,9,7,8,10,5,11,7,16,9,8,6,13,8,11,12,11,6,7,8],"age_min":5,"age_max":22,"age_mean":9.9,"species":null,"sex_distribution":{"m":263,"f":167},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005516","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":"a1f5bdb3e79c9142dbb11553dd7e31252d6cdd4d604a7702efffa5f178e26863","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 11","readme":"# The HBN-EEG Dataset\nThis is **Release 11** 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":235310606952,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:28:30.893678+00:00","dataset_created_at":"2024-09-21T14:00:08.098Z","dataset_modified_at":"2025-03-11T03:03:16.000Z"},"total_files":3397,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005516","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":"053dc34bbf3e0891","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:24:35.941261+00:00"},"tags":{"pathology":["Development"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot examples are the large clinical cohorts where the dataset goal is understanding a condition rather than a single cognitive task, e.g., (1) the Parkinson's cross-modal oddball dataset labeled Type=Clinical/Intervention because it is a large recruited clinical cohort with cognition as a biomarker focus, and (2) the Dementia resting-state dataset labeled Type=Clinical/Intervention because the dataset is organized around neuropsychiatric/neurodegenerative status. These examples guide using Clinical/Intervention when the dataset is positioned as a broad mental-health/clinical resource. For Modality, the schizophrenia visual discrimination example shows that screen-based visual paradigms map cleanly to Modality=Visual even when there are responses and feedback. For Pathology, the epilepsy pediatric dataset shows that explicit recruitment of a pediatric clinical group is not required for using a non-Healthy label when the dataset is explicitly framed around pediatric clinical/mental-health characterization; in EEGDash conventions, broad child/adolescent psychopathology framing maps best to Pathology=Development.","metadata_analysis":"Key facts from the provided README: (1) population/clinical framing: \"advance the understanding of child and adolescent mental health\" and \"participants (5-21 yo) involved in the HBN project\" and \"P-Factor, Attention, Internalization and Externalization... provide valuable insights into the psychopathology of the participants\". (2) stimulus/task modality: multiple tasks are explicitly visual/screen-based, e.g., \"Resting State... fixate on a central cross\", \"Participants viewed flashing peripheral disks\", \"Participants watched four short movies\", \"flickering disks\", \"sequences of flashed circles on the screen\", and \"computerized symbol search task\". (3) dataset purpose: described as a large-scale resource dataset: \">3000 participants\" and \"larger initiative\" to advance understanding of mental health, with derived symptom factors included.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says pediatric/adolescent mental-health focus (\"child and adolescent mental health\"; \"psychopathology of the participants\"), not a healthy-only cohort. Few-shot pattern suggests that when the dataset is about pediatric mental health broadly (not a single named diagnosis), label as Development rather than Healthy. ALIGN.\nModality: Metadata says screen-based viewing in multiple tasks (\"fixate on a central cross\", \"viewed flashing peripheral disks\", \"watched...movies\", \"flashed circles on the screen\"). Few-shot pattern suggests screen-based stimuli => Visual modality. ALIGN.\nType: Metadata says the dataset is part of an initiative focused on mental health characterization and provides psychopathology-derived factors (\"P-Factor...psychopathology\"; \"advance...mental health\"), despite including multiple cognitive tasks. Few-shot pattern suggests large cohort clinical/biomarker resources are labeled Clinical/Intervention rather than a single construct (Perception/Learning/Attention). ALIGN (though task heterogeneity makes a non-clinical 'Other' runner-up plausible).","decision_summary":"Pathology top-2: (A) Development vs (B) Healthy. Evidence for Development: \"child and adolescent mental health\"; \"participants (5-21 yo)\"; \"psychopathology of the participants\" with CBCL-derived factors. Evidence for Healthy: no explicit diagnosis list in the snippet and HBN name contains \"Healthy Brain Network\". Head-to-head: the explicit mental-health/psychopathology framing outweighs the name; select Development. \nModality top-2: (A) Visual vs (B) Resting State. Evidence for Visual: \"fixate on a central cross\"; \"viewed flashing peripheral disks\"; \"watched four short movies\"; \"flashed circles on the screen\"; \"computerized symbol search\". Evidence for Resting State: there is a resting-state task included, but it is only one of several tasks. Head-to-head: majority of tasks are visual/screen-based; select Visual. \nType top-2: (A) Clinical/Intervention vs (B) Other. Evidence for Clinical/Intervention: dataset positioned as a mental-health resource (\"advance...mental health\"), includes psychopathology factors (\"P-Factor...psychopathology\"), very large cohort (\">3000 participants\") typical of clinical characterization datasets. Evidence for Other: multiple heterogeneous cognitive tasks (perception/learning/attention/rest) could make a general-purpose label plausible. Head-to-head: stated purpose is mental-health characterization; select Clinical/Intervention. \nConfidence justification: Pathology has 2+ explicit mental-health/psychopathology quotes; Modality has multiple explicit visual-task quotes; Type has explicit mental-health resource framing but less explicit diagnosis/recruitment detail, lowering confidence."}},"computed_title":"Healthy Brain Network (HBN) EEG - Release 11","nchans_counts":[{"val":129,"count":3397}],"sfreq_counts":[{"val":500.0,"count":3397}],"stats_computed_at":"2026-04-22T23:16:00.309830+00:00","total_duration_s":901626.654,"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_R11"}}