{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33f0","dataset_id":"ds005506","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.ds005506.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":150,"ages":[13,11,5,9,5,16,10,9,16,12,13,13,12,9,7,17,6,11,8,18,8,8,10,13,7,7,6,14,10,12,5,6,5,5,11,12,10,5,11,7,8,10,15,5,8,10,10,7,11,7,11,13,6,6,5,5,12,6,9,7,11,11,13,14,9,8,9,9,9,11,6,12,5,9,8,5,8,18,11,5,13,11,6,5,17,8,9,9,16,8,9,8,7,6,8,7,8,5,7,9,13,8,7,9,6,9,16,7,10,8,7,8,11,9,5,14,15,6,6,6,8,18,13,11,7,6,6,10,6,7,6,8,10,10,8,5,6,7,7,8,5,8,12,5,10,14,12,15,14,10],"age_min":5,"age_max":18,"age_mean":9.213333333333333,"species":null,"sex_distribution":{"m":96,"f":54},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005506","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":"6d53611b869a9a77e587b72ae28747a08b19a24a3c7ffc349fe536b8cfa202ed","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 2","readme":"# The HBN-EEG Dataset\nThis is **Release 2** 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":120126392039,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","seqLearning6target","seqLearning8target","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:27:47.855377+00:00","dataset_created_at":"2024-09-21T13:22:07.363Z","dataset_modified_at":"2025-03-11T02:11:30.000Z"},"total_files":1405,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005506","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":"d6b7acb09cea5d75","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:19:43.238532+00:00"},"tags":{"pathology":["Development"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.85,"type":0.75},"reasoning":{"few_shot_analysis":"Closest convention match is the large-cohort, disorder-focused datasets labeled as Type=Clinical/Intervention (e.g., the few-shot \"A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects\" where the primary purpose is clinical characterization/biomarker-style reuse, not a single cognitive construct). Here, HBN-EEG similarly frames the dataset as an initiative to understand mental health and includes psychopathology-derived factors. For Pathology, the few-shot epilepsy example shows that pediatric-focused clinical recruitment is not labeled Healthy; instead the pathology reflects the recruited condition/clinical framing. This supports using Development rather than Healthy when the project is explicitly centered on child/adolescent mental health.","metadata_analysis":"Key recruitment/clinical framing facts:\n- \"advance the understanding of child and adolescent mental health\".\n- \"participants (5-21 yo) involved in the HBN project\".\n- \"P-Factor, Attention, Internalization and Externalization... derived from the CBCL questionnaire... insights into the psychopathology of the participants\".\n\nKey task/stimulus modality facts (stimulus-centric):\n- \"Surround Suppression: Participants viewed flashing peripheral disks\".\n- \"Movie Watching: Participants watched four short movies\".\n- \"Contrast Change Detection: Participants identified flickering disks with dominant contrast changes\".\n- \"Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen\".\n- \"Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols\".\n- Rest condition also present: \"Resting State... open or close their eyes and fixate on a central cross.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n1) Metadata says: \"child and adolescent mental health\" and notes CBCL-derived \"psychopathology\" factors.\n2) Few-shot pattern suggests: large clinically framed cohorts are not labeled Healthy; pediatric mental-health framing maps well to Development.\n3) Alignment: ALIGN.\n\nModality:\n1) Metadata says: \"viewed flashing peripheral disks\", \"watched four short movies\", \"flickering disks\", \"flashed circles on the screen\", \"computerized symbol search\" (all visual stimuli).\n2) Few-shot pattern suggests: visual discrimination/movies/visual tasks -> Modality=Visual.\n3) Alignment: ALIGN.\n\nType:\n1) Metadata says: dataset is \"part of a larger initiative\" to understand \"child and adolescent mental health\" and provides CBCL-derived \"psychopathology\" factors.\n2) Few-shot pattern suggests: when the overarching purpose is clinical/biomarker/psychopathology characterization in a large cohort (e.g., dementia dataset), Type=Clinical/Intervention.\n3) Alignment: ALIGN.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n- Candidate 1: Development\n  Evidence: \"child and adolescent mental health\"; participants are \"5-21 yo\"; CBCL-derived \"psychopathology\" factors.\n- Candidate 2: Healthy\n  Evidence: project name includes \"Healthy Brain Network\" and includes resting-state/task data without a single named diagnosis.\nDecision: Development wins because the dataset is explicitly framed around child/adolescent mental health and psychopathology assessment rather than a normative-only cohort. (Alignment with few-shot conventions: pediatric/mental-health framing supports non-Healthy labeling.)\nConfidence basis: 3 explicit supporting snippets (mental health; age range; psychopathology factors).\n\nModality:\n- Candidate 1: Visual\n  Evidence: \"viewed flashing peripheral disks\"; \"watched four short movies\"; \"identified flickering disks\"; \"flashed circles on the screen\"; \"computerized symbol search\".\n- Candidate 2: Resting State\n  Evidence: includes \"Resting State\" with eyes open/closed and fixation.\nDecision: Visual wins because the majority of described tasks involve visual stimuli; resting-state is only one of six tasks.\nConfidence basis: 4+ explicit visual-stimulus snippets.\n\nType:\n- Candidate 1: Clinical/Intervention\n  Evidence: \"advance the understanding of child and adolescent mental health\"; CBCL-derived \"psychopathology\" factors (P-factor/internalization/externalization) included as key dataset feature.\n- Candidate 2: Other\n  Evidence: multi-task battery spanning resting-state, perception, learning, and attention without a single cognitive construct.\nDecision: Clinical/Intervention wins because the dataset’s primary stated purpose and added derived measures are oriented toward psychopathology/mental-health characterization across a very large cohort, consistent with few-shot labeling of clinically oriented cohort resources.\nConfidence basis: 2 explicit clinical/psychopathology snippets; multi-task nature keeps runner-up plausible, lowering confidence slightly."}},"nemar_citation_count":1,"computed_title":"Healthy Brain Network (HBN) EEG - Release 2","nchans_counts":[{"val":129,"count":1405}],"sfreq_counts":[{"val":500.0,"count":1405}],"stats_computed_at":"2026-04-22T23:16:00.309725+00:00","total_duration_s":459087.278,"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_R2"}}