{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33f5","dataset_id":"ds005512","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.ds005512.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":257,"ages":[21,9,13,5,8,8,14,9,6,14,7,14,6,6,10,7,13,8,13,5,9,10,10,9,7,8,11,10,5,7,10,6,9,10,7,21,5,12,8,7,10,10,6,14,7,9,6,7,15,16,10,10,5,6,11,13,8,7,8,7,5,7,9,8,10,13,5,7,10,7,10,6,8,13,15,7,9,18,11,14,6,11,9,11,8,7,17,6,6,13,9,5,12,8,15,5,6,5,12,10,7,18,7,8,6,10,7,11,9,14,18,10,11,5,13,8,8,10,5,8,7,10,15,16,7,9,17,7,11,10,12,11,5,8,11,8,5,8,6,10,7,10,8,9,12,12,9,11,10,5,16,7,9,12,13,7,6,9,10,9,8,14,10,6,10,9,8,5,9,8,10,12,8,9,8,7,5,13,10,15,13,6,14,13,21,12,10,7,5,9,8,12,6,9,6,6,7,8,6,14,8,9,13,6,7,12,7,9,7,8,8,8,16,8,7,6,6,6,16,6,6,13,11,14,9,14,10,15,7,7,6,13,6,12,13,10,11,8,13,7,13,6,9,12,12,12,10,15,12,7,8,16,8,10,17,14,10],"age_min":5,"age_max":21,"age_mean":9.560311284046692,"species":null,"sex_distribution":{"f":91,"m":166},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005512","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":"c02a5cecdf4834e9d2497cf708bc9413550dbe2495d577894eaae1d7525b7511","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 8","readme":"# The HBN-EEG Dataset\nThis is **Release 8** 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":168778412421,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","seqLearning6target","seqLearning8target","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:28:14.768495+00:00","dataset_created_at":"2024-09-21T13:39:35.378Z","dataset_modified_at":"2025-03-11T02:52:49.000Z"},"total_files":2320,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005512","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":"a3fbeca1d6a345c8","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:22:44.584390+00:00"},"tags":{"pathology":["Development"],"modality":["Multisensory"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot conventions: (1) The Parkinson's \"Cross-modal Oddball Task\" example maps a large clinically-relevant cohort study to Type=\"Clinical/Intervention\" (even though it uses a cognitive task) and Modality=\"Multisensory\" because it explicitly includes both auditory and visual cues. This guides labeling HBN-EEG as Clinical/Intervention given its explicit child/adolescent mental health focus and as Multisensory/Visual depending on whether multiple stimulus channels are central. (2) The Dementia resting-state cohort example also uses Type=\"Clinical/Intervention\" for a dataset framed around a clinical population/diagnosis comparison, reinforcing that when the dataset goal is a clinical/psychopathology cohort resource (rather than a single cognitive construct), Clinical/Intervention is appropriate.","metadata_analysis":"Key facts from the README: (1) Population and clinical framing: \"part of a larger initiative to advance the understanding of child and adolescent mental health\" and participants are \"from >3000 participants (5-21 yo) involved in the HBN project.\" (2) Multi-paradigm task battery: \"six distinct tasks\" including \"Resting State\" and multiple visually-driven tasks like \"Surround Suppression\" (\"Participants viewed flashing peripheral disks\"), \"Movie Watching\" (\"Participants watched four short movies\"), \"Contrast Change Detection\" (\"Participants identified flickering disks\"), \"Sequence Learning\" (\"memorized and repeated sequences of flashed circles\"), and \"Symbol Search\" (\"performed a computerized symbol search task\"). (3) Psychopathology-relevant derived measures: \"P-Factor, Attention, Internalization and Externalization\" derived from CBCL \"provide valuable insights into the psychopathology of the participants.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says child/adolescent mental-health-focused cohort (\"understanding of child and adolescent mental health\"; \"participants (5-21 yo)\") rather than an adult healthy-only sample. Few-shot pattern: pediatric/clinical cohorts are not labeled Healthy; convention supports using a non-Healthy label when recruitment is about developmental/mental-health context. ALIGN (both point away from Healthy), so choose Development.\nModality: Metadata says tasks are predominantly visual (flashing disks, flickering disks, movies, flashed circles, symbol search) plus a resting-state condition (eyes open/closed fixation). Few-shot pattern: when both auditory and visual are explicit (e.g., cross-modal oddball), label Multisensory; when a single channel dominates, label Visual. PARTIAL ALIGN but ambiguous because movies typically include audiovisual content while most tasks are visual; thus choose Multisensory with moderate confidence.\nType: Metadata says dataset purpose is mental health/psychopathology resource (\"advance the understanding of child and adolescent mental health\"; CBCL-derived \"psychopathology\" factors; very large cohort). Few-shot pattern: large clinically-framed cohort datasets are labeled Clinical/Intervention even if tasks are cognitive. ALIGN, so choose Clinical/Intervention.","decision_summary":"Top-2 candidates per category:\n- Pathology: (A) Development vs (B) Healthy. Evidence for Development: \"child and adolescent mental health\"; age range \"5-21 yo\"; psychopathology factors from CBCL. Evidence for Healthy: not explicitly stated that participants are diagnosed patients. Head-to-head: the dataset is explicitly framed around developmental mental health and includes psychopathology measures; per guidelines, childhood/adolescence mental health => Development. \n- Modality: (A) Multisensory vs (B) Visual. Evidence for Visual: multiple tasks described as visual (\"viewed flashing peripheral disks\", \"watched four short movies\", \"identified flickering disks\", \"flashed circles\", \"symbol search\"). Evidence for Multisensory: inclusion of movie-watching likely involves audiovisual stimulation and the dataset is explicitly multi-task rather than single-modality. Head-to-head: overall battery spans at least visual + resting, and likely audiovisual movies; choose Multisensory as best single label for dominant stimulus channels across tasks.\n- Type: (A) Clinical/Intervention vs (B) Other. Evidence for Clinical/Intervention: explicit mental health initiative (\"understanding of child and adolescent mental health\"); explicit \"psychopathology\" factors (P-factor/internalization/externalization); very large cohort (>3000). Evidence for Other: multiple cognitive constructs (resting, perception, learning, attention) could make a generic label plausible. Head-to-head: primary framing is a mental-health cohort resource, matching few-shot convention for Clinical/Intervention.\nConfidence justification: Pathology supported by 2+ direct quotes about age range and child/adolescent mental health focus; Modality has multiple task quotes but ambiguity about whether movies are audiovisual in metadata; Type supported by 2+ direct quotes about mental health initiative and psychopathology factors plus cohort scale."}},"nemar_citation_count":2,"computed_title":"Healthy Brain Network (HBN) EEG - Release 8","nchans_counts":[{"val":129,"count":2320}],"sfreq_counts":[{"val":500.0,"count":2320}],"stats_computed_at":"2026-04-22T23:16:00.309788+00:00","source_url":"https://openneuro.org/datasets/ds005512","total_duration_s":644841.622,"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_R8"}}