{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33f4","dataset_id":"ds005510","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.ds005510.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":135,"ages":[10,5,12,9,6,8,16,11,14,9,16,14,9,16,15,7,7,11,16,9,20,11,17,15,6,11,9,10,8,7,13,7,8,8,9,8,5,8,8,14,12,9,13,13,7,5,9,5,7,9,19,14,18,9,10,13,19,13,6,13,8,6,8,8,7,15,14,8,8,11,11,8,7,13,5,6,7,10,13,14,14,6,9,11,13,15,6,18,10,10,13,5,11,11,7,8,6,5,5,16,12,11,10,6,17,8,9,11,11,9,15,6,9,11,13,9,14,11,8,5,19,9,6,15,6,6,9,9,8,9,7,8,7,21,6],"age_min":5,"age_max":21,"age_mean":10.22962962962963,"species":null,"sex_distribution":{"f":52,"m":83},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005510","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":"4e30e2a747f5354dfd1ca20d2294f90e950c4b31ac86bf1ccd86bf5c520dfd73","license":"CC-BY-SA 4.0","n_contributing_labs":null,"name":"Healthy Brain Network (HBN) EEG - Release 6","readme":"# The HBN-EEG Dataset\nThis is **Release 6** 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":97492911148,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DespicableMe","DiaryOfAWimpyKid","FunwithFractals","RestingState","ThePresent","contrastChangeDetection","seqLearning6target","seqLearning8target","surroundSupp","symbolSearch"],"timestamps":{"digested_at":"2026-04-22T12:28:05.484960+00:00","dataset_created_at":"2024-09-21T13:34:17.158Z","dataset_modified_at":"2025-03-11T02:42:03.000Z"},"total_files":1227,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005510","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":"bd986afbfc744b2d","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:22:09.919148+00:00"},"tags":{"pathology":["Development"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.7,"modality":0.85,"type":0.75},"reasoning":{"few_shot_analysis":"Closest few-shot convention matches are the large-cohort clinical/biomarker datasets: (1) “A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects” labeled Type=Clinical/Intervention because the dataset’s primary purpose is clinical characterization/biomarker development rather than a single cognitive construct. (2) “Cross-modal Oddball Task” in Parkinson’s labeled Type=Clinical/Intervention despite being a task dataset, because it is embedded in a large clinical cohort and the primary framing is clinical (PD-related cognitive dysfunction). HBN-EEG is similarly framed as a broad child/adolescent mental-health initiative with derived psychopathology factors, suggesting Type=Clinical/Intervention by the same convention. For Modality, the few-shot examples show that when tasks are clearly visual (e.g., visual discrimination/motion dots; motor task with visual targets), Modality is assigned by stimulus channel; HBN tasks are predominantly screen-based/visual. For Pathology, few-shots emphasize using the recruited clinical population when explicit; here it is not a single diagnosis but a child/adolescent mental health cohort, aligning with the catalog’s “Development” label convention.","metadata_analysis":"Key facts from the dataset README: (1) Population framing is developmental/mental-health: “Healthy Brain Network (HBN) Project… to advance the understanding of child and adolescent mental health” and “>3000 participants (5-21 yo) involved in the HBN project.” (2) Tasks/stimuli are predominantly visual and screen-based: “Participants viewed flashing peripheral disks…”, “Participants watched four short movies…”, “Participants identified flickering disks…”, “Participants memorized and repeated sequences of flashed circles on the screen…”, and “Participants performed a computerized symbol search task.” (3) Clinical/psychopathology emphasis is explicit via derived symptom dimensions: “P-Factor, Attention, Internalization and Externalization… derived from the CBCL questionnaire… provide valuable insights into the psychopathology of the participants.”","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says the project is about “child and adolescent mental health” and includes “psychopathology” factors; it does not name one specific diagnosis. Few-shot convention suggests using a clinical label when a clinical population is recruited; when the cohort is developmental mental health rather than a single disorder, this aligns with Pathology=Development. ALIGN.\n\nModality: Metadata lists multiple tasks that are explicitly visual/screen-based (e.g., “viewed flashing peripheral disks”, “watched… movies”, “flickering disks”, “flashed circles on the screen”, “computerized symbol search”). Few-shot convention assigns modality by stimulus channel; thus Visual. ALIGN.\n\nType: Metadata frames the dataset as a broad mental-health resource and includes psychopathology-derived factors (“P-Factor… derived from the CBCL… psychopathology”). Few-shot convention labels large clinical cohort datasets as Clinical/Intervention even when tasks are cognitive (e.g., Parkinson’s oddball; dementia resting-state). This matches HBN-EEG’s primary purpose (mental-health/developmental cohort characterization). ALIGN.","decision_summary":"Pathology (top-2):\n1) Development — Evidence: “child and adolescent mental health”; “>3000 participants (5-21 yo)”; “psychopathology of the participants”.\n2) Healthy — Evidence: project name contains “Healthy Brain Network”, but this is a project title and does not explicitly state participants are healthy.\nHead-to-head: Development wins because metadata explicitly emphasizes child/adolescent mental health and psychopathology rather than a normative-only sample.\n\nModality (top-2):\n1) Visual — Evidence: “viewed flashing peripheral disks”; “watched four short movies”; “identified flickering disks”; “flashed circles on the screen”; “computerized symbol search task”.\n2) Resting State — Evidence: includes a “Resting State” task with eyes open/closed, but it is one of multiple tasks and most active paradigms described are visual.\nHead-to-head: Visual wins as the dominant stimulus modality across the task battery.\n\nType (top-2):\n1) Clinical/Intervention — Evidence: “advance the understanding of child and adolescent mental health”; “psychopathology of the participants”; large cohort “>3000 participants”.\n2) Other — Evidence: multi-task battery spanning resting-state, perception, learning, and attention could be treated as mixed-purpose.\nHead-to-head: Clinical/Intervention wins because the dataset’s primary framing is a large developmental mental-health cohort resource rather than a single cognitive construct.\n\nConfidence notes: Pathology confidence is moderate because no single diagnosis is specified; Modality confidence is high due to multiple explicit visual-task descriptions; Type confidence is moderate-high due to explicit mental-health/psychopathology framing plus large-cohort structure."}},"nemar_citation_count":1,"computed_title":"Healthy Brain Network (HBN) EEG - Release 6","nchans_counts":[{"val":129,"count":1227}],"sfreq_counts":[{"val":500.0,"count":1227}],"stats_computed_at":"2026-04-22T23:16:00.309776+00:00","total_duration_s":372433.218,"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_R6"}}