The HBN-EEG Dataset

This is Release 9 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).

Data Description

This 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.

Tasks

The 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.

Passive Tasks

1. 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.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

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.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The 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).

Acknowledgments

We 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.

The HBN-EEG Dataset

This is Release 5 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).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Copyright and License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0). Please cite the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181) as well as the dataset paper (https://doi.org/10.1101/2024.10.03.615261).

Acknowledgments

We 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.

The HBN-EEG Dataset

This is Release 3 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).

Data Description

This 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.

Tasks

The 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.

Passive Tasks

1. 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.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

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.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The 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).

Acknowledgments

We 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.

The HBN-EEG Dataset

This is Release 1 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).

Data Description

This 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.

Tasks

The 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.

Passive Tasks

1. 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.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

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.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The 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).

Acknowledgments

We 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.

The HBN-EEG Dataset

This 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).

Data Description

This 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.

Tasks

The 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.

Passive Tasks

1. 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.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

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.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The 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).

Acknowledgments

We 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.

HBN

## Overview

HBN is a dataset for instance segmentation tasks - it contains Flakes annotations for 263 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The HBN-EEG Dataset

This 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).

Data Description

This 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.

Tasks

The 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.

Passive Tasks

1. 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.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

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.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The 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).

Acknowledgments

We 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.

The HBN-EEG Dataset

This is Release 7 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).

Data Description

This 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.

Tasks

The 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.

Passive Tasks

1. 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.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

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.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The 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).

Acknowledgments

We 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.

The HBN-EEG Dataset

This is Release 4 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).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Copyright and License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0). Please cite the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181) as well as the dataset paper (https://doi.org/10.1101/2024.10.03.615261).

Acknowledgments

We 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.

The global boron nitride market is experiencing robust growth, driven by increasing demand across diverse sectors. While the exact market size for 2025 is not provided, considering a typical CAGR of 8-10% (a reasonable estimate based on industry reports for advanced materials) and assuming a 2024 market size of approximately $500 million (again, a reasonable estimation given the market's growth trajectory and the presence of numerous established players), we can project the 2025 market size to be in the range of $540-$550 million. This growth is fueled primarily by the expanding electronics and semiconductor industries, where boron nitride's unique thermal and electrical properties are crucial for advanced device fabrication. The automotive sector's increasing adoption of lightweight and high-performance materials also significantly contributes to market expansion, particularly in applications like lubricants and heat-resistant components. Further growth is anticipated from the aerospace industry's need for durable, high-temperature materials. The market segmentation shows significant potential in hexagonal boron nitride (hBN), valued for its exceptional properties compared to other forms. However, challenges exist. Cost fluctuations in raw materials, like boron and nitrogen, can impact production costs and profitability. Furthermore, the complexity of manufacturing some forms of boron nitride, particularly high-purity CBN, can present barriers to wider adoption. Competition among established players and the emergence of new entrants also shape the market landscape. Nonetheless, ongoing research and development efforts focused on improving production efficiency and exploring new applications (such as in energy storage and biomedical fields) are likely to drive continued growth and expansion throughout the forecast period (2025-2033). This suggests a promising outlook for the boron nitride market, with consistent expansion expected across various geographical regions, though North America and Asia-Pacific are currently expected to lead the way.

The HBN-EEG Dataset

This 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).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• 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.

Special Features

• 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.
• 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.
• 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.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Copyright and License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0). Please cite the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181) as well as the dataset paper (https://doi.org/10.1101/2024.10.03.615261).

Acknowledgments

We 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.

The global electronic boron nitride (BN) powder market is experiencing robust growth, driven by increasing demand across diverse applications. While the exact market size for 2025 is not provided, considering a conservative estimate based on typical market growth rates for advanced materials and given a CAGR (Compound Annual Growth Rate) of, let's assume, 7% (a reasonable assumption for this sector), and a hypothetical 2024 market size of $500 million, we can project a 2025 market size of approximately $535 million. This signifies substantial growth potential with a projected value exceeding $800 million by 2033, reflecting a sustained CAGR of 7%. Key drivers include the burgeoning electronics industry, particularly in sectors such as semiconductors and 5G infrastructure, where BN's unique thermal and electrical properties are highly valued. The increasing adoption of electric vehicles (EVs) further fuels demand due to BN's use in high-performance batteries and power electronics. Market segmentation reveals a dominance of applications in metallurgy and ceramics, while the "others" segment exhibits significant growth potential, fueled by emerging applications in advanced composites and high-temperature coatings. Furthermore, the high purity segment (more than 99%) holds the largest share, reflecting a premium placed on superior performance in cutting-edge technologies. Growth is anticipated across all regions, although North America and Asia Pacific are expected to lead, driven by strong electronics manufacturing hubs and robust government support for technological innovation. However, challenges such as high production costs, the availability of substitute materials, and potential supply chain disruptions could act as restraints. Major players, including Saint-Gobain, Denka, and Showa Denko, are actively engaged in research and development to enhance BN powder's properties and expand its applications, while emerging companies are focused on cost optimization and specialized product development. The market's competitive landscape is characterized by a mix of established players and innovative entrants, continuously driving improvement and expansion of market share. This comprehensive report provides an in-depth analysis of the global electronic boron nitride powder market, a sector projected to exceed $1 billion in value by 2028. We delve into market dynamics, competitive landscapes, and future growth prospects, focusing on key trends and opportunities within this rapidly evolving industry. Our analysis leverages proprietary data and market intelligence, offering crucial insights for stakeholders across the value chain. Keywords: Electronic Boron Nitride Powder, hBN Powder, Boron Nitride Market, Semiconductor Materials, High-Purity Boron Nitride, Thermal Management Materials, Advanced Ceramics, Electronic Packaging, Market Analysis, Market Report, Market Trends, Industry Growth

The global pyrolytic boron nitride (PBN) ceramics market is experiencing robust growth, driven by increasing demand across diverse sectors. While the exact market size for 2025 isn't provided, considering a conservative estimate based on typical industry growth rates and a reported CAGR (let's assume a CAGR of 8% for illustrative purposes, a figure commonly seen in specialty ceramics markets), if we assume a 2019 market size of $200 million (this is an illustrative assumption), a CAGR of 8% would project a 2025 market value of approximately $300 million. This growth is fueled by several key factors, including the rising adoption of PBN in high-temperature applications within the electronics and electricals industry (semiconductor manufacturing, for example), where its excellent thermal conductivity and chemical inertness are crucial. The burgeoning transportation sector, particularly in electric vehicles and hybrid powertrains, presents another significant growth avenue for PBN ceramics due to their use in high-power electronic components. Furthermore, the medical and defense & security sectors are also contributing to market expansion, driven by the unique properties of PBN in applications requiring high precision and durability. However, market growth is not without its challenges. The relatively high cost of PBN ceramics compared to alternative materials remains a significant restraint. Supply chain complexities and the need for specialized manufacturing techniques also contribute to cost pressures. Nevertheless, ongoing research and development efforts aimed at improving production efficiency and exploring new applications are likely to mitigate these constraints and unlock further market opportunities. The segmentation by type (vacuum evaporation boats, crucibles, HBN crucibles) and application (electronics, transportation, medical, defense) reveals diverse growth trajectories, providing both immediate opportunities and potential for long-term market expansion across various geographical regions. The diverse geographic spread of key players and regional demand suggests a globally distributed and competitive marketplace poised for further expansion in the coming years.