AGI addresses the critical gap between increasingly complex naturalistic stimuli and the need for standardized, reusable annotations. Through GitHub-based version control and modern web interfaces, AGI enables researchers to share, refine, and build upon stimulus annotations across studies.
An annotation bank for the NSD Shared 1000 images generated using open-source Vision Language Models. The tool supports batch processing across multiple VLMs with plans to integrate Hierarchical Event Descriptors (HED) for standardized semantic annotations.
This dashboard provides comprehensive analysis of dataset citations within the NEMAR ecosystem, revealing collaboration patterns, research trends, and the impact of open neuroscience data sharing on the research community.
This is a data visualization dashboard for exploring the Healthy Brain Network EEG dataset. Features age and sex distributions, task availability metrics, mental health correlations, and per-release analysis across 11 dataset releases with over 3,600 participants.
The Hyser dataset provides 256-channel HD-sEMG recordings from 20 subjects across five distinct tasks, including gesture recognition and force control paradigms, making it ideal for developing advanced neural interfaces and prosthetic control algorithms.
The Healthy Brain Network EEG Datasets (HBN-EEG) includes 11 dataset releases containing EEG, behavioral data, and rich event annotations from participants aged 5-21 years, supporting large-scale analyses and machine-learning research on mental health.
The HBN-EEG dataset provides a comprehensive collection of high-density EEG recordings from the Healthy Brain Network project, formatted in the Brain Imaging Data Structure (BIDS) standard. This dataset includes annotated behavioral and task-condition events, making it ready for various types of analysis without the need for extensive preprocessing. With data from over 2,600 participants, the HBN-EEG dataset supports the development and validation of EEG analysis methods, including machine learning and deep learning approaches. Additionally, it aims to facilitate the creation of EEG-based biomarkers for psychiatric disorders, offering valuable insights into brain function and mental health.