The Healthy Brain Network EEG Datasets (HBN-EEG) includes 9 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.
The Lab Streaming Layer (LSL) presents a software-based solution for synchronizing data streams across multiple instruments in neurophysiological research. Utilizing per-sample time stamps and LAN-based time synchronization, LSL ensures accurate, continuous recording despite varying device clocks. It automatically corrects for network delays and jitters, maintaining data integrity through disruptions. Supporting over 150 device classes and compatible with numerous programming languages, LSL has become a vital tool for integrating diverse data acquisition systems. Its robustness and adaptability have extended its application beyond research, into art, performance, and commercial realms, making it a cornerstone for multimodal data collection and synchronization.
We developed a novel EEG system with a dual-electrode net structure for noise reduction and precise biosignal capture. Incorporating advanced software for signal processing, this invention enhances EEG accuracy, reduces setup complexity, and broadens EEG applications, including brain-computer interfaces, through real-time noise separation and immersive noise layering techniques.
We show that InfoMuNet, a novel functional biomarker based on a nonlinear network graph of muscle connectivity, can quantify the role of sensory information on motor performance. We demonstrate its potential use in precision rehabilitation interventions.
We demonstrate that seated locomotor perturbations produce differential theta-band responses in the anterior cingulate and supplementary motor areas, suggesting that tuning perturbation parameters can potentially modify electrocortical responses.
In exploring EEG re-referencing techniques, it’s emphasized that re-referencing may unintentionally remove common mode biological signals, crucial for accurate data interpretation. This document from the BRaIN Lab at the University of Central Florida discusses the implications of such data loss and proposes methods to mitigate these effects, ensuring reliable EEG analysis.
Download Paper Code and data Abstract Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations....
Mismarking fiducial locations can systematically change EEG source locations. We inestigated this effect by systematically moving the fiducial locations to simulate such errors.