Setting Up and Managing Git-Annex with Backblaze B2 Storage
This guide walks you through setting up git-annex with Backblaze B2 as remote storage, and covers common usage scenarios for managing large files in git repositories.
Seyed Yahya Shirazi
A Standardized Framework for Sensor Placement in Human Motion Capture and Wearable Applications
We present a comprehensive framework that standardizes sensor placement in human movement and physiological monitoring applications. Through precise definitions of anatomical landmarks, coordinate systems, and placement protocols, our framework enables reproducible sensor positioning across different applications and laboratories. The system provides quantifiable levels of placement precision and is compatible with existing data-sharing standards like BIDS and HED. This standardization addresses the critical need for consistent sensor placement across applications ranging from clinical biomechanics to consumer wearables, enhancing data quality, reproducibility, and interoperability in human biosensing research.
HBN-EEG: Healthy Brain Network EEG Datasets
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.
HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset
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.
Older adults use fewer muscles to overcome perturbations during a seated locomotor task
Older adults often demonstrate greater co-contraction and motor errors than young adults in response to motor perturbations. We demonstrated that older adults reduced their motor errors more than young adults with brief perturbations during recumbent stepping while maintaining greater muscle co-contraction. In doing so, older adults largely used one muscle pair to drive the stepper, tibialis anterior and soleus, while young adults used all muscles. These two muscles are crucial for maintaining upright balance.
The Lab Streaming Layer for Synchronized Multimodal Recording
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.
System and methods for biosignal detection and active noise cancellation
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.
Non-Parametric Functional Muscle Network as a Robust Biomarker of Fatigue
We show that the effects of fatigue on muscle coordination and neural drive can be reliably characterized using a non-parametric functional muscle network. The network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. The proposed approach has the potential to be a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.
Re-Referencing Methods for High-Density EEG
This project investigates different re-referencing approaches for high-density EEG recordings, evaluating their effectiveness in reducing artifacts and improving source localization accuracy. The work contributes to best practices for EEG preprocessing pipelines.
Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke
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.