Github
Neuromonitoring Analytics
Open-source tools for signal processing, interoperability, data visualization, and AI/ML in neurocritical care.
Neurominitoring Analytics Network Github
Open-Source Tools for Neuromonitoring Development
This space brings together open-source projects for working with complex physiologic data in both research and clinical settings. It’s built for clinicians, researchers, and engineers who want practical tools they can use, adapt, and build on.
Projects are organized to make development easier to share and reuse, so you can apply them across different workflows without starting from scratch. Many can be modified to fit specific use cases or combined with other tools depending on your needs.
Explore the projects below to access code, contribute to ongoing work, or use these tools in your own workflows.
Run and adapt open-source code for data workflows
Collaborate on multimodal signal workflows
Reuse algorithms for visualization, preprocessing, and features
Implement shared methods in analytics pipelines
FAQs
GitHub is a platform used to store, share, and collaborate on code and software projects. It is widely used in research and engineering to build and maintain open-source tools.
This space exists to support the development and sharing of open-source tools for analytics and data workflows.
It brings together clinicians, researchers, and engineers to build practical resources for working with complex physiologic datasets, including signal processing, interoperability, visualization, and AI/ML workflows.
The goal is to make high-quality tools more accessible, reproducible, and adaptable across research and clinical environments.
This resource is intended for a wide range of professionals working with physiologic data and analytics, including:
- Neurointensivists
- Neuroscientists
- Data scientists
- Biomedical engineers
- Software developers
- Neurocritical care fellows
- Students and trainees
It is especially useful for those building or working with code, data pipelines, and analytics workflows.
Anyone interested in computational approaches to brain monitoring will find this valuable, but it remains open and accessible to all users.
This space contains open-source tools and frameworks for working with complex physiologic data from critical care environments. It includes resources for signal processing, data visualization, interoperability between formats, event detection, and example research workflows.
These tools are designed to support EEG, ICP, ABP, rCBF, NIRS, and other multimodal data streams commonly used in both clinical and research settings.
The content focuses on tools and workflows for working with multimodal data, including:
- Signal processing for physiologic data (e.g., EEG, ICP, ABP)
- Data conversion, formatting, and interoperability across systems
- Event detection and physiologic pattern analysis
- Data visualization, dashboards, and exploratory analysis tools
- Example notebooks and reproducible research workflows
These resources are designed to support clinical research, ICU analytics, and development of new methods for interpreting multimodal physiologic data.
You can engage with this resource in several ways depending on your experience and goals. You can explore repositories, use tools in your own workflows, or adapt existing code for your use cases. If you’d like to get involved, you can suggest improvements, propose new features, or contribute code and examples.
Participation can range from reviewing existing work to building new tools. Discussion and collaboration are part of how the resource grows.
No contribution is too small.
This repository is part of a broader analytics ecosystem, where each resource serves a distinct role.
- GitHub provides tools, code, and reproducible implementations for working with data and enabling practical application
- The Wiki focuses on interpretation and understanding of signals and derived analytics, building shared context around how data is used
- Discord supports discussion, questions, and real-world edge cases from users, helping refine ideas through community input
Together, these resources form a continuous loop between implementation, knowledge, and collaboration, moving from practical development, to shared understanding, to ongoing discussion and refinement.
No. These tools are designed to be system-agnostic and are not dependent on any specific platform or infrastructure.
They are intended to work across a variety of neuromonitoring systems and data environments, making them broadly applicable to both research and clinical workflows regardless of the underlying monitoring setup.
