A deep learning framework for physiological data processing and understanding.
- [NEW🔥] Pretrained checkpoint for paper
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modelingis now available here. Learning Topology-Agnostic EEG Representations with Geometry-Aware Modelingis now available here.ContiFormer: Continuous-Time Transformer for Irregular Time Series Modelingis now available here.
To install from source code:
pip install .To install in development mode:
pip install --editable .[dev]See quick start for a quick start guide.
An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Creating a system that is fit for its intended purpose requires an understanding of how the technology works, its capabilities and limitations, and how to achieve the best performance. Microsoft has a broad effort to put our AI principles into practice. To find out more, see Responsible AI principles from Microsoft.
Our goal in publishing this code is to facilitate AI research on time-series data, especially in the field of physiology.
This code should not be used in clinical settings to influence treatment decisions.
We do not provide any data or trained models with this project. Users need to train models with their own data.
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When systems are deployed, Responsible AI testing should be performed to ensure safe and fair operation for the specific use case. No Responsible AI testing has been done to evaluate this method including validating fair outcomes across different groups of people. Responsible AI testing should be done before using this code in any production scenario.
Note: The documentation included in this ReadMe file is for informational purposes only and is not intended to supersede the applicable license terms.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT license.