This library has moved! This is essentially a legacy library that contains functions, scripts and notebooks for machine learning related to EMGs (electromyograms). The library was begun as the work of a research group at University of Twente headed by Dr. Eline Mos-Oppersma, PhD (UT-TNW). The work of the research group includes interfaces for clinicians as well as other researchers which are now housed in ReSurfEMG.
The code here is legacy, however we maintain this site for an upcoming meeting where researchers will build collaborative documents on analysis of respiratory surface electromyography in the hope of creating more standards in the field.
researcher_interface:
- These are a growing series of interactive notebooks that allow researchers to investigate questions about their own EMG data
open_work:
- This folder contains experimental work by core members of the rsemg team
The main program in this repository contains functions for analysis of EMG.
The notebooks are configured to run on various datasets. Contact Candace Makeda Moore (c.moore@esciencecenter.nl) to discuss any questions on data configuration.
How to get the notebooks running? Assuming the raw data set and metadata is available.
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Install all Python packages required, using conda and the environment.yml file. 1a. The command for Windows/Anaconda users can be something like: conda env create -f environment.yml 1b. Linux users can create their own environment by hand
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Open a notebook in researcher_interface and interactively run the cells.
Up to date documentation can be generated in command-line as follows (in bash terminal):
sphinx-apidoc -o ./docs -f --separate ./rsemg
rm -rf ./build_documentation
mkdir ./build_documentation
sphinx-build -b html ./docs ./built_documentation