docs: update readme credits, description, and usage

Author: danfke

README.md

@@ -1,9 +1,9 @@
 # EMGdecomPy
 
-[![ci-cd](https://github.com/UBC-SPL-MDS/emg-decomPy/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/UBC-SPL-MDS/emg-decomPy/actions/workflows/ci-cd.yml)
-[![codecov](https://codecov.io/gh/UBC-SPL-MDS/EMGdecomPy/branch/main/graph/badge.svg?token=78ZU40UEOE)](https://codecov.io/gh/UBC-SPL-MDS/EMGdecomPy)
+[![ci-cd](https://github.com/UBC-SPL-MDS/emgdecompy/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/UBC-SPL-MDS/emgdecompy/actions/workflows/ci-cd.yml)
+[![codecov](https://codecov.io/gh/UBC-SPL-MDS/emgdecompy/branch/main/graph/badge.svg?token=78ZU40UEOE)](https://codecov.io/gh/UBC-SPL-MDS/emgdecompy)
 
-A package for decomposing raw EMG signals into individual motor unit activity.
+A package for decomposing multi-channel intramuscular and surface EMG signals into individual motor unit activity based off the blind source algorithm described in [`Negro et al. (2016)](https://iopscience.iop.org/article/10.1088/1741-2560/13/2/026027/meta).
 
 ## Proposal
 
@@ -19,20 +19,34 @@ To generate the proposal locally, run the following command from the root direct
 pip install emgdecompy
 ```
 
-## Usage
+## Usage and Example
 
-- TODO
+After installing `EMGdecomPy`, to use the package import it with the following commands:
+
+```
+from emgdecompy.decomposition import decomposition
+```
+
+**Run the blind source separation algorithm on your data to extract out the separation vectors, motor unit firing times, and associated silhouette scores and pulse-to-noise ratios.**
+
+```
+decomposition(data)
+```
 
 ## Contributing
 
 Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
 
 ## License
 
-`emgdecompy` was created by Daniel King, Jasmine Ortega, Rada Rudyak, and Rowan Sivanandam. It is licensed under the terms of the MIT license.
+`EMGdecomPy` was created by [Daniel King](github.com/danfke), [Jasmine Ortega](github.com/jasmineortega), [Rada Rudyak](github.com/Radascript), and [Rowan Sivanandam](github.com/Rowansiv). It is licensed under the terms of the MIT license.
 
 ## Credits
 
-`emgdecompy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
+`EMGdecomPy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
+
+The blind source separation algorithm in this package was based off of [`Negro et al. (2016)](https://iopscience.iop.org/article/10.1088/1741-2560/13/2/026027/meta).
 
 The data used for validation was obtained from [`Hug et al. (2021)`](https://figshare.com/articles/dataset/Analysis_of_motor_unit_spike_trains_estimated_from_high-density_surface_electromyography_is_highly_reliable_across_operators/13695937).
+
+[Guilherme Ricioli](https://github.com/guilhermerc) was consulted for his work on [`semg-decomposition`](https://github.com/guilhermerc/semg-decomposition).