Kalman filters provide a straightforward and interpretable means to estimate
hidden or latent variables, and have found numerous applications in control,
robotics, signal processing, and machine learning. One such application is
neural decoding for neuroprostheses. In 2020, Burkhart et al. thoroughly
evaluated their new version of the Kalman filter that leverages Bayes' theorem
to improve filter performance for highly non-linear or non-Gaussian observation
models. This work provides an open-source Python alternative to the authors'
MATLAB algorithm. Specifically, we reproduce their most salient results for
neuroscientific contexts and further examine the efficacy of their filter using
multiple random seeds and previously unused trials from the authors' dataset.
All experiments were performed offline on a single computer.

Kalman filters are widely used for estimating hidden variables and their application in neural decoding is explored in this paper, presenting a new version that leverages Bayes' theorem for improved filter performance. The paper provides an open-source Python alternative to the authors' MATLAB algorithm, and the efficacy of the new filter is examined in neuroscientific contexts using multiple random seeds and previously unused trials from the authors' dataset.