This project uses machine learning methods to create a neural decoder. A neural decoder is a model that has the ability to predict what stimiulus evoked some neural response. Neural decoders are useful in brain-computer interfaces (BCI) as the software that drives the transformation of a user's neural input into the BCI into intention to affect the environemnt. This project is guided by Kording Lab's paper on Machine Learning for Neural Decoding.
For easy execution, the Google Colab notebook corresponding to the iPython notebook containing my data analysis and machine learning code can be found here. Feel free to add comments or point out references to improve my methods!