Last week was successful in altering the pre-processing steps so that the model would process data in regards to the 4 labels included with the sample dataset instead of the 12 labels I had used for a previous dataset in past usage of the model. The model also ran successfully in the conda environment used by MNE-Python which includes several dependencies specific to MNE-Python. I had to add a single dependency used for calculating derivatives and performing back propagation. Moving forward, I will begin to reconstruct the API I had working in week 8 one step at a time and ensuring that I can compute the same output after each processing stop. The first step is an SNR boosting step in which a small number of epochs (per experimental condition) are averaged together in order de-noise the data. Once I check that the averaged epochs are the same from my API output compared to my original model output (computed strictly using command line), I can move forward toward computing PCA on the data. I will ensure that the same number of principal components are computed from my API and command line, and again, ensure from this step that I can produce the same output as produced from my model in command line.