Last week was successful. I was able to run PCA to decrease the dimensionality of the sensor data, covariance matrix, and forward matrix. This reduced dimension data is able to run through the connectivity model smoothly. I was also able to take a list of cortical labels from the user, which informs the model which cortical regions the functional connectivity should be measured. I implemented a graphing tool in order to plot the connectivity values between the chosen cortical regions over time. This week I am attending a research conference. Once I return, I will ensure the code is aligned with MNE-Python's formatting guidelines and work to publish it.