Jonathan’s Status Report for 03/26/2022

I played around with adding more features to the data set and testing logistic regression and random forest models to distinguish any feature we are looking for out of a baseline signal. I mainly wanted to find ways to quickly compute “tall” peaks from an EEG stream, since these are very indicative of a feature we care about. However, when hooked up to a real-time system, I also realized we need to distinguish between disconnected noise, baseline signal without features, and samples with a feature we need to react to since oftentimes the headset will be significantly affected by noise due to a bad connection, which indicates any features generated are likely false. My next step is to find features that allow me to distinguish between different movements, like distinguishing left wink versus right wink, and organize the models I am training into a single decisioning process. I also need to reduce latency of data processing and prediction to ensure the model reacts quickly to data. However, this step should really take place after we have fully solidified our feature set, so we can optimize for parsing those specific features out of a sample. I feel a bit squeezed on time with respect to figuring out what features make the model reliable for prediction, but I will have more time in the upcoming week to do more experimentation and decide a good model.