This week I spent a couple hours working with Arnav to finalize our data collection and labeling system to prepare for our meeting with Professor Dueck. Once this system was implemented, I spent time with two different music students to get the headset calibrated and ready to record the raw EEG data. Finally, on Monday and Wednesday I brought it all together with the music students and Professor Dueck to orchestrate the data collection and labeling process. This involved getting the headset set up and calibrated on each student, helping Professor Dueck get the data labeling system running, and observing as the music students practiced and Professor Dueck labeled them as focused, distracted, or neutral. I watched Professor Dueck observe her students and tried to pick up on the kinds of things she was looking for while also making sure that she was using the system correctly/not encountering any issues.
I also spent a significant amount of time working on the design report. This involved doing some simple analysis on our first set of data we collected on Monday and making some key design decisions. Once we collected data for the first time on Monday, I looked through the EEG quality on the readings and found that we were generally hovering between 63 and 100 on overall EEG quality. Initially, I figured we would just live with the variable EEG quality, and go forward with our plan to pass in the power readings from each of the EEG frequency bands from each of the 5 sensors in the headset as input into the model and also add in the overall EEG quality value as input so that the model could take into account EEG quality variability. However, on Wednesday when we went to collect data again, we realized that the EEG quality from the two sensors on the forehead (AF3 and AF4) tended to be at 100 for a significant portion of the readings in our dataset. We also learned that brain activity in the prefrontal cortex(located near the forehead) is highly relevant to focus levels. This led us to decide to only work with readings where the EEG quality for both the AF3 and AF4 sensors were 100 and therefore avoid having to pass in the EEG quality as input into the model and depend on the model learning to account for variable levels of SNR in our training data. This was a key design decision because it means that we can have much higher confidence in the quality of our data going into the model because according to Emotiv, the contact quality and EEG quality is as strong as possible.
My progress is on schedule, and this week I plan to link the raw EEG data with the ground truth labels from Professor Dueck as well as implement an initial CNN for focus, distracted, or neutral state detection based on EEG power values from the prefrontal cortex. At that point, I will continue to fine tune the model and retrain as we accumulate more training data from our collaboration with Professor Dueck and her students in the School of Music.