I continued to work on doing more modeling with the double blink signal and implemented the proper logic to determine a double blink signal. I trained a model with about 15% error in detecting a double blink but included a single blink detection model that will process user signals before the double blink detection model to ensure better classification of double blink signals. This way, the double blink signal can only be detected once we already see a blink and some hard-coded factors within the decision model will ensure the double blinking doesn’t falsely trigger too often. To do this, if a single blink is detected, we switch to using the double blink detection since we should no longer be attempting to detect winks, and the double blink detection loop is run with a timeout of 1 second. Then the system goes back to detecting simpler signals and we get a coherent detection of double blinks that can be sent to the front end. I also worked with Jean to integrate the EMG code into the master application so that EMG can be run concurrently with EEG. Our system now operates with 4 software threads: 2 for I/O, 1 for ML model prediction, and 1 for managing the frontend application. We are now on target to do testing on our final integrated product next week. This is inline with our schedule as we are now within the slack period, and we hope to begin polishing our product next week.