Raw_EOG_2Ch_classified.csv

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMI). Among them, electrooculograms (EOG) measured by wearable devices are used for eye movement-enabled HMI. Most prior studies have utilized conventional gel electrodes for EOG recording. However, the gel is problematic due to skin irritation, while separate bulky electronics cause motion artifacts. Here, we introduce a low-profile, headband-type soft wearable electronic system with embedded stretchable electrodes and a flexible wireless circuit to detect EOG signals for persistent HMI. The headband with embedded dry electrodes is printed with flexible thermoplastic polyurethane. Nanomembrane electrodes are prepared by thin film deposition and laser cutting techniques. A set of signal processing data from dry electrodes demonstrates successful real-time classification of eye motions, including blink, up, down, left, and right. Our study shows that the convolutional neural network performs exceptionally well compared to other machine learning methods, showing 98.3% accuracy with six classes: the highest performance to date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a 2-wheeled RC car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.