Privacy-Preserving Eye Movement Classification with Camera-Free, Non-Invasive EOG-EEG Glasses

Eye movements are vital non-verbal cues for communication and for understanding human internal states. This paper investigates eye-based interaction, focusing on blinks and gaze, using a pair of wireless glasses that measures electrooculogram (EOG) and electroencephalogram (EEG) signals. Our system addresses privacy concerns in digital health tech through a camera-free, microphone-free setup. We report on signal processing, machine learning algorithms, and decision-support systems, filling gaps in the research around EOG and EEG for eye blink and gaze classification in a wearable form-factor and using only 4 electrodes. We believe that our paper supports groundwork for privacy-preserving, multimodal wearables at the intersection of technology and human interaction.