Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human-Machine Interfaces

• Type of Material: Article
• Authors: Seunghyeb Ban, Washington State University; Yoon Jae Lee, Georgia Institute of Technology; Shinjae Kwon, Georgia Institute of Technology; Yun-Soung Kim, Icahn School of Medicine at Mount Sinai; Jae Won Chang, Emory University; Jong-Hoon Kim, Washington State University; Woon-Hong Yeo, Georgia Institute of Technology
• Language: English
• Date: 2023-02-28
• Publisher: AMER CHEMICAL SOC
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors. Published by American Chemical Society
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1021/acsaelm.2c01436877
• Title of Journal or Parent Work: ACS APPLIED ELECTRONIC MATERIALS
• Volume: 5
• Issue: 2
• Start Page: 877
• End Page: 886
• Keywords: SYSTEMS; Materials Science, Multidisciplinary; flexible headband; electrooculography; Engineering; human-machine interface; ELECTRONICS; Materials Science; Engineering, Electrical & Electronic; Technology; real-time classification; deep learning; Science & Technology; EOG; soft materials; wireless bioelectronics
• Research Categories: Health Sciences, Medicine and Surgery; Computer Science; Engineering, Mechanical

Abstract

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMIs). Among them, electrooculography (EOG), measured by wearable devices, is 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 HMIs. The headband with 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 demonstrate 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 till date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a two-wheeled radio-controlled car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.