Motion artifact–controlled micro–brain sensors between hair follicles for persistent augmented reality brain–computer interfaces
Abstract
Modern brain–computer interfaces (BCI), utilizing electroencephalograms for bidirectional human–machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin–electrode impedance, and bulky electronics, diminishing the system’s continuous use and portability. Here, we introduce motion artifact–controlled micro–brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm −2 ) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject’s excessive motions, including standing, walking, and running. A demonstration captures this system’s capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI’s applications for interactive digital environments.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (19)
Hodam Kim
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Ju Hyeon Kim
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Yoon Jae Lee
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Jimin Lee
Hyojeong Han
Department of Biomedical Engineering, Hanyang University
Hoon Yi
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Hyeonseok Kim
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Hojoong Kim
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Tae Woog Kang
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Suyeong Chung
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Seunghyeb Ban
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Byeongjun Lee
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Haran Lee
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology
Chang-Hwan Im
Department of Biomedical Engineering, Hanyang University
Seong J. Cho
Department of Mechanical Engineering, Chungnam National University
Jung Woo Sohn
School of Mechanical System Engineering, Kumoh National Institute of Technology
Ki Jun Yu
Functional Bio-integrated Electronics and Energy Management Laboratory, School of Electrical and Electronic Engineering, Yonsei University
Tae June Kang
Department of Mechanical Engineering, Inha University
Woon-Hong Yeo
Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology