Motion artifact–controlled micro–brain sensors between hair follicles for persistent augmented reality brain–computer interfaces

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 122; no. 15; p. e2419304122
• Main Authors: Kim, Hodam, Kim, Ju Hyeon, Lee, Yoon Jae, Lee, Jimin, Han, Hyojeong, Yi, Hoon, Kim, Hyeonseok, Kim, Hojoong, Kang, Tae Woog, Chung, Suyeong, Ban, Seunghyeb, Lee, Byeongjun, Lee, Haran, Im, Chang-Hwan, Cho, Seong J., Sohn, Jung Woo, Yu, Ki Jun, Kang, Tae June, Yeo, Woon-Hong
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 15.04.2025
• Subjects: Algorithms; Augmented Reality; Brain - physiology; Brain-Computer Interfaces; Conducting polymers; Density; EEG; Electrodes; Electroencephalography - instrumentation; Electroencephalography - methods; Electronics; Evoked Potentials, Visual - physiology; Flexible components; Follicles; Hair; Hair Follicle - physiology; Human motion; Human-computer interface; Humans; Impedance; Interfaces; Motion; Polymers; Sensors; Signal classification; conducting polymer; electroencephalography; augmented reality; brain–computer interfaces; micro–brain sensors
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2419304122

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.