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

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<sup>-2</sup>) 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.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-04-07
DOI
https://doi.org/10.1073/pnas.2419304122
Citations
5
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
7.16

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article

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

Ki Jun Yu, Hodam Kim, Chang‐Hwan Im, Seong Jin Cho et al.
5 citations
Proceedings of the National Academy of Sciences
EEG and Brain-Computer Interfaces
7.16
article

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

Ki Jun Yu, Hodam Kim, Chang‐Hwan Im, Seong Jin Cho, Woon‐Hong Yeo, Ju Hyeon Kim, Hyeonseok Kim, Hoon Yi, S. Chung, Seunghyeb Ban, Haran Lee, Jimin Lee, Hojoong Kim, Yoon Jae Lee, Jung Woo Sohn, Hyojeong Han, Tae Woog Kang, Byeongjun Lee
article en
5 citations

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<sup>-2</sup>) 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.

Proceedings of the National Academy of SciencesVol. 122(15)
Kumoh National Institute of Technology (KR), Georgia Institute of Technology (US), Inha University (KR), Yonsei University (KR), Chungnam National University (KR), The Wallace H. Coulter Department of Biomedical Engineering (US), Convergence (US), Hanyang University (KR)
National Science Foundation, Korea Institute for Advancement of Technology
Openalex Percentile: Top 3%
EEG and Brain-Computer Interfaces
7.16
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