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 portab...
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Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 122; no. 15; p. e2419304122 |
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Main Authors | , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
15.04.2025
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Subjects | |
Online Access | Get full text |
ISSN | 0027-8424 1091-6490 1091-6490 |
DOI | 10.1073/pnas.2419304122 |
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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. |
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AbstractList | 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. 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.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. 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 ) 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. 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. |
Author | Im, Chang-Hwan Kim, Hojoong Lee, Byeongjun Sohn, Jung Woo Kim, Ju Hyeon Lee, Jimin Cho, Seong J. Yeo, Woon-Hong Chung, Suyeong Han, Hyojeong Kang, Tae June Kim, Hodam Lee, Yoon Jae Yu, Ki Jun Kang, Tae Woog Ban, Seunghyeb Lee, Haran Kim, Hyeonseok Yi, Hoon |
Author_xml | – sequence: 1 givenname: Hodam orcidid: 0000-0003-0439-6236 surname: Kim fullname: Kim, Hodam – sequence: 2 givenname: Ju Hyeon orcidid: 0000-0001-7703-0468 surname: Kim fullname: Kim, Ju Hyeon – sequence: 3 givenname: Yoon Jae orcidid: 0000-0002-4159-5966 surname: Lee fullname: Lee, Yoon Jae – sequence: 4 givenname: Jimin surname: Lee fullname: Lee, Jimin – sequence: 5 givenname: Hyojeong orcidid: 0009-0006-4437-7274 surname: Han fullname: Han, Hyojeong – sequence: 6 givenname: Hoon surname: Yi fullname: Yi, Hoon – sequence: 7 givenname: Hyeonseok surname: Kim fullname: Kim, Hyeonseok – sequence: 8 givenname: Hojoong surname: Kim fullname: Kim, Hojoong – sequence: 9 givenname: Tae Woog surname: Kang fullname: Kang, Tae Woog – sequence: 10 givenname: Suyeong orcidid: 0009-0008-5801-4647 surname: Chung fullname: Chung, Suyeong – sequence: 11 givenname: Seunghyeb surname: Ban fullname: Ban, Seunghyeb – sequence: 12 givenname: Byeongjun surname: Lee fullname: Lee, Byeongjun – sequence: 13 givenname: Haran surname: Lee fullname: Lee, Haran – sequence: 14 givenname: Chang-Hwan orcidid: 0000-0003-3795-3318 surname: Im fullname: Im, Chang-Hwan – sequence: 15 givenname: Seong J. orcidid: 0000-0002-3528-2808 surname: Cho fullname: Cho, Seong J. – sequence: 16 givenname: Jung Woo surname: Sohn fullname: Sohn, Jung Woo – sequence: 17 givenname: Ki Jun surname: Yu fullname: Yu, Ki Jun – sequence: 18 givenname: Tae June surname: Kang fullname: Kang, Tae June – sequence: 19 givenname: Woon-Hong orcidid: 0000-0002-5526-3882 surname: Yeo fullname: Yeo, Woon-Hong |
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SubjectTerms | 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 |
Title | Motion artifact–controlled micro–brain sensors between hair follicles for persistent augmented reality brain–computer interfaces |
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