Event-Driven Neuromorphic Gaze Decoding via e-Skin Electrooculography
Wearable eye-tracking technologies remain constrained by bulky optics, high power consumption, and reliance on external computation. We present a hardware-software codesigned electrooculography (EOG) interface that integrates ultrathin conformal e-skin sensors with resistive random-access memory (RRAM) crossbar, used to implement synaptic vector-matrix multiplication within a neuromorphic processing pipeline for real-time gaze decoding. Conformal e-skin sensors provide stable and continuous acquisition of both vertical and horizontal oculomotor signals, which are transformed into attention-guided spike features for classification by a lightweight spiking neural network (SNN). Implemented on an RRAM array with quantized weights after noise-aware training, the system achieves robust inference while substantially lowering latency and energy demand. The proposed framework is designed for local edge-level computation, thereby preserving user privacy and eliminating the need for cloud-based inference. By eliminating head-mounted optics, this glassless architecture enables unobtrusive and energy-efficient wearable interfaces. These results establish flexible bioelectronics with neuromorphic processors to advance immersive computing, assistive interfaces, and mobile health monitoring.
Authors
- Young-Woo Suh (ORCID: https://orcid.org/0000-0003-4857-206X)
- Sungchul Mun (ORCID: https://orcid.org/0000-0003-4596-9889)
- Min chul Park
- Min Seong Chae (ORCID: https://orcid.org/0009-0008-5198-8246)
- Bo‐In Park (ORCID: https://orcid.org/0000-0002-9084-3516)
- Paul Hongsuck Seo (ORCID: https://orcid.org/0000-0002-6747-1101)
- Kyusang Lee (ORCID: https://orcid.org/0000-0001-7123-3395)
- Inbo Sim
- Jonghyeon Ko (ORCID: https://orcid.org/0000-0002-8322-8056)
- Siwoo Jeong (ORCID: https://orcid.org/0000-0001-7081-6900)
- Sang‐Hoon Bae (ORCID: https://orcid.org/0000-0002-1518-7635)
- S Kim
- Ji‐Hoon Kang (ORCID: https://orcid.org/0000-0001-6805-9840)
- Jae Won Shim (ORCID: https://orcid.org/0000-0001-8387-160X)
- Yeni Kim (ORCID: https://orcid.org/0009-0002-9249-4636)
- YunSeok Choi (ORCID: https://orcid.org/0000-0002-9971-1501)
- Hyun Woo Ko
- Suyeon Jo
- Jeehwan Kim
- Hanwool Yeon
- Abdul Rehman
- Hyeonu Jeong
- Zhihao Xu
- Hwa Lee
Institutions
- Kunsan National University (KR)
- Inha University (KR)
- Korea University (KR)
- Washington University in St. Louis (US)
- Korea University Medical Center (KR)
- Netaji Subhas National Institute of Sports (IN)
- Gwangju Institute of Science and Technology (KR)
- Kyungil University (KR)
- Department of Physics, Mathematics and Informatics (BY)
- Division of Materials Science and Engineering (AU)
- Material Sciences (United States) (US)
- ON Semiconductor (South Korea) (KR)
- Convergence (US)
- International Data Group (Sweden) (SE)
- University of Virginia (US)
- Korea Institute of Science and Technology (KR)
- Massachusetts Institute of Technology (US)
- Jeonju University (KR)
- Jeonbuk National University (KR)
Publication Details
- Journal
- ACS Nano
- Published
- 2026-04-15
- DOI
- https://doi.org/10.1021/acsnano.5c19720
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Research Foundation
- Ministry of Trade, Industry and Energy
- Korea Institute of Science and Technology
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- U.S. Air Force
- Division of Electrical, Communications and Cyber Systems
- Air Force Office of Scientific Research