Evidence map›Paper›PMID 41985122›Full record

ArticleACS nano2026

Event-Driven Neuromorphic Gaze Decoding via e-Skin Electrooculography.

Siwoo Jeong, Hyun Woo Ko, Ji-Hoon Kang, Jonghyeon Ko, Inbo Sim, Zhihao Xu, Hyeonu Jeong, Seung-Il Kim, Yunseok Choi, Suyeon Jo and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Siwoo JeongDepartment of Sports Rehabilitation Medicine, Kyungil University, Gyeongsan 38428, Republic of Korea.
Hyun Woo KoCenter for Quantum Technology, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
Ji-Hoon KangDepartment of Electrical and Computer Engineering, and Program in Semiconductor Convergence, Inha University, Incheon 22212, Republic of Korea.ORCID 0000-0001-6805-9840
Jonghyeon KoDepartment of AI Convergence, Kunsan National University, Gunsan 54150, Republic of Korea.
Inbo SimDepartment of Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia 22904, United States.
Zhihao XuDepartment of Mechanical Engineering and Materials Science and the Institution of Materials Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Hyeonu JeongDepartment of Electrical and Computer Engineering, and Program in Semiconductor Convergence, Inha University, Incheon 22212, Republic of Korea.
Seung-Il KimDepartment of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Yunseok ChoiDepartment of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Suyeon JoCenter for Quantum Technology, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.ORCID 0009-0000-8138-594X
Jae Won ShimSchool of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.ORCID 0000-0001-8387-160X
Paul Hongsuck SeoDepartment of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
Min Seong ChaeDepartment of Industrial Engineering, Jeonju University, Jeonju 55069, Republic of Korea.ORCID 0009-0008-5198-8246
Yeni KimDepartment of Industrial Engineering, Jeonju University, Jeonju 55069, Republic of Korea.ORCID 0009-0002-9249-4636
Abdul RehmanConvergence Institute of Human Data Technology, Jeonju University, Jeonju 55069, Republic of Korea.
Hanwool YeonSchool of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
Bo-In ParkDepartment of Semiconductor Science & Technology, Jeonbuk National University, Jeonju 54896, Republic of Korea.ORCID 0000-0002-9084-3516
Young-Woo SuhDepartment of Ophthalmology, Korea University College of Medicine, Guro Hospital, Seoul 08308, Republic of Korea.
Hwa LeeDepartment of Ophthalmology, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea.
Jeehwan KimDepartment of Mechanical Engineering, Research Laboratory of Electronics, and Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID 0000-0002-1547-0967
Kyusang LeeDepartment of Electrical and Computer Engineering, and Material Science and Engineering, University of Virginia, Charlottesville, Virginia 22904, United States.
Sang-Hoon BaeDepartment of Mechanical Engineering and Materials Science and the Institution of Materials Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.ORCID 0000-0002-1518-7635
Min-Chul ParkCenter for Quantum Technology, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
Sungchul MunDepartment of Industrial Engineering, Convergence Institute of Human Data Technology, and Department of Data Science, Jeonju University, Jeonju 55069, Republic of Korea.ORCID 0000-0003-4596-9889

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

ElectrooculographyNeural Networks, ComputerWearable Electronic DevicesHumansattention mechanismelectronic skinelectrooculographyextended realityneuromorphic computingresistive random-access memoryspiking neural network

Identifiers

PMID41985122
PMCPMC13131047

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.