Evidence map›Paper›PMID 42845760›Full record

ArticleNanophotonics (Berlin, Germany)2026

Noninvasive Intelligent Brain-Metasurface System for Real-Time Electromagnetic Wave Manipulation.

Xiao Hai, Ruiyang Shen, Xiaoyi Wang, Guo-Min Yang

Abstract read
In one paragraph

Article in Nanophotonics (Berlin, Germany), 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

4 authors.

Xiao HaiKey Laboratory for Information Science of Electromagnetic Waves School of Information Science and Technology Fudan University Shanghai China.
Ruiyang ShenKey Laboratory for Information Science of Electromagnetic Waves School of Information Science and Technology Fudan University Shanghai China.ORCID https://orcid.org/0009-0008-5845-9195
Xiaoyi WangCollege of Electronic and Information Engineering Tongji University Shanghai China.
Guo-Min YangKey Laboratory for Information Science of Electromagnetic Waves School of Information Science and Technology Fudan University Shanghai China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A noninvasive intelligent brain-computer interface (BCI) is integrated with a reconfigurable metasurface to establish a direct neural-to-electromagnetic transduction pathway for real-time, multimodal wave control. By combining convolutional-neural-network-based decoding of motor-imagery electroencephalography (EEG) signals with threshold-based recognition of electrooculography (EOG) commands, the proposed system converts neural and ocular activities into high-speed control commands, which are subsequently implemented through an FPGA-driven dual-polarized reconfigurable metasurface for real-time electromagnetic-wave manipulation. This architecture concurrently enables beam steering, polarization conversion, and radar cross-section (RCS) reduction within a unified platform. Three distinct operational modes are demonstrated: polarization-invariant beam deflection, joint steering and polarization conversion, and broadband RCS reduction via random phase coding. Experimental results reveal a peak gain of 18.54-19.29 dBi with a 3 dB beamwidth of 12°-15° at a +20° steering angle, and an RCS reduction exceeding 10 dB across 3.7-3.85 GHz. The proposed intelligent BCI-metasurface paradigm validates the feasibility of brain-controlled electromagnetic manipulation, opening new avenues for adaptive wireless communications, immersive virtual environments, and next-generation assistive technologies.

Indexed as

BCICNNmetasurfacesmotor imagery

Identifiers

PMID42845760
PMCPMC13642466

What OpenQuestion holds

Textmetadata
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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.