Issue

Vol. 18 (2026)

Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration

https://doi.org/10.1007/s40820-025-02042-2
Sheng Wang
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, People’s Republic of China
Xiaobin Song
Department of Cardiothoracic Surgery, Jinling Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210007, People’s Republic of China
Xiaopan Song
School of Electronics Science and Engineering, Nanjing University, Nanjing 210023, People’s Republic of China
Yang Gu
School of Electronics Science and Engineering, Nanjing University, Nanjing 210023, People’s Republic of China
Zhuangzhuang Cong
Department of Cardiothoracic Surgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210007, People’s Republic of China
Yi Shen
Department of Cardiothoracic Surgery, Jinling Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210007, People’s Republic of China
Linwei Yu
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, People’s Republic of China

Corresponding Author: Linwei Yu

yulinwei@nju.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 193

Abstract

The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.


Highlights:
1 The latest advancements in neural signal decoding and the integration of flexible bioelectronics for non-invasive brain-computer interfaces are reviewed.
2 Multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies are critical for enhancing the robustness, adaptability, and real-time performance of brain-computer interface (BCI) systems.
3 The robust real-world deployment of BCIs requires breakthroughs in cross-subject generalization, environmental adaptability, and system reproducibility.

Keywords

Non-invasive BCIs Deep learning Neural signal decoding Nanowires Flexible bioelectronics
Wang, Sheng, Xiaobin Song, Xiaopan Song, Yang Gu, Zhuangzhuang Cong, Yi Shen, and Linwei Yu. 2026. “Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration”. Nano-Micro Letters 18 (January):193. https://doi.org/10.1007/s40820-025-02042-2.
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