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Updated: Apr 24, 2026

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From Bio-Interface Materials to Neural Integration: The Next-Generation Brain-Machine Interfaces Powered by

Zhenchun Li1, Rongfeng Ge1, Zhiyuan Zhao1

  • 1Key Laboratory of Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Changchun, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)
|April 23, 2026
PubMed
Summary
This summary is machine-generated.

Hydrogel electrodes offer a breakthrough for brain-machine interfaces (BMIs) by overcoming the mechanical mismatch of traditional metal electrodes. This review explores their properties, applications in neurological disorders, and future development for stable, clinically translatable BMI systems.

Keywords:
biocompatibilitybrain–machine interfacedisease treatmenthydrogel electrode

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Area of Science:

  • Biomaterials Science
  • Neuroscience
  • Medical Devices

Background:

  • Brain-machine interfaces (BMIs) are crucial for neural recording, modulation, and rehabilitation.
  • Traditional metal electrodes exhibit poor biocompatibility and mechanical mismatch with neural tissue, causing inflammation and instability.
  • Hydrogel electrodes present a promising alternative due to their softness, high ionic conductivity, and tissue-like compliance.

Purpose of the Study:

  • To systematically review the material properties of hydrogel-integrated BMIs.
  • To categorize hydrogel-based BMIs into non-invasive and invasive systems.
  • To discuss critical issues and therapeutic applications of hydrogel-based BMIs.

Main Methods:

  • In-depth investigation of hydrogel characteristics: toughness, adhesion, conductivity, and biocompatibility.
  • Categorization of hydrogel-based BMIs based on operational principles and signal acquisition.
  • Review of surgical implantation, data fusion, AI integration, and clinical translation challenges.

Main Results:

  • Hydrogels mitigate mechanical mismatch issues associated with traditional electrodes.
  • Hydrogel-based BMIs show potential in treating neurological disorders like Alzheimer's, Parkinson's, epilepsy, stroke, neuropathic pain, and depression.
  • Identified persistent challenges and proposed innovative strategies for hydrogel-based BMI development.

Conclusions:

  • Hydrogel electrodes represent a significant advancement for brain-machine interfaces.
  • Further development is needed to address challenges in surgical implantation, data fusion, and AI integration for clinical translation.
  • A roadmap for next-generation hydrogel-based biotherapeutic technologies is outlined for high-fidelity, stable, and clinically translatable BMI systems.