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Peer-reviewedCC BY-NCScience Advances

Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing

  • Ju-Chun Hsieh1
  • Mengmeng Yao1
  • Hussein Alawieh2
  • Victoria Koptelova1
  • Satyam Kumar2
  • Kai Wing Kevin Tang1
  • Wenliang Wang1
  • Jinmo Jeong1
  • Hong Ding1
  • Zoya Ahmad1
  • Derek Wang1
  • Tlali Engrav1
7 more authors
  • Reyna Wang1
  • Ashwin Gupta1
  • Weilong He1
  • William D. Moscoso-Barrera1
  • Ayden Grimes1
  • José del R. Millán1,2,3,4
  • Huiliang Wang1
4 affiliations
  1. 1Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
  2. 2Chandra Department of Electrical and Computer Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
  3. 3Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.
  4. 4Mulva Clinic for the Neurosciences, The University of Texas at Austin, Austin, TX 78712, USA.
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Abstract

Reliable brain-computer interfaces (BCIs) require electroencephalography (EEG) sensors that maintain conformal, low-impedance contact through hair, motion, and repeated use. Here, we present MindStretcH, a stretchable and hair-compatible EEG platform integrating a kirigami-patterned elastomeric mesh, deformable eutectic gallium-indium (EGaIn) interconnects, and detachable hydrogel-based conical electrodes. The system adapts to variations in head geometry, preserves electrical performance under substantial mechanical deformation, and maintains stable electrode-scalp contact over prolonged use. Longitudinal motor-imagery recordings and repeated online BCI experiments showed consistent task-related EEG activity without measurable deterioration in recording quality. These results establish MindStretcH as a reusable platform for long-term EEG monitoring and BCI applications in laboratory, clinical, and home settings.

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Cite

BibTeXRIS
BibTeX
@article{hsieh2026highly,
  title = {{Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing}},
  author = {Ju-Chun Hsieh and Mengmeng Yao and Hussein Alawieh and Victoria Koptelova and Satyam Kumar and Kai Wing Kevin Tang and Wenliang Wang and Jinmo Jeong and Hong Ding and Zoya Ahmad and Derek Wang and Tlali Engrav and Reyna Wang and Ashwin Gupta and Weilong He and William D. Moscoso-Barrera and Ayden Grimes and José del R. Millán and Huiliang Wang},
  journal = {Science Advances},
  year = {2026},
  month = oct,
  doi = {10.1126/sciadv.aee1540},
  url = {https://www.science.org/doi/10.1126/sciadv.aee1540},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing
AU  - Ju-Chun Hsieh
AU  - Mengmeng Yao
AU  - Hussein Alawieh
AU  - Victoria Koptelova
AU  - Satyam Kumar
AU  - Kai Wing Kevin Tang
AU  - Wenliang Wang
AU  - Jinmo Jeong
AU  - Hong Ding
AU  - Zoya Ahmad
AU  - Derek Wang
AU  - Tlali Engrav
AU  - Reyna Wang
AU  - Ashwin Gupta
AU  - Weilong He
AU  - William D. Moscoso-Barrera
AU  - Ayden Grimes
AU  - José del R. Millán
AU  - Huiliang Wang
PY  - 2026
DA  - 2026/10/02
JO  - Science Advances
DO  - 10.1126/sciadv.aee1540
UR  - https://www.science.org/doi/10.1126/sciadv.aee1540
AB  - Reliable brain-computer interfaces (BCIs) require electroencephalography (EEG) sensors that maintain conformal, low-impedance contact through hair, motion, and repeated use. Here, we present MindStretcH, a stretchable and hair-compatible EEG platform integrating a kirigami-patterned elastomeric mesh, deformable eutectic gallium-indium (EGaIn) interconnects, and detachable hydrogel-based conical electrodes. The system adapts to variations in head geometry, preserves electrical performance under substantial mechanical deformation, and maintains stable electrode-scalp contact over prolonged use. Longitudinal motor-imagery recordings and repeated online BCI experiments showed consistent task-related EEG activity without measurable deterioration in recording quality. These results establish MindStretcH as a reusable platform for long-term EEG monitoring and BCI applications in laboratory, clinical, and home settings.
SN  - 2375-2548
ER  -