Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing
Year: 2026
Type: article
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.
Source: Science Advances
Authors: Ju-Chun Hsieh, Mengmeng Yao, Hussein Alawieh, Victoria Koptelova, Satyam Kumar, Kai Wing Kevin Tang, Wenliang Wang, Jinmo Jeong, Hong Ding, Zoya Ahmad, Derek Wang, Tlali Engrav, Reyna Wang, Ashwin Gupta, Weilong He, William D. Moscoso-Barrera, Ayden Grimes, José del R. Millán, Huiliang Wang
Institution: The University of Texas at Austin
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W7215877349