Stretchable, hair-compatible, and long-term stable wearable EEG system
Hsieh, J.-C.; Yao, M.; Alawieh, H.; Koptelova, V.; Kumar, S.; Tang, K. K. W.; Wang, W.; Jeong, J.; Ding, H.; Chae, T.; Ahmad, Z.; Wang, D.; Engrav, T.; Wang, R.; Gupta, A.; He, W.; Moscoso-Barrera, W. D.; Grimes, A.; Millan, J. d. R.; Wang, H.
Show abstract
Electroencephalography (EEG) is a cornerstone in both neuroscience research and clinical diagnostics. However, conventional EEG monitoring faces hardware limitations, particularly its adaptability and stability. Headsets either require complicated wiring or do not have enough stretchability and wearability to comply with the diverse head anthropometry and hair conditions of the user population. Additionally, there is an inherent tradeoff between wet and dry electrodes in capturing high-fidelity signals from hair-covered scalp regions while ensuring continuous and long-term recording quality. Here, we present a Mesh-integrated, Stretchable, and Hair-compatible EEG system engineered to overcome these limitations. By incorporating a kirigami-inspired mesh design and stretchable eutectic Gallium-Indium interconnects, MindStretcH adapts to various head sizes and allows for easy wearing and removal. Moreover, its uniquely designed porous, conical, soft 3D-printed mold, embedded with hydrogel electrodes, effectively penetrates hair layers to deliver low impedance and sustained signal integrity with minimal discomfort. We validate MindStretcH through offline and online EEG-based brain-computer interface tasks over a month, demonstrating its exceptional stability in continuous monitoring and dynamic applications. These results mark a promising advance toward non-invasive neural interfaces in both clinical and everyday use.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Stiffness-tunable neurotentacles for minimally invasive implantation and long-term neural activity recordings 94%
- Multimodal layer-crossing interrogation of brain circuits enabled by microfluidic axialtrodes 94%
- Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C 93%
Similar papers in this journal
- Bioadhesive Hydrogel-Coupled and Miniaturized Ultrasound Transducer System for Long-Term, Wearable Neuromodulation 96%
- A biodegradable and restorative peripheral neural interface for the interrogation of neuropathic injuries 95%
- Shape-changing electrode array for minimally invasive large-scale intracranial brain activity mapping 95%
Similar papers in this journal
- A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings 92%
- Time-Domain Diffuse Optical Tomography for Precision Neuroscience 92%
- Neuronal avalanches as a predictive biomarker of BCI performance- towards a tool to guide tailored training 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.