Wearable 16-channel electroencephalography for 8-day continuous monitoring

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Authors: Zongman Zhang, Yi Liu, Wenqing Yan, H Wang, Shihong Lin, Jiawei Yang, Zonglei Wang, Yuli Wang, Jiansheng Zhang, Yumiao Xu, Pengcheng Zhou, Yujie Zhang
+9 more Mingzhe Wang, Zichong Ji, Meiqiong Zheng, Shuaizhe Chen, Y K Li, Tomoyuki Yokota, Sunghoon Lee, Takao Someya, Yan Wang
Cited by: 1

Abstract

Abstract Monitoring cognitive state in everyday life requires unobtrusive electroencephalography (EEG), yet conventional systems rely on non-breathable contacts that dry and slip on hair and on bulky, low-channel hardware that compromises comfort and signal fidelity. We introduce a fully breathable, integrated wearable EEG system that enables week-scale daily brain recording with smartphone-based attention assessment. An in situ-gelled, macroporous hydrogel coupled to a thin, flexible Ag/AgCl mesh electrode establishes a hair-compatible, anti-drying, friction-resistant scalp–hydrogel–electrode interface that maintains stable long-term signals. An earhook wireless module with macroporous silicone encapsulation enhances breathability and comfort, while a wirelessly rechargeable battery supports continuous operation in everyday settings . During treadmill running (6.5 km h −1 ), the hydrogel interface suppresses stride-locked artefacts (3.02 Hz) and increases signal-to-noise ratio relative to commercial EEG paste electrodes. Overnight wireless recordings yield sleep staging and spectral metrics comparable to polysomnography benchmarking. Extending on these validations, we demonstrate continuous 16-channel scalp EEG over 8 days of naturalistic activity and use machine-learning algorithms to generate real-time attention assessment via a smartphone interface. This breathable, durable architecture enables unobtrusive, longitudinal neurophysiology and opens avenues for neurological disease monitoring, learning and performance support, workplace safety, and ecologically valid brain research.

Fields of Study & Subjects

EEG and Brain-Computer Interfaces Advanced Sensor and Energy Harvesting Materials Advanced Chemical Sensor Technologies

Publication details

Journal / source
Research Square
Publisher
Springer Science and Business Media LLC
Publication date
10 June 2026
Document type
Preprint