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Additive Manufacturing-Enabled Wireless Flexible Hybrid Electronics
This paper reports an ensemble of strategies for the successful miniaturization of EEG in a fully-flexible, wearable and wireless platform.
Analysis Lab
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Authored By:
Musa Mahmood, Saswat Mishra
George W. Woodruff School of Mechanical Engineering, Institute for Electronics and
Nanotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332
Woon-Hong Yeo
Petit Institute for Bioengineering and Biosciences, Center for Flexible Electronics,
Bioengineering Program, Neural Engineering Center, Institute for Materials, Georgia Institute of
Technology, Atlanta, Georgia 30332
Summary
Inherent variation among human brains causes difficulty in the design of electroencephalography (EEG)-enabled universal brain-machine interfaces (BMI). Existing EEG systems suffer from inconsistent signal quality, while requiring many rigid wires and metal electrodes on a hair cap. Although recent machine learning techniques offer a simpler EEG arrangement with fewer electrodes, these EEG devices still involve intrusive and heavy headgear, equipped with separate non-portable electrical hardware. Here, we introduce a fully portable, wireless, flexible hybrid system on a soft elastomeric membrane, which represents an ergonomic, comfortable, long-term wearable BMI. Additive manufacturing, based on aerosol jet printing, fabricates an ultrathin, open mesh electrode that can be mounted on the skin for biopotential recording, while a wireless electronic circuit is manufactured by the combination of material transfer printing and hard-soft materials integration.
These imperceptible soft electronics incorporates a nanomembrane electrode on non-hair-bearing skin, flexible electrodes on hair-bearing scalp, and flexible circuit on the neck for wireless data acquisition. Analytical and computational studies of materials and mechanics establish the fundamental design criteria of the flexible, skin-like hybrid electronics (SHE), which enables seamless, portable EEG recording with significantly enhanced signal quality over commercial systems. With six human participants, this portable system achieves the most efficient information transfer rate (111.75 ± 1.15 bits per minute per channel). An in vivo demonstration of the SHE-enabled BMI shows precise, low-latency control of a wireless wheelchair via two-channel EEG.
Conclusions
Collectively, this paper reports an ensemble of strategies for the successful miniaturization of EEG in a fully-flexible, wearable and wireless platform. Such a device can improve many rehabilitation and therapeutic applications for patients and physicians. Future study would focus on addition of fully elastomeric, wireless self-adhesive electrodes that can be mounted on the hairy scalp with integrated electronics toward motor imagery applications and another long-term EEG study.
Initially Published in the SMTA Proceedings
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