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Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring

Nature Biomedical Engineering volume 3, pages 194–205 (2019)Cite this article

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A Publisher Correction to this article was published on 04 April 2019

A Publisher Correction to this article was published on 14 March 2019

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Abstract

Skin-interfaced medical devices are critically important for diagnosing disease, monitoring physiological health and establishing control interfaces with prosthetics, computer systems and wearable robotic devices. Skin-like epidermal electronic technologies can support these use cases in soft and ultrathin materials that conformally interface with the skin in a manner that is mechanically and thermally imperceptible. Nevertheless, schemes so far have limited the overall sizes of these devices to less than a few square centimetres. Here, we present materials, device structures, handling and mounting methods, and manufacturing approaches that enable epidermal electronic interfaces that are orders of magnitude larger than previously realized. As a proof-of-concept, we demonstrate devices for electrophysiological recordings that enable coverage of the full scalp and the full circumference of the forearm. Filamentary conductive architectures in open-network designs minimize radio frequency-induced eddy currents, forming the basis for structural and functional compatibility with magnetic resonance imaging. We demonstrate the use of the large-area interfaces for the multifunctional control of a transhumeral prosthesis by patients who have undergone targeted muscle-reinnervation surgery, in long-term electroencephalography, and in simultaneous electroencephalography and structural and functional magnetic resonance imaging.

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Fig. 1: Large-area epidermal electrodes for electrophysiological mapping and prosthetic control.
Fig. 2: Large-area epidermal electrodes for multichannel EMG and prosthetic control.
Fig. 3: Large-area epidermal electrodes for multichannel EEG.
Fig. 4: EEG/ECG performed inside an MRI scanner.
Fig. 5: Correlation of EMG and functional MRI.

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Code availability

EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for non-commercial use and open source development, together with sample data, a user’s tutorial and extensive documentation. In-house MATLAB tools were adapted from the software tools developed at Duke University (https://wiki.biac.duke.edu/biac:tools) and are available on request.

Data availability

The authors declare that all data supporting the findings of this study are available within the paper and its Supplementary Information.

Change history

  • 04 April 2019

    In Fig. 4c of this Article, the scale bar units were incorrectly stated as ‘μV’; the correct units are ‘mV’. The figure has now been amended accordingly.

  • 14 March 2019

    In Fig. 4c of this Article originally published, the bottom y axis was incorrectly labelled as ‘MRI–ECG (μV)’; the correct label is ‘MRI/ECG’. In addition, in Fig. 4d, the bottom y axis was incorrectly labelled as ‘ECG (μV)’; the correct label is ‘ECG (mV)’. The scale bar units were also incorrectly stated as ‘mV’, the correct units are ‘μV’. The figure has now been amended accordingly.

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Acknowledgements

Device fabrication and development were carried out in part in the Frederick Seitz Materials Research Laboratory Central Research Facilities and Micro-Nano-Mechanical Systems Cleanroom, University of Illinois. L.T. acknowledges the support from Beckman Institute Postdoctoral Fellowship at UIUC. The materials and device engineering aspects of the research were supported by the Center for Bio-Integrated Electronics at Northwestern University. MRI experiments were performed at the Biomedical Imaging Center at the Beckman Institute, which also provided pilot hour support. K.J.Y. acknowledges the support from the National Research Foundation of Korea (grant nos NRF-2017R1C1B5017728 and NRF-2018M3A7B4071109) and the Yonsei University Future-leading Research Initiative (grant nos RMS2 2018-22-0028). Z.X. acknowledges the support from National Natural Science Foundation of China (grant no. 11402134). Y.H. acknowledges the support from NSF (grant nos 1400169, 1534120 and 1635443). J.W.L. gratefully acknowledges support from National Research Foundation of Korea (grant nos NRF-2017M3A7B4049466 and NRF-2018R1C1B5045721). M.M. acknowledges the support provided by Beckman Institute Predoctoral and Postdoctoral Fellowships. F.D. acknowledges the support provided by a Helen Corley Petit Scholarship in Liberal Arts and Sciences and an Emanuel Donchin Professorial Scholarship in Psychology from the University of Illinois. The authors would like to thank F. Lam for valuable discussions on electromagnetic simulations. We thank representatives of BrainVision LLC and Easycap GmbH for helpful conversations related to the equipment adaptations required for this project.

Author information

Author notes

  1. These authors contributed equally: Limei Tian, Benjamin Zimmerman, Aadeel Akhtar, Ki Jun Yu.

Authors and Affiliations

  1. Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA

    Limei Tian

  2. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA

    Limei Tian, Benjamin Zimmerman, Aadeel Akhtar, Matthew Moore, Ryan J. Larsen, Brian Metzger, Florin Dolcos, Monica Fabiani, Gabriele Gratton & John A. Rogers

  3. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea

    Ki Jun Yu

  4. Applied Mechanics Laboratory, Department of Engineering Mechanics, Center for Mechanics and Materials and Center for Flexible Electronics Technology, Tsinghua University, Beijing, China

    Jian Wu, Xiaogang Guo, Yinji Ma & Yihui Zhang

  5. Department of Materials Science and Engineering, Pusan National University, Busan, Republic of Korea

    Jung Woo Lee

  6. Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL, USA

    Jinghua Li, Yuhao Liu, Subing Qu, Jue Zhang, Paul V. Braun & John A. Rogers

  7. Department of Psychology, University of Alberta, Edmonton, Alberta, Canada

    Kyle E. Mathewson

  8. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Jonathan A. Fan

  9. Department of Electrical and Computer Engineering, University of Illinois at Urbana–Champaign, Urbana, IL, USA

    Jesse Cornman & Michael Fatina

  10. Departments of Civil and Environmental Engineering, Mechanical Engineering, and Materials Science and Engineering, Northwestern University, Evanston, IL, USA

    Zhaoqian Xie & Yonggang Huang

  11. Department of Aerospace Engineering, University of Illinois at Urbana–Champaign, Urbana, IL, USA

    Timothy Bretl

  12. Feinberg School of Medicine, Northwestern University, Shirley Ryan AbilityLab, Chicago, IL, USA

    Levi J. Hargrove

  13. Departments of Materials Science and Engineering, Biomedical Engineering, Neurological Surgery, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Simpson Querrey Institute and Feinberg Medical School Center for Bio-Integrated Electronics, Northwestern University, Evanston, IL, USA

    John A. Rogers

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Contributions

L.T., B.Z., A.A., K.J.Y. and J.A.R. designed the project. L.T., B.Z., B.M., K.E.M, M.F. and G.G. conceived and performed long-term EEG studies. L.T., A.A., J.C., M.F., T.B. and L.J.H. conceived and performed EMG experiments on patients for prosthesis control. L.T., B.Z., M.M., R.J.L., J.A.F. and F.D. performed simultaneous electrophysiological recordings and MRI. J.W.L., J.L., Y.L., S.Q., J.Z. and P.V.B. assisted in the fabrication of devices and materials characterization. X.G., J.W., Z.X., Y.M., Y.Z. and Y.H. performed the mechanical and electromagnetic simulations. L.T., B.Z., A.A., M.M. and J.A.R. wrote the manuscript and K.E.M, R.J.L., J.A.F. J.W., Y.H. and F.D. provided feedback on the manuscript.

Corresponding author

Correspondence to John A. Rogers.

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Supplementary information

Supplementary Information

Supplementary figures, tables and references.

Reporting Summary

Supplementary Video 1

Signals obtained with the epidermal EMG array allow an amputee to control elbow, wrist and hand movements on the prosthesis.

Supplementary Video 2

Robustness of motion classification on vigorous tapping on the electrodes.

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Tian, L., Zimmerman, B., Akhtar, A. et al. Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring. Nat Biomed Eng 3, 194–205 (2019). https://doi.org/10.1038/s41551-019-0347-x

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