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Biphasic quasistatic brain communication for energy-efficient wireless neural implants

Nature Electronics volume 6, pages 703–716 (2023)Cite this article

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Abstract

Wearable devices typically use electromagnetic fields for wireless information exchange. For implanted devices, electromagnetic signals suffer from a high amount of absorption in tissue, and alternative modes of transmission (ultrasound, optical and magneto-electric) cause large transduction losses due to energy conversion. To mitigate this challenge, we report biphasic quasistatic brain communication for wireless neural implants. The approach is based on electro-quasistatic signalling that avoids transduction losses and leads to an end-to-end channel loss of only around 60 dB at a distance of 55 mm. It utilizes dipole-coupling-based signal transfer through the brain tissue via differential excitation in the transmitter (implant) and differential signal pickup at the receiver (external hub). It also employs a series capacitor before the signal electrode to block d.c. current flow through the tissue and maintain ion balance. Since the electrical signal transfer through the brain is electro-quasistatic up to the several tens of megahertz, it provides a scalable (up to 10 Mbps), low-loss and energy-efficient uplink from the implant to an external wearable. The transmit power consumption is only 0.52 μW at 1 Mbps (with 1% duty cycling)—within the range of possible energy harvesting in the downlink from a wearable hub to an implant.

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Fig. 1: Need for wireless communication in brain implants.
Fig. 2: System-level analysis of BP-QBC versus other available methods.
Fig. 3: Implementation and modelling of BP-QBC.
Fig. 4: Channel TF for the BP-QBC implant.
Fig. 5: BP-QBC implant/node architecture and characterization.
Fig. 6: BP-QBC transmitter and receiver performances in the SoC.

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Data availability

The data that support the plots within this paper and other findings of this study are available via GitHub at https://github.com/WISE-Lab-UF/NatE23_BP-QBC. Further details can be obtained from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Custom codes used to process the data are available via GitHub at https://github.com/WISE-Lab-UF/NatE23_BP-QBC. Further details can be obtained from the corresponding authors upon reasonable request.

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Acknowledgements

This work was supported by the Air Force Office of Scientific Research YIP Award (FA9550-17-1-0450, to S.S.), the National Science Foundation CAREER Award (grant no. 1944602, to S.S.) and the National Science Foundation CRII Award (CNS 1657455, to S.S.). We would like to thank D. Das (Purdue University) and N. Modak (Purdue University) for their co-operation and support during the development of the ICs for the neural node. The experiments in K.J. lab were supported by a Purdue Institute for Integrative Neuroscience seed grant.

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  1. Baibhab Chatterjee

    Present address: Department of Electrical Engineering, University of Florida, Gainesville, FL, USA

Authors and Affiliations

  1. Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    Baibhab Chatterjee, Mayukh Nath, Gaurav Kumar K & Shreyas Sen

  2. Center for Internet of Bodies (C-IoB), Purdue University, West Lafayette, IN, USA

    Baibhab Chatterjee, Krishna Jayant & Shreyas Sen

  3. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA

    Shulan Xiao, Krishna Jayant & Shreyas Sen

  4. Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA

    Krishna Jayant

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Contributions

B.C. and S.S. conceived the idea. B.C., S.S. and M.N. contributed to the design of the experiments for BP-QBC. B.C., M.N., G.K.K. and S.S. conducted the theoretical analysis, numerical simulations and node design, as well as performed the characterization experiments. B.C., S.X., K.J. and G.K.K. performed the in vitro and in vivo animal experiments. B.C., S.S., M.N. and K.J. analysed the experimental data. B.C., M.N. and S.S. wrote the paper. All the authors contributed to reviewing and revising the manuscript.

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Correspondence to Baibhab Chatterjee or Shreyas Sen.

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Chatterjee, B., Nath, M., Kumar K, G. et al. Biphasic quasistatic brain communication for energy-efficient wireless neural implants. Nat Electron 6, 703–716 (2023). https://doi.org/10.1038/s41928-023-01000-3

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