Brain–machine interfaces (BMIs) offer the potential for the development of communication tools between the brain and external devices. The current BMI technologies for recording and modulation of electric signals from the brain have made significant contributions to areas such as neuroscience, disease diagnosis, and rehabilitation. Next-generation BMIs require long-term stable recording and modulation of electrical signals from statistically significant neuron populations with millisecond single-cell spatiotemporal resolution. However, there are challenges to achieving this stability due to the mechanical and geometrical mismatches between electronics and the brain tissue. In addition, the requirement to achieve cell-type-specific neuromodulation and transmit and process the ever-increasing volume of data on-the-fly necessitates the implementation of smart electronics. In this review, we first summarize the requirements, challenges, and current limitations of BMIs. We then highlight three major approaches to the fabrication of flexible electronics as implantable electronics, aimed at enabling long-term stable and gliosis-free BMIs. The progress of multifunctional electronics for multimodal recording and modulation of cell-type-specific components in the brain is also discussed. Furthermore, we discuss the integration of wireless and closed-loop modulation, and on-chip processing as smart electronic components for BMIs. Finally, we examine the remaining challenges in this field and the future perspectives for how flexible and smart electronics can address these problems and continue to advance the field of BMIs.
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Flexible and smart electronics for single-cell resolved brain–machine interfaces
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March 2023
Review Article|
March 10 2023
Flexible and smart electronics for single-cell resolved brain–machine interfaces

Special Collection:
Flexible and Smart Electronics
Ariel J. Lee
;
Ariel J. Lee
(Conceptualization, Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing)
1
John A. Paulson School of Engineering and Applied Sciences, Harvard University
, Boston, 02134 Massachusetts, USA
2
Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)
, Cambridge, 02139 Massachusetts, USA
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Wenbo Wang
;
Wenbo Wang
(Conceptualization, Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing)
1
John A. Paulson School of Engineering and Applied Sciences, Harvard University
, Boston, 02134 Massachusetts, USA
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Jia Liu
Jia Liu
a)
(Conceptualization, Data curation, Funding acquisition, Writing – original draft, Writing – review & editing)
1
John A. Paulson School of Engineering and Applied Sciences, Harvard University
, Boston, 02134 Massachusetts, USA
a)Author to whom correspondence should be addressed: jia_liu@seas.harvard.edu
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a)Author to whom correspondence should be addressed: jia_liu@seas.harvard.edu
Note: This paper is part of the special collection on Flexible and Smart Electronics.
Appl. Phys. Rev. 10, 011314 (2023)
Article history
Received:
July 27 2022
Accepted:
February 13 2023
Connected Content
A companion article has been published:
Not just mind reading: improving the stability and scalability of brain-machine interfaces
Citation
Ariel J. Lee, Wenbo Wang, Jia Liu; Flexible and smart electronics for single-cell resolved brain–machine interfaces. Appl. Phys. Rev. 1 March 2023; 10 (1): 011314. https://doi.org/10.1063/5.0115879
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