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Challenges for Large-Scale Brain-Machine Interfaces

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Handbook of Neuroengineering

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Abstract

The execution of complex, naturalistic neural tasks relies on the coordinated operation of cortical microcircuits across multiple related functional areas of the brain. Cortical BCI technologies aimed at accessing these distributed computations are thus anticipated to require a large number of spatially diverse, implanted electronic listening posts or nodes, appropriately positioned in physical proximity to the sources of these neural signals. From a neuroengineering perspective, key aspects of the specifications for a next-generation BCI system include considerations of the channel counts that may be safely implanted chronically in vivo subjects, as well as efficient approaches for physical implementations of large arrays of microscale electronic probes. Data rates for extracting brain signals at a useful resolution have to be contemplated in the context of designing a commensurate communication link facilitating low-latency forward transmission for decoding by external computing platforms. This must of course occur in concert with the reverse processes, whereby the same implanted probes would provide a means to “write-in” feedback information into the brain through injection of electronic signals directly into the cortex. This chapter reviews contemporary examples and recent accomplishments in the field, from the viewpoint of systems level engineering, and discusses both the challenges and opportunities ahead to build next generations of brain-computer interfaces.

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Authors and Affiliations

  1. School of Engineering, Brown University, Providence, RI, USA

    Farah Laiwalla, Lawrence Larson & Arto Nurmikko

  2. Qualcomm Institute, University of California San Diego, La Jolla, CA, USA

    Vincent Leung

Authors
  1. Farah Laiwalla
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  2. Vincent Leung
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  3. Lawrence Larson
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  4. Arto Nurmikko
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Corresponding author

Correspondence to Arto Nurmikko .

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Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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Laiwalla, F., Leung, V., Larson, L., Nurmikko, A. (2023). Challenges for Large-Scale Brain-Machine Interfaces. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_103

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