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Critical Reviews™ in Biomedical Engineering

年間 6 号発行

ISSN 印刷: 0278-940X

ISSN オンライン: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Brain-Machine Interfaces: Electrophysiological Challenges and Limitations

巻 39, 発行 1, 2011, pp. 5-28
DOI: 10.1615/CritRevBiomedEng.v39.i1.20
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要約

Brain-machine interfaces (BMI) seek to directly communicate with the human nervous system in order to diagnose and treat intrinsic neurological disorders. While the first generation of these devices has realized significant clinical successes, they often rely on gross electrical stimulation using empirically derived parameters through open-loop mechanisms of action that are not yet fully understood. Their limitations reflect the inherent challenge in developing the next generation of these devices. This review identifies lessons learned from the first generation of BMI devices (chiefly deep brain stimulation), identifying key problems for which the solutions will aid the development of the next generation of technologies. Our analysis examines four hypotheses for the mechanism by which brain stimulation alters surrounding neurophysiologic activity. We then focus on motor prosthetics, describing various approaches to overcoming the problems of decoding neural signals. We next turn to visual prosthetics, an area for which the challenges of signal coding to match neural architecture has been partially overcome. Finally, we close with a review of cortical stimulation, examining basic principles that will be incorporated into the design of future devices. Throughout the review, we relate the issues of each specific topic to the common thread of BMI research: translating new knowledge of network neuroscience into improved devices for neuromodulation.

によって引用された
  1. Tang-Schomer Min D., Hu Xiao, Hronik-Tupaj Marie, Tien Lee W., Whalen Michael J., Omenetto Fiorenzo G., Kaplan David L., Film-Based Implants for Supporting Neuron-Electrode Integrated Interfaces for The Brain, Advanced Functional Materials, 24, 13, 2014. Crossref

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  5. Hughes Christopher, Herrera Angelica, Gaunt Robert, Collinger Jennifer, Bidirectional brain-computer interfaces, in Brain-Computer Interfaces, 168, 2020. Crossref

  6. Kalnoor Gauri, The brain-machine interface, nanosensor technology, and artificial intelligence: Their convergence with a novel frontier, in Handbook of Nanomaterials for Sensing Applications, 2021. Crossref

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