图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
生物医学工程评论综述™
SJR: 0.26 SNIP: 0.375 CiteScore™: 1.4

ISSN 打印: 0278-940X
ISSN 在线: 1943-619X

生物医学工程评论综述™

DOI: 10.1615/CritRevBiomedEng.v39.i1.20
pages 5-28

Brain-Machine Interfaces: Electrophysiological Challenges and Limitations

Bradley C. Lega
Department of Neurosurgery, Hospital of the University of Pennsylvania, University of Pennsylvania
Mijail D. Serruya
Department of Neurology, Jefferson University, Philadelphia, PA, USA
Kareem Zaghloul
Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda

ABSTRACT

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.


Articles with similar content:

The Evolution of Neuroprosthetic Interfaces
Critical Reviews™ in Biomedical Engineering, Vol.44, 2016, issue 1-2
Mijail D. Serruya, D. Kacy Cullen, Dayo O. Adewole, H. Isaac Chen, Justin C. Burrell, James P. Harris, Dmitriy Petrov, John A. Wolf
Signal Processing and Physiological Modeling-Part II: Depth Model-Driven Analysis
Critical Reviews™ in Biomedical Engineering, Vol.30, 2002, issue 1-3
Jean-Louis Coatrieux
Rhythm and Music in Rehabilitation: A Critical Review of Current Research
Critical Reviews™ in Physical and Rehabilitation Medicine, Vol.23, 2011, issue 1-4
A. Blythe LaGasse, Andrew Knight
In Vitro Microelectrode Array Technology and Neural Recordings
Critical Reviews™ in Biomedical Engineering, Vol.39, 2011, issue 1
Bruce C. Wheeler, Yoonkey Nam
Biological, Mechanical, and Technological Considerations Affecting the Longevity of Intracortical Electrode Recordings
Critical Reviews™ in Biomedical Engineering, Vol.41, 2013, issue 6
Dustin J. Tyler, James P. Harris