Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses

被引:96
作者
Gilja, Vikash [3 ,4 ]
Chestek, Cindy A. [1 ,4 ]
Diester, Ilka [2 ]
Henderson, Jaimie M. [5 ,6 ]
Deisseroth, Karl [7 ,8 ,9 ,10 ]
Shenoy, Krishna V. [1 ,2 ,9 ]
机构
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[4] Stanford Univ, SINTN, Stanford, CA 94305 USA
[5] Stanford Univ, Dept Neurosurg & Neurol, Stanford, CA 94305 USA
[6] Stanford Univ, Dept Neurol Sci, Stanford, CA 94305 USA
[7] Stanford Univ, Dept Bioengn & Psychiat, Stanford, CA 94305 USA
[8] Stanford Univ, Dept Behav Sci, Stanford, CA 94305 USA
[9] Stanford Univ, Neurosci Program, Stanford, CA 94305 USA
[10] Howard Hughes Med Inst, Chevy Chase, MD 20815 USA
关键词
BRAIN-MACHINE INTERFACE; MILLISECOND-TIMESCALE; RECORDING-SYSTEM; CORTICAL CONTROL; OPTICAL CONTROL; CONTROL SIGNALS; MOVEMENT; DISCRIMINATION; CIRCUITS; NEURONS;
D O I
10.1109/TBME.2011.2107553
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.
引用
收藏
页码:1891 / 1899
页数:9
相关论文
共 50 条
  • [31] Identification of the perpetrator among identical twins using next-generation sequencing technology: A case report
    Yuan, Lijuan
    Chen, Xihui
    Liu, Ziyu
    Liu, Qingbo
    Song, An
    Bao, Guoqiang
    Wei, Gang
    Zhang, Sijia
    Lu, Jianguo
    Wu, Yuanming
    [J]. FORENSIC SCIENCE INTERNATIONAL-GENETICS, 2020, 44
  • [32] Theta-Nested Gamma Oscillations in Next Generation Neural Mass Models
    Segneri, Marco
    Bi, Hongjie
    Olmi, Simona
    Torcini, Alessandro
    [J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2020, 14 (14)
  • [33] Novel Arithmetics in Deep Neural Networks Signal Processing for Autonomous Driving: Challenges and Opportunities
    Cococcioni, Marco
    Rossi, Federico
    Ruffaldi, Emanuele
    Saponara, Sergio
    de Dinechin, Benoit Dupont
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2021, 38 (01) : 97 - 110
  • [34] Review of memristor based neuromorphic computation: opportunities, challenges and applications
    Archita, S. Shekinah
    Ravi, V
    [J]. ENGINEERING RESEARCH EXPRESS, 2024, 6 (03):
  • [35] Integrated photonic building blocks for next-generation astronomical instrumentation II: the multimode to single mode transition
    Spaleniak, Izabela
    Jovanovic, Nemanja
    Gross, Simon
    Ireland, Michael J.
    Lawrence, Jon S.
    Withford, Michael J.
    [J]. OPTICS EXPRESS, 2013, 21 (22): : 27197 - 27208
  • [36] Heterogeneously-Integrated Optical Phase Shifters for Next-Generation Modulators and Switches on a Silicon Photonics Platform: A Review
    Kim, Younghyun
    Han, Jae-Hoon
    Ahn, Daehwan
    Kim, Sanghyeon
    [J]. MICROMACHINES, 2021, 12 (06)
  • [37] Opportunities and Challenges of Compensation and Governance of Network Neural Ecological Environment Damage in the Era of Artificial Intelligence
    Liu, Dongyue
    [J]. IEEE CONSUMER ELECTRONICS MAGAZINE, 2023, 12 (02) : 57 - 63
  • [38] Towards organism-level systems biology by next-generation genetics and whole-organ cell profiling
    Minami, Yoichi
    Yuan, Yufei
    Ueda, Hiroki R.
    [J]. BIOPHYSICAL REVIEWS, 2021, 13 (06) : 1113 - 1126
  • [39] Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities
    Ma, Zhengjing
    Mei, Gang
    Xu, Nengxiong
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2024, 57 (06)
  • [40] Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion and discrimination
    Shubitidze, Fridon
    Karkashadze, David
    Fernandez, Juan Pablo
    Barrowes, Benjamin E.
    O'Neill, Kevin
    Grzegorczyk
    Shamatava, Irma
    [J]. DETECTION AND SENSING OF MINES, EXPLOSIVE OBJECTS, AND OBSCURED TARGETS XV, 2010, 7664