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文献精选 > 近年高分"脑机接口"研究（IF≥30，近5年）

Plug-and-play control of a brain-computer interface through neural map stabilization. ​

文献信息 ​

DOI10.1038/s41587-020-0662-5
PMID32895549
期刊Nature biotechnology
影响因子41.7
JCR 分区Q1
发表年份2021
被引次数57
关键词脑机接口, 电生理学, 神经塑性
文献类型Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
ISSN1087-0156
页码326-335
期号39(3)
作者Daniel B Silversmith, Reza Abiri, Nicholas F Hardy, Nikhilesh Natraj, Adelyn Tu-Chan, Edward F Chang, Karunesh Ganguly

一句话小结 ​

本研究探讨了一种基于128通道慢性皮层电图（ECoG）的脑机接口（BCI）方法，以解决BCI在现实应用中的长期可靠性和频繁重新校准的问题。结果表明，通过长期闭环解码器适应性，能够实现稳定的控制性能，促进神经图谱的巩固，从而为BCI的实际应用提供了一种更可靠和高效的解决方案。

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脑机接口 · 电生理学 · 神经塑性

摘要 ​

脑机接口（BCIs）使得重度运动障碍个体能够控制辅助设备。然而，BCIs的一项限制因素是长期可靠性差和每天需要较长时间的重新校准，这阻碍了其在现实世界中的应用。为了开发能够在不进行重新校准的情况下实现稳定性能的方法，我们在一名瘫痪个体身上使用了128通道的慢性皮层电图（ECoG）植入物，从而实现了信号的稳定监测。我们表明，长期的闭环解码器适应性，即在多个会话中跨越多天传递解码器权重，能够导致神经图谱的巩固和“即插即用”的控制。相比之下，日常的重新初始化导致了性能的下降和可变的重新学习。巩固还允许在数天内增加控制特征，即长期维度的叠加。我们的结果为通过利用ECoG接口的稳定性和神经可塑性提供了一种可靠、稳定的BCI控制方法。

英文摘要 ​

Brain-computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and 'plug-and-play' control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.

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主要研究问题 ​

  1. 如何进一步优化神经映射的稳定性以提高BCI的长期可靠性？
  2. 在不同类型的脑电图接口中，ECoG的优势是什么，如何与其他技术相比？
  3. 长期闭环解码器适应性如何影响不同患者的BCI控制效果？
  4. 神经可塑性在BCI中的作用是什么，如何利用这一特性来改善设备的适应性？
  5. 未来BCI技术的发展方向是什么，是否会出现更高效的信号监测和处理方法？

核心洞察 ​

1. 研究背景和目的 ​

脑机接口（BCI）为严重运动障碍患者提供了控制助残设备的可能性，然而其在实际应用中的普遍性受到长期可靠性不足和每日重新校准时间过长等问题的限制。本研究旨在开发一种方法，使BCI在不需要频繁重新校准的情况下，能够在长期使用中保持稳定的性能，从而推动其在临床和日常生活中的应用。

2. 主要方法和发现 ​

本研究使用了128通道的慢性皮层电图（ECoG）植入物，对一名瘫痪个体的信号进行了稳定监测。研究者采用了长期闭环解码器适应性的方法，将解码器的权重在多天的会话中持续使用，结果表明这种方法能够实现神经图谱的巩固，并实现“即插即用”的控制。相比之下，日常重新初始化则导致性能下降和学习过程的变异性。此外，巩固过程还允许在多天内逐步增加控制特征，实现了维度的长期叠加。

3. 核心结论 ​

本研究的结果表明，通过利用ECoG接口的稳定性和神经可塑性，可以实现可靠且稳定的BCI控制。这种长期解码器适应性的方法不仅提高了BCI的性能，还减轻了使用者的负担，使其在实际应用中更加便捷和高效。

4. 研究意义和影响 ​

该研究为BCI技术的实际应用提供了新的思路和方法，尤其是在提升设备长期稳定性和可靠性方面具有重要意义。通过实现“即插即用”的控制，减少了患者的重新校准时间，这对于改善其生活质量至关重要。此外，该研究的成果可能对其他神经康复领域的技术发展产生积极影响，推动脑机接口的更广泛应用，为更多有需要的人群提供助力。

参考文献 ​

  1. Cortical neural prosthetics. - Andrew B Schwartz - Annual review of neuroscience (2004)
  2. Direct cortical control of 3D neuroprosthetic devices. - Dawn M Taylor;Stephen I Helms Tillery;Andrew B Schwartz - Science (New York, N.Y.) (2002)
  3. A high-performance neural prosthesis enabled by control algorithm design. - Vikash Gilja;Paul Nuyujukian;Cindy A Chestek;John P Cunningham;Byron M Yu;Joline M Fan;Mark M Churchland;Matthew T Kaufman;Jonathan C Kao;Stephen I Ryu;Krishna V Shenoy - Nature neuroscience (2012)
  4. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. - Leigh R Hochberg;Mijail D Serruya;Gerhard M Friehs;Jon A Mukand;Maryam Saleh;Abraham H Caplan;Almut Branner;David Chen;Richard D Penn;John P Donoghue - Nature (2006)
  5. Learning to control a brain-machine interface for reaching and grasping by primates. - Jose M Carmena;Mikhail A Lebedev;Roy E Crist;Joseph E O'Doherty;David M Santucci;Dragan F Dimitrov;Parag G Patil;Craig S Henriquez;Miguel A L Nicolelis - PLoS biology (2003)
  6. Remapping cortical modulation for electrocorticographic brain-computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis. - Alan D Degenhart;Shivayogi V Hiremath;Ying Yang;Stephen Foldes;Jennifer L Collinger;Michael Boninger;Elizabeth C Tyler-Kabara;Wei Wang - Journal of neural engineering (2018)
  7. An electrocorticographic brain interface in an individual with tetraplegia. - Wei Wang;Jennifer L Collinger;Alan D Degenhart;Elizabeth C Tyler-Kabara;Andrew B Schwartz;Daniel W Moran;Douglas J Weber;Brian Wodlinger;Ramana K Vinjamuri;Robin C Ashmore;John W Kelly;Michael L Boninger - PloS one (2013)
  8. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. - Tyson Aflalo;Spencer Kellis;Christian Klaes;Brian Lee;Ying Shi;Kelsie Pejsa;Kathleen Shanfield;Stephanie Hayes-Jackson;Mindy Aisen;Christi Heck;Charles Liu;Richard A Andersen - Science (New York, N.Y.) (2015)
  9. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. - Jonathan R Wolpaw;Dennis J McFarland - Proceedings of the National Academy of Sciences of the United States of America (2004)
  10. A brain-computer interface for long-term independent home use. - Eric W Sellers;Theresa M Vaughan;Jonathan R Wolpaw - Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases (2010)

引用本文的文献 ​

  1. Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia. - John D Simeral;Thomas Hosman;Jad Saab;Sharlene N Flesher;Marco Vilela;Brian Franco;Jessica N Kelemen;David M Brandman;John G Ciancibello;Paymon G Rezaii;Emad N Eskandar;David M Rosler;Krishna V Shenoy;Jaimie M Henderson;Arto V Nurmikko;Leigh R Hochberg - IEEE transactions on bio-medical engineering (2021)
  2. A Framework for Optimizing Co-adaptation in Body-Machine Interfaces. - Dalia De Santis - Frontiers in neurorobotics (2021)
  3. Reinvigorating electrochemistry education. - Paul A Kempler;Shannon W Boettcher;Shane Ardo - iScience (2021)
  4. Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria. - David A Moses;Sean L Metzger;Jessie R Liu;Gopala K Anumanchipalli;Joseph G Makin;Pengfei F Sun;Josh Chartier;Maximilian E Dougherty;Patricia M Liu;Gary M Abrams;Adelyn Tu-Chan;Karunesh Ganguly;Edward F Chang - The New England journal of medicine (2021)
  5. Multi-scale neural decoding and analysis. - Hung-Yun Lu;Elizabeth S Lorenc;Hanlin Zhu;Justin Kilmarx;James Sulzer;Chong Xie;Philippe N Tobler;Andrew J Watrous;Amy L Orsborn;Jarrod Lewis-Peacock;Samantha R Santacruz - Journal of neural engineering (2021)
  6. The science and engineering behind sensitized brain-controlled bionic hands. - Chethan Pandarinath;Sliman J Bensmaia - Physiological reviews (2022)
  7. Decoding four hand gestures with a single bipolar pair of electrocorticography electrodes. - Maxime Verwoert;Mariska J Vansteensel;Zachary V Freudenburg;Erik J Aarnoutse;Frans S S Leijten;Nick F Ramsey;Mariana P Branco - Journal of neural engineering (2021)
  8. Compartmentalized dynamics within a common multi-area mesoscale manifold represent a repertoire of human hand movements. - Nikhilesh Natraj;Daniel B Silversmith;Edward F Chang;Karunesh Ganguly - Neuron (2022)
  9. Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning. - Katherine Derosier;Tess L Veuthey;Karunesh Ganguly - The Journal of neuroscience : the official journal of the Society for Neuroscience (2021)
  10. Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication. - Shiyu Luo;Qinwan Rabbani;Nathan E Crone - Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics (2022)

... (47 更多 篇文献)

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