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Curated Papers > Recent high-impact research on "brain-machine interfaces" (IF≥30, last 5 years)

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

Literature Information ​

DOI10.1038/s41587-020-0662-5
PMID32895549
JournalNature biotechnology
Impact Factor41.7
JCR QuartileQ1
Publication Year2021
Times Cited57
KeywordsBrain-computer interface, Neural mapping, Long-term stability, Closed-loop decoding, Electrophysiological monitoring
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
ISSN1087-0156
Pages326-335
Issue39(3)
AuthorsDaniel B Silversmith, Reza Abiri, Nicholas F Hardy, Nikhilesh Natraj, Adelyn Tu-Chan, Edward F Chang, Karunesh Ganguly

TL;DR ​

This study addresses the challenge of poor long-term reliability in brain-computer interfaces (BCIs) for individuals with severe motor impairments by demonstrating that long-term closed-loop decoder adaptation using a chronic electrocorticography (ECoG) implant enables stable performance without daily recalibration. The findings suggest that leveraging the stability of ECoG signals and neural plasticity can significantly enhance BCI control, facilitating reliable and efficient operation of assistive devices over extended periods.

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Brain-computer interface · Neural mapping · Long-term stability · Closed-loop decoding · Electrophysiological monitoring

Abstract ​

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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Primary Questions Addressed ​

  1. How does neural map stabilization specifically enhance the reliability of brain-computer interfaces compared to traditional methods?
  2. What are the implications of long-term closed-loop decoder adaptation for the future development of assistive technologies in BCIs?
  3. In what ways can the concept of 'plug-and-play' control be applied to other neuroprosthetic devices beyond BCIs?
  4. How might the findings regarding ECoG interfaces and neural plasticity influence the design of future rehabilitation protocols for individuals with motor impairments?
  5. What challenges remain in achieving widespread adoption of BCIs in clinical settings, despite advancements in neural map stabilization techniques?

Key Findings ​

Key Insights ​

1. Research Background and Objective ​

Brain-computer interfaces (BCIs) serve as critical tools for individuals with severe motor impairments, allowing them to control assistive devices through brain signals. However, the long-term reliability of BCIs has been a significant barrier to their widespread adoption. Frequent recalibration processes are necessary due to signal variability, leading to inconsistent performance. This research aims to address these limitations by exploring methods for stable BCI performance without the need for daily recalibration, focusing on the use of chronic electrocorticography (ECoG) as a monitoring tool.

2. Main Methods and Findings ​

The study utilized a 128-channel chronic ECoG implant in a paralyzed individual to facilitate stable signal monitoring over time. A key aspect of the methodology was the implementation of long-term closed-loop decoder adaptation, where the decoder weights were preserved across multiple sessions. This approach led to the consolidation of a neural map, which enabled 'plug-and-play' control of devices. In contrast, the practice of daily reinitialization resulted in degraded performance and inconsistent relearning of control strategies. Notably, the consolidation of the neural map allowed for the gradual addition of control features over several days, demonstrating a long-term stacking of control dimensions.

3. Core Conclusions ​

The study concludes that leveraging the stability of ECoG interfaces and the principles of neural plasticity can lead to reliable and stable BCI control. The findings underscore the importance of maintaining decoder weights across sessions to enhance the performance and usability of BCIs, effectively reducing the dependency on daily recalibration. This approach not only improves the consistency of control but also facilitates the expansion of BCI capabilities over time.

4. Research Significance and Impact ​

This research has significant implications for the development and implementation of BCIs in real-world applications, particularly for individuals with severe disabilities. By demonstrating a method for achieving stable and reliable BCI control, the study contributes to the ongoing efforts to enhance user experience and operational efficiency of assistive devices. The ability to adaptively consolidate neural maps may lead to more intuitive and effective control systems, potentially improving the quality of life for users. Additionally, the insights gained from this work could inform future innovations in BCI technology, paving the way for broader applications beyond assistive devices, including neurorehabilitation and enhanced human-computer interaction.

References ​

  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)

Literatures Citing This Work ​

  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 more literatures)

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