MaltSci 麦伴科研

Appearance

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

A brain-computer interface that evokes tactile sensations improves robotic arm control. ​

Literature Information ​

DOI10.1126/science.abd0380
PMID34016775
JournalScience (New York, N.Y.)
Impact Factor45.8
JCR QuartileQ1
Publication Year2021
Times Cited173
Keywordsbrain-computer interface, tactile feedback, robotic arm, tetraplegia, motor control
Literature TypeJournal Article, Research Support, Non-U.S. Gov't
ISSN0036-8075
Pages831-836
Issue372(6544)
AuthorsSharlene N Flesher, John E Downey, Jeffrey M Weiss, Christopher L Hughes, Angelica J Herrera, Elizabeth C Tyler-Kabara, Michael L Boninger, Jennifer L Collinger, Robert A Gaunt

TL;DR ​

This study explores the enhancement of prosthetic arm control for individuals with tetraplegia through a bidirectional brain-computer interface that provides tactile feedback via intracortical microstimulation. The findings demonstrate that this approach significantly improved the speed and efficiency of robotic limb use, halving the time required for grasping tasks, thus suggesting that integrating tactile feedback can lead to performance levels closer to those of able-bodied individuals.

Search for more papers on MaltSci.com ​

brain-computer interface · tactile feedback · robotic arm · tetraplegia · motor control

Abstract ​

Prosthetic arms controlled by a brain-computer interface can enable people with tetraplegia to perform functional movements. However, vision provides limited feedback because information about grasping objects is best relayed through tactile feedback. We supplemented vision with tactile percepts evoked using a bidirectional brain-computer interface that records neural activity from the motor cortex and generates tactile sensations through intracortical microstimulation of the somatosensory cortex. This enabled a person with tetraplegia to substantially improve performance with a robotic limb; trial times on a clinical upper-limb assessment were reduced by half, from a median time of 20.9 to 10.2 seconds. Faster times were primarily due to less time spent attempting to grasp objects, revealing that mimicking known biological control principles results in task performance that is closer to able-bodied human abilities.

MaltSci.com AI Research Service ​

Intelligent ReadingAnswer any question about the paper and explain complex charts and formulas
Locate StatementsFind traces of a specific claim within the paper
Add to KBasePerform data extraction, report drafting, and advanced knowledge mining

Primary Questions Addressed ​

  1. How do different types of tactile feedback impact the performance of brain-computer interfaces in controlling robotic limbs?
  2. What are the potential applications of this technology beyond prosthetic limbs, such as in rehabilitation or virtual reality?
  3. How does the integration of tactile sensations with visual feedback compare to traditional methods of controlling robotic arms?
  4. What are the challenges and limitations faced in the development of brain-computer interfaces that evoke tactile sensations?
  5. How might this technology evolve to improve the independence and quality of life for individuals with severe mobility impairments?

Key Findings ​

Research Background and Purpose ​

Tetraplegia, often resulting from spinal cord injuries, significantly impairs upper limb function, creating a high demand for effective rehabilitation methods. Traditional brain-computer interfaces (BCIs) have shown promise in enabling individuals with tetraplegia to control robotic limbs, but these systems typically rely solely on visual feedback. This study aimed to enhance BCI functionality by integrating tactile feedback through a bidirectional BCI that stimulates the somatosensory cortex, thus improving robotic arm control.

Main Methods/Materials/Experimental Design ​

The study involved a 28-year-old male participant with tetraplegia due to a C5/C6 spinal cord injury. Two microelectrode arrays with 88 electrodes were implanted in the motor cortex to decode movement intent, while two additional arrays with 32 electrodes were placed in the somatosensory cortex to provide tactile feedback via intracortical microstimulation (ICMS).

Experimental Design: ​

  1. Device Setup:

    • Motor Cortex: Record neural activity for controlling the robotic arm.
    • Somatosensory Cortex: Deliver tactile sensations to mimic touch.

  2. Tasks:

    • Action Research Arm Test (ARAT): Modified to assess upper limb function with and without ICMS feedback.
    • Object Transfer Task: Involved picking up and transferring objects to measure performance under different feedback conditions.

  3. Feedback Conditions:

    • With ICMS: Tactile feedback was provided during tasks.
    • Without ICMS: Only visual feedback was available.

  4. Data Analysis: Performance metrics were compared using statistical tests, including the Wilcoxon rank sum test.

Technical Route (Mermaid Code) ​

Mermaid diagram

Key Results and Findings ​

  • Performance Improvement: The participant's ARAT scores significantly improved from a median of 17 (without ICMS) to 21 (with ICMS), indicating enhanced task performance.
  • Trial Times: Median completion times for tasks reduced by 51.2% (from 20.9 seconds to 10.2 seconds) when tactile feedback was provided.
  • Grasping Efficiency: Time spent attempting to grasp objects decreased by 66% with ICMS, accounting for 88% of the total time improvement.
  • Confidence and Speed: The participant exhibited increased confidence, leading to faster and more efficient task execution.

Main Conclusions/Significance/Innovation ​

This study demonstrates that integrating tactile feedback through a bidirectional BCI can significantly enhance the performance of individuals with tetraplegia using robotic limbs. The results indicate that artificial tactile sensations can effectively mimic natural sensory feedback, leading to improved functional capabilities. This innovation suggests a promising direction for future BCI designs aimed at restoring motor functions in individuals with severe impairments.

Research Limitations and Future Directions ​

  • Single-Subject Study: The findings are based on a single participant, limiting generalizability. Future studies should include larger sample sizes to validate results.
  • Task Variability: The tasks were designed around specific objects and movements; exploring a broader range of activities may provide further insights.
  • Stimulation Encoding Schemes: Future research should investigate different ICMS encoding strategies to optimize tactile feedback for various tasks, particularly those requiring fine motor control.

Overall, this research lays the groundwork for advanced BCI systems that incorporate tactile feedback, potentially revolutionizing rehabilitation approaches for individuals with spinal cord injuries.

References ​

  1. The role of visual deprivation and experience on the performance of sensory substitution devices. - H Christiaan Stronks;Amy C Nau;Michael R Ibbotson;Nick Barnes - Brain research (2015)
  2. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. - A Bolu Ajiboye;Francis R Willett;Daniel R Young;William D Memberg;Brian A Murphy;Jonathan P Miller;Benjamin L Walter;Jennifer A Sweet;Harry A Hoyen;Michael W Keith;P Hunter Peckham;John D Simeral;John P Donoghue;Leigh R Hochberg;Robert F Kirsch - Lancet (London, England) (2017)
  3. The effects of digital anesthesia on force control using a precision grip. - Joël Monzée;Yves Lamarre;Allan M Smith - Journal of neurophysiology (2003)
  4. Active tactile exploration using a brain-machine-brain interface. - Joseph E O'Doherty;Mikhail A Lebedev;Peter J Ifft;Katie Z Zhuang;Solaiman Shokur;Hannes Bleuler;Miguel A L Nicolelis - Nature (2011)
  5. Neural Basis of Touch and Proprioception in Primate Cortex. - Benoit P Delhaye;Katie H Long;Sliman J Bensmaia - Comprehensive Physiology (2018)
  6. Restoring cortical control of functional movement in a human with quadriplegia. - Chad E Bouton;Ammar Shaikhouni;Nicholas V Annetta;Marcia A Bockbrader;David A Friedenberg;Dylan M Nielson;Gaurav Sharma;Per B Sederberg;Bradley C Glenn;W Jerry Mysiw;Austin G Morgan;Milind Deogaonkar;Ali R Rezai - Nature (2016)
  7. A performance test for assessment of upper limb function in physical rehabilitation treatment and research. - R C Lyle - International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation (1981)
  8. Restoration of motor control and proprioceptive and cutaneous sensation in humans with prior upper-limb amputation via multiple Utah Slanted Electrode Arrays (USEAs) implanted in residual peripheral arm nerves. - Suzanne Wendelken;David M Page;Tyler Davis;Heather A C Wark;David T Kluger;Christopher Duncan;David J Warren;Douglas T Hutchinson;Gregory A Clark - Journal of neuroengineering and rehabilitation (2017)
  9. Restoring natural sensory feedback in real-time bidirectional hand prostheses. - Stanisa Raspopovic;Marco Capogrosso;Francesco Maria Petrini;Marco Bonizzato;Jacopo Rigosa;Giovanni Di Pino;Jacopo Carpaneto;Marco Controzzi;Tim Boretius;Eduardo Fernandez;Giuseppe Granata;Calogero Maria Oddo;Luca Citi;Anna Lisa Ciancio;Christian Cipriani;Maria Chiara Carrozza;Winnie Jensen;Eugenio Guglielmelli;Thomas Stieglitz;Paolo Maria Rossini;Silvestro Micera - Science translational medicine (2014)
  10. Home Use of a Neural-connected Sensory Prosthesis Provides the Functional and Psychosocial Experience of Having a Hand Again. - Emily L Graczyk;Linda Resnik;Matthew A Schiefer;Melissa S Schmitt;Dustin J Tyler - Scientific reports (2018)

Literatures Citing This Work ​

  1. Short reaction times in response to multi-electrode intracortical microstimulation may provide a basis for rapid movement-related feedback. - Joseph T Sombeck;Lee E Miller - Journal of neural engineering (2019)
  2. Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex. - Darrel R Deo;Paymon Rezaii;Leigh R Hochberg;Allison M Okamura;Krishna V Shenoy;Jaimie M Henderson - IEEE transactions on haptics (2021)
  3. Advancing sensory neuroprosthetics using artificial brain networks. - David Haslacher;Khaled Nasr;Surjo R Soekadar - Patterns (New York, N.Y.) (2021)
  4. Perception of microstimulation frequency in human somatosensory cortex. - Christopher L Hughes;Sharlene N Flesher;Jeffrey M Weiss;Michael Boninger;Jennifer L Collinger;Robert A Gaunt - eLife (2021)
  5. Evoking highly focal percepts in the fingertips through targeted stimulation of sulcal regions of the brain for sensory restoration. - Santosh Chandrasekaran;Stephan Bickel;Jose L Herrero;Joo-Won Kim;Noah Markowitz;Elizabeth Espinal;Nikunj A Bhagat;Richard Ramdeo;Junqian Xu;Matthew F Glasser;Chad E Bouton;Ashesh D Mehta - Brain stimulation (2021)
  6. Influence of visual feedback persistence on visuo-motor skill improvement. - Alyssa Unell;Zachary M Eisenstat;Ainsley Braun;Abhinav Gandhi;Sharon Gilad-Gutnick;Shlomit Ben-Ami;Pawan Sinha - Scientific reports (2021)
  7. Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand. - Chad Bouton;Nikunj Bhagat;Santosh Chandrasekaran;Jose Herrero;Noah Markowitz;Elizabeth Espinal;Joo-Won Kim;Richard Ramdeo;Junqian Xu;Matthew F Glasser;Stephan Bickel;Ashesh Mehta - Frontiers in neuroscience (2021)
  8. The science and engineering behind sensitized brain-controlled bionic hands. - Chethan Pandarinath;Sliman J Bensmaia - Physiological reviews (2022)
  9. Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications. - Santosh Chandrasekaran;Matthew Fifer;Stephan Bickel;Luke Osborn;Jose Herrero;Breanne Christie;Junqian Xu;Rory K J Murphy;Sandeep Singh;Matthew F Glasser;Jennifer L Collinger;Robert Gaunt;Ashesh D Mehta;Andrew Schwartz;Chad E Bouton - Bioelectronic medicine (2021)
  10. Plasticity in Cervical Motor Circuits following Spinal Cord Injury and Rehabilitation. - John R Walker;Megan Ryan Detloff - Biology (2021)

... (163 more literatures)

© 2025 MaltSci - We reshape scientific research with AI technology