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Curated Papers > High-impact authoritative literature on "brain-computer interfaces"

Brain-computer interfaces for communication and rehabilitation. ​

Literature Information ​

DOI10.1038/nrneurol.2016.113
PMID27539560
JournalNature reviews. Neurology
Impact Factor33.1
JCR QuartileQ1
Publication Year2016
Times Cited205
KeywordsBrain-computer interfaces, Communication, Rehabilitation, Electroencephalography, Neural function
Literature TypeJournal Article, Review
ISSN1759-4758
Pages513-25
Issue12(9)
AuthorsUjwal Chaudhary, Niels Birbaumer, Ander Ramos-Murguialday

TL;DR ​

This review examines the evolution and current technologies of brain-computer interfaces (BCIs), focusing on their applications for communication and motor rehabilitation in severely disabled patients, particularly those with locked-in syndrome and following strokes or spinal cord injuries. The findings highlight the potential of BCIs to enhance patient interaction with their environment and support neural recovery, underscoring their significance in improving the quality of life for individuals with severe disabilities.

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Brain-computer interfaces · Communication · Rehabilitation · Electroencephalography · Neural function

Abstract ​

Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

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Primary Questions Addressed ​

  1. What are the latest advancements in noninvasive techniques for controlling brain-computer interfaces?
  2. How do different types of brain-computer interfaces compare in their effectiveness for communication in locked-in syndrome patients?
  3. What role do neurophysiological mechanisms play in the success of brain-computer interfaces for motor rehabilitation?
  4. How can brain-computer interfaces be integrated with robotic devices to enhance the quality of life for severely disabled patients?
  5. What are the challenges and limitations currently faced in the clinical application of brain-computer interfaces for rehabilitation?

Key Findings ​

Background and Purpose ​

Brain-computer interfaces (BCIs) represent a significant advancement in the field of neuroscience and rehabilitation, allowing individuals with severe motor impairments to communicate and interact with their environment. This review discusses the development, types, and applications of BCIs, particularly focusing on their role in assisting communication for paralyzed patients and facilitating rehabilitation post-stroke or spinal cord injury.

Main Methods/Materials/Experimental Design ​

The study categorizes BCIs into invasive and noninvasive types, each employing different techniques to detect and decode brain signals.

BCI Types and Processes ​

Mermaid diagram
  • Invasive BCIs: Require surgical implantation of electrodes to measure neural activity directly. Techniques include electrocorticography (ECoG), single-unit activity (SUA), and multi-unit activity (MUA).
  • Noninvasive BCIs: Utilize external devices to capture brain signals without surgery. Common methods include EEG, NIRS, and BOLD fMRI. These systems typically involve signal acquisition, processing, and decoding algorithms to translate brain activity into control signals.

Key Results and Findings ​

  1. Assistive BCIs for Communication: BCIs have shown efficacy in enabling communication for patients with conditions like amyotrophic lateral sclerosis (ALS) and locked-in syndrome. Noninvasive systems using EEG have been effective in some cases, while invasive systems have demonstrated higher performance in controlled environments.
  2. Rehabilitative BCIs for Motor Recovery: BCIs combined with behavioral physiotherapy have facilitated motor recovery in stroke patients by promoting neuroplasticity. Studies indicated that patients using BCIs could significantly improve motor function over time.
  3. Learning Mechanisms: The review identifies the neurophysiological and learning mechanisms that underpin the efficacy of BCIs, highlighting the importance of feedback and reinforcement in learning to control these interfaces.

Main Conclusions/Significance/Innovation ​

The review emphasizes that BCIs are promising tools for both communication and rehabilitation in severely disabled patients. They can provide critical communication pathways for individuals with locked-in syndrome and facilitate motor recovery in stroke patients. The integration of classical conditioning paradigms and advanced neuroimaging techniques holds potential for improving BCI functionality and user experience, particularly in patients with complete locked-in syndrome.

Research Limitations and Future Directions ​

  1. Limited Clinical Trials: There is a notable lack of large-scale randomized controlled trials, particularly for noninvasive BCIs in diverse patient populations.
  2. Need for Comprehensive Understanding: Further research is required to elucidate the neurophysiological mechanisms behind BCI learning and the specific patient populations that could benefit from these technologies.
  3. Innovative Approaches: Future studies should explore hybrid approaches that integrate BCIs with existing rehabilitation methods and investigate lower-limb rehabilitation, which remains underexplored.

In conclusion, while BCIs present a revolutionary approach to assistive technology and rehabilitation, ongoing research and clinical trials are essential to maximize their potential and efficacy for a broader range of neurological conditions.

References ​

  1. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? - A Kübler;N Birbaumer - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2008)
  2. Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing. - Giuseppina Rota;Giacomo Handjaras;Ranganatha Sitaram;Niels Birbaumer;Grzegorz Dogil - Brain and language (2011)
  3. A high-performance brain-computer interface. - Gopal Santhanam;Stephen I Ryu;Byron M Yu;Afsheen Afshar;Krishna V Shenoy - Nature (2006)
  4. Real-time self-regulation of emotion networks in patients with depression. - David E J Linden;Isabelle Habes;Stephen J Johnston;Stefanie Linden;Ranjit Tatineni;Leena Subramanian;Bettina Sorger;David Healy;Rainer Goebel - PloS one (2012)
  5. Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. - Elizabeth A Felton;J Adam Wilson;Justin C Williams;P Charles Garell - Journal of neurosurgery (2007)
  6. Amyotrophic lateral sclerosis: lower motor neuron disease spreading to upper motor neurons. - S M Chou;F H Norris - Muscle & nerve (1993)
  7. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. - Caroline Schnakers;Fabien Perrin;Manuel Schabus;Roland Hustinx;Steve Majerus;Gustave Moonen;Melanie Boly;Audrey Vanhaudenhuyse;Marie-Aurelie Bruno;Steven Laureys - Neurocase (2009)
  8. 'Thought'--control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. - Gert Pfurtscheller;Gernot R Müller;Jörg Pfurtscheller;Hans Jürgen Gerner;Rüdiger Rupp - Neuroscience letters (2003)
  9. Review of stroke rehabilitation. - John Young;Anne Forster - BMJ (Clinical research ed.) (2007)
  10. Stroke rehabilitation. - Peter Langhorne;Julie Bernhardt;Gert Kwakkel - Lancet (London, England) (2011)

Literatures Citing This Work ​

  1. Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram. - Beomjun Min;Jongin Kim;Hyeong-Jun Park;Boreom Lee - BioMed research international (2016)
  2. Brain-Computer Interface-Based Communication in the Completely Locked-In State. - Ujwal Chaudhary;Bin Xia;Stefano Silvoni;Leonardo G Cohen;Niels Birbaumer - PLoS biology (2017)
  3. Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces. - David M Brandman;Sydney S Cash;Leigh R Hochberg - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2017)
  4. Subthalamic nucleus beta and gamma activity is modulated depending on the level of imagined grip force. - Petra Fischer;Alek Pogosyan;Binith Cheeran;Alexander L Green;Tipu Z Aziz;Jonathan Hyam;Simon Little;Thomas Foltynie;Patricia Limousin;Ludvic Zrinzo;Marwan Hariz;Michael Samuel;Keyoumars Ashkan;Peter Brown;Huiling Tan - Experimental neurology (2017)
  5. Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback. - Yang Jiang;Reza Abiri;Xiaopeng Zhao - Frontiers in aging neuroscience (2017)
  6. Motor Imagery Impairment in Postacute Stroke Patients. - Niclas Braun;Cornelia Kranczioch;Joachim Liepert;Christian Dettmers;Catharina Zich;Imke Büsching;Stefan Debener - Neural plasticity (2017)
  7. Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task. - Lorraine Perronnet;Anatole Lécuyer;Marsel Mano;Elise Bannier;Fabien Lotte;Maureen Clerc;Christian Barillot - Frontiers in human neuroscience (2017)
  8. Learned control of inter-hemispheric connectivity: Effects on bimanual motor performance. - Diljit Singh Kajal;Christoph Braun;Jürgen Mellinger;Matthew D Sacchet;Sergio Ruiz;Eberhard Fetz;Niels Birbaumer;Ranganatha Sitaram - Human brain mapping (2017)
  9. Estimated Prevalence of the Target Population for Brain-Computer Interface Neurotechnology in the Netherlands. - Elmar G M Pels;Erik J Aarnoutse;Nick F Ramsey;Mariska J Vansteensel - Neurorehabilitation and neural repair (2017)
  10. Wearable and modular functional near-infrared spectroscopy instrument with multidistance measurements at four wavelengths. - Dominik Wyser;Olivier Lambercy;Felix Scholkmann;Martin Wolf;Roger Gassert - Neurophotonics (2017)

... (195 more literatures)

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