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

Brain-computer interfaces in medicine.

Literature Information

DOI	10.1016/j.mayocp.2011.12.008
PMID	22325364
Journal	Mayo Clinic proceedings
Impact Factor	6.7
JCR Quartile	Q1
Publication Year	2012
Times Cited	125
Keywords	Brain-computer interfaces, Neuromuscular disorders, Signal acquisition, Rehabilitation, Technological development
Literature Type	Journal Article, Review
ISSN	0025-6196
Pages	268-79
Issue	87(3)
Authors	Jerry J Shih, Dean J Krusienski, Jonathan R Wolpaw

TL;DR

This paper discusses the development and potential of brain-computer interfaces (BCIs) as a transformative technology for individuals with neuromuscular disorders, enabling control of devices that can restore or enhance functionality. The research highlights the need for advancements in signal acquisition, validation in real-world scenarios, and improvements in reliability to ensure BCIs can effectively replace natural muscle functions.

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Brain-computer interfaces · Neuromuscular disorders · Signal acquisition · Rehabilitation · Technological development

Abstract

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.

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Primary Questions Addressed

1. How can brain-computer interfaces be integrated into existing rehabilitation programs for stroke patients?
2. What are the ethical considerations surrounding the use of BCIs in medical settings, particularly regarding consent and autonomy?
3. In what ways can BCIs enhance the training and performance of medical professionals, such as surgeons?
4. What advancements in signal-acquisition hardware are currently being researched to improve the functionality of BCIs in diverse environments?
5. How do long-term studies on BCI effectiveness inform the development of protocols for their widespread clinical use?

Key Findings

Research Background and Purpose

Brain-computer interfaces (BCIs) are systems that allow individuals to control devices using brain signals, bypassing normal neuromuscular pathways. The primary aim of BCIs is to restore or replace functions in individuals with neuromuscular disorders, such as amyotrophic lateral sclerosis and spinal cord injuries. This review provides an overview of the BCI technology, including its components, methods, applications, and future challenges.

Main Methods/Materials/Experimental Design

The BCI system consists of four sequential components:

1. Signal Acquisition: Measuring brain signals using modalities such as EEG, ECoG, or intracortical electrodes.
2. Feature Extraction: Analyzing the acquired signals to identify features that correlate with user intent.
3. Feature Translation: Converting the extracted features into commands for output devices.
4. Device Output: Executing the commands to control external devices.

Key Results and Findings

• BCIs have progressed from basic applications like cursor control to more complex tasks, including controlling robotic arms and enabling communication for individuals with severe disabilities.
• Different types of brain signals, including EEG, ECoG, and intracortical signals, have been explored for BCI applications, each with distinct advantages and limitations.
• Current BCI systems are primarily limited to research settings, with few successful long-term applications in everyday environments.

Main Conclusions/Significance/Innovation

The review highlights the potential of BCIs to significantly improve the quality of life for individuals with severe disabilities. However, for BCIs to be widely adopted, advances are needed in three critical areas:

1. Development of reliable and user-friendly signal acquisition hardware.
2. Validation of BCI systems through long-term studies in real-world settings.
3. Improvement of BCI reliability to match natural muscle-based actions.

Research Limitations and Future Directions

• The translation of BCI technology from laboratory settings to practical, everyday use remains a significant challenge.
• Current BCI systems are primarily focused on users with severe disabilities, limiting the broader application and commercial viability of the technology.
• Future research should address the need for better signal acquisition technologies, effective validation methods, and increased reliability to ensure that BCIs can meet the diverse needs of users in various environments.

Aspect	Current Status	Future Directions
Signal Acquisition	Limited to laboratory settings; various methods explored (EEG, ECoG, intracortical)	Development of portable, user-friendly, and reliable systems
Validation	Few long-term studies; primarily confined to research settings	Comprehensive studies in real-world environments
Reliability	Poor reliability for complex tasks; limited to basic communication functions	Enhance reliability to match natural muscle-based actions
User Population	Focus on individuals with severe disabilities	Explore applications for a broader population

The advancements in BCI technology hold great promise for enhancing communication and control capabilities for individuals with disabilities and potentially for the general population in the future.

References

1. Cognitive control signals for neural prosthetics. - S Musallam;B D Corneil;B Greger;H Scherberger;R A Andersen - Science (New York, N.Y.) (2004)
2. An efficient P300-based brain-computer interface for disabled subjects. - Ulrich Hoffmann;Jean-Marc Vesin;Touradj Ebrahimi;Karin Diserens - Journal of neuroscience methods (2008)
3. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. - Kai J Miller;Gerwin Schalk;Eberhard E Fetz;Marcel den Nijs;Jeffrey G Ojemann;Rajesh P N Rao - Proceedings of the National Academy of Sciences of the United States of America (2010)
4. Brain-computer interfaces based on the steady-state visual-evoked response. - M Middendorf;G McMillan;G Calhoun;K S Jones - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2000)
5. Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. - Simon P Kelly;Edmund C Lalor;Richard B Reilly;John J Foxe - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2005)
6. Multichannel EEG-based brain-computer communication. - J R Wolpaw;D J McFarland - Electroencephalography and clinical neurophysiology (1994)
7. Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG). - Peter Brunner;Anthony L Ritaccio;Joseph F Emrich;Horst Bischof;Gerwin Schalk - Frontiers in neuroscience (2011)
8. Brain-computer interfaces in neurological rehabilitation. - Janis J Daly;Jonathan R Wolpaw - The Lancet. Neurology (2008)
9. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. - Christian Enzinger;Stefan Ropele;Franz Fazekas;Marisa Loitfelder;Faton Gorani;Thomas Seifert;Gudrun Reiter;Christa Neuper;Gert Pfurtscheller;Gernot Müller-Putz - Experimental brain research (2008)
10. Neurophysiologic correlates of fMRI in human motor cortex. - Dora Hermes;Kai J Miller;Mariska J Vansteensel;Erik J Aarnoutse;Frans S S Leijten;Nick F Ramsey - Human brain mapping (2012)

Literatures Citing This Work

1. Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects. - Olesya A Mokienko;Alexander V Chervyakov;Sofia N Kulikova;Pavel D Bobrov;Liudmila A Chernikova;Alexander A Frolov;Mikhail A Piradov - Frontiers in computational neuroscience (2013)
2. Low-latency multi-threaded processing of neuronal signals for brain-computer interfaces. - Jörg Fischer;Tomislav Milekovic;Gerhard Schneider;Carsten Mehring - Frontiers in neuroengineering (2014)
3. Empirical models of scalp-EEG responses using non-concurrent intracranial responses. - Komalpreet Kaur;Jerry J Shih;Dean J Krusienski - Journal of neural engineering (2014)
4. Classification of four-class motor imagery employing single-channel electroencephalography. - Sheng Ge;Ruimin Wang;Dongchuan Yu - PloS one (2014)
5. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. - Kai Keng Ang;Cuntai Guan;Kok Soon Phua;Chuanchu Wang;Longjiang Zhou;Ka Yin Tang;Gopal J Ephraim Joseph;Christopher Wee Keong Kuah;Karen Sui Geok Chua - Frontiers in neuroengineering (2014)
6. Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis. - Peter J Grahn;Grant W Mallory;B Michael Berry;Jan T Hachmann;Darlene A Lobel;J Luis Lujan - Frontiers in neuroscience (2014)
7. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury. - Rüdiger Rupp - Frontiers in neuroengineering (2014)
8. "Messing with the mind": evolutionary challenges to human brain augmentation. - Arthur Saniotis;Maciej Henneberg;Jaliya Kumaratilake;James P Grantham - Frontiers in systems neuroscience (2014)
9. Non-invasive control interfaces for intention detection in active movement-assistive devices. - Joan Lobo-Prat;Peter N Kooren;Arno H A Stienen;Just L Herder;Bart F J M Koopman;Peter H Veltink - Journal of neuroengineering and rehabilitation (2014)
10. Future think: cautiously optimistic about brain augmentation using tissue engineering and machine interface. - E Paul Zehr - Frontiers in systems neuroscience (2015)

... (115 more literatures)

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