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

Brain-computer interfaces for communication and control.

Literature Information

DOI	10.1016/s1388-2457(02)00057-3
PMID	12048038
Journal	Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Impact Factor	3.6
JCR Quartile	Q1
Publication Year	2002
Times Cited	1105
Keywords	Brain-computer interface, Electrophysiological signals, Communication technology, Neuromuscular disorders
Literature Type	Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, P.H.S., Review
ISSN	1388-2457
Pages	767-91
Issue	113(6)
Authors	Jonathan R Wolpaw, Niels Birbaumer, Dennis J McFarland, Gert Pfurtscheller, Theresa M Vaughan

TL;DR

This research explores the development of brain-computer interfaces (BCIs) as a means for individuals with severe neuromuscular disorders to communicate and control devices, leveraging various electrophysiological signals to translate brain activity into commands. The findings highlight the potential of BCIs to significantly enhance communication for paralyzed users while emphasizing the need for interdisciplinary collaboration and improved signal processing methods to increase information transfer rates for broader applications.

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Brain-computer interface · Electrophysiological signals · Communication technology · Neuromuscular disorders

Abstract

For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.

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Primary Questions Addressed

1. What are the latest advancements in BCI technology that improve communication for users with severe disabilities?
2. How do different electrophysiological signals compare in terms of their effectiveness for BCI applications?
3. What are the key challenges in increasing the information transfer rates of current BCI systems?
4. How can interdisciplinary collaboration enhance the development of more effective BCI systems?
5. What user-centered design principles should be considered to improve the acceptance of BCI technology among users with disabilities?

Key Findings

1. Research Background and Objective

The research explores the potential of brain-computer interfaces (BCIs) as a novel non-muscular channel for communication and control. Grounded in the understanding that electroencephalographic (EEG) activity and other electrophysiological measures can reflect brain function, the study aims to develop technology that empowers individuals with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injuries. The primary objective is to enable users, who may be completely paralyzed or 'locked in,' to communicate their needs and operate devices through brain signals, thus enhancing their quality of life and autonomy.

2. Main Methods and Findings

The research highlights several approaches adopted in BCI systems, focusing on the interpretation of various electrophysiological signals, including slow cortical potentials, P300 potentials, and mu or beta rhythms. These signals, captured from the scalp or via implanted electrodes, are translated in real-time into actionable commands for computers or other devices. The study notes that successful BCI operation necessitates a mutual adaptation process between the user and the system, requiring both parties to continuously adjust to ensure effective communication. Current BCIs achieve information transfer rates of 10-25 bits per minute, which, while limited, can still be beneficial for users unable to use conventional communication methods.

3. Core Conclusions

The findings underscore that BCI research is inherently interdisciplinary, requiring insights from neurobiology, psychology, engineering, mathematics, and computer science. Future advancements hinge on identifying the most controllable brain signals, developing user training methods, and optimizing algorithms for signal translation. The need for precise evaluation of BCI performance, alongside the identification of suitable applications and user preferences, is critical. Moreover, the study warns against the potential pitfalls of exaggerated media portrayals which can mislead the public and create skepticism among researchers.

4. Research Significance and Impact

The significance of this research lies in its potential to redefine communication for individuals with severe disabilities, providing them with a new means to express their thoughts and control their environment. As BCI technology evolves, it could also extend its applications to individuals without disabilities, offering supplementary control options in various situations. The findings advocate for a balanced approach that prioritizes rigorous research and realistic expectations, which is essential for fostering trust and advancing BCI technologies. Ultimately, addressing these issues could lead to groundbreaking improvements in assistive technologies, thereby enhancing the lives of those with motor disabilities and expanding the capabilities of all users.

Literatures Citing This Work

1. Predictors of successful self control during brain-computer communication. - N Neumann;N Birbaumer - Journal of neurology, neurosurgery, and psychiatry (2003)
2. Basic advances and new avenues in therapy of spinal cord injury. - Bruce H Dobkin;Leif A Havton - Annual review of medicine (2004)
3. 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)
4. Motor imagery classification by means of source analysis for brain-computer interface applications. - Lei Qin;Lei Ding;Bin He - Journal of neural engineering (2004)
5. Motor-related cortical dynamics to intact movements in tetraplegics as revealed by high-resolution EEG. - Donatella Mattia;Febo Cincotti;Marco Mattiocco;Giorgio Scivoletto;Maria Grazia Marciani;Fabio Babiloni - Human brain mapping (2006)
6. Encoding of movement direction in different frequency ranges of motor cortical local field potentials. - Jörn Rickert;Simone Cardoso de Oliveira;Eilon Vaadia;Ad Aertsen;Stefan Rotter;Carsten Mehring - The Journal of neuroscience : the official journal of the Society for Neuroscience (2005)
7. An enhanced time-frequency-spatial approach for motor imagery classification. - Nobuyuki Yamawaki;Christopher Wilke;Zhongming Liu;Bin He - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2006)
8. The beat goes on: rhythmic modulation of cortical potentials by imagined tapping. - Allen Osman;Robert Albert;K Richard Ridderinkhof;Guido Band;Maurits van der Molen - Journal of experimental psychology. Human perception and performance (2006)
9. Adaptive feature extraction for EEG signal classification. - Shiliang Sun;Changshui Zhang - Medical & biological engineering & computing (2006)
10. Study of discriminant analysis applied to motor imagery bipolar data. - Carmen Vidaurre;Reinhold Scherer;Rafael Cabeza;Alois Schlögl;Gert Pfurtscheller - Medical & biological engineering & computing (2007)

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