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Brain-computer interfaces for communication and control

Authors:

Dennis J. McFarland,

Jonathan R. WolpawAuthors Info & Claims

Communications of the ACM, Volume 54, Issue 5

Pages 60 - 66

https://doi.org/10.1145/1941487.1941506

Published: 01 May 2011 Publication History

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Abstract

The brain's electrical signals enable people without muscle control to physically interact with the world.

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Index Terms

Brain-computer interfaces for communication and control

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Reviews

Reviewer: George Michael White

Think about what you have to go through to read this review. You may be reading it in a journal or on a computer screen, but in any case, you have to move your hands and arms to either turn pages or click mouse buttons. Imagine, though, that you have little or no muscle control. Turning a page or clicking a mouse button is a major-perhaps impossible-activity. This is exactly what people with amyotrophic lateral sclerosis (ALS) must do. ALS is a disease that attacks the pathways between the brain and the limbs. Everyday actions that most people perform almost without thinking are impossible for people with ALS. Is there a way to bypass the damaged pathway__?__ If the pathways are not completely damaged (for example, if the eyes or eyebrows are still under the control of the brain), then a person can use the position of the eyes to give information about what the brain wishes to perform. If the disease is so advanced that eye control is no longer possible, then noninvasive electrodes placed on the scalp, or invasive electrodes placed directly on selected parts of the brain, can detect electrical signals. External machines can then use the complex signals received to perform the desired actions. This is all very complicated. The desired actions must correlate with the signals and then be effected. This short article summarizes progress in this field, starting from the early years in the last century to 2010. Those interested in this highly specialized field will enjoy reading it. Online Computing Reviews Service

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Published In

Communications of the ACM Volume 54, Issue 5

May 2011

134 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1941487

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 May 2011

Published in CACM Volume 54, Issue 5

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Chen LYin ZGu XZhang XCao XZhang CLi X(2025)Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic modelComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2025.108594261(108594)Online publication date: Apr-2025
https://doi.org/10.1016/j.cmpb.2025.108594
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https://doi.org/10.1016/j.bspc.2024.107189
Qin YZhang LYu B(2025)A cross-domain-based channel selection method for motor imageryMedical & Biological Engineering & Computing10.1007/s11517-025-03298-xOnline publication date: 25-Jan-2025
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Abstract

References

Cited By

Index Terms

Recommendations

Advanced brain computer interface for communication and control

Towards ambulatory brain-computer interfaces: a pilot study with P300 signals

Brain-Computer Interfaces: Current Trends and Applications

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