Brain-computer interfaces for communication and control
TLDR
The brain's electrical signals enable people without muscle control to physically interact with the world through the use of their brains' electrical signals.Abstract:
The brain's electrical signals enable people without muscle control to physically interact with the world.read more
Citations
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Journal ArticleDOI
Brain Computer Interfaces, a Review
TL;DR: The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.
Journal ArticleDOI
Single-Trial Analysis and Classification of ERP Components - a Tutorial
TL;DR: This tutorial proposes to use shrinkage estimators and shows that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification.
Journal ArticleDOI
Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.
José del R. Millán,Rüdiger Rupp,Gernot Müller-Putz,Rod Murray-Smith,Claudio Giugliemma,Michael Tangermann,Carmen Vidaurre,Febo Cincotti,Andrea Kübler,Robert Leeb,Christa Neuper,Klaus-Robert Müller,Donatella Mattia +12 more
TL;DR: This paper focuses on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT) and identifies four application areas where disabled individuals could greatly benefit from advancements inBCI technology, namely, “Communication and Control”, ‘Motor Substitution’, ”Entertainment” and “Motor Recovery”.
Journal ArticleDOI
fNIRS-based brain-computer interfaces: a review
Noman Naseer,Keum-Shik Hong +1 more
TL;DR: In this paper, the most common brain areas for fNIRS-based BCI are the primary motor cortex and prefrontal cortex, and the motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided.
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An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method
TL;DR: The positive characteristics of the proposed SSVEP-based BCI system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.
References
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Book
The Mathematical Theory of Communication
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
Book
The global burden of disease: a comprehensive assessment of mortality and disability from diseases injuries and risk factors in 1990 and projected to 2020.
TL;DR: This is the first in a planned series of 10 volumes that will attempt to "summarize epidemiological knowledge about all major conditions and most risk factors" and use historical trends in main determinants to project mortality and disease burden forward to 2020.
Journal ArticleDOI
Brain-computer interfaces for communication and control.
Jonathan R. Wolpaw,Jonathan R. Wolpaw,Niels Birbaumer,Niels Birbaumer,Dennis J. McFarland,Gert Pfurtscheller,Theresa M. Vaughan +6 more
TL;DR: With adequate recognition and effective engagement of all 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.
Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI
Event-related EEG/MEG synchronization and desynchronization: basic principles.
TL;DR: Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously.