Enhanced performance by a hybrid NIRS–EEG brain computer interface
Siamac Fazli,Jan Mehnert,Jan Mehnert,Jens Steinbrink,Gabriel Curio,Arno Villringer,Arno Villringer,Arno Villringer,Klaus-Robert Müller,Benjamin Blankertz +9 more
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TLDR
The results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01).About:
This article is published in NeuroImage.The article was published on 2012-01-02 and is currently open access. It has received 536 citations till now. The article focuses on the topics: Sensorimotor rhythm & Electroencephalography.read more
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Closed-loop brain training: the science of neurofeedback
Ranganatha Sitaram,Tomas Ros,Luke E. Stoeckel,Sven Haller,Frank Scharnowski,Jarrod A. Lewis-Peacock,Nikolaus Weiskopf,Nikolaus Weiskopf,Maria Laura Blefari,Mohit Rana,Ethan Oblak,Niels Birbaumer,James Sulzer +12 more
TL;DR: How neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints is discussed.
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Review of the BCI Competition IV
Michael Tangermann,Klaus-Robert Müller,Ad Aertsen,Niels Birbaumer,Christoph Braun,Christoph Braun,Clemens Brunner,Clemens Brunner,Robert Leeb,Carsten Mehring,Carsten Mehring,Kai J. Miller,Gernot Müller-Putz,Guido Nolte,Gert Pfurtscheller,Hubert Preissl,Hubert Preissl,Gerwin Schalk,Alois Schlögl,Carmen Vidaurre,Stephan Waldert,Stephan Waldert,Stephan Waldert,Benjamin Blankertz +23 more
TL;DR: The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community and it is the hope that winning entries may enhance the analysis methods of future BCIs.
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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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Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
TL;DR: Brain-machine interfaces research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema.
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Brain computer interface: control signals review
TL;DR: A review to the state-of-the-art work in the field of BCI on the Brain control signals, their types and classifications and the current BCI technology in terms of hardware and software is reviewed.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
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Information Theory, Inference and Learning Algorithms
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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Information theory, inference, and learning algorithms
TL;DR: In this paper, the mathematics underpinning the most dynamic areas of modern science and engineering are discussed and discussed in a fun and exciting textbook on the mathematics underlying the most important areas of science and technology.
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.
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Neurophysiological investigation of the basis of the fMRI signal
TL;DR: These findings suggest that the BOLD contrast mechanism reflects the input and intracortical processing of a given area rather than its spiking output, and that LFPs yield a better estimate of BOLD responses than the multi-unit responses.