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  Enhanced performance by a hybrid NIRS–EEG brain computer interface

Fazli, S., Mehnert, J., Steinbrink, J., Curio, G., Villringer, A., Mueller, K.-R., et al. (2012). Enhanced performance by a hybrid NIRS–EEG brain computer interface. NeuroImage, 59(1), 519-529. doi:10.1016/j.neuroimage.2011.07.084.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-13FF-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-CF7F-7
Genre: Journal Article

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 Creators:
Fazli, Siamac1, 2, Author
Mehnert, Jan3, 4, Author              
Steinbrink, Jens2, 3, Author
Curio, Gabriel2, 5, Author
Villringer, Arno3, 4, 6, Author              
Mueller, Klaus-Robert2, 7, Author
Blankertz, Benjamin1, 2, 8, Author
Affiliations:
1Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
2Bernstein Focus: Neurotechnology Berlin, Germany, ou_persistent22              
3Berlin Neuroimaging Center, Charité University Medicine Berlin, Germany, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
5Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
6Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
7Institute for Pure and Applied Mathematics (IPAM), University of California, Los Angeles, CA, USA, ou_persistent22              
8Fraunhofer Institute for Open Communication Systems (FOKUS), Berlin, Germany, ou_persistent22              

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Free keywords: Combined NIRS-EEG; Hybrid BCI; Meta-classifier
 Abstract: Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our 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). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI.

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Language(s): eng - English
 Dates: 2011-08-042012-01-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2011.07.084
PMID: 21840399
Other: Epub 2011
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 59 (1) Sequence Number: - Start / End Page: 519 - 529 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166