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Concurrent spatiotemporal analysis of functional near-infrared spectroscopy data using independent component analysis
Zhen Yuan
2013-12-01
Conference Name6th International Congress on Image and Signal Processing (CISP)
Source Publication2013 6TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), VOLS 1-3
Volume2
Pages794-798
Conference DateDEC 16-18, 2013
Conference PlaceHangzhou, PEOPLES R CHINA
Abstract

Functional near infrared spectroscopy (fNIRS) recordings are the hemodynamic signals that come from the latent neural sources and physiological noise that are spatially and temporally mixed across the brain. The purpose of this work was to extract the temporal characteristics and the spatial activation patterns of human brains captured by independent component analysis (ICA). Both of the raw and filtered fNIRS datasets are processed with ICA. In particular the temporal components and spatial activity maps identified by ICA for a right finger tapping task were investigated in detail.

KeywordCognitive Neurosciences Fnirs Ica Neuroimaging
DOI10.1109/CISP.2013.6745273
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000341115000148
Scopus ID2-s2.0-84897769503
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Citation statistics
Document TypeConference paper
CollectionBiological Imaging and Stem Cell Core
Faculty of Health Sciences
DEPARTMENT OF PUBLIC HEALTH AND MEDICINAL ADMINISTRATION
Corresponding AuthorZhen Yuan
AffiliationUniversity of Macau, Bioimaging Core, Faculty of Health Sciences, Macau SAR, China
First Author AffilicationFaculty of Health Sciences
Corresponding Author AffilicationFaculty of Health Sciences
Recommended Citation
GB/T 7714
Zhen Yuan. Concurrent spatiotemporal analysis of functional near-infrared spectroscopy data using independent component analysis[C], 2013, 794-798.
APA Zhen Yuan.(2013). Concurrent spatiotemporal analysis of functional near-infrared spectroscopy data using independent component analysis. 2013 6TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), VOLS 1-3, 2, 794-798.
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