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Journal of SpectroscopyVolume 2013 2013, Article ID 950302, 9 pages

Research Article

School of Electrical Engineering and Automaton, Harbin Institute of Technology, Harbin 150001, China

School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Received 26 September 2013; Accepted 27 October 2013

Academic Editor: Yehia Mechref

Copyright © 2013 Yan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Brain-computer interface BCI is one technology that allows a user to communicate with external devices through detecting brain activity. As a promising noninvasive technique, functional near-infrared spectroscopy fNIRS has recently earned increasing attention in BCI studies. However, in practice fNIRS measurements can suffer from significant physiological interference, for example, arising from cardiac contraction, breathing, and blood pressure fluctuations, thereby severely limiting the utility of the method. Here, we apply the multidistance fNIRS method, with short-distance and long-distance optode pairs, and we propose the combination of independent component analysis ICA and least squares LS with the fNIRS recordings to reduce the interference. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Least squares is used to optimize the reconstruction value for brain activity signal. Monte Carlo simulations of photon propagation through a five-layered slab model of a human adult head were implemented to evaluate our methodology. The results demonstrate that the ICA method can separate the brain signal and interference; the further application of least squares can significantly recover haemodynamic signals contaminated by physiological interference from the fNIRS-evoked brain activity data.





Autor: Yan Zhang, Xin Liu, Chunling Yang, Kuanquan Wang, Jinwei Sun, and Peter Rolfe

Fuente: https://www.hindawi.com/



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