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Kim, Sung-Phil
Brain-Computer Interface (BCI) Lab
Research Interests
  • Brain-computer interface, Statistical Signal Processing, Neural Code, Neuromarketing

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Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

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Title
Noise reduction in functional near-infrared spectroscopy signals by independent component analysis
Author
Santosa, HendrikHong, Melissa JiyounKim, Sung-PhilHong, Keum-Shik
Issue Date
2013-07
Publisher
AMER INST PHYSICS
Citation
REVIEW OF SCIENTIFIC INSTRUMENTS, v.84, no.7, pp.1 - 9
Abstract
Functional near-infrared spectroscopy (fNIRS) is used to detect concentration changes of oxyhemoglobin and deoxy-hemoglobin in the human brain. The main difficulty entailed in the analysis of fNIRS signals is the fact that the hemodynamic response to a specific neuronal activation is contaminated by physiological and instrument noises, motion artifacts, and other interferences. This paper proposes independent component analysis (ICA) as a means of identifying the original hemodynamic response in the presence of noises. The original hemodynamic response was reconstructed using the primary independent component (IC) and other, less-weighting-coefficient ICs. In order to generate experimental brain stimuli, arithmetic tasks were administered to eight volunteer subjects. The t-value of the reconstructed hemodynamic response was improved by using the ICs found in the measured data. The best t-value out of 16 low-pass-filtered signals was 37, and that of the reconstructed one was 51. Also, the average t-value of the eight subjects' reconstructed signals was 40, whereas that of all of their low-pass-filtered signals was only 20. Overall, the results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping.
URI
https://scholarworks.unist.ac.kr/handle/201301/9536
DOI
10.1063/1.4812785
ISSN
0034-6748
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