Open Access
1 September 2009 Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals
Jaakko Virtanen, Tommi E.J. Noponen, Pekka Meriläinen
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Abstract
Near-infrared spectroscopy (NIRS) is a method for noninvasive estimation of cerebral hemodynamic changes. Principal component analysis (PCA) and independent component analysis (ICA) can be used for decomposing a set of signals to underlying components. Our objective is to determine whether PCA or ICA is more efficient in identifying and removing scalp blood flow interference from multichannel NIRS signals. Concentration changes of oxygenated (HbO2) and deoxygenated (HbR) hemoglobin are measured on the forehead with multichannel NIRS during hyper- and hypocapnia. PCA and ICA are used separately to identify and remove signal contribution from extracerebral tissue, and the resulting estimates of cerebral responses are compared to the expected cerebral responses. Both methods were able to reduce extracerebral contribution to the signals, but PCA typically performs equal to or better than ICA. The improvement in 3-cm signal quality achieved with both methods is comparable to increasing the source-detector separation from 3 to 5 cm. Especially PCA appears to be well suited for use in NIRS applications where the cerebral activation is diffuse, such as monitoring of global cerebral oxygenation and hemodynamics. Performance differences between PCA and ICA could be attributed primarily to different criteria for identifying the surface effect.
©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)
Jaakko Virtanen, Tommi E.J. Noponen, and Pekka Meriläinen "Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals," Journal of Biomedical Optics 14(5), 054032 (1 September 2009). https://doi.org/10.1117/1.3253323
Published: 1 September 2009
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Cited by 104 scholarly publications.
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KEYWORDS
Independent component analysis

Principal component analysis

Near infrared spectroscopy

Hemodynamics

Natural surfaces

Electronic filtering

Sensors

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