Abstract
Functional near infrared spectroscopy can measure hemodynamic
signals, and the results are similar to functional magnetic resonance
imaging of blood-oxygen-level-dependent signals. Thus, functional near
infrared spectroscopy can be employed to investigate brain activity by
measuring the absorption of near infrared light through an intact skull.
Recently, a general linear model, which is a standard method for
functional magnetic resonance imaging, was applied to functional near
infrared spectroscopy imaging analysis. However, the general linear model
fails when functional near infrared spectroscopy signals retain noise,
such as that caused by the subject's movement during measurement.
Although wavelet-based denoising and hemodynamic response function
smoothing are popular denoising methods for functional near infrared
spectroscopy signals, these methods do not exhibit impressive performances
for very noisy environments and a specific class of noise. Thus, this
paper proposes a new denoising algorithm that uses multiple wavelet
shrinkage and a multiple threshold function based on a hemodynamic
response model. Through the experiments, the performance of the proposed
algorithm is verified using graphic results and objective indexes, and it
is compared with existing denoising algorithms.
© 2018 The Author(s)
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