19 September 2014 An ARMA model based motion artifact reduction algorithm in fNIRS data through a Kalman filtering approach
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Abstract
Functional Near infrared spectroscopy (fNIRS) is a newly noninvasive way to measure oxy hemoglobin and deoxy hemoglobin concentration changes of human brain. Relatively safe and affordable than other functional imaging techniques such as fMRI, it is widely used for some special applications such as infant examinations and pilot’s brain monitoring. In such applications, fNIRS data sometimes suffer from undesirable movements of subject’s head which called motion artifact and lead to a signal corruption. Motion artifact in fNIRS data may result in fallacy of concluding or diagnosis. In this work we try to reduce these artifacts by a novel Kalman filtering algorithm that is based on an autoregressive moving average (ARMA) model for fNIRS system. Our proposed method does not require to any additional hardware and sensor and also it does not need to whole data together that once were of ineluctable necessities in older algorithms such as adaptive filter and Wiener filtering. Results show that our approach is successful in cleaning contaminated fNIRS data.
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M. Amian, S. Kamaledin Setarehdan, H. Yousefi, "An ARMA model based motion artifact reduction algorithm in fNIRS data through a Kalman filtering approach", Proc. SPIE 9216, Optics and Photonics for Information Processing VIII, 921614 (19 September 2014); doi: 10.1117/12.2058587; https://doi.org/10.1117/12.2058587
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