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      How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation.

      Physiological measurement

      Artifacts, Computer Simulation, Humans, Movement, physiology, Oxyhemoglobins, Time Factors, metabolism, Reproducibility of Results, Spectroscopy, Near-Infrared, methods, Algorithms

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          Abstract

          Near-infrared imaging (NIRI) is a neuroimaging technique which enables us to non-invasively measure hemodynamic changes in the human brain. Since the technique is very sensitive, the movement of a subject can cause movement artifacts (MAs), which affect the signal quality and results to a high degree. No general method is yet available to reduce these MAs effectively. The aim was to develop a new MA reduction method. A method based on moving standard deviation and spline interpolation was developed. It enables the semi-automatic detection and reduction of MAs in the data. It was validated using simulated and real NIRI signals. The results show that a significant reduction of MAs and an increase in signal quality are achieved. The effectiveness and usability of the method is demonstrated by the improved detection of evoked hemodynamic responses. The present method can not only be used in the postprocessing of NIRI signals but also for other kinds of data containing artifacts, for example ECG or EEG signals.

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          Author and article information

          Journal
          20308772
          10.1088/0967-3334/31/5/004

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