The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation
Open accessAbstract
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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Cites
Comment on the modified Beer–Lambert law for scattering media
Sassaroli, A., & Fantini, S. (2004) · Physics in Medicine and Biology · doi:10.1088/0031-9155/49/14/n07 · Cited by 352
Hamaoka, T., McCully, K. K., Quaresima, V., Yamamoto, K., & Chance, B. (2007) · Journal of Biomedical Optics · doi:10.1117/1.2805437 · Cited by 329
Cited by
Herold, F., Wiegel, P., Scholkmann, F., & Müller, N. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7120466 · Cited by 482
Self-citationCooper, R. J., Selb, J., Gagnon, L., Phillip, D., Schytz, H. W., Iversen, H. K., Ashina, M., & Boas, D. A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00147 · Cited by 421
Reindl, V., Gerloff, C., Scharke, W., & Konrad, K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.060 · Cited by 411
Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS
Barker, J. W., Aarabi, A., & Huppert, T. J. (2013) · Biomedical Optics Express · doi:10.1364/boe.4.001366 · Cited by 337
A Review on the Use of Wearable Functional Near‐Infrared Spectroscopy in Naturalistic Environments
Pinti, P., Aichelburg, C., Gilbert, S., Hamilton, A., Hirsch, J., Burgess, P., & Tachtsidis, I. (2018) · Japanese Psychological Research · doi:10.1111/jpr.12206 · Cited by 325
Metadata
- Authors
- Scholkmann, F., Spichtig, S., Muehlemann, T., & Wolf, M.
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2010-03-22
- Volume
- 31
- Issue
- 5
- Pages
- 649-662
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W1996776986
- Cited by
- 683
- References
- 2
- FWCI
- 9.40
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Scholkmann, F., Spichtig, S., Muehlemann, T., & Wolf, M. (2010). How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiological Measurement. https://doi.org/10.1088/0967-3334/31/5/004
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