Abstract

Functional near-infrared spectroscopy (fNIRS) has been increasingly utilized for detecting human cerebral activity in many disciplines because of the potential for less-restraining conditions. However, users often suffer from motion artifacts originating from optode fluctuation during task execution when the task includes motion. In such cases, the optode fluctuation induces changes both in the reflection by hair and in the transmission between the optode and scalp. If part of the reflected light is directly received by the detector optode (short-circuited light), it will contaminate the fNIRS signal. The transmittance change at the optode–scalp gap will also contaminate the signal. In this study, we proposed an optical model on the influence of optode fluctuation on the fNIRS signal and a method for removing the influence. The model revealed the following: (1) the received short-circuited light and the gap transmittance change generated a baseline change in the detected light intensity, and (2) the signal from the tissues was downscaled with increases in the receiving intensity of short-circuited light. To avoid erroneous detection of short-circuited light, we developed a method that optically eliminated hair-reflected light from the detection using linearly polarized light sources and an orthogonally polarized analyzer. The method was validated with an optical phantom possessing a haired surface. The optical absorbance change of a close source–detector (S-D) pair equipped with polarizers was very similar to that of distant S-D pairs, even though these optodes were artificially fluctuated. By combining the multidistance optode arrangement technique with the short-circuited light elimination method, the measurement could effectively eliminate motion artifacts originating from optode fluctuation.

© 2015 Optical Society of America

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References

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    [Crossref] [PubMed]

2015 (2)

A. M. Chiarelli, E. L. Maclin, M. Fabiani, and G. Gratton, “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” Neuroimage 112, 128–137 (2015).
[Crossref] [PubMed]

S. Brigadoi and R. J. Cooper, “How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics 2(2), 025005 (2015).
[Crossref] [PubMed]

2014 (5)

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1350066 (2014).
[Crossref] [PubMed]

F. Scholkmann, A. J. Metz, and M. Wolf, “Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy--how robust are the different calculation methods against movement artifacts?” Physiol. Meas. 35(4), 717–734 (2014).
[Crossref] [PubMed]

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

2013 (2)

S. Umeyama and T. Yamada, “Detection of an unstable and/or a weak probe contact in a multichannel functional near-infrared spectroscopy measurement,” J. Biomed. Opt. 18(4), 047003 (2013).
[Crossref] [PubMed]

J. W. Barker, A. Aarabi, and T. J. Huppert, “Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS,” Biomed. Opt. Express 4(8), 1366–1379 (2013).
[Crossref] [PubMed]

2012 (4)

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. A. Sato, “Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography,” Opt. Express 20(18), 20427–20446 (2012).
[Crossref] [PubMed]

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33(2), 259–270 (2012).
[Crossref] [PubMed]

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

2011 (1)

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

2010 (3)

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

2009 (1)

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
[Crossref] [PubMed]

2007 (2)

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study,” J. Biomed. Opt. 12(4), 044014 (2007).
[Crossref] [PubMed]

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

2005 (1)

1995 (1)

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Aarabi, A.

Amita, T.

Ashina, M.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Atsumori, H.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Barker, J. W.

Berger, A. J.

Blasi, A.

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

Boas, D. A.

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1350066 (2014).
[Crossref] [PubMed]

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Brigadoi, S.

S. Brigadoi and R. J. Cooper, “How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics 2(2), 025005 (2015).
[Crossref] [PubMed]

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

Brown, E. N.

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study,” J. Biomed. Opt. 12(4), 044014 (2007).
[Crossref] [PubMed]

Brühl, R.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Cash, S. S.

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

Ceccherini, L.

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

Chiarelli, A. M.

A. M. Chiarelli, E. L. Maclin, M. Fabiani, and G. Gratton, “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” Neuroimage 112, 128–137 (2015).
[Crossref] [PubMed]

Cooper, C. E.

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Cooper, R. J.

S. Brigadoi and R. J. Cooper, “How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics 2(2), 025005 (2015).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1350066 (2014).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Cope, M.

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Culver, J. P.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Cutini, S.

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

Darzi, A. W.

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

Dehghani, H.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Delpy, D. T.

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Dumont, G. A.

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33(2), 259–270 (2012).
[Crossref] [PubMed]

Elwell, C. E.

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Fabiani, M.

A. M. Chiarelli, E. L. Maclin, M. Fabiani, and G. Gratton, “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” Neuroimage 112, 128–137 (2015).
[Crossref] [PubMed]

Funane, T.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Gagnon, L.

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Gratton, G.

A. M. Chiarelli, E. L. Maclin, M. Fabiani, and G. Gratton, “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” Neuroimage 112, 128–137 (2015).
[Crossref] [PubMed]

Heine, A.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Hiroe, N.

Huppert, T. J.

Ilmoniemi, R. J.

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

Inoue, Y.

Ittermann, B.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Iversen, H. K.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Jacobs, A. M.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

James, D. R. C.

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

Jelzow, A.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Katura, T.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Kiguchi, M.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Kirilina, E.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Kosaka, T.

Kotilahti, K.

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

Leff, D. R.

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

Lloyd-Fox, S.

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

Maclin, E. L.

A. M. Chiarelli, E. L. Maclin, M. Fabiani, and G. Gratton, “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” Neuroimage 112, 128–137 (2015).
[Crossref] [PubMed]

Matcher, S. J.

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Matsuda, K.

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
[Crossref] [PubMed]

Metz, A. J.

F. Scholkmann, A. J. Metz, and M. Wolf, “Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy--how robust are the different calculation methods against movement artifacts?” Physiol. Meas. 35(4), 717–734 (2014).
[Crossref] [PubMed]

Molavi, B.

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33(2), 259–270 (2012).
[Crossref] [PubMed]

Muehlemann, T.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

Niessing, M.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Noponen, T.

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

Obata, A. N.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Okada, E.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Orihuela-Espina, F.

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

Phillip, D.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Saager, R. B.

Sato, H.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Sato, M. A.

Scarpa, F.

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

Scatturin, P.

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

Schlaggar, B. L.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Scholkmann, F.

F. Scholkmann, A. J. Metz, and M. Wolf, “Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy--how robust are the different calculation methods against movement artifacts?” Physiol. Meas. 35(4), 717–734 (2014).
[Crossref] [PubMed]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

Schytz, H. W.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Selb, J.

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1350066 (2014).
[Crossref] [PubMed]

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Shimokawa, T.

Spichtig, S.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

Strangman, G. E.

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study,” J. Biomed. Opt. 12(4), 044014 (2007).
[Crossref] [PubMed]

Tachtsidis, I.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

Tanikawa, Y.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Umeyama, S.

S. Umeyama and T. Yamada, “Detection of an unstable and/or a weak probe contact in a multichannel functional near-infrared spectroscopy measurement,” J. Biomed. Opt. 18(4), 047003 (2013).
[Crossref] [PubMed]

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
[Crossref] [PubMed]

Virtanen, J.

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

Wabnitz, H.

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

White, B. R.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Wolf, M.

F. Scholkmann, A. J. Metz, and M. Wolf, “Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy--how robust are the different calculation methods against movement artifacts?” Physiol. Meas. 35(4), 717–734 (2014).
[Crossref] [PubMed]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

Yamada, T.

S. Umeyama and T. Yamada, “Detection of an unstable and/or a weak probe contact in a multichannel functional near-infrared spectroscopy measurement,” J. Biomed. Opt. 18(4), 047003 (2013).
[Crossref] [PubMed]

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
[Crossref] [PubMed]

Yamashita, O.

Yang, G. Z.

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

Yücel, M. A.

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1350066 (2014).
[Crossref] [PubMed]

Zeff, B. W.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Zhang, Q.

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study,” J. Biomed. Opt. 12(4), 044014 (2007).
[Crossref] [PubMed]

Anal. Biochem. (1)

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227(1), 54–68 (1995).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Front. Neurosci. (1)

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

J. Biomed. Opt. (4)

J. Virtanen, T. Noponen, K. Kotilahti, J. Virtanen, and R. J. Ilmoniemi, “Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy,” J. Biomed. Opt. 16(8), 087005 (2011).
[Crossref] [PubMed]

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study,” J. Biomed. Opt. 12(4), 044014 (2007).
[Crossref] [PubMed]

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
[Crossref] [PubMed]

S. Umeyama and T. Yamada, “Detection of an unstable and/or a weak probe contact in a multichannel functional near-infrared spectroscopy measurement,” J. Biomed. Opt. 18(4), 047003 (2013).
[Crossref] [PubMed]

J. Innov. Opt. Health Sci. (1)

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1350066 (2014).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (1)

Neuroimage (5)

A. M. Chiarelli, E. L. Maclin, M. Fabiani, and G. Gratton, “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” Neuroimage 112, 128–137 (2015).
[Crossref] [PubMed]

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage 85(Pt 1), 192–201 (2014).
[Crossref] [PubMed]

S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” Neuroimage 85(Pt 1), 181–191 (2014).
[Crossref] [PubMed]

Neurophotonics (1)

S. Brigadoi and R. J. Cooper, “How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics 2(2), 025005 (2015).
[Crossref] [PubMed]

Neurosci. Biobehav. Rev. (1)

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

Opt. Express (1)

Phys. Med. Biol. (1)

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

Physiol. Meas. (3)

F. Scholkmann, A. J. Metz, and M. Wolf, “Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy--how robust are the different calculation methods against movement artifacts?” Physiol. Meas. 35(4), 717–734 (2014).
[Crossref] [PubMed]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33(2), 259–270 (2012).
[Crossref] [PubMed]

Proc. Natl. Acad. Sci. U.S.A. (1)

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Other (2)

T. Yamada, M. Ohashi, and S. Umeyama, “Development of a fiber-less fNIRS system and its aplicatinapplication to hair-covered head,” Proc. SPIE Int. Soc. Opt. Eng., 8928, 8928R (2014).

T. Yamada, S. Umeyama, and K. Matsuda, “A multidistance probe arrangement NIRS for detecting absorption changes in cerebral gray matter,” Proc. SPIE Int. Soc. Opt. Eng., 7557, 75570X (2010).

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Figures (10)

Fig. 1
Fig. 1

Illustration of the optical model for motion artifact generation. The light illuminated by the source optode is either partly reflected at hairs to get short-circuited, or travels through the tissues. Both are received by detector optode. I 0 , illumination intensity; I 1 (t) , intensity of short-circuited light; r s (t) , transmittance at source–scalp gap; R(t) , transmittance at head tissues; r d (t) , transmittance at optode–scalp gap; I 2 (t) , intensity of tissue-traveling light; I(t) , total detection intensity.

Fig. 2
Fig. 2

Elimination effect on the hair reflected light using a pair of polarizers. (a) Parallel configuration of polarizers. (b) Orthogonal configuration of polarizers. (c) Setup of optical elements for the demonstration. P1, polarizer for illumination light; P2, polarizer for reflected or scattered light.

Fig. 3
Fig. 3

Schematic illustration of the proposed method with combined use of the multidistance optode arrangement and the short-circuited light elimination method. For eliminating short-circuited light from the source to detector optodes at S-D pairs of 15 mm distance, polarizers P1 and P2 were arranged in orthogonal directions with each other. r s 30 (t) and r s 15 (t) , transmittances of optode–scalp gaps at source optodes of 30 mm and 15 mm distance from the detector optode, respectively; R 30 (t) and R 15 (t) , transmittances of head tissues when S-D pairs of 30 mm and 15 mm were used, respectively; r d (t) , transmittance between the detector optode and scalp surface; Δ A 30 (t) and Δ A 15 (t) , absorbance changes when S-D pairs of 30 mm and 15 mm were used, respectively.

Fig. 4
Fig. 4

(a) Photograph and (b) illustration of the implementation system. S1-S3, source optodes; D1 and D2, detector optodes. S3-S1-D1 and S3-S2-D2 were used for the proposed and original multidistance optode arrangements, respectively. Distances of far and close S-D pairs were 30 mm and 15 mm, respectively. S1 and D1 were equipped with polarizer films of orthogonal directions with each other. D2 was equipped with polarizer film so as to equalize the light intensity detected by S3-D2 with that by S3-D1. S2 was equipped with a ND filter film so as to equalize the light intensity detected by S2-D2 with that by S1-D1.

Fig. 5
Fig. 5

(a) Structure of the phantom with hair covered surface. (b) Four configurations of optodes against the phantom.

Fig. 6
Fig. 6

Typical absorbance changes, including motion artifacts. Lines in upper and lower frames were obtained at wavelengths of 770 nm and 840 nm, respectively. In each frame, red solid, blue solid, red dashed, and blue dashed lines were obtained with S1-D1, S3-D1, S2-D2, and S3-D2 pairs, respectively. Black and gray arrows indicate the beginning and the end of fluctuating the optodes, respectively.

Fig. 7
Fig. 7

Pearson correlation coefficients of absorbance changes at distant and close S-D pairs. Red circles represent correlations in absorbance changes at S1-D1 and S3-D1. Blue circles represent those at S2-D2 and S3-D2. The region larger than 0.8 for both measurement wavelengths in the lower frame was graphically magnified and drawn in the upper frame.

Fig. 8
Fig. 8

Hemoglobin changes obtained with conventional and two multidistance fNIRS measurements. Results for each trial are indicated in individual column. In each column, results of conventional fNIRS at S3-D1, the proposed multidistance measurement at S3-S1-D1, conventional fNIRS at S3-D2, and the original multidistance measurement at S3-S2-D2 are presented in descending order. The results calculated on the basis of the data in Fig. 6 were shown in the trial #1. The trail #3 and #8 were the best cases in the motion artifact reduction with the proposed and the original multidistance measurements, respectively. The k values and the artifact reduction ratios in each case are indicated in Figs. 9 and 10, respectively.

Fig. 9
Fig. 9

Difference in k values estimated with the proposed and original multidistance technique. Red and blue circles represent values with the arrangements S3-S1-D1 and S3-S2-D2, respectively. The filled circle in each color corresponds to the cases shown in Figs. 6 and 8. Circles of the best cases in artifact reduction used the proposed (#3) and the original (#8) multidistance measurements were indicated with colored letters, respectively.

Fig. 10
Fig. 10

Improvement in baseline flatness evaluated by the relative reduction ratio of standard deviation in hemoglobin changes calculated with the multidistance arrangement technique. Red and blue circles represent values obtained with arrangements S3-S1-D1 and S3-S2-D2, respectively. The filled circle in each color corresponds to the cases shown in Figs. 6 and 8. Circles of the best cases in artifact reduction used the proposed (#3) and the original (#8) multidistance measurements were indicated with colored letters, respectively.

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

ΔA(t)=log I(t) I(0) =log I 1 (t)+ I 2 (t) I 1 (0)+ I 2 (0) =log I 1 (t)+ I 0 r s (t)R(t) r d (t) I 1 (0)+ I 0 r s (0)R(0) r d (0) =log I 1 (t)+ I 0 r s (t)R(0) r d (t) I 1 (0)+ I 0 r s (0)R(0) r d (0) log{ 1+ I 0 r s (t)ΔR(t) r d (t) I 1 (t)+ I 0 r s (t)R(0) r d (t) } ,
ΔA(t)=log r s (t) r d (t) r s (0) r d (0) log R(t) R(0)
Δ a d (t)=log R d (t) R d (0)
Δ a 30 (t)kΔ a 15 (t)=( l gm 30 k l gm 15 )Δ μ a gm (t),
1 N t=1 N Δ a rest 30 (t)kΔ a rest 15 (t) 2 min,
Δ A rest 30 (t)kΔ A rest 15 (t) =log r s 30 (t) r d (t) r s 30 (0) r d (0) log R 30 (t) R 30 (0) +klog r s 15 (t) r d (t) r s 15 (0) r d (0) +klog R 15 (t) R 15 (0) =log r s 30 (t) r d (t) { r s 15 (t) r d (t) } k +( l gm 30 k l gm 15 )Δ μ a gm (t).

Metrics