1 December 2014 Algorithm for removing scalp signals from functional near-infrared spectroscopy signals in real time using multidistance optodes
Author Affiliations +
J. of Biomedical Optics, 19(11), 110505 (2014). doi:10.1117/1.JBO.19.11.110505
A real-time algorithm for removing scalp-blood signals from functional near-infrared spectroscopy signals is proposed. Scalp and deep signals have different dependencies on the source-detector distance. These signals were separated using this characteristic. The algorithm was validated through an experiment using a dynamic phantom in which shallow and deep absorptions were independently changed. The algorithm for measurement of oxygenated and deoxygenated hemoglobins using two wavelengths was explicitly obtained. This algorithm is potentially useful for real-time systems, e.g., brain-computer interfaces and neuro-feedback systems.
Kiguchi and Funane: Algorithm for removing scalp signals from functional near-infrared spectroscopy signals in real time using multidistance optodes



Functional near-infrared spectroscopy (fNIRS)1 has been used for observing brain activity in the fields that require everyday measurements because the equipment is smaller, more inexpensive, and requires less restriction of subjects than other neuro-imaging modalities such as functional MRI and PET. Since the light is irradiated and detected from the brain through the scalp, the effects of hemodynamic changes in the scalp on the fNIRS signals have been reported.2,3 Therefore, an experimental design that compensates these effects using adequate control conditions is required.

To lift or relax this limitation, several engineering techniques have been developed.45.6 The multidistance independent analysis (MD-ICA) method using multidistance optodes7 has an advantage in that scalp signals can be quantitatively separated from the obtained signals. However, the real-time process of MD-ICA has not been achieved due to ICA, which requires time series data, although some other techniques are applicable for real-time processing. Because the scalp-signal effect potentially changes during experiments due to changes in emotion, the real-time separation is helpful to determine whether to stop or redo the experiment to increase measurement throughput. The real-time separation is also required for the brain-computer interface and neuro-feedback systems that have been applied in rehabilitation. We modified the algorithm of MD-ICA for real-time processing and validated the algorithm through an experiment using a dynamic phantom.



The absorbance change ΔA is proportional to the sum of the product of change in hemoglobin concentration ci and partial path length li in region i according to the modified Beer–Lambert equation in the case where the changes in partial path lengths are negligible


where ε is the molecular extinction coefficient for hemoglobin. Since the continuous-wave fNIRS (cw-fNIRS) cannot separately measure ci and li, the effective values of c and l are defined when the hemoglobin concentration changes in the scalp and the deep tissue



The dependency of the partial path length on the source-detector (SD) distance d has been calculated using a slab model7 and the real-head anatomical model obtained through MRI.8 The partial path length in scalp is approximately constant when d is larger than about 10 mm. The partial path length in the deep tissue linearly increases when d is larger than d0.


Here, d0 and l0 are the x-intercept and the minus value of y-intercept, respectively, when the partial path length in the deep tissue is plotted to d.

In MD-ICA, independent components were calculated by time-delayed decorrelation ICA (TDD-ICA) using absorbance changes observed with multiple values of d, then each weight factor for each independent component was divided into a deep subcomponent and a scalp subcomponent according to the ratio of the partial path length for deep tissue and scalp calculated using the d dependency of signal intensity. The deep and scalp components were reconstructed using all deep subcomponents and scalp subcomponents, respectively. The TDD algorithm requires a certain time period of measured absorbance changes, usually the period of a measurement cycle. Therefore, MD-ICA was used in the postprocess.

The MD-ICA algorithm can be easily modified for real-time processing by removing the ICA process. We call the new algorithm the real-time scalp signal separating (RT-SSS) algorithm. In MD-ICA, the separation process was applied to each independent component obtained by ICA. In RT-SSS, the separation process was applied not to the independent component, but directly to the absorbance change. The absorbance change observed with SD distance d, ΔA[d,t] can be separated into those for the deep and scalp layers, ΔAdeepRTSSS[d,t] and ΔAscalpRTSSS[d,t] using Eqs. (2) and (3).







The ΔAdeepRTSSS[d1,t] and ΔAscalpRTSSS[d1,t] can be calculated for each time t from ΔA[d1,t] and ΔA[d2,t] observed with different two values of d, d1 and d2, when d0 is given as well as MD-ICA. The parameters Δ(cl0)deep and Δ(cl)scalp are effective ones for the two homogeneous layer models. The deviation of d0 caused by the inhomogeneity in the depth direction leads to the cross talk between the separated signals of scalp and deep regions. The errors of separation were investigated in the previous paper for MD-ICA.7 Although two values of d are required for solving equations, using more than three values of d potentially reduces the error of separation due to the lateral inhomogeneity of absorbance in the scalp caused by the inhomogeneous distribution of scalp veins.

In RT-SSS, the noise is artificially divided into scalp and deep components depending on its SD-distance dependency. Each noise included in scalp or deep signals can be reduced by the usual algorithms of noise reduction in the postprocess.


Experimental Validation Using a Dynamic Phantom

The RT-SSS algorithm was validated using a dynamic phantom.9 The dynamic phantom had two (upper and lower) scattering and absorbing layers, and the absorption changes in the two layers were independently created by their motions driven by two moving stages. We measured the absorbance changes by using the phantom irradiated by a diode-laser light with a wavelength of 695 nm at d=15 and 30 mm under three conditions: (1) only the absorption of the lower layer was changed with a specified activation pattern, (2) only the absorption of the upper layer was changed with another activation pattern, and (3) the absorptions of both the lower and upper layers were changed with the activation patterns used for the conditions 1 and 2, respectively.

When considering that d=30mm is commonly employed in fNIRS, the absorbance change measured at d=30mm under condition 1 was the pure signal originating from the deep (lower) layer. The absorbance change measured at d=30mm under condition 2 was the pure signal originating from the scalp (upper) layer. The absorbance change measured at d=30mm under condition 3 was the mixed signal originating from both the deep and scalp layers. These three absorbance changes are expressed as ΔAdeeppure, ΔAscalppure, and ΔAmixed, respectively. The absorbance changes in the deep and scalp layers obtained using the RT-SSS algorithm with ΔAmixed[15,t] and ΔAmixed[30,t] are also expressed as ΔAdeepRTSSS and ΔAscalpRTSSS, respectively.

The value of d0 for the phantom required the use of Eq. (5). It was experimentally measured as follows. Under condition 1, ΔAdeepRTSSS of Eq. (5) can be replaced by ΔAdeeppure. Then, we can write the following equation:


By the least square fitting with Eq. (7), d0 was obtained as 9.92 mm. Note that these values d0 change depending on the characteristics of the phantom, i.e., materials, structure, and so on.

The mixed absorbance change measured with d=30mm, ΔAmixed[30,t] and the separated deep and scalp absorbance changes using the RT-SSS algorithm, ΔAdeepRTSSS[30,t] and ΔAscalpRTSSS[30,t] are compared with the pure absorbance changes ΔAdeeppure[30,t] and ΔAscalppure[30,t] in Fig. 1. Each absorbance change separated using the RT-SSS algorithm agreed with each pure absorbance change for both deep and scalp tissues. Figure 2 shows the correlation between (a) the deep absorbance change, (b) scalp absorbance change and each corresponding pure absorbance change, and (c) the mixed absorbance change and pure deep absorbance change. The fitting coefficients, slope α and y-intercept β, are shown in Fig. 2. The correlation coefficients r between the RT-SSS absorbance change and pure absorbance change for deep tissue and scalp were 0.997 and 0.991, respectively. The chi-squares were small enough and the residual waveform was flat. For the mixed absorbance change, however, the correlation coefficient was smaller, the chi-square was larger, and the residual waveform was more distorted than those for RT-SSS absorbance change because the mixed absorbance change includes both deep and scalp ones. Therefore, we can conclude that the RT-SSS algorithm successfully separated the mixed absorbance change into deep and scalp absorbance changes.

Fig. 1

(a) Time series of deep absorbance change obtained using RT-SSS algorithm, mixed absorbance change, and pure deep absorbance change. (b) Time series of scalp absorbance change obtained using RT-SSS algorithm and pure scalp absorbance change.


Fig. 2

Correlation between (a) deep absorbance change separated using RT-SSS algorithm, (b) scalp absorbance change separated using RT-SSS algorithm, (c) mixed absorbance change and respective pure absorbance change.



Discussions and Conclusions

The RT-SSS algorithm was explicitly applied to oxygenated and deoxygenated hemoglobin measurements using two wavelengths. From Eqs. (4) to (6), each absorbance change at wavelengths λ1 and λ2 measured with d1 and d2 was written as










where εoxyλ and εdeoxyλ represent the molecular extinction coefficient at wavelength λ for oxygenated and deoxygenated hemoglobins, respectively. Also Δcoxy and Δcdeoxy are the change in concentration of oxygenated and deoxygenated hemoglobins, respectively. Here, d0 was assumed to be independent of the wavelength in the case of human measurement. When d0 and ε are given, the deep and scalp hemoglobin signals are separated by solving Eq. (8) for four unknowns of (Δcoxyl)deep[d,t], (Δcdeoxyl)deep[d,t], (Δcoxyl)scalp[d,t] and (Δcdeoxyl)scalp[d,t] since the elements of the matrix on the left-hand side of Eq. (8) are directly measurable at each time, t. The literature values of ε for hemoglobin can be used. The value of d0 for the human forehead was estimated to be 10.5±1.6mm by stanching the scalp blood flow, and the errors in the calculated hemoglobin caused by the variation in the value of d0 were also estimated in the previous paper on MD-ICA.7 Because the concept of RT-SSS is almost the same as that of MD-ICA, the effect of the variation of d0 must also be almost the same.

In conclusion, we proposed the RT-SSS algorithm using two d measurements and validated it through phantom experiments. This algorithm will be applied to human experiments to verify its effectiveness.



F. Scholkmannet al., “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85, 6–27 (2014).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2013.05.004Google Scholar


P. Smielewskiet al., “Clinical evaluation of near-infrared spectroscopy for testing cerebrovascular reactivity in patients with carotid artery disease,” Stroke 28, 331–338 (1997).SJCCA70039-2499http://dx.doi.org/10.1161/01.STR.28.2.331Google Scholar


L. Minatiet al., “Intra- and extra-cranial effects of transient blood pressure changes on brain near-infrared spectroscopy (NIRS) measurements,” J. Neurosci. Methods 197, 283–288 (2011).JNMEDT0165-0270http://dx.doi.org/10.1016/j.jneumeth.2011.02.029Google Scholar


D. A. BoasA. M. DaleM. A. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy,” Neuroimage 23(Suppl 1), S275–S288 (2004).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2004.07.011Google Scholar


R. B. SaagerN. L. TelleriA. J. Berger, “Two-detector corrected near infrared spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS,” Neuroimage 55, 1679–1685 (2011).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2011.01.043Google Scholar


T. YamadaS. UmeyamaK. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14, 064034 (2009).JBOPFO1083-3668http://dx.doi.org/10.1117/1.3275469Google Scholar


T. Funaneet al., “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85, 150–165 (2014).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2013.02.026Google Scholar


G. E. StrangmanQ. ZhangZ. Li, “Scalp and skull influence on near infrared photon propagation in the Colin27 brain template,” Neuroimage 85, 136–149 (2014).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2013.04.090Google Scholar


T. Funaneet al., “Dynamic phantom with two stage-driven absorbers for mimicking hemoglobin changes in superficial and deep tissues,” J. Biomed. Opt. 17, 047001 (2012).JBOPFO1083-3668http://dx.doi.org/10.1117/1.JBO.17.4.047001Google Scholar

Masashi Kiguchi, Tsukasa Funane, "Algorithm for removing scalp signals from functional near-infrared spectroscopy signals in real time using multidistance optodes," Journal of Biomedical Optics 19(11), 110505 (1 December 2014). http://dx.doi.org/10.1117/1.JBO.19.11.110505


Near infrared spectroscopy



Independent component analysis

Magnetic resonance imaging


Back to Top