JP6028100B2 - Biological light measurement data analysis apparatus, analysis method, and program therefor - Google Patents

Description

  The present invention relates to a biological light measurement method for obtaining in-vivo information, and in particular, optical measurement is performed at a plurality of points on the head in order to evaluate a brain activity state, and the measurement data depends on changes in blood flow in the brain. The present invention relates to a biological light measurement data analysis apparatus and an analysis method for calculating a signal and displaying the result as an image.

  When light having a peak wavelength of light intensity in the visible to near-infrared region with high permeability to a living body is used, information inside the living body can be measured non-invasively. This is based on the Lambert-Beer law which showed that the logarithm of the measured optical signal is proportional to the product of the concentration of the light-absorbing substance and its optical path length. By developing this law, the relative concentration change of “oxygenated hemoglobin (Hb)” and “deoxygenated Hb” and “total Hb (sum of oxygenated and deoxygenated Hb)” Techniques for measuring (referred to as Hb signals) have been developed. In particular, a technique for non-invasive simultaneous measurement of multiple Hb signals in the human cerebral cortex using this technique has been proposed (Non-Patent Document 1) and used in research and clinical aspects. The above document discloses a method for measuring a human brain function by measuring an Hb signal of the cerebral cortex. Specifically, with the activation of human perceptual function and motor function, the blood volume in the cortical cortex area that controls the function increases locally, and the oxygenated Hb signal and deoxygenated Hb signal at the corresponding site change Therefore, the activity status of the brain can be evaluated. This technology is expected to be an unprecedented method for measuring brain function because it has features that it can measure brain function easily and non-invasively with low restraint on subjects, and it can evaluate hemodynamics and blood circulation. Has been.

  However, the signals measured by this measurement technology include biological signals that originate from parts other than the brain, such as skin blood flow, and signals that are irrelevant to the target brain function, even if signals originate from the brain. Several studies have pointed out that noise components such as artifacts due to head movement are mixed. For example, a study using a language fluency task shows that the conventional activity signal disappears when the skin in the measurement area is compressed. Most of the Hb signal change in the forehead is due to skin blood flow. The possibility of doing was pointed out (nonpatent literature 2).

  As measures for improving these noise components, for example, many methods using an apparatus capable of measuring a plurality of irradiation point-detection point (Source-Detector: S-D) distances have been proposed. Usually, in order to measure the depth in the vicinity of the cerebral cortex, the S-D distance is set to about 3 cm in many cases. However, if a shorter S-D distance is set, a signal in a shallow part can be measured. Such a method is disclosed in Patent Document 3, for example. However, in order to measure a plurality of S-D distances, it is necessary to increase the number of detectors or light sources, and the apparatus configuration becomes complicated, resulting in a problem of increased cost.

  Further, the apparatus configuration is only one conventional S-D distance, and several methods for removing noise component signals have been proposed. For example, after separating components by independent component analysis (ICA), a component considered to be systemic hemodynamics is removed (Patent Document 1-2).

  In addition, there is a method of using a known condition for a blood hemoglobin concentration change associated with a brain activity to be observed by an optical topography measurement signal. That is, it is known from conventional research that when the “oxygenated Hb concentration” in the blood increases with brain activity, the “deoxygenated Hb concentration” decreases accordingly. Therefore, the signal at the time when the correlation coefficient between the time series signal of oxygenated Hb concentration change and deoxygenated Hb concentration change obtained by optical topography measurement is positive is multiplied by a coefficient smaller than 1. A method of reducing the influence of noise components in the entire time series by compressing the signal amplitude has been proposed (Non-Patent Document 3). However, with this method, when the target signal is hidden and superimposed on the noise signal, the signal component is also compressed with the signal amplitude equivalent to the noise component, so the signal / noise ratio at that time is improved. It has not been.

  Also, combining the above-mentioned inverse correlation system between oxygenated Hb concentration change and deoxygenated Hb concentration change and signal component separation methods such as the above independent component analysis, oxygenated Hb and deoxygenated from the separated signal components A method of selecting only brain activity signal components whose Hb relationship is inversely correlated is easily analogized, but there are the following two problems. One problem is that, as described above, statistical measurement signal characteristics do not always lead to correct signal component separation. The other is that, in the conventional signal separation method, the signal component separation calculation of oxygenated Hb and that of deoxygenated Hb are executed as separate processes, so the features are not necessarily similar (although it is an inverse correlation). It is a problem that it is not always possible to extract the signal component.

  Of the two problems described above, a new technique is proposed in Non-Patent Document 4 for the problem related to the former signal separation method. This is a technique for extracting signal changes that appear in synchronization with repeated repetition of an experimental task that induces brain activity, and is more effective than an approach that uses signal independence. Synchronized signal components can be extracted. However, no solution to the latter problem has been shown.

Japanese Patent No. 4379155 Japanese Patent No. 4631510 US Pat. No. 7,072,701 B2

Maki, A et al., “Spatial and temporal analysis of human motor activity using noninvasive NIR topography” Medical Physics 22, 1997-2005 (1995). Takahashi, T et al., “Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task,” NeuroImage 59, 991-1002 (2011). Cui X et al., ”Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics.” Neuroimage 15:49 (4): 3039-46 (2010) Tanaka H et al., “Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data.” Neuroimage 1; 64: 308-27 (2013)

  As described in the background art, in the conventional biological light measurement method, the apparatus configuration is complicated, or statistical assumptions such as independent component analysis are required. It has been difficult to optimally derive a brain activity signal from a biological light measurement signal mixed with a noise signal other than the oxygenated hemoglobin concentration change signal.

  An object of the present invention is to provide a biological optical data analysis device and an analysis method capable of deriving a brain activity signal from which noise is removed from a biological optical measurement signal including a noise signal without complicating the device configuration. And

  According to the present invention, the brain activity signal corresponding to the repeated experiment task shows the same signal change at each repetition, and the oxygenated hemoglobin concentration change signal and the deoxygenated hemoglobin concentration change signal which are brain activity signals are Under the condition that there is an inverse correlation relationship, a signal obtained by multiplying and synthesizing each of the signals obtained from a plurality of measurement regions observed almost simultaneously with optimized weighting factors is calculated as a brain activity signal component.

  If a typical example of the present invention is given, in the biological light measurement data analysis apparatus for analyzing time-series data obtained by detecting light transmitted, scattered, and reflected in a living body at a plurality of measurement points, the plurality of points The time series data is multiplied by the mixing coefficient to create mixed time series data, and an evaluation function creating unit that creates an evaluation function representing the similarity of the repetition period of the mixed time series data, and the mixing coefficient of the mixed time series data Is calculated so that the similarity between signals repeatedly measured in time calculated from the mixed time-series data is the highest, and the weight coefficient calculation is performed on the time-series data of the plurality of points. A signal synthesizing unit that obtains time-series data obtained by removing the noise component by multiplying the mixing coefficient derived by the unit, and is substantially simultaneously measured at each of the plurality of points in the derivation of the mixing coefficient. And it is characterized in the use of time-series data of two or more can be derived positively correlated with each other by using a specific conversion.

  As another example of the present invention, in the biological light measurement data analysis method for analyzing time-series data obtained by detecting light transmitted, scattered, and reflected in a living body at a plurality of measurement points, A time series data of points is multiplied by a mixing coefficient to create mixed time series data, and an evaluation function creating step for creating an evaluation function representing a similarity in a repetition period of the mixed time series data, and the mixing of the mixed time series data A weight coefficient calculating step for deriving a coefficient so that the similarity between signals repeatedly measured in time calculated from the mixed time series data is the highest, and the weight coefficient in the time series data of the plurality of points A signal synthesis step for obtaining time series data obtained by removing the noise component by multiplying the mixing coefficient derived by the arithmetic unit, and in each of the plurality of points in the mixing coefficient derivation process There are those characterized by using the time-series data of two or more can be derived positively correlated with each other by using a substantially be measured simultaneously, and specific conversion.

  Since the biological light measurement data analysis apparatus and analysis method of the present invention is a calculation method using only the start and end times of the repeated experimental task, it does not require statistical assumptions such as independent component analysis, Brain activity signals can be derived more powerfully.

It is a block diagram which shows the basic composition of the biological light measurement data analysis apparatus of Example 1 of this invention. It is a flowchart of the biological light measurement data analysis method of Example 1 of this invention. It is a figure of the experiment task sequence of the biological light measurement data used in the present Example. It is a figure which shows the example which applied the biological optical measurement data analysis method of Example 1 of this invention, (a) The example of original data (time series data) of biological optical measurement data, (b) The conventional TRCA method is applied And (c) an example in which noise is removed by applying the channel extension TRCA method of the method of the present invention. It is a screen structural example of the biological light measurement data analysis apparatus of Example 3 of this invention.

  Embodiments of the present invention will be described with reference to the drawings.

  The basic form of the present invention is that when data X optically measured at a plurality of points at spatially different positions and observation data Y that has a positive or negative correlation with it are input, noise other than brain activity signals is input. It is a biological light data analysis apparatus and analysis method for outputting corrected data Z from which components have been removed.

  First, a method for calculating a noise component which is the core of the present invention will be described in detail.

  First, as original data, time series data X (measurement time T) of biological light measurement data measured at N measurement points (channel: ch) is shown in a matrix (Formula 1).

ここで、x_i(t)は平均値が0、標準偏差が１になるよう標準化した。また、必要に応じて、高周波雑音を除く平滑化処理や低周波揺らぎを除くハイパスフィルタなどの前処理も実施する。各チャンネルch(i)のデータx_i(t)に任意の係数（混合係数、重み係数）w(i)をかけて和を取ると、一つの混合時系列データy(t)が作成できる（数式2）。

Here, x _i (t) was standardized so that the average value was 0 and the standard deviation was 1. In addition, preprocessing such as a smoothing process that removes high-frequency noise and a high-pass filter that removes low-frequency fluctuations is performed as necessary. When data x _i (t) of each channel ch (i) is multiplied by an arbitrary coefficient (mixing coefficient, weighting coefficient) w (i) and summed, one mixed time series data y (t) can be created ( Formula 2).

ここで、Wは混合係数列を、添え字iはch番号を表す。

Here, W represents a mixing coefficient sequence, and the subscript i represents a ch number.

The conventional method TRCA (Task-Related Component Analysis) will be described. The TRCA method is an arithmetic method based on the assumption that a plurality of time series signals (for example, signal changes for repeated experimental tasks) show similar changes. Based on this hypothesis, the mixed signal y1 derived by multiplying the time series signal X observed from a plurality of observation points as the input signal by the optimum weighting factor w so as to reduce the noise of X is synthesized. It is considered that each of the ^N time-series signal segments y ^{(1 to N)} cut out from the mixed signal y1 in accordance with the start / end times of the N repetition experiment tasks shows the same change. Thus, for example, similar y when the similarity ^{(1 to N)} and expressed as C of the covariance between the time series division as shown in Equation 3, C that indicates a large value y ^{(1 to N)} Means high. Therefore, in the TRCA method, an arithmetic processing is performed by setting a problem of deriving a mixing coefficient w of X that maximizes C.

For N time series segments y ^{(1 to N)} , the covariance of the k th time series segment y ^(k) and the l th time series segment y ^(l) is

と定義され、評価関数となる。なお、共分散を平均で割れば、相関係数となる。

And is an evaluation function. Note that the correlation coefficient is obtained by dividing the covariance by the average.

  The sum R of the round-robin covariances is

と表わされる。ここで、

It is expressed as here,

である。ただし、i、 jは計測点番号を意味する。この評価関数を共分散和とよぶ。この共分散和が最大となるような混合係数wを求めることにより、繰り返し事象に対応して再現される脳活動信号を抽出することが可能であると考える。ただし、最大化計算における発散を防ぐため、境界条件として、

It is. However, i and j mean measurement point numbers. This evaluation function is called a covariance sum. It is considered that a brain activity signal reproduced in response to a repetitive event can be extracted by obtaining a mixing coefficient w that maximizes the covariance sum. However, to prevent divergence in the maximization calculation, as a boundary condition,

を同時に満たすものとする。

At the same time.

  This maximization is expressed by the following equation.

これはRayleigh-Ritz定理から

From the Rayleigh-Ritz theorem

の固有ベクトルがｗとなる。このとき、固有値は各固有ベクトルにより合成されるｙの妥当性を示す指標となる。以上が、従来のTRCA法の説明である。

The eigenvector of is w. At this time, the eigenvalue is an index indicating the validity of y synthesized by each eigenvector. The above is an explanation of the conventional TRCA method.

In the conventional TRCA method, calculation is performed using only oxygenated Hb or deoxygenated Hb. In the present invention, both signals are used for calculation simultaneously. That is, the relationship of oxygenated Hb and deoxygenated Hb utilizes the fact that the ideally inverse correlation, the signals obtained from M measurement points (channel), from a signal X _{the oxy} oxygenated Hb

とし、脱酸素化Hbの信号X_Deoxy

And deoxygenated Hb signal X _Deoxy

とし、共分散の演算を行う。つまり、見かけ上の計測点数を増やし計算を行う。なお、酸素化Hbおよび脱酸素化Hbの計測は、異なる波長の光を当てて、同時に計測すればよい。そして、導出された共分散和を用い以降の計算は通常のTRCA法と同様に行い、最適化された混合係数ｗを求める。これは時刻情報を保持した計算方法であり、そのため酸素化Hbと脱酸素化Hbとで同時刻に生じている雑音成分の除去に効果的である。例えば体や頭部の動きに伴う雑音信号が対象となる。本手法をチャンネル拡張TRCAとする。

And calculate the covariance. That is, the number of apparent measurement points is increased and calculation is performed. Note that oxygenated Hb and deoxygenated Hb may be measured simultaneously by applying light of different wavelengths. Then, the subsequent calculation using the derived covariance sum is performed in the same manner as the normal TRCA method, and the optimized mixing coefficient w is obtained. This is a calculation method that retains time information, and is therefore effective in removing noise components generated at the same time in oxygenated Hb and deoxygenated Hb. For example, a noise signal accompanying the movement of the body or head is targeted. This method is called channel expansion TRCA.

  FIG. 1 shows the basic configuration of the biological light measurement data analysis apparatus according to the first embodiment of the present invention, including the biological light measurement apparatus.

  In the biological light measurement device, light having a wavelength belonging to the visible to infrared region is generated by the light irradiation unit 10, and the light is guided to the measurement interface unit 40 through the optical fiber 30 to irradiate the head of the subject. The plurality of lights that have passed through the inside of the head are received by the interface unit 40, the light is guided to the light detection unit 20 through the optical fiber 30, and the light detection unit 20 detects it as an electrical signal. The interface unit 40 obtains blood change amount signals at a plurality of points by alternately arranging irradiation units and detection units at lattice points at predetermined intervals. The biological light measurement data detected by the light detection unit 20 is stored in the storage unit 50 and used for subsequent analysis.

  The biological light measurement data analysis apparatus of the present invention is mainly composed of a calculation unit 60. The calculation unit 60 includes an evaluation function creation unit 62, a weight coefficient calculation unit 64, a signal synthesis unit 66, and a user interface unit 68.

  The biological light measurement data stored in the storage unit 50 is processed by the calculation unit 60. In the calculation unit 60, parameters set by the user in the user interface unit 68 can be used. Parameters to be set include selection of an evaluation function and TRCA. Since the calculation unit 60 uses two types of biological light data, the storage unit 50 stores two or more types of biological light measurement data.

  The evaluation function creating unit 62 creates an evaluation function R (Formula 4) relating to reproducibility with respect to the experimental task for the two types of biological light data (X, Y) read from the storage unit 50. The weighting factor calculation unit 64 uses the created evaluation function R to derive a weighting factor W for synthesis that maximizes the reproducibility of the synthesized signal for the experimental task. The signal synthesis unit 66 multiplies the input biological light data (X, Y) and the synthesis weight coefficient W to derive the synthesis signal Z expressed by Equation 1.

  The display unit 70 displays the obtained composite signal Z. In addition, it is good also as a biological light measurement data analysis apparatus of this invention including the memory | storage part 50 and the display part 70. FIG.

FIG. 2 shows a flowchart of the biological light measurement data analysis method corresponding to the first embodiment of the present invention. First, in the evaluation function creation step S110, two types of biological light data X and Y are read, and an evaluation function R relating to reproducibility for the experiment task is created. In this case, the analysis parameter input from the input unit of the user interface unit 68 may be used.
Next, in the weighting factor calculation step S120, a weighting factor W for synthesis that maximizes the reproducibility of the synthesized signal for the experimental task is derived using the created evaluation function R.
Then, in the signal synthesis step S130, the input biological light data (X, Y) and the synthesis weight coefficient W are multiplied to derive a synthesized signal Z.

  The program of the present invention causes a computer to execute the processing described in the flowchart of FIG.

  3 and 4 show an example in which this analysis method is applied to actual brain function measurement data. Here, a signal observed from an actual human head is used. In the experiment, as shown in FIG. 3, the subject (task) 301 to memorize the position of the graphic presented on the screen was repeated 8 times for 8 seconds with a rest period 302 of about 30-40 seconds. The measurement site is the forehead portion of the subject.

  The biological light measurement data (original data) from which noise components are not removed in FIG. 4 (a), the conventional TRCA method in FIG. 4 (b) is used, and the method according to the present invention in FIG. 4 (c) is applied. Compare the correction data. In each figure, the horizontal axis represents the passage of time (seconds), and the vertical axis represents the amplitude. In FIG. 4A, reference numeral 401 indicates a deoxygenated hemoglobin signal, reference numeral 402 indicates an oxygenated hemoglobin signal, and a response signal of eight tasks 403. In the original data, a large signal change is observed around 150 seconds during measurement, but it is considered to be a noise component because its amplitude is large and it is not synchronized with repeated experimental tasks.

  On the other hand, in the result (reference numeral 411) of removing noise by applying the conventional TRCA method shown in FIG. 4B, those noises are greatly reduced compared to the original data, but around 150 seconds. There is also a change that seems to be due to noise and spike-like noise components around 220 seconds.

  On the other hand, in the result of removing noise by applying the method of the present invention shown in FIG. 4 (c) (reference numeral 421), the influence of noise in the vicinity of 150 seconds is reduced, and spike noise is also removed. ing.

  FIG. 4B and FIG. 4C show the results of averaging the signal changes corresponding to the experimental task repeated eight times. In the conventional TRCA method indicated by reference numeral 412, the signal decreases after completion of the task due to the influence of noise. However, in the method of the present invention indicated by reference numeral 422, the influence of noise is reduced and an ideal signal pattern is obtained. From this result, it is clear that the method of the present invention is effective.

The second embodiment of the present invention will be described below. In Example 2, the time-series division y ^(1-N) corresponding to the repeated experiment task used when deriving the covariance sum represented by Formula 4 is changed to x ^(1-N) when oxygenated Hb. Series division

と、脱酸素化Hbの時系列信号にマイナスを乗じた時系列区分

And time series division of deoxygenated Hb time series signal minus

とし、ｘ^{（１〜２N）}から導出される共分散和を用い最適な混合係数ｗを算出する。この手法では、酸素化Hbと脱酸素化Hbの計測点に関する情報、すなわち空間的な位置情報は保持されており、特定の観測部位において、酸素化Hbと脱酸素化Hbの信号変化が同一である混合信号ｙを導出する。雑音除去に関しても同様に、酸素化Hbと脱酸素化Hbとで共通した計測部位に特異的な成分を低減することができる。本手法を時系列拡張TRCAとする。

And an optimal mixing coefficient w is calculated using a covariance sum derived from x ^{(1 to 2N)} . In this method, information about the measurement points of oxygenated Hb and deoxygenated Hb, that is, spatial position information, is retained, and the signal changes of oxygenated Hb and deoxygenated Hb are the same at a specific observation site. A certain mixed signal y is derived. Similarly, with respect to noise removal, it is possible to reduce components specific to the measurement site common to oxygenated Hb and deoxygenated Hb. This method is called time-series extended TRCA.

  By combining the first and second embodiments, it is possible to effectively remove both noise generated at the same time by oxygenated Hb and deoxygenated Hb and noise generated at the same measurement site. FIG. 5 illustrates an input screen configuration of the user interface unit according to the third embodiment of the present invention. The screen can be set to select both time-series expansion TRCA and channel expansion TRCA, perform both, or repeat two processes. On the monitor screen 501, the time series expansion TRCA and the channel expansion TRCA can be selected by the analysis method selection function 1 indicated by reference numeral 502 and the analysis method selection function 2 indicated by reference numeral 503. In addition, the function for setting the number of repetitions indicated by reference numeral 504 can set the number of repetitions for the analysis method selection function 1 and the analysis method selection function 2. By pressing the execution button 505, the selected process is repeated.

DESCRIPTION OF SYMBOLS 10 Light irradiation part 20 Light detection part 30 Optical fiber 40 Measurement interface part 50 Storage part 60 Calculation part 62 Evaluation function creation part 64 Weight coefficient calculation part 66 Signal composition part 68 User interface part 70 Display part 301 Experimental task period 302 Rest period 401 Departure Oxygenated hemoglobin signal 402 Oxygenated hemoglobin signal 403 Task (task)
411 Signal 412 after removal of noise by conventional TRCA method Signal 421 obtained by repeatedly adding and averaging signal 411 Signal 422 after removal of noise by TRCA method of the present invention Signal 501 by repeatedly adding and averaging signal 421 Monitor screen 502 Analysis method Selection function 1
503 Analysis method selection function 2
504 Function for setting the number of repetitions 505 Execution function

Claims (15)

In a biological light measurement data analysis device that analyzes time-series data obtained by detecting light transmitted, scattered, and reflected in a living body at a plurality of measurement points,
An evaluation function creating unit that creates mixed time series data by multiplying the time series data of the plurality of points by a mixing coefficient, and creates an evaluation function representing the similarity of the repetition period of the mixed time series data;
A weighting factor calculation unit for deriving the mixing coefficient of the mixed time series data so that the similarity between signals repeatedly measured in time calculated from the mixed time series data is the highest,
A signal synthesizing unit that obtains time series data obtained by multiplying the time series data of the plurality of points by the mixing coefficient derived by the weighting factor computing unit to remove noise components;
In the derivation of the mixing coefficient, two or more types of time-series data that are measured almost simultaneously at each of the plurality of points and are capable of deriving a positive correlation with each other by using a specific conversion process are used. Biological light measurement data analysis device. The biological light measurement data analysis apparatus according to claim 1,
The biological light measurement data analysis apparatus characterized by using a value of covariance or correlation coefficient as the similarity between the repeatedly measured signals. The biological light measurement data analysis apparatus according to claim 1,
The oxygenated hemoglobin concentration change and the deoxygenated hemoglobin concentration change are used as two or more types of time series data from which a positive correlation can be derived, and the sign of the deoxygenated hemoglobin is reversed as a conversion process. Biological light measurement data analysis device. The biological light measurement data analysis apparatus according to claim 1,
The biological light measurement data analysis apparatus characterized in that the derivation of the mixing coefficient that maximizes the similarity is calculated as an eigenvalue problem. The biological light measurement data analysis apparatus according to claim 1,
The biological light measurement data analysis apparatus characterized in that two or more types of time-series data capable of deriving a positive correlation with each other are used for extending a measurement channel. The biological light measurement data analysis apparatus according to claim 1,
The biological light measurement data analysis apparatus characterized in that two or more types of time series data capable of deriving a positive correlation with each other are used for extending measurement time. The biological light measurement data analysis apparatus according to claim 1,
The biological optical measurement data analysis apparatus characterized in that two or more types of time series data capable of deriving a positive correlation with each other are used for extending a measurement channel and a measurement time. In the biological light measurement data analysis method for analyzing time-series data obtained by detecting light transmitted, scattered, and reflected in a living body at a plurality of measurement points,
An evaluation function creating step of creating a mixed time series data by multiplying the time series data of the plurality of points by a mixing coefficient, and creating an evaluation function representing the similarity of the repetition period of the mixed time series data;
A weighting factor calculation step for deriving the mixing coefficient of the mixed time series data so that the similarity between the signals repeatedly measured in time calculated from the mixed time series data is the highest;
A signal synthesis step of obtaining time series data obtained by multiplying the time series data of the plurality of points by the mixing coefficient derived in the weighting factor calculation step to remove noise components;
The mixing coefficient derivation process uses two or more types of time-series data that are measured almost simultaneously at each of the plurality of points and that can derive a positive correlation with each other by using a specific conversion process. To analyze biological light measurement data. The biological light measurement data analysis method according to claim 8,
The biological light measurement data analysis method characterized by using a covariance or a correlation coefficient value as the similarity between the repeatedly measured signals. The biological light measurement data analysis method according to claim 8,
The oxygenated hemoglobin concentration change and the deoxygenated hemoglobin concentration change are used as two or more types of time series data from which a positive correlation can be derived, and the sign of the deoxygenated hemoglobin is reversed as a conversion process. The biological optical measurement data analysis method. The biological light measurement data analysis method according to claim 8,
The biological light measurement data analysis method characterized in that the derivation of the mixing coefficient that maximizes the similarity is calculated as an eigenvalue problem. The biological light measurement data analysis method according to claim 8,
The biological light measurement data analysis method, wherein two or more types of time series data capable of deriving a positive correlation with each other are used for extending a measurement channel. The biological light measurement data analysis method according to claim 8,
A biological light measurement data analysis method, wherein two or more types of time series data capable of deriving a positive correlation with each other are used for extending measurement time. The biological light measurement data analysis method according to claim 8,
The biological optical measurement data analysis method, wherein two or more types of time series data capable of deriving a positive correlation with each other are used for extending a measurement channel and a measurement time. A program for causing a computer to analyze time-series data obtained by detecting light transmitted, scattered, and reflected in a living body at a plurality of measurement points,
An evaluation function creating step of creating a mixed time series data by multiplying the time series data of the plurality of points by a mixing coefficient, and creating an evaluation function representing the similarity of the repetition period of the mixed time series data;
A weighting factor calculation step for deriving the mixing coefficient of the mixed time series data so that the similarity between the signals repeatedly measured in time calculated from the mixed time series data is the highest;
A signal synthesizing step for obtaining time-series data obtained by multiplying the time-series data of the plurality of points by the mixing coefficient derived by the weighting factor calculation unit to remove noise components;
The mixing coefficient derivation process uses two or more types of time-series data that are measured almost simultaneously at each of the plurality of points and that can derive a positive correlation with each other by using a specific conversion process. Program to do.

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