CN116269366A - ROI brain region channel optimization screening method based on fNIRS analysis - Google Patents
ROI brain region channel optimization screening method based on fNIRS analysis Download PDFInfo
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Abstract
The invention discloses an optimal screening method for ROI brain region channels based on fNIRS analysis, which specifically comprises the following steps: synchronously collecting multichannel near infrared signals, and preprocessing the multichannel near infrared signals to obtain brain blood oxygen concentration signals; calculating multiple types of time domain feature sets for the preprocessed multichannel cerebral blood oxygen signals, and carrying out feature fusion on the time domain multiple feature sets by adopting a feature fusion algorithm of a least square method to obtain a time domain optimal feature set; constructing a first set of all the ROI area channels based on an permutation and combination algorithm, and further constructing a second set of the ROI area channels by adopting an preface traversal algorithm; determining a second set element of the ROI channel which enables the local evaluation index and the global evaluation index of the brain to be maximum by adopting a local-global optimizing method to obtain an optimal channel combination of each ROI of the brain, and realizing effective monitoring of the brain function state; the invention can reflect the brain real nerve activity.
Description
Technical Field
The invention relates to an optimal screening method for ROI brain region channels based on fNIRS analysis, and belongs to the technical field of functional near infrared spectroscopy.
Background
Functional near infrared spectroscopy (fnrs) is a multifunctional neuroimaging tool, and has been widely used in scientific research and clinical medicine in recent years. fnigs is a novel noninvasive brain function detection technology that can indirectly reflect brain neural activity by detecting changes in concentration signals of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total oxyhemoglobin (HbT) in the cortex of the brain in real time. Besides the obvious portability, the fNIRS also has the advantages of higher time sampling rate and wider application range in the research of the nerve field, thereby providing favorable conditions for analysis of brain function imaging, providing a safe and effective imaging mode for brain function research by the characteristics of being more suitable for large-scale data acquisition and the like.
Brain function imaging is one of the basic means to study brain activity, which can be divided into two major categories, external stimulus-induced neural activity and spontaneous neural activity independent of external stimulus. Evoked neural activity is the brain's response to external stimuli when performing a particular task. Traditional brain function imaging studies have focused on inducing neural activity to find brain regions responsible for specific functions through specific activation. But even when not engaged in a particular task, the brain spontaneously develops neural activity. Through previous studies, humans have realized that the brain is a complex dynamic interactive system, and that many brain regions that are structurally and functionally interrelated work together to ensure efficient information processing and information interaction. In recent years, brain region-based research is widely applied to research in various fields, determination of traditional brain regions of interest (regions of interest, ROI) is affected by subjective factors of researchers, and due to the fact that resting brain imaging has a characteristic of no task, an existing task state imaging analysis method (such as a general linear model based on experimental design information and the like) is not applicable any more, and an activation channel in the range of the ROI cannot be selected by a general linear model activation method. Therefore, the limitation of the traditional method for screening the channels in the ROI is that the unified selection standard is lacking, and the research result is greatly influenced by subjective factors of researchers.
In the conventional determination of the ROI area, all channel signals covering the functional brain area are generally regarded as the brain area signals, and the method may reduce the overall activation degree of the brain area due to the low activation degree of part of channels, so that the real neural activity of the ROI area cannot be reflected, and therefore, it is necessary to provide an optimization screening method of the ROI brain area channel based on fnrs analysis to study the brain activation condition of the ROI area.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an optimal screening method for the ROI brain region channel based on fNIRS analysis, which can reflect the real nerve activity of the brain, and is used for solving the problem that the whole activation degree of the brain region is reduced and the limitation of the real nerve activity of the ROI region cannot be revealed due to the low activation degree of part of channels in the traditional ROI region.
In order to solve the technical problems, the invention adopts the following technical scheme:
an optimization screening method of ROI brain region channels based on fNIRS analysis comprises the following steps:
step 3, constructing a first set of all the ROI area channels based on an permutation and combination algorithm, and further constructing a second set of the ROI area channels by adopting an order traversal algorithm;
and 4, calculating the regional evaluation index of the ROI and the global evaluation index of the brain of the second set of elements of the regional channel of the ROI by adopting a local-global optimizing method, screening the second set of elements of the regional channel of the ROI, which make the regional evaluation index and the global evaluation index of the brain maximum, obtaining the optimal channel combination of each regional region of the brain, and realizing the effective monitoring of the brain function state.
The technical scheme of the invention is further improved as follows: in step 1, the pretreatment of bad guide, bad section and motion artifact removal, filtering and blood oxygen concentration conversion specifically comprises the following steps:
step 11, bad guide detection and elimination, adopting variation coefficient to judge and eliminate the channel with bad signal, the variation coefficient CV is defined as
Wherein sigma is the standard deviation of the signal, mu is the mean value of the signal, and when the CV value is more than 15%, judging the guide as bad guide, and eliminating the channel data;
and 15, converting the optical density signal into an oxygen concentration signal, and obtaining oxyhemoglobin HbO, deoxyhemoglobin HbR and total oxyhemoglobin HbT concentration signals according to the modified Beer-Lambert law, wherein a path difference factor is set to be-6 to 6.
The technical scheme of the invention is further improved as follows: the specific steps of the step 2 are as follows:
step 21, performing time domain multi-feature extraction on the preprocessed multi-channel cerebral blood oxygen signal to obtain multi-class time domain feature sets of each channel, wherein the time domain feature sets x= [ X1, X2, X3, X4, X5, X6, … ] include but are not limited to: an energy set X1, a kurtosis set X2, a mean value set X3, a skewness set X4, a standard deviation set X5 and a variance set X6;
step 22, respectively carrying out normalization processing on each element in the time domain feature set X, wherein the formula is as follows:
wherein xi= [ Xi1, xi2, …, xij, …, xiM ] (i=1, 2, …; 1.ltoreq.j.ltoreq.M), i is a time domain feature class, M is the number of channels, and normalized time domain feature set X ' = [ X1', X2', X3', X4', X5', X6', … ] is obtained after normalization processing;
step 23, performing feature fusion on the normalized time domain feature set X' by using a constraint least square method to obtain a time domain optimal feature set X ", wherein the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
α k is a weight value and l is a time domain feature class.
The technical scheme of the invention is further improved as follows: the specific steps of the step 3 are as follows:
step 31, initializing the number of ROI areas and a channel set in the ROI areas according to electrode positioning;
step 32, constructing a first set of channels of each ROI area by using a permutation and combination algorithm based on the channel sets in each ROI area, wherein the permutation and combination formula C is as follows:
in the method, in the process of the invention,
representing the combination of m (m=1, 2,., n) channels arbitrarily selected from the case where the number of channels in the ROI area is n;
step 33, adopting a preface traversal algorithm to randomly select an element from the first set of the ROI area channels to form a new set of the ROI area channels of the brain, namely a second set of the ROI area channels.
The technical scheme of the invention is further improved as follows: the step 31 specifically includes: determining the number of initialized ROI (region of interest) areas and a channel set in each ROI area according to a preset brain region division principle and an electrode positioning file, wherein the preset brain region division principle comprises, but is not limited to: AAL brain division criteria, brodmann brain division criteria.
The technical scheme of the invention is further improved as follows: the local-global optimizing method in the step 4 specifically comprises the following steps:
step 41, calculating a local evaluation index of the ROI and a global evaluation index of the brain for the ROI channel combination corresponding to each element in the second set of the ROI channels, wherein the local evaluation index is determined by the time domain optimal feature set, and quantitatively describing the activation condition of each ROI by calculating the mean value of the time domain optimal feature of the channel in the ROI corresponding to the current element; the brain global evaluation index is determined by the preprocessed brain blood oxygen concentration signals, a correlation coefficient matrix between the ROI areas corresponding to the current elements is calculated through a Pearson correlation method, the average value of the correlation coefficient matrix is calculated, and the association relation between the ROI areas is quantitatively described;
and 42, comparing the regional evaluation index of the ROI of the second set element of the regional channel of the ROI with the global evaluation index of the brain to obtain the maximum value of the regional evaluation index of the ROI and the global evaluation index of the brain, and obtaining the corresponding second set element of the regional channel of the ROI, namely the optimal channel combination of each regional ROI of the brain according to the maximum value.
By adopting the technical scheme, the invention has the following technical progress:
the invention provides an optimization screening method for ROI brain region channels based on fNIRS analysis, which is used for solving the problems that the whole activation degree of a traditional ROI region is reduced due to low activation degree of partial channels, the limitation of real activities of the ROI region cannot be revealed, meanwhile, the number of channels in the ROI region after the channels are screened is not more than the original number, thereby being beneficial to reducing huge and complex calculated amount and improving the calculation efficiency on a certain program.
Drawings
FIG. 1 is a schematic flow chart provided in an embodiment of the present invention;
fig. 2 is a schematic diagram of electrode distribution of a near infrared acquisition device according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a pretreatment method according to an embodiment of the present invention;
FIG. 4 is a flow chart of a feature extraction and feature fusion method according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of constructing a ROI region channel set according to an embodiment of the present invention;
fig. 6 is a flow chart of a local-global optimizing method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
In view of the fact that the whole activation degree of the brain region is reduced due to the low activation degree of part of channels in the traditional ROI region, the limitation of the real nerve activity of the ROI region cannot be revealed. The embodiment of the invention provides an optimal screening scheme for ROI brain region channels based on fNIRS analysis, which is used for selecting a channel set with highest activation degree from each functional brain region of a cerebral cortex as an effective channel of the brain region.
The following describes in detail the ROI brain region channel optimization screening scheme based on fnrs analysis according to the embodiment of the present invention with reference to the accompanying drawings and specific embodiments.
In order to verify the optimal screening method for the ROI brain region channels based on the fNIRS analysis, 10 healthy elderly people are selected to be tested (the average age is 55+/-5 years old), and the NIRSmart near infrared spectrum measuring instrument of Dan Yang Hui invasive medical equipment company is used for collecting 44 channel data of the tested in a resting state task. Near infrared (760 nm,860 nm) signals were recorded at two different wavelengths in a continuous waveform with a sampling frequency of 11Hz. The tested person is required to look at the front, avoid moving, wear the head cap in a comfortable and quiet environment, keep sitting still, avoid regular thinking and collect resting state data for 8 minutes.
As shown in fig. 1, the ROI brain region channel optimization screening scheme based on fnrs analysis provided by the embodiment of the present invention may include the following steps:
step 100, synchronously collecting multichannel near infrared signals, and carrying out pretreatment of bad conduction, bad section and motion artifact removal, filtering and blood oxygen concentration conversion on the multichannel near infrared signals to obtain oxygenated hemoglobin HbO, deoxygenated hemoglobin HbR and total oxygenated hemoglobin HbT concentration signals.
Near infrared data acquisition: a functional near infrared data acquisition experimental platform is built, and cerebral blood oxygen signals in a resting state are acquired, wherein the specific process is as follows: the specific electrode distribution is shown in fig. 2, based on near infrared spectroscopy equipment (NIRSmart) with 18 light sources by 16 detectors, using international 10-20 system standard with binaural mastoid as reference. In the fnrs study, a few marker points on the skull are typically used as references to assist in describing and locating the optode position for configuring and placing the fnrs optodes plate. The existing brain skull reference points are mainly from the international 10-20 reference system. The near infrared spectrum device uses a continuous wave method to place a light source and a detector on a light pole which is not far away, and the hemoglobin change amount on the cortex is measured by near infrared light with two wavelengths, wherein a pair of near infrared light emitters and an absorption sensor form a channel. The multichannel cerebral blood oxygen signals of the forehead leaves and the movement area are collected.
The pretreatment of the multichannel near infrared signal comprises the following specific steps of:
step 101, bad guide detection and elimination, adopting variation coefficient to judge and eliminate the channel with bad signal, the variation coefficient CV is defined as
Wherein sigma is the standard deviation of the signal, mu is the mean value of the signal, and when the CV value is more than 15%, the derivative is judged to be bad, and the derivative is removed.
Step 102, detecting and eliminating bad segment data by adopting abnormal points, comparing the amplitude of signals at any time point with the average amplitude of signals in any period of time, setting a threshold value to mark the abnormal points, and further removing the bad segment data by adopting a cubic spline interpolation method.
And 103, detecting and removing the motion artifact, setting a standard deviation threshold value of a signal to be 6, setting a peak threshold value to be 0.5, and identifying and removing the motion artifact by adopting a cubic spline interpolation method.
Step 104, filtering, namely removing interference components including noise caused by heartbeat, respiration, mel wave and the like by adopting a six-order butterworth band-pass filter with the frequency of 0.01 to 0.1 Hz.
Step 105, converting the optical density into blood oxygen concentration data, and obtaining oxygenated hemoglobin HbO, deoxygenated hemoglobin HbR and total oxygenated hemoglobin HbT concentration signals according to the modified Beer-Lambert law, wherein a path difference factor is set to be-6 to 6.
Step 200, calculating a multi-type time domain feature set for the preprocessed multi-channel cerebral blood oxygen signals, and carrying out feature fusion on the time domain multi-feature set by adopting a feature fusion algorithm of a least square method to obtain a time domain optimal feature set, wherein the specific steps are as shown in fig. 4:
step 201, performing time domain multi-feature extraction on the preprocessed multi-channel cerebral blood oxygen signal to obtain a multi-class time domain feature set of each channel, wherein the time domain feature set x= [ X1, X2, X3, X4, X5, X6. ], including but not limited to: an energy set X1, a kurtosis set X2, a mean value set X3, a skewness set X4, a standard deviation set X5 and a variance set X6;
step 202, respectively carrying out normalization processing on each element in the time domain feature set X, wherein the formula is as follows:
where xi= [ Xi1, xi2,..;
step 203, performing feature fusion on the normalized time domain feature set X' by using a constraint least squares method (Constrained Least Squares, CLS) to obtain a time domain optimal feature set X ", where the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
α k is a weight value and l is a time domain feature class.
Step 300, constructing a first set of all the ROI area channels based on an permutation and combination method, and further constructing a second set of the ROI area channels by adopting a preface traversal algorithm, wherein the specific steps are as shown in fig. 5:
step 301, initializing the number of ROI areas and the channel set in the ROI areas according to the electrode positioning, specifically including: determining the number of initialized ROI areas and a channel set in each ROI area according to a preset brain region division principle and an electrode positioning file, wherein the preset brain region division principle comprises, but is not limited to: AAL brain region division criteria, brodmann brain region division criteria;
step 302, constructing a first set of channels of each ROI area by using a permutation and combination algorithm based on the channel sets in each ROI area, wherein the permutation and combination formula C is as follows:
in the method, in the process of the invention,
representing the combination of m (m=1, 2,., n) channels arbitrarily selected from the case where the number of channels in the ROI area is n;
step 303, adopting a preface traversal algorithm to randomly select an element from the first set of the ROI area channels to form a new set of the ROI area channels of the brain, namely a second set of the ROI area channels.
In the embodiment of the invention, the brain region covered by 44 channels acquired by near infrared spectrum equipment is divided into a left forehead, a right forehead, a left movement region and a right movement region by using a Brodmann brain region division standard, and 4 ROI regions are all shown in figure 2. And dividing the acquired 44 channels into four brain-dividing regions, namely a left forehead, a right forehead, a left movement region and a right movement region, respectively according to the channel positioning file provided by the near infrared spectrum acquisition equipment. Wherein channels 1-7 are located in the right forehead, channels 8-14 are located in the left forehead, channels 15-29 are located in the right movement region, and channels 30-44 are located in the left movement region.
Step 400, calculating the local evaluation index of the ROI region and the global evaluation index of the brain of the second set of elements of the ROI region channel by adopting a local-global optimizing method, screening the second set of elements of the ROI region channel which enable the local evaluation index and the global evaluation index of the brain to be maximum, obtaining the optimal channel combination of each ROI region of the brain, and realizing the effective monitoring of the brain function state, wherein the specific steps are shown in fig. 6:
step 401, calculating a local evaluation index of the ROI and a global evaluation index of the brain for the ROI channel combination corresponding to each element in the second set of the ROI channels, wherein the local evaluation index is determined by a time domain optimal feature set, and quantitatively describing the activation condition of each ROI by calculating the mean value of the time domain optimal feature of the channel in the ROI corresponding to the current element; the global brain evaluation index is determined by the preprocessed brain blood oxygen concentration signals, a correlation coefficient matrix between the ROI areas corresponding to the current elements is calculated through a Pearson correlation method, the average value of the correlation coefficient matrix is calculated, and the association relation between the ROI areas is quantitatively described.
Step 402, comparing the ROI regional local evaluation index and the brain global evaluation index of the second set element of the ROI regional channel, obtaining the maximum value of the ROI regional local evaluation index and the brain global evaluation index, and obtaining the corresponding second set element of the ROI regional channel according to the maximum value, namely, the optimal channel combination of each ROI regional of the brain.
In the embodiment provided by the invention, the total of the initial ROI areas is 4, namely the right forehead is marked as ROI1 (channels 1-7), the left forehead is marked as ROI2 (channels 8-14), the left movement area is marked as ROI3 (channels 15-29), and the right movement area is marked as ROI4 (channels 30-44).
Firstly, based on the internal channel set of each initial ROI region, adopting an array combination algorithm to construct a first set of ROI region channels, taking the ROI1 region as an example, and generating a first set C1 of the ROI1 region channels, which mainly comprises { [1], [2], [3], [4], [5], [6], [7], [1,2], [1,3], [1,4], [1,5], [1,6], [1,7], [2,2], [2,3], [2,4], [2, 2...
Secondly, an element is selected from the first channel sets C1, C2, C3 and C4 of the 4 ROI areas by adopting an advanced traversal algorithm to form a new ROI area set, and a second ROI area channel set is formed.
Then, calculating a local evaluation index of the ROI and a global evaluation index of the brain for the ROI channel combination corresponding to each element in the second set of the ROI channels, wherein the local evaluation index is determined by a time domain optimal feature set, and quantitatively describing the activation condition of each ROI by calculating the average value of the time domain optimal feature of the channel in the ROI corresponding to the current element; the global brain evaluation index is determined by the preprocessed brain blood oxygen concentration signals, a correlation coefficient matrix between the ROI areas corresponding to the current elements is calculated through a Pearson correlation method, the average value of the correlation coefficient matrix is calculated, and the association relation between the ROI areas is quantitatively described.
And finally, comparing the regional evaluation index of the ROI of the second set element of the regional channel of the ROI with the global evaluation index of the brain to obtain the maximum value of the regional evaluation index of the ROI and the global evaluation index of the brain, and obtaining the corresponding second set element of the regional channel of the ROI according to the maximum value, namely, the optimal channel combination of each regional ROI of the brain.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (6)
1. An optimization screening method for ROI brain region channels based on fNIRS analysis is characterized by comprising the following steps: the method comprises the following steps:
step 1, synchronously acquiring multichannel near infrared signals, and carrying out pretreatment of bad conduction, bad section and motion artifact removal, filtering and blood oxygen concentration conversion on the multichannel near infrared signals to obtain oxygenated hemoglobin HbO, deoxygenated hemoglobin HbR and total oxygenated hemoglobin HbT concentration signals;
step 2, calculating multiple types of time domain feature sets for the preprocessed multichannel cerebral blood oxygen signals, and carrying out feature fusion on the time domain multiple feature sets by adopting a feature fusion algorithm of a least square method to obtain a time domain optimal feature set;
step 3, constructing a first set of all the ROI area channels based on an permutation and combination algorithm, and further constructing a second set of the ROI area channels by adopting an order traversal algorithm;
and 4, calculating the regional evaluation index of the ROI and the global evaluation index of the brain of the second set of elements of the regional channel of the ROI by adopting a local-global optimizing method, screening the second set of elements of the regional channel of the ROI, which make the regional evaluation index and the global evaluation index of the brain maximum, obtaining the optimal channel combination of each regional region of the brain, and realizing the effective monitoring of the brain function state.
2. The method for optimizing and screening the brain region channel of the ROI based on the fNIRS analysis according to claim 1, wherein the method comprises the following steps of: in step 1, the pretreatment of bad guide, bad section and motion artifact removal, filtering and blood oxygen concentration conversion specifically comprises the following steps:
step 11, bad guide detection and elimination, adopting variation coefficient to judge and eliminate the channel with bad signal, the variation coefficient CV is defined as
Wherein sigma is the standard deviation of the signal, mu is the mean value of the signal, and when the CV value is more than 15%, judging the guide as bad guide, and eliminating the channel data;
step 12, bad segment detection and elimination, namely detecting a data bad segment by adopting an abnormal point, comparing the amplitude of a signal at any time point with the average amplitude of the signal in any period of time, setting a threshold value to mark an abnormal point, and further removing the bad segment data by adopting a cubic spline interpolation method;
step 13, removing motion artifacts, setting a signal standard deviation threshold value to be 6, setting a peak threshold value to be 0.5, and identifying and removing the motion artifacts in the signals by adopting a cubic spline interpolation method;
step 14, filtering, namely removing interference components including noise caused by heartbeat, respiration and Mel waves by adopting a six-order Butterworth band-pass filter with the frequency of 0.01 to 0.1 Hz;
and 15, converting the optical density signal into an oxygen concentration signal, and obtaining oxyhemoglobin HbO, deoxyhemoglobin HbR and total oxyhemoglobin HbT concentration signals according to the modified Beer-Lambert law, wherein a path difference factor is set to be-6 to 6.
3. The method for optimizing and screening the brain region channel of the ROI based on the fNIRS analysis according to claim 1, wherein the method comprises the following steps of: the specific steps of the step 2 are as follows:
step 21, performing time domain multi-feature extraction on the preprocessed multi-channel cerebral blood oxygen signal to obtain multi-class time domain feature sets of each channel, wherein the time domain feature sets x= [ X1, X2, X3, X4, X5, X6, … ] include but are not limited to: an energy set X1, a kurtosis set X2, a mean value set X3, a skewness set X4, a standard deviation set X5 and a variance set X6;
step 22, respectively carrying out normalization processing on each element in the time domain feature set X, wherein the formula is as follows:
where xi= [ Xi1, xi2,..;
step 23, performing feature fusion on the normalized time domain feature set X' by using a constraint least square method to obtain a time domain optimal feature set X ", wherein the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
α k is a weight value and l is a time domain feature class.
4. The method for optimizing and screening the brain region channel of the ROI based on the fNIRS analysis according to claim 1, wherein the method comprises the following steps of: the specific steps of the step 3 are as follows:
step 31, initializing the number of ROI areas and a channel set in the ROI areas according to electrode positioning;
step 32, constructing a first set of channels of each ROI area by using a permutation and combination algorithm based on the channel sets in each ROI area, wherein the permutation and combination formula C is as follows:
in the method, in the process of the invention,
representing the combination of m (m=1, 2, …, n) channels arbitrarily selected from the n number of channels within the ROI area;
step 33, adopting a preface traversal algorithm to randomly select an element from the first set of the ROI area channels to form a new set of the ROI area channels of the brain, namely a second set of the ROI area channels.
5. The method for optimizing and screening the brain region channel of the ROI based on the fNIRS analysis according to claim 4, wherein the method comprises the following steps: the step 31 specifically includes: determining the number of initialized ROI (region of interest) areas and a channel set in each ROI area according to a preset brain region division principle and an electrode positioning file, wherein the preset brain region division principle comprises, but is not limited to: AAL brain division criteria, brodmann brain division criteria.
6. The method for optimizing and screening the brain region channel of the ROI based on the fNIRS analysis according to claim 1, wherein the method comprises the following steps of: the local-global optimizing method in the step 4 specifically comprises the following steps:
step 41, calculating a local evaluation index of the ROI and a global evaluation index of the brain for the ROI channel combination corresponding to each element in the second set of the ROI channels, wherein the local evaluation index is determined by the time domain optimal feature set, and quantitatively describing the activation condition of each ROI by calculating the mean value of the time domain optimal feature of the channel in the ROI corresponding to the current element; the brain global evaluation index is determined by the preprocessed brain blood oxygen concentration signals, a correlation coefficient matrix between the ROI areas corresponding to the current elements is calculated through a Pearson correlation method, the average value of the correlation coefficient matrix is calculated, and the association relation between the ROI areas is quantitatively described;
and 42, comparing the regional evaluation index of the ROI of the second set element of the regional channel of the ROI with the global evaluation index of the brain to obtain the maximum value of the regional evaluation index of the ROI and the global evaluation index of the brain, and obtaining the corresponding second set element of the regional channel of the ROI, namely the optimal channel combination of each regional ROI of the brain according to the maximum value.
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