Cognitive disorder screening method and device based on XR eye tracker and near-infrared brain imaging

Abstract

The invention discloses a cognitive impairment screening method and device based on an XR eye tracker and near-infrared brain imaging. The device comprises an augmented reality scene generation module, an eye tracker data acquisition module, a near-infrared brain imaging module, a data analysis processing module and a cognitive impairment screening module. The augmented reality scene generation module is used for generating and displaying a virtual reality scene and an augmented reality scene containing cognitive tasks; the eye tracker data acquisition module is used for acquiring an eye movement data information set in real time when a user completes a cognitive task in a virtual reality scene and an augmented reality scene; the near-infrared brain imaging module is used for collecting and obtaining a blood oxygen information set of cerebral cortex when a user completes a cognitive task in a virtual reality scene and an augmented reality scene in real time; and the cognitive impairment screening module is used for performing cognitive impairment screening processing on the evaluation process information, the eye movement data information set and the blood oxygen information set of cerebral cortex to obtain cognitive impairment information of the user.

Description

Technical Field

[0001] The present invention relates to the fields of extended reality and brain cognition, and in particular to a method and device for screening cognitive impairment based on an XR eye tracker and near-infrared brain imaging. Background Art

[0002] With the acceleration of the trend of aging society, more and more people are suffering from cognitive impairment. Cognitive impairment is a common neurological abnormality. Early and accurate screening is crucial for the diagnosis and treatment of cognitive impairment. At present, commonly used screening methods for cognitive impairment include neuropsychological tests, imaging examinations, etc., but these methods have certain limitations, such as strong subjectivity, complex operation, and insensitivity to early lesions.

[0003] How to achieve rapid, accurate and simple early screening of cognitive impairment is an urgent problem that needs to be solved in the current context of an aging society. Summary of the invention

[0004] The present invention mainly solves the problem of how to achieve rapid, accurate and simple early screening of cognitive impairment. The present invention discloses a method and device for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging.

[0005] The purpose of the present invention is to provide a method and device for screening cognitive impairment based on an extended reality eye tracker and a brain-computer interface, so as to address the limitations of existing cognitive impairment screening methods and improve the accuracy, objectivity and efficiency of screening.

[0006] In a first aspect of the embodiment of the present application, a cognitive impairment screening device based on an XR eye tracker and near-infrared brain imaging is disclosed, comprising: an extended reality scene generation module, an eye tracker data acquisition module, a near-infrared brain imaging module, a data analysis and processing module, and a cognitive impairment screening module;

[0007] The extended reality scene generation module is used to generate and display virtual reality scenes and augmented reality scenes containing cognitive tasks;

[0008] The eye tracker data acquisition module is used to acquire in real time an eye movement data information set when the user completes a cognitive task in a virtual reality scene or an augmented reality scene; the eye movement data information set includes a fixation point information sequence, a fixation time information sequence, and an eye saccade frequency information sequence; the fixation point information sequence includes fixation point position information; the fixation time information sequence includes fixation time information; the eye saccade frequency information sequence includes eye saccade frequency information;

[0009] The near-infrared brain imaging module is used to collect in real time a set of blood oxygen information of the cerebral cortex when the user completes cognitive tasks in a virtual reality scene or an augmented reality scene; the set of blood oxygen information of the cerebral cortex includes a brain blood oxygen information sequence;

[0010] The data analysis and processing module is connected to the extended reality scene generation module, the eye tracker data acquisition module, the near-infrared brain imaging module and the cognitive impairment screening module, respectively, and is used to analyze and process the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract the first abnormal characteristic signal;

[0011] The cognitive impairment screening module is used to perform cognitive impairment screening on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information; the cognitive impairment information includes whether there is cognitive impairment and the degree of cognitive impairment.

[0012] The data analysis and processing module is used to analyze and process the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal, including:

[0013] The data analysis and processing module performs abnormality discrimination processing on the collected eye movement data information set to obtain a first abnormality discrimination value;

[0014] Determine whether the first abnormal discrimination value is greater than a preset first discrimination threshold value, and obtain a first abnormal characteristic signal; if the first abnormal characteristic signal is negative, send the collected eye movement data information set and the cerebral cortex blood oxygen information set to a cognitive impairment screening module;

[0015] If the first abnormal characteristic signal is yes, the cognitive impairment information is confirmed to indicate the presence of cognitive impairment, and the cognitive impairment degree information is determined as the first abnormality discrimination value.

[0016] The extended reality scene generation module includes a virtual reality scene submodule and an augmented reality scene submodule;

[0017] The virtual reality scene submodule is used to display a virtual reality scene containing cognitive tasks to the user;

[0018] The augmented reality scene submodule is used to display an augmented reality scene containing cognitive tasks to the user.

[0019] The cognitive impairment screening module is used to perform cognitive impairment screening on the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0020] The cognitive impairment screening module preprocesses the eye movement data information set and the cerebral cortex blood oxygen information set respectively to obtain a preprocessed eye movement data information set and a cerebral cortex blood oxygen information set;

[0021] The preprocessed eye movement data information set and the cerebral cortex blood oxygen information set are subjected to cognitive impairment assessment processing to obtain cognitive impairment information of the user.

[0022] In a second aspect of an embodiment of the present invention, a method for screening cognitive impairment based on an XR eye tracker and near-infrared brain imaging is disclosed, which is implemented using the cognitive impairment screening device based on an XR eye tracker and near-infrared brain imaging, and includes:

[0023] S1, using the extended reality scene generation module to generate and display a virtual reality scene and an augmented reality scene containing cognitive tasks;

[0024] S2, using the eye tracker data acquisition module to acquire a collection of eye movement data information of the user when completing cognitive tasks in virtual reality scenes and augmented reality scenes in real time;

[0025] S3, using the near-infrared brain imaging module to collect in real time a collection of blood oxygen information of the cerebral cortex of the user when the user completes cognitive tasks in a virtual reality scene and an augmented reality scene;

[0026] S4, using the data analysis and processing module, analyzing and processing the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal;

[0027] S5, using the cognitive impairment screening module, performing cognitive impairment screening processing on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information.

[0028] The step of analyzing and processing the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal includes:

[0029] S41, performing abnormality discrimination processing on the collected eye movement data information set to obtain a first abnormality discrimination value;

[0030] S42, determining whether the first abnormal discrimination value is greater than a preset first discrimination threshold, and obtaining a first abnormal characteristic signal; if the first abnormal characteristic signal is negative, sending the collected eye movement data information set and the cerebral cortex blood oxygen information set to a cognitive impairment screening module;

[0031] If the first abnormal characteristic signal is yes, the cognitive impairment information is confirmed to indicate the presence of cognitive impairment, and the cognitive impairment degree information is determined as the first abnormality discrimination value.

[0032] The cognitive impairment screening process is performed on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0033] S51, preprocessing the eye movement data information set and the cerebral cortex blood oxygen information set respectively to obtain a preprocessed eye movement data information set and a cerebral cortex blood oxygen information set;

[0034] S52, performing cognitive impairment assessment processing on the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain cognitive impairment information of the user.

[0035] The pre-processing operation comprises:

[0036] Normalizing each information set to obtain a corresponding normalized information set;

[0037] Perform data cleaning on each normalized information set to obtain a corresponding cleaned data set;

[0038] Perform pattern recognition and discrimination processing on each cleaned data set to obtain a corresponding pre-processed information set.

[0039] The cognitive impairment assessment process is performed on the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0040] S521, establishing a conditional probability association model;

[0041] S522, substituting the historical evaluation data set into the conditional probability association model, training the conditional probability association model, and obtaining a trained conditional probability association model;

[0042] S523, using the trained conditional probability association model, processing the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain cognitive impairment assessment result information;

[0043] S524, determining whether the cognitive impairment assessment result information is greater than a preset second discrimination threshold, and obtaining a second abnormal characteristic signal; if the second abnormal characteristic signal is negative, confirming that the cognitive impairment information does not contain cognitive impairment;

[0044] If the second abnormal characteristic signal is yes, confirming that the cognitive impairment information is the existence of cognitive impairment, and determining that the cognitive impairment assessment information is cognitive impairment assessment result information;

[0045] S525 , constructing cognitive impairment information of the user by using the cognitive impairment assessment information and the information on whether cognitive impairment exists.

[0046] The step of establishing a conditional probability association model comprises:

[0047] S5211, establish a network of influencing factors;

[0048] S5212, using the evaluation process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set, assigning conditional probability parameters to the influencing factor association relationship network;

[0049] S5213, assigning evidence values ​​to the influencing factor nodes in the influencing factor association relationship network to obtain a conditional probability association model.

[0050] The beneficial effects of the present invention are:

[0051] The present invention introduces extended reality display technology, eye tracker and near-infrared brain imaging technology into the field of screening for cognitive impairment, thereby achieving rapid and accurate screening for cognitive impairment.

[0052] The present invention can achieve a perfect fusion of virtual scenes and display scenes through mixed reality technology, create an environment that is attached to reality and dynamically reconfigurable, increase the user's immersive experience, and thus improve the credibility of the test results.

[0053] The present invention can monitor the user's eye movement trajectory in real time through an eye tracker to obtain information such as visual attention; and can directly detect the blood activity in the brain through near-infrared brain imaging technology to accurately reflect the state of cognitive function from the physiological indicator level.

[0054] The present invention uses a conditional probability association model to perform cognitive impairment assessment on the assessment process information, the preprocessed eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, realizes fusion processing from the assessment data level and the acquisition signal level, uses the assessment process information to perform feedback processing on the assessment results, improves the accuracy and comprehensiveness of the cognitive impairment screening model, and expands the applicable scenarios of the model.

[0055] The present invention utilizes a data analysis and processing module to firstly perform an advance screening of the eye movement data, and introduces a difference calculation process to obtain a first abnormal discrimination value, thereby reducing the amount of data to be screened and improving the screening efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a composition diagram of the device of the present invention;

[0057] Figure 2 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0058] In order to better understand the content of the present invention, an embodiment is given here.

[0059] Figure 1 It is a composition diagram of the device of the present invention; Figure 2 It is a flow chart for implementing the method of the present invention.

[0060] In a first aspect of the embodiment of the present application, a cognitive impairment screening device based on an XR eye tracker and near-infrared brain imaging is disclosed, comprising: an extended reality scene generation module, an eye tracker data acquisition module, a near-infrared brain imaging module, a data analysis and processing module, and a cognitive impairment screening module;

[0061] The extended reality scene generation module is used to generate and display virtual reality scenes and augmented reality scenes containing cognitive tasks; the cognitive tasks include object recognition, spatial navigation, memory testing, etc.;

[0062] The eye tracker data acquisition module is used to acquire the user's eye movement data information set in real time in the extended reality scene; the eye movement data information set includes a fixation point information sequence, a fixation time information sequence, an eye saccade frequency information sequence, etc. The fixation point information sequence includes fixation point position information; the fixation time information sequence includes fixation time information; the eye saccade frequency information sequence includes eye saccade frequency information;

[0063] The near-infrared brain imaging module is used to collect a set of blood oxygen information of the cerebral cortex of the user when completing a cognitive task. The set of blood oxygen information of the cerebral cortex includes a brain blood oxygen information sequence;

[0064] The data analysis and processing module is connected to the extended reality scene generation module, the eye tracker data acquisition module, the near-infrared brain imaging module and the cognitive impairment screening module respectively, and is used to analyze and process the collected eye movement data information set to extract the first abnormal feature signal;

[0065] The cognitive impairment screening module is used to perform cognitive impairment screening on the process evaluation information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information; the cognitive impairment information includes whether there is cognitive impairment and the degree of cognitive impairment;

[0066] The near-infrared brain imaging module can be implemented using a near-infrared brain imaging device, the extended reality scene generation module can be implemented using an XR device, and the eye tracker data acquisition module can be implemented using an eye tracker.

[0067] The data analysis and processing module is used to analyze and process the collected eye movement data information set to extract a first abnormal feature signal, including:

[0068] The data analysis and processing module performs abnormality discrimination processing on the collected eye movement data information set to obtain a first abnormality discrimination value;

[0069] Determine whether the first abnormal discrimination value is greater than a preset first discrimination threshold value, and obtain a first abnormal characteristic signal; if the first abnormal characteristic signal is negative, send the collected eye movement data information set and the cerebral cortex blood oxygen information set to a cognitive impairment screening module;

[0070] If the first abnormal characteristic signal is yes, confirming that the cognitive impairment information indicates the presence of cognitive impairment, and determining the cognitive impairment degree information as a first abnormality discrimination value;

[0071] The extended reality scene generation module includes a virtual reality scene submodule and an augmented reality scene submodule;

[0072] The virtual reality scene submodule is used to display a virtual reality scene containing cognitive tasks to the user;

[0073] The augmented reality scene submodule is used to display an augmented reality scene containing cognitive tasks to the user;

[0074] The cognitive impairment screening module is used to perform cognitive impairment screening on the process evaluation information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0075] Preprocessing the eye movement data information set and the cerebral cortex blood oxygen information set respectively to obtain a preprocessed eye movement data information set and a cerebral cortex blood oxygen information set;

[0076] Performing cognitive impairment assessment processing on the process evaluation information, the pre-processed eye movement data information set, and the cerebral cortex blood oxygen information set to obtain cognitive impairment information of the user;

[0077] The pre-processing operation comprises:

[0078] Normalizing each information set to obtain a corresponding normalized information set;

[0079] Perform data cleaning on each normalized information set to obtain a corresponding cleaned data set;

[0080] Perform pattern recognition and discrimination processing on each cleaned data set to obtain a corresponding pre-processed information set;

[0081] The cognitive impairment assessment process is performed on the process assessment information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0082] Establish a conditional probability association model;

[0083] Substituting the historical evaluation data set into the conditional probability association model, training the conditional probability association model, and obtaining a trained conditional probability association model;

[0084] Using the trained conditional probability association model, the process evaluation information, the preprocessed eye movement data information set and the cerebral cortex blood oxygen information set are processed to obtain cognitive impairment evaluation result information;

[0085] Determine whether the cognitive impairment assessment result information is greater than a preset second discrimination threshold, and obtain a second abnormal characteristic signal; if the second abnormal characteristic signal is negative, confirm that the cognitive impairment information does not contain cognitive impairment;

[0086] If the second abnormal characteristic signal is yes, confirming that the cognitive impairment information is the existence of cognitive impairment, and determining that the cognitive impairment assessment information is cognitive impairment assessment result information;

[0087] The cognitive impairment information is constructed by using the cognitive impairment assessment information and the cognitive impairment information.

[0088] The step of establishing a conditional probability association model comprises:

[0089] Establish a network of influencing factors;

[0090] Using the evaluation process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set, conditional probability parameter assignment is performed on the influencing factor association relationship network;

[0091] Evidence values ​​are assigned to the influencing factor nodes in the influencing factor association network to obtain a conditional probability association model.

[0092] The evaluation process information includes whether the classical method is adopted, whether mature tools are adopted, whether supervised samples are adopted, whether the evaluation process is adapted to the characteristics of the evaluation data, whether the evaluation process is adapted to the characteristics of the evaluation business, whether the analysis of the evaluation purpose is sufficient, whether the analysis of the evaluation object is accurate, whether the evaluation subject is fully managed, whether the evaluation results are verified, whether the indicators are complete, whether the indicators are independent, whether the indicators are consistent, whether the indicators are objective, the credibility of the evaluation process, and the technical level of the personnel, and the result values ​​and probability values ​​of all the above factors;

[0093] The evaluation process information can be obtained by conducting a prior evaluation of the cognitive impairment screening method.

[0094] The establishment of the influencing factor association network specifically includes: using a hierarchical Bayesian network method, introducing one root node: evaluation credibility; and then introducing seven intermediate nodes: evaluation process credibility, evaluation method credibility, primary data product quality, intermediate data product quality, final data product quality, evaluation indicator construction quality, and quantitative indicator credibility;

[0095] According to the knowledge information of business domain, data domain and evaluation domain, a relationship network of influencing factors of 20 influencing factor nodes and 7 intermediate nodes to the root node is established;

[0096] Using the root node, the intermediate node and the influencing factor node, an influencing factor association network is constructed; the 20 influencing factor nodes include whether the classical method is adopted, whether the mature tool is adopted, whether the supervised sample is adopted, whether it is adapted to the evaluation data characteristics, whether it is adapted to the evaluation business characteristics, whether the evaluation purpose analysis is sufficient, whether the evaluation object analysis is accurate, whether the evaluation subject is fully controlled, whether the evaluation result is tested, whether the indicator is complete, whether the indicator is independent, whether the indicator is consistent, whether the indicator is objective, the credibility of the evaluation process, the technical level of the personnel, the mean and variance ratio of the data of the pre-processed eye movement data information set, the mean and variance ratio of the data of the pre-processed cerebral cortex blood oxygen information set, whether data cleaning is performed, the median and variance ratio of the data of the pre-processed eye movement data information set, and the median and variance ratio of the data of the pre-processed cerebral cortex blood oxygen information set;

[0097] In the influencing factor association network, the credibility factor of the evaluation method is jointly determined by whether the classical method is adopted, whether the mature tool is adopted, whether the supervised sample is adopted, whether it is suitable for the evaluation data characteristics, and whether it is suitable for the evaluation business characteristics;

[0098] The credibility of the assessment process is determined by whether the assessment purpose analysis is sufficient, whether the assessment object analysis is accurate, whether the assessment subject is fully controlled, and whether the assessment results are verified;

[0099] The quality factors of evaluation index construction are determined by six factors: whether the index is complete, whether the index is independent, whether the index is consistent, whether the index is objective, the credibility of the evaluation process and the technical level of the personnel;

[0100] The credibility of the quantitative indicators is determined by the quality of the evaluation indicator construction and the quality of the final data product. The quality of the primary data product is determined by the mean and variance ratio of the preprocessed eye movement data information set and the mean and variance ratio of the preprocessed cerebral cortex blood oxygen information set.

[0101] The quality of the intermediate data product is determined by three factors: whether data cleaning is performed, the median and variance ratio of the preprocessed eye movement data information set, and the median and variance ratio of the preprocessed cerebral cortex blood oxygen information set;

[0102] The quality of the final data product is determined by two factors: the quality of the intermediate data product and the technical level of the personnel;

[0103] The credibility of the final root node assessment is determined by the credibility of the assessment process, the credibility of the assessment method, the quality of the primary data product, the quality of the intermediate data product, the quality of the final data product, the quality of the assessment indicator construction, and the credibility of the quantitative indicators;

[0104] According to the above decision relationship, the influencing factor correlation network is constructed;

[0105] In the influencing factor association network, the values ​​of the 20 influencing factor nodes are determined based on the evaluation process information, the preprocessed eye movement data information set and the cerebral cortex blood oxygen information set, and then the value of the evaluation credibility of a root node is calculated. The value of the evaluation credibility is used to represent the cognitive impairment evaluation result information.

[0106] The step of using the evaluation process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to assign conditional probability parameters to the influencing factor association relationship network includes:

[0107] The influencing factor association relationship network is limited to a Boolean network, and all node values ​​are True or False, representing the occurrence or non-occurrence of the negative aspects of the corresponding influencing factor events;

[0108] For any non-leaf node of the network, i.e., 1 root node and 7 intermediate nodes, a conditional probability dependency relationship between the non-leaf node and all its corresponding parent nodes is established, i.e., P(non-leaf node|all parent nodes), which is expressed in the form of a conditional probability expression or a LeakyNoisyor function to assign values ​​to conditional probability parameters; when the number of parent nodes is less than or equal to 2, the conditional probability expression is directly used to express it; when the number of parent nodes is greater than 2, the Leaky Noisyor function noisyor() is used to express it; the parent node corresponding to the non-leaf node is the parent node of the non-leaf node;

[0109] The specific expression of the Leaky Noisyor function noisyor() function is:

[0110] Let X be any node in a Boolean Bayesian network, and its N parent nodes form the set n is the parent node number, is the nth parent node of X, let express All nodes in the set have the value False. express Remove Node Except for True, all other nodes are False. express All the node indices whose values ​​are True in , and then let:

[0111]

[0112] Represents all parent nodes of X The conditional probability that X takes the value True when both take the value False. Represents all parent nodes of X Remove Node The conditional probability that X takes the value True when all other nodes take the value False except True; when the number of parent nodes is less than or equal to 2, let X and its parent node set The conditional probability expression is:

[0113]

[0114] Correspondingly, when the number of parent nodes is greater than 2, the expression of the conditional probability dependency relationship between non-leaf nodes and all parent nodes adopts the Leaky Noisyor function noisyor(), and its specific expression is:

[0115]

[0116] The function input is all parent nodes of X Value pair and p0.

[0117] Calculate the conditional probability values ​​of the non-leaf node and all its corresponding parent nodes according to the probability values ​​in the evaluation process information, the pre-processed eye movement data information set and the blood oxygen information set of the cerebral cortex;

[0118] The step of assigning evidence values ​​to the influencing factor nodes in the influencing factor association relationship network to obtain a conditional probability association model includes:

[0119] Evidence values ​​are assigned to the 20 influencing factor nodes in the influencing factor association network. The evidence value is the probability of the influencing factor occurring, that is, the probability of the influencing factor node taking True. For any influencing factor node Y, its evidence is divided into hard evidence and soft evidence. The hard evidence takes the value of 1 or 0, that is, P(Y=True)=1 or P(Y=True)=0, which is used to indicate the occurrence or non-occurrence of the influencing factor event. The soft evidence takes the value of α∈(0,1), that is, 0<P(Y=True)=α<1, which is used to express the objective or subjective probability of the occurrence of the influencing factor event, or the proportion of the segment range of the influencing factor event in the overall space or time of cognitive impairment screening. The evidence of all influencing factor nodes constitutes the network evidence set Evidence.

[0120] The conditional probability P (A = False | Evidence) that the network root node takes False is obtained when the network evidence set Evidence is known, wherein A represents the root node, and the conditional probability P (A = False | Evidence) is used as the cognitive impairment degree information.

[0121] The conditional probability P(A=False|Evidence) that the network root node takes False when the network evidence set Evidence is known can be obtained by using Bayesian network modeling software.

[0122] Substituting the historical evaluation data set into the conditional probability association model, training the conditional probability association model, and obtaining the trained conditional probability association model, comprises:

[0123] The historical evaluation data set includes a collection data set and a corresponding cognitive impairment evaluation value set; the collection data set includes collection data; the collection data includes historical evaluation process information, eye movement data history information and blood oxygen history information; the cognitive impairment evaluation value set includes a cognitive impairment evaluation value corresponding to each collection data, which is also a label value corresponding to each collection data;

[0124] The historical assessment process information includes whether the classical method is adopted, whether mature tools are adopted, whether supervised samples are adopted, whether the method is adapted to the characteristics of the assessment data, whether the method is adapted to the characteristics of the assessment business, whether the assessment purpose analysis is sufficient, whether the assessment object analysis is accurate, whether the assessment subject has full control over the entire process, whether the assessment results are verified, whether the indicators are complete, whether the indicators are independent, whether the indicators are consistent, whether the indicators are objective, the credibility of the assessment process, and the technical level of the personnel, and the historical data of the result values ​​and probability values ​​of all the above factors;

[0125] Initialize the number of training iterations;

[0126] Inputting the collected data in the historical evaluation data set as input data into the conditional probability association model;

[0127] Using the conditional probability association model, the input data is processed to obtain a predicted value;

[0128] Performing difference calculation processing on the obtained predicted value and the label information corresponding to the input data to obtain a difference value;

[0129] Determine whether the difference value satisfies a convergence condition, and obtain a first determination result;

[0130] When the first judgment result is no, judging whether the training iteration number value is equal to the training number threshold, and obtaining a second judgment result;

[0131] When the second judgment result is no, determining that the model training state does not meet the training termination condition;

[0132] When the second judgment result is yes, determining that the model training state satisfies the training termination condition;

[0133] When the first judgment result is yes, determining that the model training state satisfies a training termination condition;

[0134] When the model training status does not meet the termination training condition, the parameter update model is used to update the parameters of the conditional probability association model, the training iteration number value is increased by 1, and the execution is triggered to input the collected data in the historical evaluation data set as input data into the conditional probability association model;

[0135] When the model training state satisfies the training termination condition, the training process of the conditional probability association model is completed to obtain a trained conditional probability association model.

[0136] The difference value satisfies the convergence condition, which means that the difference value is less than a preset convergence threshold; the difference value does not satisfy the convergence condition, which means that the difference value is not less than the preset convergence threshold.

[0137] The difference calculation process may be implemented using a loss function.

[0138] The loss function may adopt a cross entropy loss function.

[0139] The parameter updating model is:

[0140]

[0141] θ←θ+v;

[0142] In the formula, is the difference value calculated for the i-th collected data in the historical evaluation data set, v is the parameter update value, θ is the parameter to be updated, η is the initial parameter learning rate, α is the momentum angle parameter, 0≤α≤π / 4, It means to find partial derivatives with respect to variable θ;

[0143] The parameters to be updated of the conditional probability association model are the conditional probability values ​​between each node;

[0144] The normalization process is to map data in different value ranges to a specified value range, and its mathematical expression is:

[0145]

[0146] Among them, t is a data value in the data set, t max is the maximum value of all data in the data set, t min is the minimum value of all data in the data set, t 归一化 is the value of data t after normalization;

[0147] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0148] The pattern recognition and discrimination processing includes:

[0149] For each type of data attribute of the cleaned data set, the data collection information of the data is used as a known independent variable, and the data value of the data is used as a known dependent variable. The known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain the best consistent approximation polynomial f(Ix) of the type of data attribute; the best consistent approximation polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the data is deleted from the cleaned data set; if it is less than or equal to the second regression discrimination threshold, the data is not processed;

[0150] Performing fusion processing on all data of the cleaned data set that perform feature pre-analysis to obtain a corresponding pre-processed information set;

[0151] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0152] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0153] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0154] The collected eye movement data information set is subjected to abnormality discrimination processing to obtain a first abnormality discrimination value, including

[0155] Using the eye movement data information set, an eye movement data matrix is ​​constructed; the row vector of the eye movement data matrix is ​​a fixation point information sequence, a fixation time information sequence, and an eye saccade frequency information sequence;

[0156] Obtaining a standard eye movement data sequence; the row vector of the standard eye movement data sequence is a standard fixation point information sequence, a standard fixation time information sequence, and a standard eye saccade frequency information sequence;

[0157] Performing difference calculation processing on the standard eye movement data sequence and the eye movement data matrix to obtain a first abnormal discrimination value;

[0158] The difference calculation includes:

[0159] Subtracting the standard eye movement data sequence from the eye movement data matrix to obtain a difference matrix;

[0160] The difference matrix is ​​subjected to cross-correlation calculation processing to obtain a cross-correlation matrix R; i,j represents the i-th row and j-th element of the cross-correlation matrix R, r i,j The calculation expression is:

[0161] r i,j =E(e i e j ),

[0162] Among them, E() means finding the mean, e i represents the i-th row of the difference matrix;

[0163] For the mutual correlation matrix R, each row vector thereof is normalized and calculated to obtain a normalized correlation value sequence; the expression of the normalization calculation is:

[0164]

[0165] in, represents the jth value of the i-th normalized correlation value sequence, and N is the number of elements contained in the normalized correlation value sequence;

[0166] Perform logarithmic summation on each normalized correlation value sequence to obtain the uncertainty value of each normalized correlation value sequence;

[0167] The calculation expression of the logarithmic summation process is:

[0168]

[0169] Among them, H i is the uncertainty value of the i-th normalized correlation value sequence;

[0170] Performing angle normalization processing on the uncertainty value of each normalized correlation value sequence to obtain the characteristic value of each normalized correlation value sequence;

[0171] The calculation expression of the angle normalization process is:

[0172]

[0173] in, is the eigenvalue of the ith normalized correlation value sequence, and M is the total number of normalized correlation value sequences;

[0174] The eigenvalues ​​of all sequences are used to perform weighted summation on the mean of the row vectors of the difference matrix to obtain the first abnormality discrimination value.

[0175] XR (extended reality) technology includes AR (augmented reality), VR (virtual reality), and MR (mixed reality). It uses hardware equipment combined with a variety of technical means to integrate virtual content and real scenes.

[0176] In a second aspect of the embodiment of the present application, a method for screening cognitive impairment based on an XR eye tracker and near-infrared brain imaging is disclosed, which is implemented using the cognitive impairment screening device based on an XR eye tracker and near-infrared brain imaging, including:

[0177] S1, using the extended reality scene generation module to generate and display a virtual reality scene and an augmented reality scene containing cognitive tasks;

[0178] S2, using the eye tracker data acquisition module to acquire a collection of eye movement data information of the user when completing cognitive tasks in virtual reality scenes and augmented reality scenes in real time;

[0179] S3, using the near-infrared brain imaging module to collect in real time a collection of blood oxygen information of the cerebral cortex of the user when the user completes cognitive tasks in a virtual reality scene and an augmented reality scene;

[0180] S4, using the data analysis and processing module, analyzing and processing the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal;

[0181] S5, using the cognitive impairment screening module, performing cognitive impairment screening processing on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information.

[0182] The step of analyzing and processing the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal includes:

[0183] S41, performing abnormality discrimination processing on the collected eye movement data information set to obtain a first abnormality discrimination value;

[0184] S42, determining whether the first abnormal discrimination value is greater than a preset first discrimination threshold, and obtaining a first abnormal characteristic signal; if the first abnormal characteristic signal is negative, sending the collected eye movement data information set and the cerebral cortex blood oxygen information set to a cognitive impairment screening module;

[0185] If the first abnormal characteristic signal is yes, the cognitive impairment information is confirmed to indicate the presence of cognitive impairment, and the cognitive impairment degree information is determined as the first abnormality discrimination value.

[0186] The cognitive impairment screening process is performed on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0187] S51, preprocessing the eye movement data information set and the cerebral cortex blood oxygen information set respectively to obtain a preprocessed eye movement data information set and a cerebral cortex blood oxygen information set;

[0188] S52, performing cognitive impairment assessment processing on the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain cognitive impairment information of the user.

[0189] The pre-processing operation comprises:

[0190] Normalizing each information set to obtain a corresponding normalized information set;

[0191] Perform data cleaning on each normalized information set to obtain a corresponding cleaned data set;

[0192] Perform pattern recognition and discrimination processing on each cleaned data set to obtain a corresponding pre-processed information set.

[0193] The cognitive impairment assessment process is performed on the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including:

[0194] S521, establishing a conditional probability association model;

[0195] S522, substituting the historical evaluation data set into the conditional probability association model, training the conditional probability association model, and obtaining a trained conditional probability association model;

[0196] S523, using the trained conditional probability association model, processing the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain cognitive impairment assessment result information;

[0197] S524, determining whether the cognitive impairment assessment result information is greater than a preset second discrimination threshold, and obtaining a second abnormal characteristic signal; if the second abnormal characteristic signal is negative, confirming that the cognitive impairment information does not contain cognitive impairment;

[0198] If the second abnormal characteristic signal is yes, confirming that the cognitive impairment information is the existence of cognitive impairment, and determining that the cognitive impairment assessment information is cognitive impairment assessment result information;

[0199] S525 , constructing cognitive impairment information of the user by using the cognitive impairment assessment information and the information on whether cognitive impairment exists.

[0200] The step of establishing a conditional probability association model comprises:

[0201] S5211, establish a correlation network of influencing factors;

[0202] S5212, using the evaluation process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set, assigning conditional probability parameters to the influencing factor association relationship network;

[0203] S5213, assigning evidence values ​​to the influencing factor nodes in the influencing factor association relationship network to obtain a conditional probability association model.

[0204] The evaluation process information includes whether the classical method is adopted, whether mature tools are adopted, whether supervised samples are adopted, whether the evaluation process is adapted to the characteristics of the evaluation data, whether the evaluation process is adapted to the characteristics of the evaluation business, whether the analysis of the evaluation purpose is sufficient, whether the analysis of the evaluation object is accurate, whether the evaluation subject is fully managed, whether the evaluation results are verified, whether the indicators are complete, whether the indicators are independent, whether the indicators are consistent, whether the indicators are objective, the credibility of the evaluation process, and the technical level of the personnel, and the result values ​​and probability values ​​of all the above factors;

[0205] The evaluation process information can be obtained by conducting a prior evaluation of the cognitive impairment screening method.

[0206] The establishment of the influencing factor association network specifically includes: using a hierarchical Bayesian network method, introducing one root node: evaluation credibility; and then introducing seven intermediate nodes: evaluation process credibility, evaluation method credibility, primary data product quality, intermediate data product quality, final data product quality, evaluation indicator construction quality, and quantitative indicator credibility;

[0207] According to the knowledge information of business domain, data domain and evaluation domain, a relationship network of influencing factors of 20 influencing factor nodes and 7 intermediate nodes to the root node is established;

[0208] Using the root node, the intermediate node and the influencing factor node, an influencing factor association network is constructed; the 20 influencing factor nodes include whether the classical method is adopted, whether the mature tool is adopted, whether the supervised sample is adopted, whether it is adapted to the evaluation data characteristics, whether it is adapted to the evaluation business characteristics, whether the evaluation purpose analysis is sufficient, whether the evaluation object analysis is accurate, whether the evaluation subject is fully controlled, whether the evaluation result is tested, whether the indicator is complete, whether the indicator is independent, whether the indicator is consistent, whether the indicator is objective, the credibility of the evaluation process, the technical level of the personnel, the mean and variance ratio of the data of the pre-processed eye movement data information set, the mean and variance ratio of the data of the pre-processed cerebral cortex blood oxygen information set, whether data cleaning is performed, the median and variance ratio of the data of the pre-processed eye movement data information set, and the median and variance ratio of the data of the pre-processed cerebral cortex blood oxygen information set;

[0209] In the influencing factor association network, the credibility factor of the evaluation method is jointly determined by whether the classical method is adopted, whether the mature tool is adopted, whether the supervised sample is adopted, whether it is suitable for the evaluation data characteristics, and whether it is suitable for the evaluation business characteristics;

[0210] The credibility of the assessment process is determined by whether the assessment purpose analysis is sufficient, whether the assessment object analysis is accurate, whether the assessment subject is fully controlled, and whether the assessment results are verified;

[0211] The quality factors of evaluation index construction are determined by six factors: whether the index is complete, whether the index is independent, whether the index is consistent, whether the index is objective, the credibility of the evaluation process and the technical level of the personnel;

[0212] The credibility of the quantitative indicators is determined by the quality of the evaluation indicator construction and the quality of the final data product. The quality of the primary data product is determined by the mean and variance ratio of the preprocessed eye movement data information set and the mean and variance ratio of the preprocessed cerebral cortex blood oxygen information set.

[0213] The quality of the intermediate data product is determined by three factors: whether data cleaning is performed, the median and variance ratio of the preprocessed eye movement data information set, and the median and variance ratio of the preprocessed cerebral cortex blood oxygen information set;

[0214] The quality of the final data product is determined by two factors: the quality of the intermediate data product and the technical level of the personnel;

[0215] The credibility of the final root node assessment is determined by the credibility of the assessment process, the credibility of the assessment method, the quality of the primary data product, the quality of the intermediate data product, the quality of the final data product, the quality of the assessment indicator construction, and the credibility of the quantitative indicators;

[0216] According to the above decision relationship, the influencing factor correlation network is constructed;

[0217] In the influencing factor association network, the values ​​of the 20 influencing factor nodes are determined based on the evaluation process information, the preprocessed eye movement data information set and the cerebral cortex blood oxygen information set, and then the value of the evaluation credibility of a root node is calculated. The value of the evaluation credibility is used to represent the cognitive impairment evaluation result information.

[0218] The step of using the evaluation process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to assign conditional probability parameters to the influencing factor association relationship network includes:

[0219] The influencing factor association relationship network is limited to a Boolean network, and all node values ​​are True or False, representing the occurrence or non-occurrence of the negative aspects of the corresponding influencing factor events;

[0220] For any non-leaf node of the network, i.e., 1 root node and 7 intermediate nodes, a conditional probability dependency relationship between the non-leaf node and all its corresponding parent nodes is established, i.e., P(non-leaf node|all parent nodes), which is expressed in the form of a conditional probability expression or a LeakyNoisyor function to assign values ​​to conditional probability parameters; when the number of parent nodes is less than or equal to 2, the conditional probability expression is directly used to express it; when the number of parent nodes is greater than 2, the Leaky Noisyor function noisyor() is used to express it; the parent node corresponding to the non-leaf node is the parent node of the non-leaf node;

[0221] The specific expression of the Leaky Noisyor function noisyor() function is:

[0222] Let X be any node in a Boolean Bayesian network, and its N parent nodes form the set n is the parent node number, is the nth parent node of X, let express All nodes in the set have the value False. express Remove Node Except for True, all other nodes are False. express All the node indices whose values ​​are True in , and then let:

[0223]

[0224] Represents all parent nodes of X When all take the value False, the conditional probability that X takes the value True represents all the parent nodes of X except for the node takes the value True, the conditional probability that X takes the value True when all the remaining nodes take the value False; when the number of parent nodes is less than or equal to 2, let the conditional probability expression of X and its parent node set X be:

[0225]

[0226] Correspondingly, when the number of parent nodes is greater than 2, the expression of the conditional probability dependence relationship between the non-leaf node and all the parent nodes adopts the Leaky Noisyor function noisyor(), and its specific expression is:

[0227]

[0228] The input of this function is the value pairs of all the parent nodes of X and p0.

[0229] According to the probability values in the described evaluation process information, the preprocessed eye movement data information set and the blood oxygen information set of the cerebral cortex, calculate the conditional probability values of the non-leaf node and all its corresponding parent nodes;

[0230] Assign evidence values to the influencing factor nodes in the influencing factor association relationship network to obtain a conditional probability association model, including:

[0231] Assign evidence values to 20 influencing factor nodes in the influencing factor association relationship network. The evidence value is the probability of the occurrence of the influencing factor, that is, the probability that the influencing factor node takes the value True; for any influencing factor node Y, its evidence is divided into two types: hard evidence and soft evidence. The hard evidence takes the value 1 or 0, that is, P(Y = True) = 1 or P(Y = True) = 0, which is used to represent whether the influencing factor event occurs or not. The soft evidence takes the value α ∈ (0, 1), that is, 0 < P(Y = True) = α < 1, which is used to express the objective or subjective probability of the occurrence of the influencing factor event, or to express the proportion of the occurrence range of the influencing factor event in the overall space or time of cognitive impairment screening; the evidence of all influencing factor nodes constitutes the network evidence set Evidence;

[0232] Find the conditional probability P(A = False|Evidence) that the network root node takes the value False when the network evidence set Evidence is known, where A represents the root node, and use the conditional probability P(A = False|Evidence) as the cognitive impairment degree information.

[0233] The conditional probability P(A=False|Evidence) that the network root node takes False when the network evidence set Evidence is known can be obtained by using Bayesian network modeling software.

[0234] Substituting the historical evaluation data set into the conditional probability association model, training the conditional probability association model, and obtaining the trained conditional probability association model, comprises:

[0235] The historical evaluation data set includes a collection data set and a corresponding cognitive impairment evaluation value set; the collection data set includes collection data; the collection data includes historical evaluation process information, eye movement data history information and blood oxygen history information; the cognitive impairment evaluation value set includes a cognitive impairment evaluation value corresponding to each collection data, which is also a label value corresponding to each collection data;

[0236] The historical assessment process information includes whether the classical method is adopted, whether mature tools are adopted, whether supervised samples are adopted, whether the method is adapted to the characteristics of the assessment data, whether the method is adapted to the characteristics of the assessment business, whether the assessment purpose analysis is sufficient, whether the assessment object analysis is accurate, whether the assessment subject has full control over the entire process, whether the assessment results are verified, whether the indicators are complete, whether the indicators are independent, whether the indicators are consistent, whether the indicators are objective, the credibility of the assessment process, and the technical level of the personnel, and the historical data of the result values ​​and probability values ​​of all the above factors;

[0237] Initialize the number of training iterations;

[0238] Inputting the collected data in the historical evaluation data set as input data into the conditional probability association model;

[0239] Using the conditional probability association model, the input data is processed to obtain a predicted value;

[0240] Performing difference calculation processing on the obtained predicted value and the label information corresponding to the input data to obtain a difference value;

[0241] Determine whether the difference value satisfies a convergence condition, and obtain a first determination result;

[0242] When the first judgment result is no, judging whether the training iteration number value is equal to the training number threshold, and obtaining a second judgment result;

[0243] When the second judgment result is no, determining that the model training state does not meet the training termination condition;

[0244] When the second judgment result is yes, determining that the model training state satisfies the training termination condition;

[0245] When the first judgment result is yes, determining that the model training state satisfies a training termination condition;

[0246] When the model training status does not meet the termination training condition, the parameter update model is used to update the parameters of the conditional probability association model, the training iteration number value is increased by 1, and the execution is triggered to input the collected data in the historical evaluation data set as input data into the conditional probability association model;

[0247] When the model training state satisfies the training termination condition, the training process of the conditional probability association model is completed to obtain a trained conditional probability association model.

[0248] The difference value satisfies the convergence condition, which means that the difference value is less than a preset convergence threshold; the difference value does not satisfy the convergence condition, which means that the difference value is not less than the preset convergence threshold.

[0249] The difference calculation process may be implemented using a loss function.

[0250] The loss function may adopt a cross entropy loss function.

[0251] The parameter updating model is:

[0252]

[0253] θ←θ+v;

[0254] In the formula, is the difference value calculated for the i-th collected data in the historical evaluation data set, v is the parameter update value, θ is the parameter to be updated, η is the initial parameter learning rate, α is the momentum angle parameter, 0≤α≤π / 4, It means to find partial derivatives with respect to variable θ;

[0255] The parameters to be updated of the conditional probability association model are the conditional probability values ​​between each node;

[0256] The normalization process is to map data in different value ranges to a specified value range, and its mathematical expression is:

[0257]

[0258] Among them, t is a data value in the data set, t max is the maximum value of all data in the data set, t min is the minimum value of all data in the data set, t 归一化 is the value of data t after normalization;

[0259] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0260] The pattern recognition and discrimination processing includes:

[0261] For each type of data attribute of the cleaned data set, the data collection information of the data is used as a known independent variable, and the data value of the data is used as a known dependent variable. The known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain the best consistent approximation polynomial f(Ix) of the type of data attribute; the best consistent approximation polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the data is deleted from the cleaned data set; if it is less than or equal to the second regression discrimination threshold, the data is not processed;

[0262] Performing fusion processing on all data of the cleaned data set that perform feature pre-analysis to obtain a corresponding pre-processed information set;

[0263] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0264] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0265] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0266] The collected eye movement data information set is subjected to abnormality discrimination processing to obtain a first abnormality discrimination value, including

[0267] Using the eye movement data information set, an eye movement data matrix is ​​constructed; the row vector of the eye movement data matrix is ​​a fixation point information sequence, a fixation time information sequence, and an eye saccade frequency information sequence;

[0268] Obtaining a standard eye movement data sequence; the row vector of the standard eye movement data sequence is a standard fixation point information sequence, a standard fixation time information sequence, and a standard eye saccade frequency information sequence;

[0269] Performing difference calculation processing on the standard eye movement data sequence and the eye movement data matrix to obtain a first abnormal discrimination value;

[0270] The difference calculation includes:

[0271] Subtracting the standard eye movement data sequence from the eye movement data matrix to obtain a difference matrix;

[0272] The difference matrix is ​​subjected to cross-correlation calculation processing to obtain a cross-correlation matrix R; i,j represents the i-th row and j-th element of the cross-correlation matrix R, r i,j The calculation expression is:

[0273] r i,j =E(e i e j ),

[0274] Among them, E() means finding the mean, e i represents the i-th row of the difference matrix;

[0275] For the mutual correlation matrix R, each row vector thereof is normalized and calculated to obtain a normalized correlation value sequence; the expression of the normalization calculation is:

[0276]

[0277] in, represents the jth value of the i-th normalized correlation value sequence, and N is the number of elements contained in the normalized correlation value sequence;

[0278] Perform logarithmic summation on each normalized correlation value sequence to obtain the uncertainty value of each normalized correlation value sequence;

[0279] The calculation expression of the logarithmic summation process is:

[0280]

[0281] Among them, H i is the uncertainty value of the i-th normalized correlation value sequence;

[0282] Performing angle normalization processing on the uncertainty value of each normalized correlation value sequence to obtain the characteristic value of each normalized correlation value sequence;

[0283] The calculation expression of the angle normalization process is:

[0284]

[0285] in, is the eigenvalue of the i-th normalized correlation value sequence, and M is the total number of normalized correlation value sequences.

[0286] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A cognitive impairment screening device based on XR eye tracker and near-infrared brain imaging, characterized in that: include: Extended reality scene generation module, eye tracker data acquisition module, near-infrared brain imaging module, data analysis and processing module, cognitive impairment screening module; The extended reality scene generation module is used to generate and display virtual reality scenes and augmented reality scenes containing cognitive tasks; The eye tracker data acquisition module is used to acquire in real time an eye movement data information set when the user completes a cognitive task in a virtual reality scene or an augmented reality scene; the eye movement data information set includes a fixation point information sequence, a fixation time information sequence, and an eye saccade frequency information sequence; the fixation point information sequence includes fixation point position information; the fixation time information sequence includes fixation time information; the eye saccade frequency information sequence includes eye saccade frequency information; The near-infrared brain imaging module is used to collect in real time a set of blood oxygen information of the cerebral cortex when the user completes cognitive tasks in a virtual reality scene or an augmented reality scene; the set of blood oxygen information of the cerebral cortex includes a brain blood oxygen information sequence; The data analysis and processing module is connected to the extended reality scene generation module, the eye tracker data acquisition module, the near-infrared brain imaging module and the cognitive impairment screening module, respectively, and is used to analyze and process the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract the first abnormal characteristic signal; The cognitive impairment screening module is used to perform cognitive impairment screening processing on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information; The cognitive impairment information includes whether there is cognitive impairment and the degree of cognitive impairment.

2. The cognitive impairment screening device based on XR eye tracker and near-infrared brain imaging according to claim 1, characterized in that: The data analysis and processing module is used to analyze and process the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal, including: The data analysis and processing module performs abnormality discrimination processing on the collected eye movement data information set to obtain a first abnormality discrimination value; Determine whether the first abnormality discrimination value is greater than a preset first discrimination threshold, and obtain a first abnormality characteristic signal; If the first abnormal characteristic signal is negative, sending the collected eye movement data information set and the cerebral cortex blood oxygen information set to a cognitive impairment screening module; If the first abnormal characteristic signal is yes, the cognitive impairment information is confirmed to indicate the presence of cognitive impairment, and the cognitive impairment degree information is determined as the first abnormality discrimination value.

3. The cognitive impairment screening device based on XR eye tracker and near-infrared brain imaging according to claim 2, characterized in that: The extended reality scene generation module includes a virtual reality scene submodule and an augmented reality scene submodule; The virtual reality scene submodule is used to display a virtual reality scene containing cognitive tasks to the user; The augmented reality scene submodule is used to display an augmented reality scene containing cognitive tasks to the user.

4. The cognitive impairment screening device based on XR eye tracker and near-infrared brain imaging according to claim 2, characterized in that: The cognitive impairment screening module is used to perform cognitive impairment screening on the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including: The cognitive impairment screening module preprocesses the eye movement data information set and the cerebral cortex blood oxygen information set respectively to obtain a preprocessed eye movement data information set and a cerebral cortex blood oxygen information set; The preprocessed eye movement data information set and the cerebral cortex blood oxygen information set are subjected to cognitive impairment assessment processing to obtain cognitive impairment information of the user.

5. A method for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging, characterized in that: The method is implemented by using a cognitive impairment screening device based on an XR eye tracker and near-infrared brain imaging as claimed in any one of claims 1 to 4, comprising: S1, using the extended reality scene generation module to generate and display a virtual reality scene and an augmented reality scene containing cognitive tasks; S2, using the eye tracker data acquisition module to acquire a collection of eye movement data information of the user when completing cognitive tasks in virtual reality scenes and augmented reality scenes in real time; S3, using the near-infrared brain imaging module to collect in real time a collection of blood oxygen information of the cerebral cortex of the user when the user completes cognitive tasks in a virtual reality scene and an augmented reality scene; S4, using the data analysis and processing module, analyzing and processing the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal; S5, using the cognitive impairment screening module, performing cognitive impairment screening processing on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information.

6. The method for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging according to claim 5, characterized in that: The step of analyzing and processing the collected eye movement data information set and the cerebral cortex blood oxygen information set to extract a first abnormal characteristic signal includes: S41, performing abnormality discrimination processing on the collected eye movement data information set to obtain a first abnormality discrimination value; S42, determining whether the first abnormal discrimination value is greater than a preset first discrimination threshold, and obtaining a first abnormal characteristic signal; if the first abnormal characteristic signal is negative, sending the collected eye movement data information set and the cerebral cortex blood oxygen information set to a cognitive impairment screening module; If the first abnormal characteristic signal is yes, the cognitive impairment information is confirmed to indicate the presence of cognitive impairment, and the cognitive impairment degree information is determined as the first abnormality discrimination value.

7. The method for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging according to claim 5, characterized in that: The cognitive impairment screening process is performed on the assessment process information, the eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including: S51, preprocessing the eye movement data information set and the cerebral cortex blood oxygen information set respectively to obtain a preprocessed eye movement data information set and a cerebral cortex blood oxygen information set; S52, performing cognitive impairment assessment processing on the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain cognitive impairment information of the user.

8. The method for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging according to claim 7, characterized in that: The pre-processing operation comprises: Normalizing each information set to obtain a corresponding normalized information set; Perform data cleaning on each normalized information set to obtain a corresponding cleaned data set; Perform pattern recognition and discrimination processing on each cleaned data set to obtain a corresponding pre-processed information set.

9. The method for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging according to claim 7, characterized in that: The cognitive impairment assessment process is performed on the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain the user's cognitive impairment information, including: S521, establishing a conditional probability association model; S522, substituting the historical evaluation data set into the conditional probability association model, training the conditional probability association model, and obtaining a trained conditional probability association model; S523, using the trained conditional probability association model, processing the assessment process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set to obtain cognitive impairment assessment result information; S524, determining whether the cognitive impairment assessment result information is greater than a preset second discrimination threshold, and obtaining a second abnormal characteristic signal; if the second abnormal characteristic signal is negative, confirming that the cognitive impairment information does not contain cognitive impairment; If the second abnormal characteristic signal is yes, confirming that the cognitive impairment information is the existence of cognitive impairment, and determining that the cognitive impairment assessment information is cognitive impairment assessment result information; S525 , constructing cognitive impairment information of the user by using the cognitive impairment assessment information and the information on whether cognitive impairment exists.

10. The method for screening cognitive impairment based on XR eye tracker and near-infrared brain imaging according to claim 9, characterized in that: The step of establishing a conditional probability association model comprises: S5211, establish a correlation network of influencing factors; S5212, using the evaluation process information, the pre-processed eye movement data information set and the cerebral cortex blood oxygen information set, assigning conditional probability parameters to the influencing factor association relationship network; S5213, assigning evidence values ​​to the influencing factor nodes in the influencing factor association relationship network to obtain a conditional probability association model.

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