CN117159001B - Brain functional development assessment method and system - Google Patents
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Abstract
The application relates to a brain function development assessment method and system, which belong to the technical field of brain-computer interfaces, and the method comprises the following steps: obtaining evaluation indexes according to brain electrical data, wherein the brain electrical data at least comprises a group of brain electrical parameters, and the group of brain electrical parameters comprises a left brain parameter and a right brain parameter; and inputting the evaluation index and the identity information into a neural network model to obtain a classification result, wherein the electroencephalogram data and the identity information are both from the same monitoring object. The method is mainly used for quantifying and evaluating the left and right brain development balance of the individual, and has the effect of improving the evaluation accuracy and reliability.
Description
Technical Field
The application relates to the technical field of brain-computer interfaces, in particular to a brain functional development assessment method and system.
Background
Left and right brain developmental equality refers to the relative equilibrium state of the left and right hemispheres of the brain in terms of structure and function. Normally, the left brain controls the right side of the body, mainly participating in linguistic ability, logical thinking and analytic ability; the right brain controls the left side of the body, mainly involved in spatial perception, emotional processing, and creative thinking. Left and right brain developmental imbalance may lead to cognitive imbalance, learning difficulties, mood and social problems, attention problems, dyskinesia problems, and the like. Therefore, balanced development of the left and right brains is critical to the overall cognitive and behavioral performance of an individual.
Existing brain development assessment methods are typically based on neuroimaging techniques (e.g., MRI and fMRI) or behavioral measurements (e.g., cognitive tasks) to assess individual left and right brain development balance. However, the neuroimaging technology requires expensive equipment and specialized data processing and analysis, which limits the feasibility of the neuroimaging technology in large-scale application, and the method based on behavior measurement is easily affected by subjective willingness, motivation, learning effect and other factors of the participants, and may not accurately reflect the actual situation of brain development. Furthermore, behavioral measures do not provide direct information about brain structure and function, and lack an in-depth understanding of brain developmental mechanisms.
Disclosure of Invention
The application provides a brain function development assessment method and system, which are used for quantifying and assessing the left and right brain development balance of an individual and have the characteristics of improving the assessment accuracy and reliability.
The purpose of the application is to provide a method for evaluating brain functional development.
The first object of the present application is achieved by the following technical solutions:
a method of assessing brain functional development, comprising:
obtaining evaluation indexes according to brain electrical data, wherein the brain electrical data at least comprises a group of brain electrical parameters, and the group of brain electrical parameters comprises a left brain parameter and a right brain parameter;
and inputting the evaluation index and the identity information into a neural network model to obtain a classification result, wherein the electroencephalogram data and the identity information are both from the same monitoring object.
By adopting the technical scheme, the evaluation index is obtained according to the brain electrical data of the monitoring object, and the evaluation index and the identity information are used as the influence factors for evaluating whether the brain development of the monitoring object is balanced, so that the evaluation method does not need expensive neuroimaging technology to evaluate the brain development condition, can solve the problem of low accuracy caused by a behavior measurement-based method, and can analyze the actual condition of the brain development by putting the evaluation index and the identity information into a neural network model trained in advance to obtain a classification result so as to improve the accuracy and the reliability of evaluation.
The present application may be further configured in a preferred example to: the evaluation index comprises a first type of evaluation index corresponding to the frequency phase and/or a second type of evaluation index corresponding to the intensity value over the frequency phase.
By adopting the technical scheme, the evaluation indexes comprise the first type evaluation indexes and/or the second type evaluation indexes, the first type evaluation indexes correspond to the frequency stage, the second type evaluation indexes correspond to the intensity value in the frequency stage, and the actual condition of brain development is evaluated by analyzing the intensity value in the frequency stage and/or the frequency stage of the electroencephalogram data, so that data support is provided for obtaining accurate evaluation results.
The present application may be further configured in a preferred example to: the first type evaluation index corresponding to the frequency stage is determined by the frequency difference value of at least one group of electroencephalogram parameters, wherein the frequency difference value refers to the difference value of the frequency value corresponding to the maximum intensity value of the left brain parameter and the frequency value corresponding to the maximum intensity value of the right brain parameter in any group of electroencephalogram parameters.
By adopting the technical scheme, the difference value of the frequency value corresponding to the maximum intensity value of the left brain parameter and the frequency value corresponding to the maximum intensity value of the right brain parameter in any group of brain electrical parameters is calculated, and then a first type evaluation index is calculated according to the difference value, so that data support is provided for evaluating whether the development of the left brain and the development of the right brain of a monitored object are balanced.
The present application may be further configured in a preferred example to: when the evaluation index is a first type evaluation index, the obtaining the first type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, wherein the left brain parameter and the right brain parameter in any group of brain parameters comprise frequency values alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5;
the value a is calculated and the value,wherein the value A i For the value A, T corresponding to the ith brain electrode, the test duration is the function of F1 (alpha 1, alpha 2, alpha 3, alpha 4, alpha 5), the maximum intensity in each moment alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 is calculated in the test durationThe value, and take the frequency value corresponding to the maximum intensity value as the calculation result of each moment;
calculating a first type of evaluation index:wherein N is the number of brain electrodes, delta A n The frequency difference value obtained by subtracting the value A of the right brain from the value A of the left brain in the N-th group of brain electrical parameters is represented, and N is less than or equal to N/2.
The present application may be further configured in a preferred example to: the second type of evaluation index corresponding to the intensity value on the frequency phase is determined by the intensity difference value on the frequency phase of at least one group of electroencephalogram parameters, wherein the intensity difference value refers to the difference value between the maximum intensity value of the left brain parameter and the maximum intensity value of the right brain parameter in any group of electroencephalogram parameters.
By adopting the technical scheme, the difference value between the maximum intensity value of the left brain parameter and the maximum intensity value of the right brain parameter in any group of brain electrical parameters is calculated, and then a second type evaluation index is obtained according to the difference value, so that data support is provided for evaluating whether the development of the left brain and the right brain of a monitored object is balanced.
The present application may be further configured in a preferred example to: when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, wherein the left brain parameter and the right brain parameter in any group of brain parameters comprise frequency values alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 and intensity values omega 1, omega 2, omega 3, omega 4 and omega 5 respectively corresponding to the frequency values alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5;
the value B is calculated and the value,wherein the value B i For the value B, T corresponding to the ith brain electrode, the test duration is F2 (omega 1, omega 2, omega 3, omega 4, omega 5) function, and the maximum value is selected from omega 1, omega 2, omega 3, omega 4 and omega 5 at each moment in the test duration;
calculate a second type of ratingPrice index:wherein N is the number of brain electrodes, delta B n The intensity difference value obtained by subtracting the value B of the right brain from the value B of the left brain in the nth group of brain electrical parameters is represented, and N is less than or equal to N/2.
The present application may be further configured in a preferred example to: when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, and the left brain parameter and the right brain parameter in any group of brain parameters comprise a frequency interval delta and an intensity value omega 6 corresponding to the frequency interval delta;
calculating a valueWherein delta nL Intensity values ω6, δ corresponding to frequency interval δ for the nth set of left brain parameters nR Intensity value omega 6 corresponding to frequency interval delta of the nth group of right brain parameters;
calculating a second type evaluation index:wherein N is the number of the brain electrodes, and N is less than or equal to N/2.
The present application may be further configured in a preferred example to: when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, and the left brain parameter and the right brain parameter in any group of brain parameters comprise a frequency interval alpha and an intensity value omega 8 corresponding to the frequency interval alpha;
calculating a valueWherein alpha is nL Intensity value omega 8, alpha corresponding to frequency interval alpha of the nth group of left brain parameters nR Intensity value omega corresponding to frequency interval alpha of nth group right brain parameter8；
Calculating a second type evaluation index:wherein N is the number of the brain electrodes, and N is less than or equal to N/2.
By adopting the technical scheme, the second type evaluation index further comprises a plurality of calculation modes, so that when the second type evaluation index is used as an influence factor for evaluating whether the brain development of the monitored object is balanced or not, the data quantity of the influence factor is more diversified and rich, and data support is provided for obtaining an accurate evaluation result.
The present application may be further configured in a preferred example to: the frequency phase comprises frequency values of 1Hz-3Hz, 4Hz-7Hz, 8Hz-12Hz, 13Hz-30Hz, 31Hz-50Hz frequency interval and 8Hz, 9Hz, 10Hz, 11Hz and 12 Hz.
By adopting the technical scheme, because the brain wave of the brain is always positioned in the range of 1Hz-50Hz and is more intensively expressed in the range of 8Hz-12Hz when the human brain is moving, the method only analyzes the change condition of the brain wave data between 1Hz-50Hz of a monitoring object in the test duration, thereby not only reducing the calculated amount of the method, but also reducing the interference of the data and providing a guarantee for further obtaining accurate evaluation results.
The second object of the present application is to provide an evaluation system for brain functional development.
The second object of the present application is achieved by the following technical solutions:
an assessment system for brain functional development, comprising:
the data processing unit is used for obtaining evaluation indexes according to brain electrical data, wherein the brain electrical data at least comprises a group of brain electrical parameters, and the group of brain electrical parameters comprises a left brain parameter and a right brain parameter;
and the data generation unit is used for inputting the evaluation index and the identity information into the neural network model to obtain a classification result, and the electroencephalogram data and the identity information are both from the same monitoring object.
In summary, the present application includes at least one of the following beneficial technical effects:
1. firstly, calculating a difference value between a frequency value corresponding to the maximum intensity value of a left brain parameter and a frequency value corresponding to the maximum intensity value of a right brain parameter in any group of brain electrical parameters, and calculating according to the difference value to obtain a first type evaluation index, thereby providing data support for evaluating whether the development of the left brain and the right brain of a monitored object is balanced;
2. secondly, calculating a difference value between the maximum intensity value of the left brain parameter and the maximum intensity value of the right brain parameter in any group of brain electrical parameters, and calculating according to the difference value to obtain a second type evaluation index, so as to provide data support for evaluating whether the development of the left brain and the right brain of a monitored object is balanced;
3. finally, the evaluation indexes comprise a first type evaluation index and/or a second type evaluation index, and the evaluation indexes and the identity information are used as influence factors for evaluating whether the brain development of the monitored object is balanced or not, so that the method does not need expensive neuroimaging technology to evaluate the brain development condition, the problem of low accuracy caused by a behavior measurement-based method can be solved, and the classification result can be obtained by putting the evaluation indexes and the identity information into a neural network model trained in advance, and the actual condition of the brain development can be analyzed to improve the accuracy and reliability of evaluation.
Drawings
FIG. 1 illustrates a schematic diagram of an exemplary operating environment in which embodiments of the present application can be implemented.
Fig. 2 shows a flow chart of an assessment method of brain functional development according to an embodiment of the present application.
Fig. 3 shows a block diagram of an evaluation system for brain functional development according to an embodiment of the present application.
Reference numerals illustrate: 100. an electroencephalogram instrument; 200. an electroencephalogram acquisition module; 300. an MCU; 400. a wireless transmission module; 500. detecting a terminal; 510. a direction module; 520. a data receiving module; 530. a data processing module; 540. a data analysis module; 541. a data processing unit; 542. a data generation unit; 543. a data storage unit; 550. and a display module.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Fig. 1 shows a schematic diagram of an exemplary operating environment in which embodiments of the present application can be implemented, including an electroencephalograph 100, an electroencephalogram acquisition module 200, an MCU300, a wireless transmission module 400, and a detection terminal 500.
The electroencephalograph 100 is provided with a plurality of electroencephalogram electrodes, and electroencephalogram signals on the head of a monitoring object are collected by the electroencephalogram electrodes when the electroencephalograph 100 is worn on the head of the monitoring object. In this example, the position where the electroencephalograph 100 collects the electroencephalogram signals is determined according to the international standard 10-20 lead system, and the selection of the electroencephalogram electrodes follows the principle of bilateral cerebral symmetry. In a specific example, 8 electroencephalogram electrodes are selected, wherein Fp1, fp2, F3, F4, C3, C4, P3 and P4 are symmetrical left and right, F3 and F4 are symmetrical left and right, C3 and C4 are symmetrical left and right, and P3 and P4 are symmetrical left and right. Because the four groups of electroencephalogram electrodes cover the whole head of the monitored object, the electroencephalogram signals acquired by the four groups of electroencephalogram electrodes are used as the basis for judging whether the left brain and the right brain of the monitored object are balanced or not. In other examples, the electroencephalogram electrodes at other positions can be selected, but only the left-right brain symmetry of the selected electroencephalogram electrodes is guaranteed, and if the accuracy to be evaluated is higher, electroencephalogram signals acquired by a larger number of electroencephalogram electrodes can be acquired, and the number of specific electroencephalogram electrodes is not limited.
It should be noted that the monitoring object refers to a person to be evaluated for balancing brain development. It should be further noted that the above-mentioned odd numbered electroencephalogram electrodes are all placed on the left side of the brain, such as "Fp1", "F3", "C3", "P3", and the even numbered electroencephalogram electrodes are all placed on the right side of the brain, such as "Fp2", "F4", "C4", and "P4".
The electroencephalograph 100 transmits the acquired electroencephalogram signals to the electroencephalogram acquisition module 200, and the electroencephalogram acquisition module 200 is designed based on a TIADS1299 chip, namely, the electroencephalogram acquisition module 200 integrates other functional modules on the basis of the TIADS1299 chip. In this example, after the electroencephalogram signal is filtered and amplified by the electroencephalogram acquisition module 200, the electroencephalogram signal is converted into digitized electroencephalogram data, and the electroencephalogram data is transmitted to the MCU300. The MCU300 adopts STM32F407, the STM32F407 is a 32-bit embedded microprocessor based on ARM cortex M3 kernel, the MCU300 adopts an IIR filter to carry out software filtering on the electroencephalogram data, and the electroencephalogram data after digital filtering processing is transmitted to the wireless transmission module 400. The wireless transmission module 400 is designed based on the Bluetooth module RF-BM-BG22A1 of the EFR32BG22 chip, i.e. the wireless transmission module 400 integrates a module with wireless transmission function on the EFR32BG22 chip. The wireless transmission module 400 transmits the electroencephalogram data to the detection terminal 500 in a packet manner through bluetooth wireless communication.
The detection terminal 500 is an intelligent terminal such as a mobile phone, a tablet, a computer, etc., and the detection terminal 500 includes an instruction module 510, a data receiving module 520, a data processing module 530, a data analyzing module 540, and a display module 550. The instruction module 510 can play voice or video to instruct the monitored object to perform operations such as opening eyes, closing eyes or visually tracking the marks displayed on the screen according to instruction requirements. The time period during which the instruction module 510 instructs the monitoring object to perform the operation is referred to as a test duration, that is, during the test duration, the electroencephalogram signals of the monitoring object during the operation are collected by the electroencephalogram meter 100.
The data receiving module 520 is in communication connection with the wireless transmission module 400, and the data receiving module 520 is configured to receive the brain electrical data sent by the wireless transmission module 400. In order to ensure that the data receiving module 520 receives the electroencephalogram data sent by the wireless transmission module 400, the data receiving module 520 needs to be corresponding to the wireless transmission module 400, for example, when the wireless transmission module 400 adopts bluetooth to transmit the electroencephalogram data, the data receiving module 520 is a bluetooth module; when the wireless transmission module 400 adopts WIFI to transmit the brain electrical data, the data receiving module 520 is a WIFI communication module.
The data processing module 530 is connected to the data receiving module 520, and acquires electroencephalogram data through the data receiving module 520. The data processing module 530 adopts low-pass filtering to filter high-frequency artifacts, power frequency interference, and the like in the electroencephalogram data so as to obtain pure electroencephalogram data. In addition, the data processing module 530 further converts the electroencephalogram data belonging to the time domain into electroencephalogram data of the frequency domain by adopting an FFT algorithm or a wavelet transform algorithm, and in a specific example, the data processing module 530 converts the electroencephalogram data of the time domain into electroencephalogram data of 10 frequency domains in total, namely, 1Hz-3Hz, 4Hz-7Hz, 8Hz-12Hz, 13Hz-30Hz, 31Hz-50Hz, 8Hz, 9Hz, 10Hz, 11Hz, and 12 Hz. That is, after the electroencephalogram data generated by each electroencephalogram electrode is processed by the data processing module 530, electroencephalogram data in the corresponding 10 frequency domains is obtained. For convenience of explanation, the frequency ranges of 1Hz-3Hz, 4Hz-7Hz, 8Hz-12Hz, 13Hz-30Hz, 31Hz-50Hz, and the frequency values of 8Hz, 9Hz, 10Hz, 11Hz, 12Hz are denoted by delta, theta, alpha, beta, gamma, alpha 1, alpha 2, alpha 3, alpha 4, alpha 5, respectively.
It should be noted that, the purpose of converting the time domain electroencephalogram data into the 10 frequency domain electroencephalogram data is as follows: the brain waves of the brain are normally located in the above frequency phase (1 Hz-50 Hz) when the human brain is active, so the present application only analyzes the condition of the brain wave data of the monitoring object in the above frequency phase when the operation is performed. When the time domain electroencephalogram data is converted into the frequency domain electroencephalogram data, the electroencephalogram data in the frequency domain also corresponds to an intensity value, for example, the electroencephalogram data in the converted frequency domain is a two-dimensional coordinate system, the abscissa of the two-dimensional coordinate system is frequency, and the ordinate is intensity. Therefore, after the time domain electroencephalogram data is converted into the frequency domain electroencephalogram data, richer data can be obtained, so that the data analysis module 540 is facilitated to analyze.
In addition, when the data analysis module 540 uses the brain electrical data in the frequency domain as a basis for analyzing whether the brain development is balanced, the classification result obtained after the analysis is output to the display module 550 for display.
It should be noted that the operating environment illustrated in fig. 1 is merely illustrative, and is in no way intended to limit the application or uses of embodiments of the present invention. For example, the operation environment may include a plurality of electroencephalographs 100, a plurality of electroencephalogram acquisition modules 200, a plurality of MCUs 300, a plurality of wireless transmission modules 400, and a plurality of detection terminals 500.
Fig. 2 shows a flow chart of a method of assessing brain functional development according to an embodiment of the present application, which may be performed by the detection terminal 500 described above. Specifically, the main flow of the evaluation method of brain functional development is described below.
And step 100, obtaining an evaluation index according to the electroencephalogram data.
Firstly, the data analysis module 540 obtains the electroencephalogram data belonging to the frequency domain from the data processing module 530, and divides the electroencephalogram data into a plurality of groups of electroencephalogram parameters according to the principle of bilateral brain symmetry, for example, frequency domain electroencephalogram data corresponding to Fp1 and Fp2 respectively is used as a group of electroencephalogram parameters, frequency domain electroencephalogram data corresponding to F3 and F4 respectively is used as a group of electroencephalogram parameters, frequency domain electroencephalogram data corresponding to C3 and C4 respectively is used as a group of electroencephalogram parameters, and frequency domain electroencephalogram data corresponding to P3 and P4 respectively is used as a group of electroencephalogram parameters, namely four groups of electroencephalogram parameters are obtained in total. In other examples, if there are N electroencephalogram electrodes, the N electroencephalogram electrodes will obtain N/2 sets of electroencephalogram parameters, where N is greater than or equal to 2, under the principle of following left-right brain symmetry.
In this application, taking the above four sets of electroencephalogram parameters as an example, how to obtain an evaluation index according to electroencephalogram data will be described.
First, as known from four sets of electroencephalogram parameters, each set of electroencephalogram parameters includes left brain parameters, such as the above-mentioned electroencephalogram data corresponding to Fp1, F3, C3, and P3, and right brain parameters, such as the above-mentioned electroencephalogram data corresponding to Fp2, F4, C4, and P4. And because the left brain parameter and the right brain parameter both comprise the 10 frequency domains, each frequency domain corresponds to one intensity value. Therefore, the application takes the intensity value of each brain electrical data in the frequency domain stage and/or the frequency domain stage as an influence factor for calculating the evaluation index.
For convenience of distinction, the present application uses an evaluation index calculated from a frequency domain stage as a first type of evaluation index, and uses an evaluation index calculated from an intensity value on a frequency domain stage as a second type of evaluation index.
The method for taking the evaluation index calculated according to the frequency domain stage as the first type of evaluation index comprises the following steps:
in a specific example, the first type of evaluation index is calculated according to 5 frequency values of α1, α2, α3, α4, and α5. Specifically, the value A is calculated first,wherein the value A i And (3) taking the numerical value A and T corresponding to the ith electroencephalogram electrode as a test duration, and taking the frequency value corresponding to the maximum intensity value as a calculation result of each moment when the function F1 (alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5) is in the test duration, wherein the maximum intensity value in each moment alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5 is calculated. Then, a first type of evaluation index is calculated: />Wherein N is the number of brain electrodes, delta A n The difference value obtained by subtracting the value A of the right brain from the value A of the left brain in the N-th group of brain electrical parameters is represented, and N is less than or equal to N/2.
It can be seen that the magnitude of the first type of evaluation index is associated with the frequency difference value of each group of electroencephalogram parameters. In addition, the present application also refers to the first type of evaluation index as a first evaluation index P1.
The method for taking the evaluation index obtained by calculating according to the intensity value in the frequency domain stage as the second type of evaluation index comprises the following steps: in a specific example, the second type of evaluation index is calculated according to the intensity values corresponding to 5 frequency values of α1, α2, α3, α4, and α5, and the intensity values corresponding to 5 frequency intervals of δ, θ, α, β, and γ. For the convenience of distinction, the intensity values corresponding to α1, α2, α3, α4, α5, δ, θ, α, β, γ are denoted by ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8, ω9, ω10 in this order. Meanwhile, for convenience of description, the second type of evaluation index calculated according to the intensity value is named sequentially by the second evaluation index, the third evaluation index, the fourth evaluation index, the fifth evaluation index, the sixth evaluation index, the seventh evaluation index, the eighth evaluation index, and the ninth evaluation index, specifically as shown in steps S110 to S180:
and step S110, calculating to obtain a second evaluation index according to 5 intensity values of omega 1, omega 2, omega 3, omega 4 and omega 5. Specifically, the value B is calculated first,i is less than or equal to 8, wherein the value B i For the i-th electroencephalogram electrode, the corresponding numerical value B, T is the test duration, and the function of F2 (omega 1, omega 2, omega 3, omega 4, omega 5) is that the maximum value of the intensity values is selected from omega 1, omega 2, omega 3, omega 4 and omega 5 at each moment in the test duration. Then, a second evaluation index is calculated: />Wherein N is the number of brain electrodes, delta B n The difference value obtained by subtracting the value B of the right brain from the value B of the left brain in the nth group of brain electrical parameters is represented, and N is less than or equal to N/2.
And step S120, calculating according to omega 6 to obtain a third evaluation index. Specifically, a numerical value is calculated firstWherein delta nL Intensity values ω6, δ corresponding to frequency interval δ for the nth set of left brain parameters nR Intensity value ω6 corresponding to frequency interval δ of the nth set of right brain parameters. Then, a third evaluation index is calculated: />。
And step S130, calculating according to omega 7 to obtain a fourth evaluation index. Specifically, a numerical value is calculated firstWherein θ nL Intensity value omega 7, theta corresponding to frequency interval theta of n-th group left brain parameter nR The intensity value ω7 corresponding to the frequency interval θ of the nth set of right brain parameters. Then, a fourth evaluation index is calculated: />。
And step S140, calculating according to omega 8 to obtain a fifth evaluation index. Specifically, a numerical value is calculated firstWherein alpha is nL Intensity value omega 8, alpha corresponding to frequency interval alpha of the nth group of left brain parameters nR Intensity value ω8 corresponding to frequency interval α of the nth set of right brain parameters. Then, a fifth evaluation index is calculated:。
and step S150, calculating according to omega 8 to obtain a sixth evaluation index. Specifically, a numerical value is calculated firstWherein alpha is nL Intensity value omega 8, alpha corresponding to frequency interval alpha of the nth group of left brain parameters nR Intensity value ω8 corresponding to frequency interval α of the nth set of right brain parameters. Then, a sixth evaluation index is calculated: />。
And step S160, calculating according to omega 9 to obtain a seventh evaluation index. Specifically, a numerical value is calculated firstWherein beta is nL Intensity values ω9, β corresponding to frequency interval β for the nth set of left brain parameters nR Intensity value ω9 corresponding to frequency interval β of the nth set of right brain parameters. Then, a seventh evaluation index is calculated: />。
And step S170, calculating according to omega 9 to obtain an eighth evaluation index. Specifically, a numerical value is calculated firstWherein beta is nL Intensity values ω9, β corresponding to frequency interval β for the nth set of left brain parameters nR Intensity value ω9 corresponding to frequency interval β of the nth set of right brain parameters. Then, an eighth evaluation index is calculated: />。
And step S180, calculating according to omega 10 to obtain a ninth evaluation index. Specifically, a numerical value is calculated firstWherein, gamma nL Intensity values ω10, γ corresponding to frequency interval γ of the nth group of left brain parameters nR Intensity value ω10 corresponding to frequency interval γ of the nth group right brain parameter. Then, a ninth evaluation index is calculated:。
it can be seen that there are three different calculation methods, such as ΔBn, when the evaluation index calculated from the intensity values in the frequency domain is used as the second type evaluation index,、In order to distinguish the three calculation results, it is also possible to use、/>、/>Is expressed by a calculation formula of (a). In other examples, other calculation manners may be used to calculate the second type of evaluation index, which is not limited herein.
In summary, the second type of evaluation index of the present application includes 8 indices in total. Based on the first type of evaluation indexes, 9 evaluation indexes are obtained in total.
And step 200, inputting the evaluation index and the identity information into the neural network model to obtain a classification result.
In one specific example, the identity information includes an age and a gender of the monitored subject. It should be noted that, during actual testing, identity information and the electroencephalogram data are required to be ensured to come from the same monitoring object.
In addition, the data analysis module 540 of the present application establishes a neural network model in advance, so that after the evaluation index and the identity information are obtained, the evaluation index and the identity information can be input into the neural network model, and the neural network model can output the corresponding classification result. In the example, the classification result is displayed in a class form, that is, the neural network model can judge that the brain function development of the monitored object is in a specific class according to the evaluation index and the identity information, the class is normal, the first class, the second class and the third class, and compared with the brain belonging to the first class, the brain development of the normal brain has higher balance; by analogy, brains belonging to the first class are more balanced in brain development than brains belonging to the second class.
Specifically, the method for building the neural network model is as follows in step S210-step S226:
step S210, firstly, performing brain function development evaluation on normal people and problem people by adopting a behavior measurement method to obtain training set data. In this embodiment, the evaluation is performed using a space oriented task, a Stroop task, and a scale. Where space-oriented tasks involve the perception and understanding of spatial directions, orientations, and spatial relationships by individuals, tasks such as rotation, mirror image drawing, space-oriented memory, etc., may evaluate the processing power of an individual on spatial information. Among these tasks, the left brain is primarily responsible for sequence and direction, and the right brain is primarily responsible for shape and spatial relationships. The Stroop task can evaluate the language interference suppression capability of individuals, and in the Stroop task, the testees (normal crowd and problem crowd) need to ignore the meaning of the characters, but only pay attention to the colors of the characters, which involves the conflict processing of the left brain on the language and the colors. The scale is a mental health scale (MHS-CA) for children and teenagers, which evaluates the testee in terms of cognition, emotion, mind, individuality, thinking and the like. And marking the brain dysfunction of the tested person as normal, primary, secondary and tertiary four classifications according to the result of the behavior measurement.
And step 220, training the neural network model according to the training set data. Specifically, the input of the neural network model is a two-dimensional matrix of 11×k, where K is the number of data samples, 11 is 9 evaluation indexes plus age and sex information of the monitored object, and the output of the model is a probability distribution marked as normal, first-order, second-order, third-order, and total four classifications.
According to the method, the neural network model is trained according to the steps S221-S226, and the trained model is the evaluation model and also represents that the neural network model is built.
Step S221, preprocessing training set data. The preprocessing stage performs normalization processing on the input data to ensure that the various features have similar dimensions and distributions. In this embodiment, a normalization method of mean normalization is adopted. Specifically, for each feature, the mean and standard deviation of the training set data are calculated, and then the value of each feature is subtracted from the mean and divided by the standard deviation. The output of the model is marked by codes, such as the normal, first level, and second level are respectively represented by [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1 ].
Step S222, initializing weight and bias parameters of the neural network model. In this embodiment, the Xavier initialization method is used to initialize the weights and bias parameters. The Xavier initialization method can adaptively adjust the initial value of the parameter according to the number of neurons of the previous layer and the number of neurons of the next layer, and helps to avoid the problem of gradient disappearance or gradient explosion.
Step S223, defining the structure of the neural network model, including an input layer, a hidden layer and an output layer. In this embodiment, a feed-forward neural network with two hidden layers and one output layer is used. The number of neurons in the input layer is 11, the first hidden layer contains 50 neurons and the second hidden layer contains 30 neurons. The number of neurons at the output layer is defined as 4 according to the multi-class label of the problem, corresponding to the normal, primary, secondary and tertiary four classes, respectively.
Step S224, selecting an appropriate activation function. ReLU (Rectified Linear Unit) is used as an activation function on the neurons of each of the hidden and output layers in this embodiment. The ReLU function is a linear function in the positive range, which can provide a nonlinear transformation, effectively capturing the complexity in the input data. The output layer can conveniently obtain probability predictions for each category using a softmax activation function.
Step S225, defining a loss function. The loss function is used to measure the difference between the output of the neural network model and the actual signature. A cross entropy loss function (Cross Entropy Loss) is used in this embodiment. The cross entropy loss function is widely used in the multi-classification problem, and can effectively measure the distance between the prediction of the model and the real label so as to improve the accuracy of the calculation result of the neural network model.
Step S226, iterating until the neural network model converges or reaches the preset training times. In the embodiment, an Adam optimization algorithm is adopted for training the model. The Adam algorithm combines the characteristics of a momentum method and an adaptive learning rate, and can accelerate the convergence rate and improve the performance of the model.
Based on the established neural network model, transmitting 11 parameters including the age and sex of the monitoring object added with 9 evaluation indexes to the neural network model, obtaining probability distribution representing normal, primary, secondary and tertiary classification by the neural network model, taking the level with the highest proportion in the probability distribution as the result of the evaluation, and providing corresponding classification labels according to the evaluation result to obtain the classification result.
Fig. 3 shows a block diagram of an evaluation system for brain functional development according to an embodiment of the present application, the evaluation system comprising a data calculation unit and a data generation unit.
The data processing unit 541 is configured to obtain an evaluation index according to electroencephalogram data, where the electroencephalogram data at least includes a set of electroencephalogram parameters, and the set of electroencephalogram parameters includes a left brain parameter and a right brain parameter.
The data generating unit 542 is configured to input the evaluation index and the identity information into the neural network model to obtain a classification result, where the electroencephalogram data and the identity information are both from the same monitoring object.
In a specific example, the evaluation system further comprises a data storage unit 543, the data storage unit 543 being configured to store identity information and a neural network model.
The modules and units involved in the embodiments of the present application may be implemented in software, or may be implemented in hardware. The modules and units described may also be provided in a processor, for example, as: a processor includes a data processing unit 541, a data generating unit 542, and a data storage unit 543. The names of these units do not constitute a limitation of the module itself in some cases, and for example, the data processing unit 542 may also be described as "a unit for obtaining an evaluation index from electroencephalogram data".
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described module may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the disclosure. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (3)
1. A method for assessing brain function development, comprising:
obtaining evaluation indexes according to electroencephalogram data, wherein the electroencephalogram data at least comprises a group of electroencephalogram parameters, the group of electroencephalogram parameters comprise left brain parameters and right brain parameters, the evaluation indexes comprise a first type of evaluation indexes corresponding to frequency phases and/or a second type of evaluation indexes corresponding to intensity values on frequency phases, the first type of evaluation indexes corresponding to frequency phases are determined by frequency differences of at least one group of electroencephalogram parameters, and the frequency differences are differences of frequency values corresponding to maximum intensity values of the left brain parameters and maximum intensity values of the right brain parameters in any group of electroencephalogram parameters; the second type evaluation index corresponding to the intensity value on the frequency phase is determined by the intensity difference value on the frequency phase of at least one group of electroencephalogram parameters, wherein the intensity difference value refers to the difference value between the maximum intensity value of the left brain parameter and the maximum intensity value of the right brain parameter in any group of electroencephalogram parameters;
when the evaluation index is a first type evaluation index, the obtaining the first type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, wherein the left brain parameter and the right brain parameter in any group of brain parameters comprise frequency values alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5;
the value a is calculated and the value,wherein the value A i For the i-th electroencephalogram electrode, the corresponding numerical value A and T is the test duration, the function of F1 (alpha 1, alpha 2, alpha 3, alpha 4, alpha 5) is in the test duration, the maximum intensity value in each moment alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 is calculated, and the frequency value corresponding to the maximum intensity value is taken as the calculation junction of each momentFruit;
calculating a first type of evaluation index:wherein N is the number of brain electrodes, delta A n The frequency difference value obtained by subtracting the value A of the right brain from the value A of the left brain in the nth group of brain electrical parameters is represented, and N is less than or equal to N/2;
when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, wherein the left brain parameter and the right brain parameter in any group of brain parameters comprise frequency values alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 and intensity values omega 1, omega 2, omega 3, omega 4 and omega 5 respectively corresponding to the frequency values alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5;
the value B is calculated and the value,wherein the value B i For the value B, T corresponding to the ith brain electrode, the test duration is F2 (omega 1, omega 2, omega 3, omega 4, omega 5) function, and the maximum value is selected from omega 1, omega 2, omega 3, omega 4 and omega 5 at each moment in the test duration;
calculating a second type evaluation index:wherein N is the number of brain electrodes, delta B n The intensity difference value obtained by subtracting the value B of the right brain from the value B of the left brain in the nth group of brain electrical parameters is represented, and N is less than or equal to N/2; and
when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, and the left brain parameter and the right brain parameter in any group of brain parameters comprise a frequency interval delta and an intensity value omega 6 corresponding to the frequency interval delta;
calculating a valueWherein delta nL Intensity values ω6, δ corresponding to frequency interval δ for the nth set of left brain parameters nR Intensity value omega 6 corresponding to frequency interval delta of the nth group of right brain parameters;
calculating a second type evaluation index:wherein N is the number of brain electrodes, and N is less than or equal to N/2; and
when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, and the left brain parameter and the right brain parameter in any group of brain parameters comprise a frequency interval alpha and an intensity value omega 8 corresponding to the frequency interval alpha;
calculating a valueWherein alpha is nL Intensity value omega 8, alpha corresponding to frequency interval alpha of the nth group of left brain parameters nR Intensity value omega 8 corresponding to frequency interval alpha of the nth group of right brain parameters;
calculating a second type evaluation index:wherein N is the number of brain electrodes, and N is less than or equal to N/2;
and inputting the evaluation index and the identity information into a neural network model to obtain a classification result, wherein the electroencephalogram data and the identity information are both from the same monitoring object.
2. The method of assessing brain functional development according to claim 1, wherein the frequency phase comprises frequency values of 1Hz-3Hz, 4Hz-7Hz, 8Hz-12Hz, 13Hz-30Hz, 31Hz-50Hz frequency intervals and 8Hz, 9Hz, 10Hz, 11Hz, 12 Hz.
3. An assessment system for brain functional development, comprising:
the data processing unit (541) is configured to obtain an evaluation index according to electroencephalogram data, where the electroencephalogram data at least includes a set of electroencephalogram parameters, and the set of electroencephalogram parameters includes a left brain parameter and a right brain parameter, the evaluation index includes a first type of evaluation index corresponding to a frequency phase and/or a second type of evaluation index corresponding to an intensity value on the frequency phase, the first type of evaluation index corresponding to the frequency phase is determined by a frequency difference value of at least one set of electroencephalogram parameters, and the frequency difference value is a difference value between a frequency value corresponding to a maximum intensity value of the left brain parameter and a frequency value corresponding to a maximum intensity value of the right brain parameter in any set of electroencephalogram parameters; the second type evaluation index corresponding to the intensity value on the frequency phase is determined by the intensity difference value on the frequency phase of at least one group of electroencephalogram parameters, wherein the intensity difference value refers to the difference value between the maximum intensity value of the left brain parameter and the maximum intensity value of the right brain parameter in any group of electroencephalogram parameters;
when the evaluation index is a first type evaluation index, the obtaining the first type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, wherein the left brain parameter and the right brain parameter in any group of brain parameters comprise frequency values alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5;
the value a is calculated and the value,wherein the value A i The method comprises the steps that (1) the maximum intensity value in each moment alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5 is calculated in the test duration, and the frequency value corresponding to the maximum intensity value is taken as the calculation result of each moment, wherein the value A and T corresponding to the ith electroencephalogram electrode are the test duration, and the function F1 (alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5) is the test duration;
calculating a first type of evaluation index:wherein N is the number of brain electrodes, delta A n Represents the nth group of brain electrical parametersThe frequency difference value obtained by subtracting the value A of the right brain from the value A of the left brain in the numbers is N less than or equal to N/2;
when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, wherein the left brain parameter and the right brain parameter in any group of brain parameters comprise frequency values alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 and intensity values omega 1, omega 2, omega 3, omega 4 and omega 5 respectively corresponding to the frequency values alpha 1, alpha 2, alpha 3, alpha 4 and alpha 5;
the value B is calculated and the value,wherein the value B i For the value B, T corresponding to the ith brain electrode, the test duration is F2 (omega 1, omega 2, omega 3, omega 4, omega 5) function, and the maximum value is selected from omega 1, omega 2, omega 3, omega 4 and omega 5 at each moment in the test duration;
calculating a second type evaluation index:wherein N is the number of brain electrodes, delta B n The intensity difference value obtained by subtracting the value B of the right brain from the value B of the left brain in the nth group of brain electrical parameters is represented, and N is less than or equal to N/2; and
when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, and the left brain parameter and the right brain parameter in any group of brain parameters comprise a frequency interval delta and an intensity value omega 6 corresponding to the frequency interval delta;
calculating a valueWherein delta nL Intensity values ω6, δ corresponding to frequency interval δ for the nth set of left brain parameters nR Intensity value omega 6 corresponding to frequency interval delta of the nth group of right brain parameters;
calculate a second type of ratingPrice index:wherein N is the number of brain electrodes, and N is less than or equal to N/2; and
when the evaluation index is a second type evaluation index, the obtaining the second type evaluation index according to the electroencephalogram data includes:
two mutually symmetrical brain electrodes generate a group of brain parameters, and the left brain parameter and the right brain parameter in any group of brain parameters comprise a frequency interval alpha and an intensity value omega 8 corresponding to the frequency interval alpha;
calculating a valueWherein alpha is nL Intensity value omega 8, alpha corresponding to frequency interval alpha of the nth group of left brain parameters nR Intensity value omega 8 corresponding to frequency interval alpha of the nth group of right brain parameters;
calculating a second type evaluation index:wherein N is the number of brain electrodes, and N is less than or equal to N/2;
and the data generation unit (542) is used for inputting the evaluation index and the identity information into a neural network model to obtain a classification result, wherein the electroencephalogram data and the identity information are from the same monitoring object.
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