CN116831598A - Brain muscle signal evaluation method and device - Google Patents
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Abstract
The embodiment of the application discloses a brain muscle signal evaluation method and device, wherein the method comprises the following steps: acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user; determining a first group of internal mean square error and a first group of inter-mean square error based on the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, and determining a second group of internal mean square error and a second group of inter-mean square error based on the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data; determining an electroencephalogram target value based on the first intra-group mean square error and the first inter-group mean square error, and determining an myoelectric target value based on the second intra-group mean square error and the second inter-group mean square error; determining an electroencephalogram reference value and a myoelectricity reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectricity frequency spectrum data and the task state myoelectricity frequency spectrum data; and determining an evaluation result of the user based on the electroencephalogram target value, the myoelectric target value, the electroencephalogram reference value and the myoelectric reference value.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a brain muscle signal evaluation method and device.
Background
In the mirroring therapy, the patient sees the mirror image of the movement of the perfect side limb, and can activate the mirror image neurons of the corresponding cortex, and the discharge form of the mirror image neurons is consistent with the brain region electric activity when the action is actually performed, so that the mirror image neurons help to restore the movement function of the affected side limb. However, the current method for evaluating the therapeutic effect of the mirror therapy generally evaluates the therapeutic effect by filling the subjective scale of the patient, so that the accuracy of the evaluation result is very low, and the evaluation of the clinical research effect is affected. In addition, the evaluation of the effect of a treatment is often performed after the whole treatment or after a plurality of treatments, and lacks real-time.
Disclosure of Invention
The application provides a brain muscle signal evaluation method and device for solving the technical problems.
To this end, an aspect of an embodiment of the present application provides a brain muscle signal evaluation method, including:
acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user;
determining a first group of internal mean square deviations and a first group of inter-mean square deviations based on the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, and determining a second group of internal mean square deviations and a second group of inter-mean square deviations based on the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data;
Determining an electroencephalogram target value based on the first intra-group mean square error and the first inter-group mean square error, and determining an myoelectric target value based on the second intra-group mean square error and the second inter-group mean square error;
determining an electroencephalogram reference value and a myoelectricity reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectricity frequency spectrum data and the task state myoelectricity frequency spectrum data;
and determining an evaluation result of the user based on the electroencephalogram target value, the myoelectric target value, the electroencephalogram reference value and the myoelectric reference value.
The acquiring the rest state electroencephalogram spectrum data, the task state electroencephalogram spectrum data, the rest state myoelectric spectrum data and the task state myoelectric spectrum data of the user comprises the following steps:
collecting a resting state electroencephalogram signal, a task state electroencephalogram signal and a resting state electromyogram signal and a task state electromyogram signal of a user;
and preprocessing the rest state electroencephalogram signal, the task state electroencephalogram signal, the rest state electromyogram signal and the task state electromyogram signal to obtain rest state electroencephalogram spectrum data, task state electroencephalogram spectrum data, rest state electromyogram spectrum data and task state electromyogram spectrum data.
Wherein, carry out the preliminary treatment to the quiet state brain electrical signal, the task state brain electrical signal, the quiet state electromyographic signal and the task state electromyographic signal, include:
Performing Fourier transformation on the resting state electroencephalogram signal, the task state electroencephalogram signal, the resting state electromyogram signal and the task state electromyogram signal to obtain candidate resting state electroencephalogram spectrum data, candidate task state electroencephalogram spectrum data, candidate resting state electromyogram spectrum data and candidate task state electromyogram spectrum data;
and selecting the data in a preset frequency range from the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data to obtain the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data.
The determining the first intra-group mean square error and the first inter-group mean square error based on the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data comprises the following steps:
determining a first average value of the rest state electroencephalogram frequency spectrum data, a second average value of the task state electroencephalogram frequency spectrum data, and a third average value of all data in the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data;
determining a first group of inner dispersion square sum and a first group of inter-dispersion square sum based on the first mean value, the second mean value, the third mean value, the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data;
Determining a degree of freedom in the first group and a degree of freedom between the first group based on the rest state electroencephalogram spectral data and the total data of the task state electroencephalogram spectral data;
the first intra-set mean square error and the first inter-set mean square error are determined based on the first intra-set sum of squares of deviations, the first inter-set sum of squares of deviations, the first intra-set degree of freedom and the first inter-set degree of freedom.
Wherein the determining the second intra-group mean square error and the second inter-group mean square error based on the rest state myoelectric spectrum data and the task state myoelectric spectrum data comprises:
determining a fourth mean value of the rest state myoelectric spectrum data, a fifth mean value of the task state myoelectric spectrum data, and a sixth mean value of all data in the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
determining a second intra-group sum of squares and a second inter-group sum of squares of dispersion based on the fourth mean, the fifth mean, the sixth mean, the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
determining a degree of freedom within the second group and a degree of freedom between the second group based on the rest state myoelectric spectrum data and the total data of the task state myoelectric spectrum data;
the second intra-set mean square error and the second inter-set mean square error are determined based on the second intra-set sum of squares of deviations, the second inter-set sum of squares of deviations, the second intra-set degree of freedom and the second inter-set degree of freedom.
Wherein the determining the evaluation result of the user based on the electroencephalogram target value, the myoelectric target value, the electroencephalogram reference value and the myoelectric reference value includes:
determining an electroencephalogram evaluation result based on the electroencephalogram target value and the electroencephalogram reference value;
determining an myoelectricity evaluation result based on the myoelectricity target value and the myoelectricity reference value;
if the electroencephalogram evaluation result and the myoelectricity evaluation result are both positive, determining that the evaluation result of the user is light;
if one of the electroencephalogram evaluation result and the myoelectricity evaluation result is positive and the other is negative, determining that the evaluation result of the user is medium;
and if the electroencephalogram evaluation result and the myoelectricity evaluation result are both negative, determining that the evaluation result of the user is heavy.
Another aspect of the embodiments of the present application provides a brain muscle signal evaluation device, including:
the acquisition module is used for acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user;
the computing module is used for determining the first group of internal mean square deviations of the rest state electroencephalogram frequency spectrum data, the second group of internal mean square deviations of the task state electroencephalogram frequency spectrum data, the first group of internal mean square deviations of the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, the third group of internal mean square deviations of the rest state myoelectric frequency spectrum data, the fourth group of internal mean square deviations of the task state myoelectric frequency spectrum data, the rest state myoelectric frequency spectrum data and the second group of internal mean square deviations of the task state myoelectric frequency spectrum data;
The calculation module is further configured to determine a first target value and a second target value based on the first intra-group mean square error, the second intra-group mean square error, and the first inter-group mean square error, and determine a third target value and a fourth target value based on the third intra-group mean square error, the fourth intra-group mean square error, and the second inter-group mean square error;
the computing module is further used for determining a first reference value, a second reference value, a third reference value and a fourth reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data;
the computing module is further configured to determine an evaluation result of the user based on the first target value, the second target value, the third target value, the fourth target value, the first reference value, the second reference value, the third reference value, and the fourth reference value.
The computing module is further configured to determine a first mean value and a first intra-group degree of freedom of the rest state electroencephalogram spectrum data, determine a first intra-group sum of squares based on the first mean value, and divide the first intra-group sum of squares by the first intra-group degree of freedom to obtain a first intra-group mean square error;
the computing module is further configured to determine a second average value of the task state electroencephalogram spectrum data and a second intra-group degree of freedom, determine a second intra-group sum of squares based on the second average value, and divide the second intra-group sum of squares by the second intra-group degree of freedom to obtain a second intra-group mean square error;
The computing module is further configured to determine a first degree of freedom between the first groups and a first sum of squares between the first groups based on the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data, and determine a first mean square error between the first groups based on the first degree of freedom between the first groups and the first sum of squares between the first groups.
The computing module is further configured to determine a third mean value and a third intra-group degree of freedom of the rest state myoelectric spectrum data, determine a third intra-group sum of squares based on the third mean value, and divide the third intra-group sum of squares by the third intra-group degree of freedom to obtain a third intra-group mean square error;
the computing module is further configured to determine a fourth mean value of the task state myoelectric spectrum data and a fourth intra-group degree of freedom, determine a fourth intra-group sum of squares based on the fourth mean value, and divide the fourth intra-group sum of squares by the fourth intra-group degree of freedom to obtain a fourth intra-group mean square error;
the computing module is further configured to determine a second degree of freedom between the second groups and a second sum of squares between the second groups based on the rest state myoelectric spectrum data and the task state myoelectric spectrum data, and determine a second mean square error between the second groups based on the second degree of freedom between the second groups and the second sum of squares between the second groups.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a flow chart of a brain muscle signal evaluation method according to one embodiment of the application;
FIG. 2 shows a schematic diagram of a distribution table according to one embodiment of the application;
FIG. 3 shows a flow chart of a brain muscle signal evaluation method according to another embodiment of the present application;
FIG. 4 shows a flow chart of a brain muscle signal evaluation method according to another embodiment of the present application;
FIG. 5 shows a flow chart of a brain muscle signal evaluation method according to another embodiment of the present application;
FIG. 6 shows a flow chart of a brain muscle signal evaluation method according to another embodiment of the present application;
FIG. 7 shows a flowchart of a brain muscle signal evaluation method according to another embodiment of the present application;
FIG. 8 illustrates a flowchart for determining an evaluation result of a user based on an electroencephalogram evaluation result and an electromyographic evaluation result, according to an embodiment of the application;
FIG. 9 shows a flowchart of a brain muscle signal evaluation method according to another embodiment of the present application;
fig. 10 shows a schematic structural diagram of a brain muscle signal evaluation device according to an embodiment of the present application.
Detailed Description
In order to make the objects, features and advantages of the present application more comprehensible, the technical solutions according to the embodiments of the present application will be clearly described in the following with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to improve the accuracy and real-time performance of the evaluation, an embodiment of the present application provides a brain muscle signal evaluation method, as shown in fig. 1, which includes:
step 101, acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user.
The rest state electroencephalogram frequency spectrum data and the rest state myoelectric frequency spectrum data are frequency spectrum data of a user in a calm state, and the task state electroencephalogram frequency spectrum data and the task state myoelectric frequency spectrum data are frequency spectrum data of the user when the user imagines movement of a patient side limb and controls the patient side limb as much as possible according to a preset instruction (for example, the user imagines the movement of the patient side limb).
The spectrum data includes amplitude values corresponding to a plurality of frequencies, for example, task state electroencephalogram spectrum data obtained by fourier transform of a task state electroencephalogram signal is:
step 102, determining a first intra-group mean square error and a first inter-group mean square error based on the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, and determining a second intra-group mean square error and a second inter-group mean square error based on the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data.
Determining a mean square error MSE in the first group based on the resting state electroencephalogram spectral data and the task state electroencephalogram spectral data 1 Determining a first inter-group mean square error MSA between resting state and task state electroencephalogram spectral data 1 。
Determining a mean square error MSE in the second group based on the rest state myoelectric spectrum data and the task state electroencephalogram spectrum data 2 Determining a second inter-group mean square error MSA between the rest state myoelectric spectrum data and the task state myoelectric spectrum data 2 。
Step 103, determining an electroencephalogram target value based on the first intra-group mean square error and the first inter-group mean square error, and determining an myoelectric target value based on the second intra-group mean square error and the second inter-group mean square error.
Specifically, the electroencephalogram target value F is determined based on the following formula 1 ：
Determining the myoelectric target value F based on the following formula 2 ：
And 104, determining an electroencephalogram reference value and an electromyogram reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state electromyogram frequency spectrum data and the task state electromyogram frequency spectrum data.
In this embodiment, there are two methods to determine the reference value.
The first method is as follows:
and determining a corresponding distribution table based on the preset confidence alpha, and subtracting 1 from the number of data groups to obtain the abscissa of the distribution table (in the embodiment, the number of data groups is 2 for the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data). Subtracting 1 from the total number of all data in the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, and subtracting the abscissa of the distribution table to obtain the ordinate of the distribution table. And determining an electroencephalogram reference value from the corresponding distribution table based on the abscissa of the distribution table and the ordinate of the distribution table.
For example, as shown in fig. 2, fig. 2 is a distribution table corresponding to a preset confidence α of 0.05, a certain evaluation sets the preset confidence α to 0.05, the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data to 2 sets of data, the total number of all data in the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data is 10, the abscissa of the distribution table is 1, the ordinate of the distribution table is 8, and the electroencephalogram reference value is 23925 based on the coordinates (1, 8) from the distribution table corresponding to the preset confidence α of 0.05.
For another example, if the preset confidence α is set to 0.05 for a certain evaluation, the rest state myoelectric spectrum data and the task state myoelectric spectrum data are 2 sets of data, the total number of all data in the rest state myoelectric spectrum data and the task state myoelectric spectrum data is 4, the abscissa of the distribution table is 1, the ordinate of the distribution table is 2, and the myoelectric reference value is 20000 from the distribution table corresponding to the preset confidence α of 0.05 based on the coordinates (1, 2).
In the second method, the electroencephalogram reference value and the myoelectric reference value are determined by statistical software such as SPSS (Statistical Package for the Social Sciences, social science statistical software package, a statistical software) or Excel (a spreadsheet software, with a statistical function).
And 105, determining an evaluation result of the user based on the electroencephalogram target value, the myoelectricity target value, the electroencephalogram reference value and the myoelectricity reference value.
The evaluation results of the user are three, namely light weight, medium weight and heavy weight, and the light weight indicates that the patient limb exercise ability of the user is better to recover, and the treatment effect of the mirror image therapy is better. The medium-sized indicates that the recovery of the motor ability of the affected limb of the user is general and the therapeutic effect of the mirror image therapy is general. Heavy instructions indicate that the user has poor recovery of the motor ability of the affected limb and poor therapeutic effect of mirror image therapy.
In the above scheme, the electroencephalogram target value of the user is determined by acquiring the resting state electroencephalogram spectrum data and the task state electroencephalogram spectrum data of the user, and the myoelectricity target value of the user is determined by acquiring the resting state myoelectricity spectrum data and the task state myoelectricity spectrum data of the user. The electroencephalogram target value can represent the difference between the mean level of the electroencephalogram spectrum data of the user in the calm state and the mean level of the electroencephalogram spectrum data of the user when the user executes a preset instruction (for example, the user imagines the movement of the affected limb and realizes movement control of the affected limb as much as possible), and similarly, the myoelectric target value can represent the difference between the mean level of the myoelectric spectrum data of the user in the calm state and the mean level of the myoelectric spectrum data of the user when executing the preset instruction. The electroencephalogram reference value and the myoelectric reference value are values for judging whether the target value exceeds a significance level (if the electroencephalogram target value is larger than the electroencephalogram reference value, the significance difference exists between mean value levels of data for determining the electroencephalogram target value is explained), and whether the significance difference exists between the mean value level of rest state electroencephalogram spectrum data and the mean value level of task state electroencephalogram spectrum data of a user can be determined by comparing the electroencephalogram target value with the electroencephalogram reference value and comparing the myoelectric target value with the myoelectric reference value. Therefore, the further determined evaluation result of the user is more accurate. After each training or treatment, the user can be evaluated through the scheme, and the real-time performance of the evaluation result is improved.
In an example of the present application, as shown in fig. 3, the acquiring resting state electroencephalogram spectrum data, task state electroencephalogram spectrum data, resting state myoelectric spectrum data, and task state myoelectric spectrum data of the user includes:
step 201, collecting a resting state brain electrical signal, a task state brain electrical signal, a resting state electromyographic signal and a task state electromyographic signal of a user.
The brain electrode cap is worn for the user, and meanwhile, the electrode sheet is attached to the skin above the muscle with larger corresponding state change when the limb moves. And acquiring the electroencephalogram signals and the electromyographic signals of the user.
In this embodiment, the brain electrical signals and the electromyographic signals are typically acquired for 2 minutes. In other embodiments, the acquisition time may be set based on demand.
In this embodiment, generally, after mirror image treatment of the user, a set of electroencephalogram signals and electromyographic signals (i.e., resting state electroencephalogram signals and resting state electromyographic signals) of the user in a calm state are collected, and a set of electroencephalogram signals and electromyographic signals (i.e., task state electroencephalogram signals and resting state electroencephalogram signals) of the user in a state according to a preset instruction (e.g., the user imagines movement of a patient side limb and realizes movement control of the patient side limb as much as possible) are collected.
Step 202, preprocessing the rest state electroencephalogram signal, the task state electroencephalogram signal, the rest state electromyogram signal and the task state electromyogram signal to obtain rest state electroencephalogram spectrum data, task state electroencephalogram spectrum data, rest state electromyogram spectrum data and task state electromyogram spectrum data.
The resting state electroencephalogram, the task state electroencephalogram, the resting state electromyogram and the task state electromyogram are preprocessed, such as Fourier transform, fast Fourier transform or wavelet transform, and the resting state electroencephalogram, the task state electroencephalogram, the resting state electromyogram and the task state electromyogram are converted into resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state electromyogram frequency spectrum data and task state electromyogram frequency spectrum data.
In an example of the present application, as shown in fig. 4, the preprocessing of the resting state electroencephalogram signal, the task state electroencephalogram signal, the resting state electromyogram signal, and the task state electromyogram signal includes:
step 301, performing fourier transform on the rest state electroencephalogram signal, the task state electroencephalogram signal, the rest state electromyogram signal and the task state electromyogram signal to obtain candidate rest state electroencephalogram spectrum data, candidate task state electroencephalogram spectrum data, candidate rest state electromyogram spectrum data and candidate task state electromyogram spectrum data.
Step 302, selecting data in a preset frequency range from the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data to obtain the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data.
In this embodiment, the preset frequency range is set to 8-30Hz, and in other embodiments, the preset frequency range may be set based on requirements.
Some specific rhythms (e.g., alpha, beta, etc.) are typically related to human thinking, attention, and movement, and these rhythms are typically distributed over a specific frequency range (e.g., alpha is typically 8-12Hz, beta is typically 15-20 Hz). In the above scheme, the data in the preset frequency range in the spectrum data is selected as the spectrum data for determining the target value by setting the preset frequency range. The calculation amount of data can be reduced, and the accuracy of the target value determined based on the frequency spectrum data can be further improved.
In an example of the present application, as shown in fig. 5, the determining the first intra-group mean square error and the first inter-group mean square error based on the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data includes:
step 401, determining a first mean value of the rest state electroencephalogram frequency spectrum data, a second mean value of the task state electroencephalogram frequency spectrum data, and a third mean value of all data in the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data.
Determining a first average AVG of resting brain electrical spectrum data 1 Second average value 1VG of task state electroencephalogram spectrum data 2 Third average value AVG of all data in resting state electroencephalogram spectrum data and task state electroencephalogram spectrum data 3 。
For example, a certain set of resting state electroencephalogram spectrum data has 4 data, respectively A 11 、A 12 、A 13 And A 14 The corresponding task state electroencephalogram frequency spectrum data also has 4 data, which are respectively A 21 、A 22 、A 23 And A 24 Then the first mean valueSecond mean->Third mean->
Step 402, determining a first intra-group variance square sum and a first inter-group variance square sum based on the first mean, the second mean, the third mean, the resting state electroencephalogram spectral data and the task state electroencephalogram spectral data.
Determining the sum of squares of the deviations SSE in the first group based on the following formula 1 ：
Wherein A is ji For the jth set of ith brain electrical spectral data, e.g. A 12 Is the 2 nd frequency spectrum data in the resting state brain electricity frequency spectrum data. AVF (Audio video frequency) j Is the mean value of the j-th group of electroencephalogram spectrum data, e.g. AVG 1 Is the mean value of the rest state electroencephalogram spectrum data. K is the number of sets of electroencephalogram spectral data,and N is the total number of the brain electrical spectrum data in the j-th group of brain electrical spectrum data.
Determining a sum of squares of differences between the first group SSA based on the following formula 1 ：
Wherein, AVG j Is the mean value of the j-th group of electroencephalogram spectrum data, e.g. AVG 1 Is the mean value of the rest state electroencephalogram spectrum data. N (N) j Is the total number of the brain electrical spectrum data in the j-th group of brain electrical spectrum data. K is the group number of the brain electrical spectrum data, AVG 3 And is the third mean value.
Step 403, determining the degrees of freedom in the first group and the degrees of freedom between the first groups based on the rest state and task state electroencephalogram data.
First inter-group degree of freedom dfA 1 =m-1, first intra-group degree of freedom dfE 1 N-m, where m is the number of sets of electroencephalogram spectral data and n is the total number of all electroencephalogram spectral data.
For example, a certain set of rest state electroencephalogram spectrum data has 4 data, and the corresponding task state electroencephalogram spectrum data has 4 data, where m is 2, n is 8, and dfA 1 1, dfE 1 6.
Step 404, determining a first intra-set mean square error and a first inter-set mean square error based on the first intra-set sum of squares of deviations, the first inter-set sum of squares of deviations, the first intra-set degree of freedom and the first inter-set degree of freedom.
Determining the mean square error MSE in the first group based on the following formula 1 ：
Determining a first inter-group mean square error MSA based on the following formula 1 ：
In an example of the present application, as shown in fig. 6, the determining the second intra-group mean square error and the second inter-group mean square error based on the rest state myoelectric spectrum data and the task state myoelectric spectrum data includes:
Step 501, determining a fourth mean value of rest state myoelectric spectrum data, a fifth mean value of task state myoelectric spectrum data, and a sixth mean value of all data in rest state myoelectric spectrum data and task state myoelectric spectrum data.
Determining a fourth mean AVG of rest myoelectric spectrum data 4 Fifth average value AVG of task state myoelectric spectrum data 5 Sixth average AVG of all data in rest state myoelectric spectrum data and task state myoelectric spectrum data 6 。
For example, a certain set of rest state myoelectric spectrum data has 5 data, B respectively 11 、B 12 、B 13 、B 14 And B 15 Corresponding task state myoelectric spectrum data also have 5 data, namely B 21 、B 22 、B 23 、B 24 And B 25 Then fourth mean valueFifth mean->Sixth mean->
Step 502, determining a second intra-group sum of squares and a second inter-group sum of squares of the deviations based on the fourth mean, the fifth mean, the sixth mean, the rest state myoelectric spectrum data, and the task state myoelectric spectrum data.
Determining the sum of squares of the deviations SSE in the second group based on the following formula 2 ：
Wherein B is ji For the j-th group of i-th myoelectric spectrum data, e.g. B 12 Is the 2 nd spectrum data in the rest state myoelectric spectrum data. AVG (Audio video graphics) j For averaging of the j-th set of myoelectric spectrum data, e.g. AVG 4 Is the mean value of the rest state myoelectric spectrum data. K is the number of sets of myoelectric spectrum data, and N is the total number of myoelectric spectrum data in the j-th set of myoelectric spectrum data.
Determining a second set of inter-set dispersion squares and SSAs based on the following equation 2 ：
Wherein, AVG j For averaging of the j-th set of myoelectric spectrum data, e.g. AVG 4 Is the mean value of the rest state electroencephalogram spectrum data. N (N) j Is the sum of the myoelectric spectrum data in the j-th set of myoelectric spectrum data. K is the group number of myoelectric spectrum data, AVG 6 And is the sixth mean.
In step 503, the degrees of freedom in the second group and the degrees of freedom between the second group are determined based on the rest state myoelectric spectrum data and the total data of the task state myoelectric spectrum data.
Second inter-group degree of freedom dfA 2 M-1, degree of freedom dfE in second group 2 N-m, where m is the number of sets of myoelectrical spectral data and n is the total number of all myoelectrical spectral data.
For example, a certain set of rest state myoelectric spectrum data has 10 data, and the corresponding task state myoelectric spectrum data has 10 data, wherein m is 2, n is 20, and dfA 2 1, dfE 2 18.
Step 504, determining a second intra-set mean square error and a second inter-set mean square error based on the second intra-set sum of squares of deviations, the second inter-set sum of squares of deviations, the second intra-set degree of freedom, and the second inter-set degree of freedom.
Determining the mean square error MSE in the second group based on the following formula 2 ：
Determining a second inter-group mean square error MSA based on the following formula 2 ：
In an example of the present application, as shown in fig. 7, there is further provided a brain muscle signal evaluation method, wherein determining an evaluation result of the user based on the brain electrical target value, the myoelectrical target value, the brain electrical reference value, and the myoelectrical reference value includes:
Step 601, determining an electroencephalogram evaluation result based on an electroencephalogram target value and an electroencephalogram reference value.
If the electroencephalogram target value is larger than the electroencephalogram reference value, determining that the electroencephalogram evaluation result is positive.
And if the electroencephalogram target value is smaller than or equal to the electroencephalogram reference value, determining that the electroencephalogram evaluation result is negative.
Step 602, determining an myoelectricity evaluation result based on the myoelectricity target value and the myoelectricity reference value.
And if the myoelectricity target value is larger than the myoelectricity reference value, determining that the myoelectricity evaluation result is positive.
And if the myoelectricity target value is smaller than or equal to the myoelectricity reference value, determining that the myoelectricity evaluation result is negative.
And step 603, if the electroencephalogram evaluation result and the myoelectricity evaluation result are both positive, determining that the evaluation result of the user is light.
Step 604, if one of the electroencephalogram evaluation result and the myoelectricity evaluation result is positive and one of the electroencephalogram evaluation result and the myoelectricity evaluation result is negative, determining that the evaluation result of the user is medium-sized.
Step 605, if the electroencephalogram evaluation result and the myoelectricity evaluation result are both negative, determining that the evaluation result of the user is heavy.
As shown in fig. 8, if the electroencephalogram evaluation result and the myoelectricity evaluation result are both positive, it is determined that the evaluation result of the user is light. And determining that the evaluation result of the user is medium-sized if the brain electricity evaluation result and the myoelectricity evaluation result are positive or negative. And if the electroencephalogram evaluation result and the myoelectricity evaluation result are both negative, determining that the evaluation result of the user is heavy.
In the above scheme, if the electroencephalogram evaluation result or the myoelectricity evaluation result is positive, it indicates that there is a significant difference between the mean level of the rest state data and the task state data for evaluation, and if the result is negative, it indicates that there is no significant difference between the mean level of the rest state data and the task state data for evaluation. If the evaluation results of the brain electricity and the myoelectricity show significant differences, the fact that the affected side limb of the user can complete the training action corresponding to the preset instruction to a certain extent is indicated, the user does not need to repeatedly execute the training action in the subsequent training plan, and the corresponding typing result is light. If only one of the signals shows a significant difference or neither of the signals shows a significant difference, the user is not able to complete the training action well, and the user also needs to perform corresponding training. The user can be accurately typed, and quantification of the evaluation result is perfected.
In an example of the present application, there is further provided a brain muscle signal evaluation method, as shown in fig. 9, after determining an electroencephalogram evaluation result and an electromyogram evaluation result, the method further includes:
and step 701, if the electroencephalogram evaluation result is positive, a first prompt tone is sent to a user.
If the electroencephalogram evaluation result is positive, the fact that the user is fully involved in the training and is excellent in performance is indicated, a first prompt tone is sent to the user, and forward feedback is given.
The first alert tone may be audio containing any motivational words.
Step 702, if the electroencephalogram evaluation result is negative, performing electric stimulation on a brain area preset by a user.
If the brain electricity evaluation result is negative, the transcranial random noise electric stimulation is implemented on the preset brain area of the user so as to further activate the mirror image neurons of the movement area.
And step 703, if the myoelectricity evaluation result is positive, sending a second prompt tone to the user.
If the myoelectricity evaluation result is positive, the user is informed that the control effect on the muscle on the affected side is greatly improved in the training, and a second prompt tone is sent to the user to give forward feedback.
The second alert tone may be audio containing any motivational words.
And step 704, if the myoelectricity evaluation result is negative, performing electric stimulation on a preset muscle position of the user.
If the myoelectricity evaluation result is negative, functional electric stimulation is applied to the preset muscle position of the user so as to further activate the muscle neurons at the position.
Step 705, retraining and evaluating the user.
After playing the alert tone or electrical stimulus, the user is re-evaluated.
In the scheme, the prompt tone or the electric stimulation is played to the user based on the electroencephalogram evaluation result and the myoelectricity evaluation result, so that the user can obtain feedback based on training, the participation of the user is improved, the neurons in the brain area or the muscle position of the user can be further activated by the electric stimulation, and the effect of the user in subsequent training is improved.
In order to implement the above-mentioned brain muscle signal evaluation method, as shown in fig. 10, an example of the present application provides a brain muscle signal evaluation device, including:
the acquisition module 10 is used for acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user;
the calculation module 20 is configured to determine a first intra-group mean square error and a first inter-group mean square error based on the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data, and determine a second intra-group mean square error and a second inter-group mean square error based on the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
the calculation module 20 is further configured to determine an electroencephalogram target value based on the first intra-group mean square error and the first inter-group mean square error, and determine an myoelectric target value based on the second intra-group mean square error and the second inter-group mean square error;
The calculation module 20 is further configured to determine an electroencephalogram reference value and an electromyogram reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state electromyogram frequency spectrum data, and the task state electromyogram frequency spectrum data;
the calculation module 20 is further configured to determine an evaluation result of the user based on the electroencephalogram target value, the myoelectric target value, the electroencephalogram reference value, and the myoelectric reference value.
Wherein, still include:
the acquisition module 10 is further configured to acquire a resting state electroencephalogram signal, a task state electroencephalogram signal, a resting state electromyogram signal, and a task state electromyogram signal of the user;
the processing module 30 is configured to pre-process the rest state electroencephalogram signal, the task state electroencephalogram signal, the rest state electromyogram signal, and the task state electromyogram signal to obtain rest state electroencephalogram spectrum data, task state electroencephalogram spectrum data, rest state electromyogram spectrum data, and task state electromyogram spectrum data.
The computing module 20 is further configured to perform fourier transform on the resting brain electrical signal, the task brain electrical signal, the resting electromyographic signal, and the task electromyographic signal to obtain candidate resting brain electrical spectrum data, candidate task brain electrical spectrum data, candidate resting electromyographic spectrum data, and candidate task electromyographic spectrum data;
The processing module 30 is further configured to select the candidate rest state electroencephalogram frequency spectrum data, the candidate task state electroencephalogram frequency spectrum data, the candidate rest state myoelectric frequency spectrum data, and data within a preset frequency range in the candidate task state myoelectric frequency spectrum data, so as to obtain the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data, and the task state myoelectric frequency spectrum data.
The calculation module 20 is further configured to determine a first average value of the rest state electroencephalogram spectrum data, a second average value of the task state electroencephalogram spectrum data, and a third average value of all data in the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data;
the computing module 20 is further configured to determine a first intra-group sum of squares and a first inter-group sum of squares based on the first mean, the second mean, the third mean, the rest state electroencephalogram spectrum data, and the task state electroencephalogram spectrum data;
the computing module 20 is further configured to determine a first intra-group degree of freedom and a first inter-group degree of freedom based on the rest state electroencephalogram spectrum data and the total data of the task state electroencephalogram spectrum data;
the calculation module 20 is further configured to determine a first intra-set mean square error and a first inter-set mean square error based on the first intra-set sum of squares of deviations, the first inter-set sum of squares of deviations, the first intra-set degree of freedom and the first inter-set degree of freedom.
The calculation module 20 is further configured to determine a fourth mean value of the rest state myoelectric spectrum data, a fifth mean value of the task state myoelectric spectrum data, and a sixth mean value of all data in the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
the computing module 20 is further configured to determine a second intra-group sum of squares of dispersion and a second inter-group sum of squares of dispersion based on the fourth mean, the fifth mean, the sixth mean, the rest state myoelectric spectrum data, and the task state myoelectric spectrum data;
the calculation module 20 is further configured to determine a second intra-group degree of freedom and a second inter-group degree of freedom based on the rest state myoelectric spectrum data and the total data of the task state myoelectric spectrum data;
the calculation module 20 is further configured to determine a second intra-set mean square error and a second inter-set mean square error based on the second intra-set sum of squares of deviations, the second inter-set sum of squares of deviations, the second intra-set degree of freedom and the second inter-set degree of freedom.
Wherein, the calculation module 20 is further configured to determine an electroencephalogram evaluation result based on the electroencephalogram target value and the electroencephalogram reference value;
the calculation module 20 is further configured to determine an myoelectricity evaluation result based on the myoelectricity target value and the myoelectricity reference value;
the computing module 20 is further configured to determine that the evaluation result of the user is light if both the electroencephalogram evaluation result and the myoelectricity evaluation result are positive;
The computing module 20 is further configured to determine that the evaluation result of the user is medium if one of the electroencephalogram evaluation result and the myoelectricity evaluation result is positive and one of the electroencephalogram evaluation result and the myoelectricity evaluation result is negative;
the calculation module 20 is further configured to determine that the evaluation result of the user is heavy if both the electroencephalogram evaluation result and the myoelectric evaluation result are negative.
The processing module 30 is further configured to send a first alert tone to a user if the electroencephalogram evaluation result is positive;
the processing module 30 is further configured to perform electrical stimulation on a brain area preset by a user if the electroencephalogram evaluation result is negative;
the processing module 30 is further configured to send a second alert tone to the user if the myoelectricity evaluation result is positive;
the processing module 30 is further configured to perform electrical stimulation on a preset muscle position of the user if the myoelectricity evaluation result is negative;
the computing module 30 is further configured to retrain and evaluate the user.
In the above scheme, the electroencephalogram target value of the user is determined by acquiring the resting state electroencephalogram spectrum data and the task state electroencephalogram spectrum data of the user, and the myoelectricity target value of the user is determined by acquiring the resting state myoelectricity spectrum data and the task state myoelectricity spectrum data of the user. The electroencephalogram target value can represent the difference between the mean level of the electroencephalogram spectrum data of the user in the calm state and the mean level of the electroencephalogram spectrum data of the user when the user executes a preset instruction (for example, the user imagines the movement of the affected limb and realizes movement control of the affected limb as much as possible), and similarly, the myoelectric target value can represent the difference between the mean level of the myoelectric spectrum data of the user in the calm state and the mean level of the myoelectric spectrum data of the user when executing the preset instruction. The electroencephalogram reference value and the myoelectric reference value are values for judging whether the target value exceeds a significance level (if the electroencephalogram target value is larger than the electroencephalogram reference value, the significance difference exists between mean value levels of data for determining the electroencephalogram target value is explained), and whether the significance difference exists between the mean value level of rest state electroencephalogram spectrum data and the mean value level of task state electroencephalogram spectrum data of a user can be determined by comparing the electroencephalogram target value with the electroencephalogram reference value and comparing the myoelectric target value with the myoelectric reference value. Therefore, the further determined evaluation result of the user is more accurate. After each training or treatment, the user can be evaluated through the scheme, and the real-time performance of the evaluation result is improved.
In one example, an embodiment of the present application also provides a mobile terminal including at least one memory, and a processor communicatively coupled to the at least one memory; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the brain muscle signal assessment method of any one of the embodiments of fig. 1-9 described above.
In addition, an embodiment of the present application further provides a computer readable storage medium storing computer executable instructions for executing the cerebral muscle signal evaluation method flow described in any one of the embodiments of fig. 1 to 9.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the embodiments of the present application, the meaning of "plurality" is two or more, unless explicitly defined otherwise.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a Processor (Processor) to perform part of the steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of brain muscle signal assessment, the method comprising:
acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user;
determining a first group of internal mean square deviations and a first group of inter-mean square deviations based on the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, and determining a second group of internal mean square deviations and a second group of inter-mean square deviations based on the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data;
determining an electroencephalogram target value based on the first intra-group mean square error and the first inter-group mean square error, and determining an myoelectric target value based on the second intra-group mean square error and the second inter-group mean square error;
determining an electroencephalogram reference value and a myoelectricity reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectricity frequency spectrum data and the task state myoelectricity frequency spectrum data;
And determining an evaluation result of the user based on the electroencephalogram target value, the myoelectric target value, the electroencephalogram reference value and the myoelectric reference value.
2. The brain muscle signal evaluation method according to claim 1, wherein the acquiring of the rest state brain electrical spectrum data, the task state brain electrical spectrum data, the rest state myoelectrical spectrum data, and the task state myoelectrical spectrum data of the user includes:
collecting a resting state electroencephalogram signal, a task state electroencephalogram signal and a resting state electromyogram signal and a task state electromyogram signal of a user;
and preprocessing the rest state electroencephalogram signal, the task state electroencephalogram signal, the rest state electromyogram signal and the task state electromyogram signal to obtain rest state electroencephalogram spectrum data, task state electroencephalogram spectrum data, rest state electromyogram spectrum data and task state electromyogram spectrum data.
3. The brain-muscle signal evaluation method according to claim 2, wherein the preprocessing of the resting-state brain electrical signal, the task-state brain electrical signal, the resting-state electromyographic signal, and the task-state electromyographic signal includes:
performing Fourier transformation on the resting state electroencephalogram signal, the task state electroencephalogram signal, the resting state electromyogram signal and the task state electromyogram signal to obtain candidate resting state electroencephalogram spectrum data, candidate task state electroencephalogram spectrum data, candidate resting state electromyogram spectrum data and candidate task state electromyogram spectrum data;
And selecting the data in a preset frequency range from the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data to obtain the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data.
4. The brain muscle signal evaluation method according to claim 1, wherein the determining the first intra-group mean square error and the first inter-group mean square error based on the resting state electroencephalogram spectrum data and the task state electroencephalogram spectrum data includes:
determining a first average value of the rest state electroencephalogram frequency spectrum data, a second average value of the task state electroencephalogram frequency spectrum data, and a third average value of all data in the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data;
determining a first group of inner dispersion square sum and a first group of inter-dispersion square sum based on the first mean value, the second mean value, the third mean value, the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data;
determining a degree of freedom in the first group and a degree of freedom between the first group based on the rest state electroencephalogram spectral data and the total data of the task state electroencephalogram spectral data;
the first intra-set mean square error and the first inter-set mean square error are determined based on the first intra-set sum of squares of deviations, the first inter-set sum of squares of deviations, the first intra-set degree of freedom and the first inter-set degree of freedom.
5. The brain myosignal evaluation method according to claim 1, the determining a second intra-group mean square error and a second inter-group mean square error based on the rest state myoelectric spectrum data and task state myoelectric spectrum data, comprising:
determining a fourth mean value of the rest state myoelectric spectrum data, a fifth mean value of the task state myoelectric spectrum data, and a sixth mean value of all data in the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
determining a second intra-group sum of squares and a second inter-group sum of squares of dispersion based on the fourth mean, the fifth mean, the sixth mean, the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
determining a degree of freedom within the second group and a degree of freedom between the second group based on the rest state myoelectric spectrum data and the total data of the task state myoelectric spectrum data;
the second intra-set mean square error and the second inter-set mean square error are determined based on the second intra-set sum of squares of deviations, the second inter-set sum of squares of deviations, the second intra-set degree of freedom and the second inter-set degree of freedom.
6. The brain-muscle signal evaluation method according to claim 1, the determining the evaluation result of the user based on the brain-electrical target value, the myoelectricity target value, the brain-electrical reference value, and the myoelectricity reference value, comprising:
Determining an electroencephalogram evaluation result based on the electroencephalogram target value and the electroencephalogram reference value;
determining an myoelectricity evaluation result based on the myoelectricity target value and the myoelectricity reference value;
if the electroencephalogram evaluation result and the myoelectricity evaluation result are both positive, determining that the evaluation result of the user is light;
if one of the electroencephalogram evaluation result and the myoelectricity evaluation result is positive and the other is negative, determining that the evaluation result of the user is medium;
and if the electroencephalogram evaluation result and the myoelectricity evaluation result are both negative, determining that the evaluation result of the user is heavy.
7. The brain muscle signal evaluation method according to claim 6, further comprising, after the determining of the brain electrical evaluation result and the myoelectrical evaluation result:
if the electroencephalogram evaluation result is positive, a first prompt tone is sent to a user;
if the electroencephalogram evaluation result is negative, performing electric stimulation on a preset brain area of a user;
if the myoelectricity evaluation result is positive, a second prompt tone is sent to the user;
if the myoelectricity evaluation result is negative, performing electric stimulation on a preset muscle position of a user;
and retraining and evaluating the user.
8. A brain muscle signal evaluation device, the device comprising:
The acquisition module is used for acquiring resting state electroencephalogram frequency spectrum data, task state electroencephalogram frequency spectrum data, resting state myoelectric frequency spectrum data and task state myoelectric frequency spectrum data of a user;
the calculation module is used for determining a first group of internal mean square deviations and a first group of inter-mean square deviations based on the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data, and determining a second group of internal mean square deviations and a second group of inter-mean square deviations based on the rest state myoelectric frequency spectrum data and the task state myoelectric frequency spectrum data;
the calculation module is further used for determining an electroencephalogram target value based on the first intra-group mean square error and the first inter-group mean square error and determining an myoelectric target value based on the second intra-group mean square error and the second inter-group mean square error;
the calculation module is further used for determining an electroencephalogram reference value and an myoelectricity reference value based on the rest state electroencephalogram frequency spectrum data, the task state electroencephalogram frequency spectrum data, the rest state myoelectricity frequency spectrum data and the task state myoelectricity frequency spectrum data;
the calculation module is further used for determining an evaluation result of the user based on the electroencephalogram target value, the myoelectricity target value, the electroencephalogram reference value and the myoelectricity reference value.
9. The brain muscle signal evaluation device according to claim 8, comprising:
The computing module is further used for determining a first average value of the rest state electroencephalogram frequency spectrum data, a second average value of the task state electroencephalogram frequency spectrum data and a third average value of all data in the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data;
the computing module is further used for determining a first group of inner dispersion square sum and a first group of inter-dispersion square sum based on the first mean value, the second mean value, the third mean value, the rest state electroencephalogram spectrum data and the task state electroencephalogram spectrum data;
the computing module is further used for determining the degree of freedom in the first group and the degree of freedom between the first group based on the total data of the rest state electroencephalogram frequency spectrum data and the task state electroencephalogram frequency spectrum data;
the computing module is further configured to determine a first intra-set mean square error and a first inter-set mean square error based on the first intra-set sum of squares of deviations, the first inter-set sum of squares of deviations, the first intra-set degree of freedom and the first inter-set degree of freedom.
10. The brain muscle signal evaluation device according to claim 8, comprising:
the computing module is further used for determining a fourth mean value of the rest state myoelectric spectrum data, a fifth mean value of the task state myoelectric spectrum data, and a sixth mean value of all data in the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
The computing module is further used for determining a second intra-group dispersion square sum and a second inter-group dispersion square sum based on the fourth mean value, the fifth mean value, the sixth mean value, the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
the computing module is further used for determining the degree of freedom in the second group and the degree of freedom between the second group based on the total data of the rest state myoelectric spectrum data and the task state myoelectric spectrum data;
the computing module is further configured to determine a second intra-set mean square error and a second inter-set mean square error based on the second intra-set sum of squares of deviations, the second inter-set sum of squares of deviations, the second intra-set degree of freedom, and the second inter-set degree of freedom.
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