RU2671869C1 - Method and system for determining development capacity of higher mental functions and human skills by means of neurometry - Google Patents

Abstract

FIELD: medicine.SUBSTANCE: group of inventions relates to medical diagnostics, the method and system for determining the potential for the development of higher mental functions and skills of a person. Carry out the following stages of the method. (A) – presenting loads to the user containing tasks to be performed by the user. (B) – registering at least one signal of the bioelectric activity of the user's brain in different states, corresponding to the user performing loads related to the studied higher mental functions. (C) – marking of at least one registered signal of the bioelectrical activity of the brain with signs of the user performing the load at the moment, as well as marking the type of load imposed on a user, or marking the absence of a user performing a shown load. (D) – verifying at least one registered signal by the criterion of sufficiency, the criterion of purity and the criterion of sensitivity. (E) – smoothing of at least one registered signal by the moving average method for a given depth of epoch to determine the average characteristics of the signal and the user's brain in each of the states of performance of the loads imposed on the user. (F) – calculating the deviations of the average characteristics of the signal and the user's brain in each of the states of the user’s performance of shown loads from the characteristics of the state of quiet wakefulness defined in (E). (G) – providing the user with the aforementioned calculated deviations of the average characteristics in (F) in the form of diagrams of values along the edges of the intellect. (H) – calculating and storing a matrix of coefficients of average characteristics for users with reliably confirmed alternative method of success in one or several edges of intelligence. (I) – using matrix (H) in the calculations (F) as an element of determining the scalar distance. System contains modules that allow you to perform the above techniques using the mathematical calculations of the neurometrics given in the claims.EFFECT: group of inventions provides high accuracy and speed in determining the potential of higher mental functions and skills of a person.9 cl, 9 dwg

Description

FIELD OF TECHNOLOGY

The invention relates to the field of computer technology and the satisfaction of human needs, including psychology and neuropsychology, and can be used to individually determine the most successful areas of development of human abilities, choose an effective method of training in accordance with the characteristics of the brain, as well as help specialists in the fields of profiling and career guidance of adults, children and adolescents as an objective diagnostic tool.

BACKGROUND

A known method for determining the functional state of the brain (see RU 2565372, publ. 10.06.2013), based on recording the delay time of the appearance of the trace of the second photostimulation signal after the start of stimulation with the first signal. The main disadvantage of this method is the limited applicability, since this method can only be used to diagnose only the fact of visual agnosia, and the visual load proposed by the mentioned method cannot be used to diagnose other higher mental functions (HF), such as speech or spatial memory.

Also known is a method of assessing the functional state of the central nervous system (see RU 2039524, publ. 07/20/1995), which determines the ratio of frequency indices and the ratios of average amplitudes in the EEG. The main disadvantages of this method are the limited results obtained with data only on the functional state (health) of the central nervous system without the possibility of studying individual higher mental functions.

There is also a known method of formulating a new psychometric tool, an assessment questionnaire (see US 20150079578, published March 19, 2015), in which EEG signals are recorded and analyzed, and in this method, mapping (mapping) of brain waves in users is carried out in order to determine levels the intensity of specific patterns of brain waves in areas and between areas with known functions when performing tasks related to specific areas of the brain, and research and development of tables neurological function for specific areas of the cerebral cortex and the binding of these functions with the data of brain mapping. The main disadvantages of this method are (1) the use of reference tables for evaluating the data obtained during testing, that is, comparing the tested individual with the norm, and (2) the dependence of the result on the level of skill development at the time of testing. The combination of (1) and (2) makes this method inapplicable for assessing the potential for the future development of higher mental functions of a person and his abilities beyond the previously acquired skills and abilities, which is a key issue of profiling (children and adolescents) and career counseling (adolescents and adults) .

Also known is a method of psychological analysis and a device for its implementation (options) (see RU presenting to a subject a number of 2319444, published March 20, 2008), including tasks and tests and measuring and recording physiological parameters, and the subject is presented with projective stimuli on a personal computer screen methods, questions and statements, united by the diagnostic scenario, and the test subject for each stimulus by drawing a hand with a touch device for entering information into a personal computer draws a performance the task and gives answers, while in the process of drawing and presenting answers to the subjects, they automatically take and process one or more electrophysiological signals that are variable to the psychoemotional response of the test person to stimuli, and at least one signal characterizing the movement of the arm with a touch input device personal computer, and the results obtained are presented in tables and graphs characterizing the psychological portrait of the subject. One of the disadvantages of this method is the need to analyze user responses in addition to electrophysiological signals that are variable to the user's psycho-emotional response to stimuli. Another disadvantage of this method is the determination of the psychological portrait (profile of semantic and physiological significance) of the user, which does not allow to evaluate the applied areas of abilities (speech, mathematics, sports and craft, creativity, music) of the user. Another disadvantage of this method is the need for the user for each incentive to draw the execution of the task and presenting answers by moving the hand with a touch device for entering information into a personal computer. The proposed system and method can overcome at least part of the above disadvantages or all of the above disadvantages, as well as realize the advantages of this technical solution, as described in the framework of this technical solution.

ESSENCE OF TECHNICAL SOLUTION

The technical result achieved by the invention consists in increasing the accuracy and speed of determining the development potential of higher mental functions and human skills.

According to one implementation option, a method is proposed for determining the development potential of higher mental functions and human skills, which includes: (A) presenting to the user loads containing tasks for the user to perform; (B) registration of at least one signal of bioelectric activity of the brain of the user in various conditions corresponding to the user's performance of loads related to the studied higher mental functions; (C) marking at least one registered signal of the bioelectrical activity of the brain with signs of a load being performed at a given moment by the user, as well as marking the type of load presented to the user, or marking the user not performing the load; (D) checking at least one registered signal against a sufficiency criterion, a purity criterion, and a sensitivity criterion; (E) smoothing at least one registered signal by the moving average method at a given epoch depth to determine the average characteristics of the user's signal and brain in each of the execution states of the loads presented to the user; (E) calculating the deviations of the average signal and brain characteristics of the user defined in (E) in each of the states of the user performing the displayed loads from the characteristics of the state of calm wakefulness; (G) providing the user with the aforementioned calculated deviations of the average characteristics in (E) in the form of diagrams of values along the edges of the intellect; (H) calculating and storing the matrix of coefficients of average characteristics for users with reliably confirmed by an alternative method success in one or more facets of intelligence; (I) the use of the matrix (3) in the calculations (E) as an element of determining the scalar distance.

In one particular embodiment, the load presented to the user comprises at least one task to the user.

In one particular embodiment, the type of load presented to the user is a verbal-linguistic type of load or a logical-mathematical type of load, or a musical type of load, or a spatial type of load, or a bodily-kinesthetic type of load, or an interpersonal type of load, or an intrapersonal type of load.

In one particular embodiment, the loads presented to a user include sets of audio tests, video tests, graphic tests, text tests, speech tests and supporting materials related to verbal-linguistic types of loads or logical-mathematical types of loads, or musical types of loads, or spatial types of loads , or bodily-kinesthetic types of load, or interpersonal types of load, or intrapersonal types of load and, in a particular case, corresponding to age groups Users who e.g., age groups of users in the range from 3 years to 7 years, 7 years to 11 years, from 11 years to 13 years, from 13 years to 15 years, from 15 years to 17 years, from 18 years and older.

In one particular embodiment, the aforementioned marking of the signals of the bioelectric activity of the brain is carried out simultaneously with the registration of signals of the bioelectric activity of the brain of the user.

In one particular embodiment, the marking includes marking the end of the presentation of the load to the user, marking the beginning of the presentation of the set of loads to the user and / or marking the end of the user performing the set of loads.

In one particular embodiment, the sufficiency criterion determines the sufficiency of the recorded data, and loads of each type are registered at least a specified amount of time.

In one particular embodiment, the amount of time is set by the operator and / or automatically by the control and marking module or the control module and / or marking module, said modules being software.

In one particular embodiment, the purity criterion determines the purity of the recorded data, and the number of vectors containing only zero values is less than the set purity threshold.

In one particular embodiment, the purity threshold is set by the operator and / or automatically by a monitoring and marking module or a monitoring and / or marking module, said modules being software.

In one particular embodiment, the sensitivity criterion determines the sensitivity of the data, with a standardly defined scalar distance between the data vectors corresponding to the moments of the user performing the load and the vectors corresponding to the moments of calm wakefulness, less than the set threshold sensitivity value.

In one particular embodiment, the threshold value of sensitivity is set by the operator and / or automatically by a monitoring and marking module or a monitoring and / or marking module, said modules being software.

In one particular embodiment, the resultant signal smoothing by the moving average method is a set of values that is an instantaneous neurometry of higher mental functions.

, причем j является индексом суммирования внешнего операнда знаменателя и принимает значения от нуля до заданной глубины эпохи N; М является количеством каналов, по крайней мере, одного сигнала или двух сигналов; к является количеством количество типов нагрузок, в частном случае заданий, которые содержат нагрузки;

является пространством векторов действительных чисел размерности М; Т является временем предъявления нагрузок; N является глубиной сглаживания, причем N<Т (N меньше Т); i принадлежит интервалу времени от 0 до Т; μ_i определяется как

, если в момент времени i пользователь выполнил задание, содержащееся в нагрузке k; где

является силой сигнала в момент времени t=i-j и

является общей силой сигналов в интервале от i-N до i.In one particular embodiment, the instantaneous neurometric of higher mental functions μ _i at time i and the resting neurometric μ ⁰ , which is a normalizing vector corresponding to instant neurometric in the user's functional state “calm awake, eyes open”, are calculated by the formula:

wherein j is the summation index of the external operand of the denominator and takes values from zero to a given depth of the epoch N; M is the number of channels of at least one signal or two signals; k is the number of the number of types of loads, in the particular case of tasks that contain loads;

is the space of vectors of real numbers of dimension M; T is the time of presentation of loads; N is the smoothing depth, with N <T (N is less than T); i belongs to a time interval from 0 to T; μ _i is defined as

if at time i the user completed the task contained in the load k; Where

is the signal strength at time t = ij and

is the total signal strength in the range from iN to i.

In one particular embodiment, the set of instantaneous neurometry μ _i is normalized to the resting neurometric μ ⁰ .

In one particular embodiment, the neurometry of the higher mental function (Ω) is calculated, which is the result of calculating the average scalar distance over the entire recording time between the instantaneous neurometrics and the resting neurometry, and the final value is the neurometry of the higher mental function.

In one particular embodiment, the final value of the neurometric of higher mental function takes values from zero to unity.

для любой k принадлежащей диапазону значений от 1 до количества предъявляемых нагрузок K, где (μ^k, μ⁰) является стандартно определенным скалярным произведением.In one particular embodiment of the neurometric implementation of the k-th higher mental function is calculated by the formula

for any k belonging to the range of values from 1 to the number of presented loads K, where (μ ^k , μ ⁰ ) is a standardly defined scalar product.

In one of the private options for implementation, the marking of the signals of bioelectric activity of the brain is carried out by the operator.

In one particular embodiment, the marking of brain bioelectric activity signals is carried out automatically by a control and marking module or a control module and / or marking module.

In one particular embodiment, the mentioned matrix of coefficients of average characteristics is defined as the average normal expected deviation at any time of the user's realization of the highest mental function of instantaneous neurometry from resting neurometry, and the obtained value of the higher mental function is a neurometric of higher mental function, and in this way defined space (the neurometric of higher mental function belongs to the range of values from zero to unity) A function has a greater development potential when its neurometric higher psychic function tends to unity, and a lower potential when its neurometric higher psychic function tends to zero

According to another implementation option, a system is proposed for determining the development potential of higher mental functions and human skills, the system comprising: a registration module, in which, by registering, at least one sensor connected with the recording module, at least one bioelectric signal activity of the user's brain in various conditions corresponding to the user's performance of the loads imposed on him, relating to the higher mental functions being studied iyam; a primary data processing module in which: marking of at least one registered signal of brain bioelectric activity with signs of a load being performed by a user at a given moment, as well as marking the type of load presented to the user, or marking the user not performing the load; checking at least one registered signal according to the sufficiency criterion, the purity criterion, and the sensitivity criterion; smoothing at least one registered signal by the moving average method to a given epoch depth to determine the average characteristics of the user's signal and brain in each of the execution states of the loads presented to the user; a secondary data processing module in which: a) calculation of deviations of average characteristics in each of the states of execution by the user of the presented loads from the characteristics of the state of calm wakefulness; b) the calculation and storage of the matrix of coefficients of average characteristics for users with reliably confirmed by an alternative method success in one or more facets of intelligence; c) using the matrix of coefficients of average characteristics calculated in b) to calculate the deviations of average characteristics in a) as an element of determining the scalar distance; a module for providing results to the user of said calculated deviations of average characteristics in the form of diagrams of values along the edges of intelligence.

In one particular embodiment, the load presented to the user comprises at least one task to the user.

In one particular embodiment, the type of load presented to the user is a verbal-linguistic type of load or a logical-mathematical type of load, or a musical type of load, or a spatial type of load, or a bodily-kinesthetic type of load, or an interpersonal type of load, or an intrapersonal type of load.

In one particular embodiment, the loads presented to the user comprise sets of audio tasks, video tasks, graphic tasks, text tasks, speech tasks and auxiliary materials related to verbal-linguistic types of loads or logical-mathematical types of loads, or musical types of loads, or spatial types of load, or bodily-kinesthetic types of load, or interpersonal types of load, or intrapersonal types of load, and, in a particular case, corresponding to age groups Upp users, for example, age groups of users in the range from 3 years to 7 years, from 7 years to 11 years, from 11 years to 13 years, from 13 years to 15 years, from 15 years to 17 years, from 18 years and older

In one particular embodiment, the marking of brain bioelectric activity signals is carried out simultaneously with the registration of bioelectrical activity signals of the brain of the user.

In one particular embodiment, the marking includes marking the end of the presentation of the load to the user, marking the beginning of the presentation of the set of loads to the user and / or marking the end of the user performing the set of loads.

In one particular embodiment, the sufficiency criterion determines the sufficiency of the recorded data, and loads of each type are registered at least a specified amount of time.

In one particular embodiment, the amount of time is set by the operator and / or automatically by the control and marking module or the control module and / or marking module, said modules being software.

In one particular embodiment, the purity criterion determines the purity of the recorded data, and the number of vectors containing only zero values is less than the set purity threshold.

In one particular embodiment, the purity threshold is set by the operator and / or automatically by a monitoring and marking module or a monitoring and / or marking module, said modules being software.

In one particular embodiment, the sensitivity criterion determines the sensitivity of the data, with a standardly defined scalar distance between the data vectors corresponding to the moments of the user performing the load and the vectors corresponding to the moments of calm wakefulness, less than the set threshold sensitivity value.

In one particular embodiment, the threshold value of sensitivity is set by the operator and / or automatically by a monitoring and marking module or a monitoring and / or marking module, said modules being software.

In one particular embodiment, the resultant signal smoothing by the moving average method is a set of values that is an instantaneous neurometry of higher mental functions.

, причем j является индексом суммирования внешнего операнда знаменателя и принимает значения от нуля до заданной глубины эпохи N; М является количеством каналов, по крайней мере, одного сигнала или двух сигналов; k является количеством количество типов нагрузок, в частном случае заданий, которые содержат нагрузки;

является пространством векторов действительных чисел размерности М; Т является временем предъявления нагрузок; N является глубиной сглаживания, причем N<Т (N меньше Т); i принадлежит интервалу времени от 0 до Т; μ_i определяется как

, если в момент времени i пользователь выполнил задание, содержащееся в нагрузке k; где

является силой сигнала в момент времени t=i-j и

является общей силой сигналов в интервале от i-N до i.In one particular embodiment, the instantaneous neurometry of higher mental functions μ _i at the time moment and the resting neurometric μ ⁰ , which is a normalizing vector corresponding to instant neurometry in the user's functional state “calm awake, eyes open”, are calculated by the formula:

wherein j is the summation index of the external operand of the denominator and takes values from zero to a given depth of the epoch N; M is the number of channels of at least one signal or two signals; k is the number of the number of types of loads, in the particular case of tasks that contain loads;

is the space of vectors of real numbers of dimension M; T is the time of presentation of loads; N is the smoothing depth, with N <T (N is less than T); i belongs to a time interval from 0 to T; μ _i is defined as

if at time i the user completed the task contained in the load k; Where

is the signal strength at time t = ij and

is the total signal strength in the range from iN to i.

In one particular embodiment, the set of instantaneous neurometry μ _i is normalized to the resting neurometric μ ⁰ .

In one particular embodiment, the neurometry of the higher mental function is calculated, which is the result of calculating the average scalar distance over the entire recording time between the instantaneous neurometrics and the resting neurometry, and the final value is the neurometry of the higher mental function.

In one particular embodiment, the final value of the neurometric of higher mental function takes values from zero to unity.

для любой k принадлежащей диапазону значений от 1 до количества предъявляемых нагрузок K, где (μ^k, μ⁰) является стандартно определенным скалярным произведением.In one particular embodiment of the neurometric implementation of the k-th higher mental function is calculated by the formula

for any k belonging to the range of values from 1 to the number of presented loads K, where (μ ^k , μ ⁰ ) is a standardly defined scalar product.

In one of the private options for implementation, the marking of the signals of bioelectric activity of the brain is carried out by the operator.

In one particular embodiment, the marking of brain bioelectric activity signals is carried out automatically by a control and marking module or a control module and / or marking module.

In one particular embodiment, the registration module is a neurocomputer interface (NKI) or an electroencephalograph, or a cardiomonitor, or an electrocardiograph, or a heart rate monitor, or a tonometer.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional objectives, features and advantages of this technical solution will be clear from reading the following description of the implementation of the technical solution with reference to the accompanying drawings, in which:

FIG. 1 illustrates an exemplary embodiment of a system that implements the method described in the framework of the present invention;

FIG. 2 illustrates another exemplary embodiment of a system that implements the method described in the framework of the present invention;

FIG. 3 illustrates another exemplary embodiment of a system that implements the method described in the framework of the present invention;

FIG. 4 illustrates another exemplary embodiment of a system that implements the method described in the framework of the present invention;

FIG. 5 illustrates another exemplary embodiment of a system implementing the method described in the framework of the present invention;

FIG. 6 illustrates an exemplary flowchart of a process for determining the development potential of higher mental functions and skills of a person (user), according to one embodiment of the present invention;

FIG. 7 illustrates an exemplary embodiment of marking each time block of source data with a test operator for data relating to moments of test execution of certain tasks and data not related to such moments, according to one embodiment of the present invention;

FIG. 8 illustrates an example embodiment of presenting diagnostic results to a user, in accordance with one embodiment of the present invention.

FIG. 9 illustrates an example of a general purpose computer system.

The objects and features of the present invention, methods for achieving these objects and features will become apparent by reference to exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below, it can be embodied in various forms. The essence described in the description is nothing more than the specific details provided to assist the specialist in the field of technology in a comprehensive understanding of the invention, and the present invention is defined only in the scope of the attached claims.

Used in the present description of the invention, the terms "component", "element", "system", "module", "part", in particular, "component", and the like are used to refer to computer entities (for example, objects associated with a computer, computing entities), which can be hardware, in particular equipment (for example, a device, tool, apparatus, equipment, part of a device, in particular, a processor, microprocessor, printed circuit board, etc.), software (for example er, executable program code, compile the application, software module, part of the software and / or code, etc.) or firmware (firmware / firmware). So, for example, a component can be a process running / executing on a processor, a processor, an object, an executable file, a program, a function, a method, a library, a subprogram and / or a computing device (for example, a microcomputer or a computer) or a combination of software or hardware. As an illustration: as an application running on a server can be a component or module, so the server can be a component or module. At least one component may be located within the process. A component may reside on a single computing device (eg, a computer) and / or may be distributed between two or more computing devices. So, for example, in a particular case, an application (component) can be represented by a server component (server part) and a client component (client part). In the particular case, the client component is installed on at least one computing device, and the server component is installed on the second computing device, from which, in the particular case, control and / or configuration of the first computing device (and / or its component components / parts.

In FIG. 1 illustrates one exemplary embodiment of a system that implements a method (or at least one part of the present method) described in the framework of the present invention. In the particular case of the invention, the described system comprises at least one sensor 117 for recording (recording) and, in the particular case, recording at least one signal, in particular, the signal (s) of the brain of the user 110, for example, in the data warehouse, through the registration module 119, as will be described in more detail below. In the particular case of the implementation of the present invention, said signal or signals (in particular, a bioelectric signal) contains data, in particular, indicators of bioelectric activity of the brain or any other activity of the user 110 and / or at least one of its body parts or user functions and / or body parts of the user, as will be described in more detail below. So, for example, the data contained in said signal (s) is data that, in a particular case, contains a bioelectric signal recorded (in a particular case, received) by at least one sensor (s) mentioned above and / or below ) It is worth noting that the registration (and recording) of the signal is carried out by at least one registration module 119 using at least one sensor 117, in the particular case during the implementation by the user of an arbitrary at least one higher mental function (VPF ) or in the process of the user’s state, which is the state of “awake, eyes open” (“calm wakefulness, eyes open”), or in the state of “calm wakefulness, eyes open”, sleep state, state of arousal, fatigue, anxiety in any other state, as described more fully hereinafter. It is worth noting that, in a particular case, the process of the user's realization of higher mental function (HFF) is the process of the user's brain working on the implementation of HFF. In a particular case, the user's brain "implements" at least one HMF, and the signal associated with this "implementation" is recorded.

Also, in the particular case of the implementation of the present invention, the state of "awake, eyes open" corresponds to the alleged absence of brain reactions (in particular, the absence of signals characteristic of HMF) of the user to external cognitive excitations, for example, when the situation does not change (the environment), there is no noise , lack of conversation, the user does not deliberately look at anything (does not focus on anything).

The higher mental functions described in the present invention (English higher mental functions, highly mental functions) are mental processes, in a particular case, social in their origin, mediated in structure, arbitrary in the nature of regulation and, in a particular case, systemically related to each other . In a particular case, higher mental functions are cultural, not natural mental processes, determined not by genetics, but by society and human culture. Examples of functions characterizing such HMF are, for example:

- social in origin (not something innate, but processes that are formed in vivo under the direct influence of culture (for example, society, family, school, etc.), and the main mechanism of this function is internalization, i.e. internalization of the external inward;

- mediated by structure, the cultural implementation of which is an internal instrument, moreover, such functions include speech, as well as the idea that culture can be accepted and understood (understood);

- arbitrary regulation by nature, when the user (person) can consciously control them.

It is worth noting that signals can also be signals, in particular, which are characteristics of the user's psycho-emotional reaction to the presented loads, which are described in more detail below. Also, the signals mentioned may be signals detected by any known sensors. So, for example, such a registered signal can be a signal containing various biometric data (in particular, parameters, characteristics, etc.) of a person, for example, pulse, pressure, electric fields generated during the work of the user's heart. The registration of these signals can be carried out using sensors 117 associated with at least one recording module (recording device, recording device, recording module) 119, in particular, being (or implemented) a neurocomputer interface (NKI), electroencephalograph, cardiomonitor , electrocardiograph, heart rate monitor, blood pressure monitor and others. It is worth noting that at least one sensor may be part of a device (in the particular case, device 115) or may be associated with such a device 115, as described in the framework of the present invention.

It should be noted that the loads presented to the user 110 may be visual loads, for example, presented to the user on at least one storage medium. Also, loads may be presented to user 110 by means of display module 475 (FIG. 4). Loads can also be presented to the user 110 by the operator 120, including through various devices (means, methods, modules, etc.), as described in the framework of the present invention. Also, the mentioned devices (means, methods, modules, etc.) can be means and methods of transmitting information from the operator 120 to the user 110 and / or from any device and / or module, component, means, etc. (for example, a computing device (328, FIG. 3, 328A, FIG. 4, 328B, FIG. 4), device 115, module 527, etc.) to user 110.

It is worth noting that the mentioned loads can be, for example, verbal-linguistic load, logical-mathematical load, musical load, spatial load, bodily-kinesthetic load, interpersonal load, intrapersonal load, as well as a state of calm wakefulness, eyes are open (which also is a type of load), etc. It should be noted that the load, in the particular case of the implementation of the present invention, can be a task (in the particular case, a test) that the user must perform and which can be presented to the user by the operator 120 and / or the load module, as described in the framework of the present invention.

In a particular case, the verbal-linguistic load is a criterion that determines the level of speech skills (speech) (also being verbal-linguistic skills, speech skills), in the particular case, the ability to speak, the user 110. So, for example, the user 110 within of the present invention can be presented verbal-linguistic load, which may include at least one task presented to the user, as described in the framework of the present invention. Such a task may include contacting user 110, for example, including part of the task, for example, "Help me remember the words. I will tell you the words, and you will remind me of them later." After which the user is presented with a few words to remember, for example, "house", "forest", "table", "ringing", "bass", etc. After presenting the user 110 tasks (in the particular case, which is the load), the user is invited to say the memorized words, for example, "Say the words that you remember." Also, a verbal-linguistic load may be presented to user 110 in the form of a task, which includes presenting the text to the user (by reading it by the operator 120 and / or by displaying by the display module 475, and / or by demonstrating on a sheet of paper, etc.). After reading the text, the user is prompted to answer a question related to the presented text. Also, the verbal-linguistic load may contain a task to the user 110 with a request to continue the phrase, sentence, etc. Also, a verbal-linguistic load can be a task in which the user is shown an image containing an object related to an event. The user must answer a question related to what is displayed on the image, for example, what the user 110 sees in the image, what kind of image it is, and may also include the need for the user to answer questions related to the qualities and / or characteristics of the objects depicted, to describe what is shown in the image, etc.

In the particular case, the logical-mathematical load is a criterion that determines the level of skills in counting and logic, in the particular case, the ability to math (a particular case of which is the ability to count and logic), user 110. Also, the user 110 may be presented with a logical-mathematical load, which may include at least one task. In the particular case, the task of the logical and mathematical load is a task on the account and logic. So, for example, such a task may include presenting to the user an image that simultaneously displays a different number of objects of various types, for example, five apples, four oranges, eight pears, etc. The user may be asked to count the number of objects of a particular type and / or may be asked to compare which objects are larger, etc. Also, setting the logical-mathematical load may include presenting to the user 110 related words spaced in space (for example, on a sheet of paper, on an image displayed by the display module 475, etc.), and the user 110 must determine such a relationship. For example, the user may be presented with the words spoon, egg, porridge, shell, stone, plum, etc., where related objects are an egg and shell, spoon and cereal, stone and plum. It is worth noting that the user 110 may be offered an example of such a relationship, for example, a spoon is associated with porridge, because porridge is eaten with a spoon. Also, the task may include presenting to the user an image on which several objects of one type and at least one object of another type are displayed simultaneously, moreover, the user 110 is asked to determine at least one different from objects of another type. In the particular case, the user 110 is invited to complete the task of determining the excess object, in particular, sets of qualities that are different from other objects, properties of such objects, etc.

In the particular case, the musical load is a criterion that determines the level of music and rhythm, in the particular case, the ability to music and rhythm, the user 110. It should be noted that in the particular case, the score as a result includes a generalized assessment of the potential of the ability in / to music (particular a case of which are abilities in the musical-rhythmic field of user practice). The user 110 may be presented with a musical load, which may include at least one task. So, for example, such a task may include presenting to the user a task in the form of audio loads (in particular, containing audio tests, audio tasks). Such loads (in the particular case, tasks) may include the reproduction of sounds, music, etc. So, for example, the user can play the sound of one note with the request of the user to repeat the played note with a musical instrument, or by the user 110 indicating the played note when playing several notes in a row. Also, such a task may include reproducing a rhythm to user 110 in order to repeat it.

In the particular case, the spatial load is a criterion that determines the level of creativity, in the particular case, the creative ability of the user 110. Thus, the user 110 may be presented with a spatial load, which may include at least one task. In the particular case, such a task may include presenting to the user an image with several objects displayed that intersect with each other, in particular in the same plane, the user 110 having to answer which objects are displayed on the image. Also, such a task may include presenting to the user 110 at least two images that are different from each other, and the user must determine how the image data differs and / or what is the main difference between these two images. Also, such a task may include presenting the image of the clock without hands to the user 110, and the user must perform the task of determining the position, for example, the minute hand of the clock, if the user was informed of one of the options for the estimated time on the clock. Also, the user may be prompted to determine the time of day from a given time on the clock. Also, this type of task may include presenting to the user 110 several images with the displayed seasons so that the user determines what time of the year is shown in the figure, or so that the user 110 answers at what time of the year he has his birthday, etc.

In a particular case, the bodily-kinesthetic load is a criterion that determines the level of “movement”, in a particular case, the ability to “move” (in particular, sport and craft), the user 110. It is worth noting that in a particular case, the ability to own the body by the user equally (simultaneously) significant for sports and crafts, such as macrame, surgery, dance, etc. Also, a body-kinesthetic load may be presented to user 110, which may include at least one task. In the particular case, such a task may include presenting to the user an image containing, for example, any geometric figure, an object folded from the fingers (and) of the figure (s), etc., which (which) the user 110 must reproduce. The user 110 can reproduce the depicted figures, objects, etc., in particular, draw them (for example, on a piece of paper, with a hand or finger in the air, on the screen of a touch screen, etc.) or by folding the fingers (hands) etc .. Body-kinesthetic load may include an oral task to the user 110 to draw some kind of geometric figure, etc. Also, the body-kinesthetic load may include presenting to the user 110 an image (visually by an operator or by means of a display module 475, on a sheet of paper, etc.) of a figure folded from the fingers of at least one hand so that the user 110 is folded with fingers at least one hand, the same figure. In the particular case, the user 110 repeats the folded figures from the operator 120 or presented to him by means of the display module 475, on a sheet of paper, etc. In the particular case of the implementation of the present image, the user must repeat the presented figures, folded from the fingers of one hand, with the fingers of his other hand. For example, the operator 120 adds the figures from the fingers of the left hand, and the user 110 adds the same figures with the fingers of the right hand.

In the particular case, interpersonal load is a criterion that determines the level of sociability (knowledge of people, 898 in FIG. 8) of the user 110, in the particular case, the ability to communicate, the user 110. For example, the user 110 may be presented with an interpersonal load, which can include at least one task. In the particular case, such a task may include presenting to the user an image containing an image of a situation in order to obtain an analysis of the situation from the user. So, for example, the task for the user may be to tell about what happened or is happening on the image, where the situation is, how the situation will end, to assume the age of the people in the image (if people are present on the image), etc. Also, an interpersonal load may include presenting to the user 110 several facial expressions in order to determine which expression refers to joy, anger, surprise, joy, crying, etc., as well as determine the age of the person whose facial expression was presented to user 110, suggest why the person is crying etc.

In the particular case, the intrapersonal load is a criterion that determines the level of the “inner self” (self-knowledge, 899 in FIG. 8) of the user 110. So, for example, the user 110 may be presented with an intrapersonal load, which may include at least one the task. In the particular case, such an interpersonal load task may include a conversation between the operator 120 and the user 110. In such a conversation, the operator can ask the user various questions that the user 110 must answer. Examples of such questions may be:

- Does it happen that you (also) are sad?

- What do you do well? The best?

- What turns out badly? Worst of all?

- Why are you upset?

- Why are you happy?

- Who's your best friend

- You help? Are you asking for help?

- What do you want to become?

- What is most important in you, how are you different?

- Will you correct the teacher’s mistake in the lesson? etc.

In the particular case of the implementation of the present invention, the user is the test, i.e. user (person), determination of the development potential of higher mental functions and skills, abilities (in particular, potential abilities), etc. which is carried out by the method (or at least one part of the method) described in the framework of the present invention.

In the particular case of the implementation of the present invention on the user's head 110 or in the immediate vicinity of the surface of the user's head (for example, at a distance of from 0.0001 millimeters to several centimeters, in particular up to 9-25 centimeters or more), at least one a sensor 117 associated with at least one recording module 119 described below, for example, an NKI (and / or at least one other device or module, means or system, in particular, determining, recording , brabotku and / or storage of the measurement results for at least one of the recorded signal, in particular, the user biometric parameters).

Further, for the purpose of describing the present invention, the registration module (or a set of registration modules) 119 will be considered as an example of NKI (in particular, NKI-module), however, it should be understood that at least one registration module can be used instead of NKI, which be a device (module, means, component, etc.) processing and / or storage of data (signal) received from sensors 117.

It is worth noting that the data from the sensor (s) are transmitted to at least one recording module 119. It is worth noting that in particular cases of the implementation of the present invention, the number of sensors 117 may vary from one to several tens, hundreds, etc. It is also worth noting that the sensor (or, in the particular case of sensors) 117 can be combined with a registration module, in particular with an NKI (and / or at least one other device, means, module or system mentioned above and described in the framework of the present invention) 119 in one case, for example, are implemented as a single device 115. Also, such a mentioned device 115 may be collapsible and / or consist of several parts, each of which may have its own case and / or be part of the case devices 115. Also, around the edge at least one part of device 115 (for example, a sensor (s), computing device 328, FIG. 3, computing device 1 328A (FIG. 4), computing device 2 328B (FIG. 4) and other computing devices, data storage ( 470, FIG. 4), communication module (s) 515 (FIG. 5) and / or any other devices, means, modules and / or systems described in the framework of the present invention, including those used for registration, processing and / or storage of user parameters 110) can not only be part of each other and / or complement other ug of each other, but can also be mobile relative to each other. So, for example, the mobility of the parts (in the particular case of the described modules), for example, devices (a) 115, with which they can be integral, can be used (implemented) to enable the position of the sensor (s) 117 or other parts of the device to be implemented 115 relative to the head (and also the surface of the head) of the user 110 and / or individual parts of the head (or any other at least one part of the body) of the user. In the particular case, a change in the position of the sensor (s) 117 (or any other part and / or module of the device 115, as well as associated with such a device 115 or any other described device and / or module) may include a change in the position of at least one sensor (s) 117 (including the module (modules, parts, etc.)) vertically and / or horizontally (in the particular case, in three-dimensional space) relative to at least one part of the user's body. It is worth noting that parts of the user's body can be the head, parts or parts of the head, parts of other parts or parts of the user’s body 110. Also, changing the position of the sensor (s) 117, etc. may include the ability to change the distance from the sensor (s) 117 to one of these parts or sections of the body of the user 110.

The computing devices described in the framework of the present invention can be various (including digital, analog, mechanical, combined, etc.) devices, in particular, but not limited to, electronic devices, personal devices, portable devices, stationary devices, including including smartphones, mobile devices, telephones, laptops, tablets, personal computers, desktop computers, all-in-one, servers, computer stand, including demonstration computer stand, computerized anny stand, the test stand, etc.

In the particular case of the implementation of the present invention, the data warehouse 470 (FIG. 4) may be a temporary data storage device (eg, Random Access Memory (RAM)), a permanent data storage, for example, (Programmable) Read Only Memory (ROM / ROM), including that implemented by at least one microcircuit or chipset, etc.

It is worth noting that the data warehouse 470, FIG. 4 may be an integral part (including a module) of at least one module (for example, a device, another module, component, etc. of the described system). In the particular case, the data warehouse is a device for storing information / data and can be implemented with at least one hard disk drive (HDD), solid-state drive (SSD, English . solid-state drive), a hybrid hard drive (SSHD, English solid-state hybrid drive), a storage area network (SHAN / SAN, English Storage Area Network), a network storage system / network storage (NAS, English Network Attached Storage) and / or any other device that allows at least writing to the device, reading from troystva and / or data storage device. The data in the data warehouse 470, FIG. 4 can be stored in any known format, for example, in a database (DB), for example, in the form of at least one table or a set of related or unrelated database tables. It is worth noting that at least one of the mentioned databases can be hierarchical, object, object-oriented, object-relational, relational, network and / or functional database, each of which can be centralized, concentrated, distributed, heterogeneous, homogeneous, fragmented (partitioned), replicated, spatial, temporal, spatio-temporal, cyclic, super-large database, etc., and for the management, creation and use of databases can be used Use This Criterion various database management systems (DBMS).

Also, the data in the data warehouse 470, FIG. 4 can be stored in at least one file, in particular, in the form of a text file, or the data can be stored in any at least one other currently known data / information storage format or in a data format invented later .

It should be noted that the position of at least one sensor 117 (and / or module, devices (a), part or parts of devices (a), etc.) relative to the user or part of the user's body and / or at least at least one point located (located) on one part of the body or body of the user 110, and / or at least one other device or sensor, and / or relative to at least one environmental object, as well as relative to devices (a) control devices (a) tracking, including devices (a), module (s) and systems (s ) tracking the position of the body parts of the user 110, can be noted (in the particular case, by applying a mark) and / or, in the particular case, recorded, etc. any of the known (or invented in the future) method.

So, for example, by the above-mentioned devices (in the particular case, implementing the modules described in the framework of the present invention), by means of which the relative position of at least one sensor, module, devices (a), part of devices (a) and t etc. may include cameras, infrared sensors, methods and devices, including tracking systems for the user's eyes, the user's gaze, in particular the direction of the user's gaze, etc.

Also, the said fixation can be the establishment of the location, including the mutual at least one sensor, as well as the module, device, part of the device, etc., and such an installation can be performed both by hardware and software. Said fixation (in the particular case, said establishment) and / or marking may include tracing (for example, along the contour) of at least one sensor 117 and / or device 115, for example, with a marker, pen, or by any other known methods. Also, a coloring agent (for example, washable paint) can be applied to the surface of sensors, for example, adjacent to the surface of the user, which after removal of the sensor and / or recording devices remains on the surface of the user, in particular on the skin of the user. It is also worth noting that the above methods may include placing at least one part of the user's body of various marks, for example, by means of a marker, by gluing various marks that allow you to set the location of the sensors and / or recording devices on the user. It is worth noting that the location of such marks (as well as the location of sensors 117 and / or registration modules 119, including NKI) can be saved by using different cameras. So, for example, the location of sensors and / or recording devices can be saved by creating a photograph and / or video recording of the user's image, in particular, in the process of registering a signal. Further, the described fixation, in the particular case in the form of photographs and / or video recordings, can be used to place sensors and / or recording devices when re-registering (and recording, including saving) the signal (and data that contains the signal) , for example, if control has not been passed, as described in more detail below.

In the particular case, the aforementioned arrangement of sensors 117 and / or registration modules 119 may be an orientation, including in three-dimensional space, for example, be coordinates in three-dimensional space. Also, in a particular case, the environment (the environment (of the user)) can be, for example, a tripod with which the sensor (s) 117 is connected or mounted, or a device that includes the sensor (s) 117, at least one registration module (for example, NKI) 119, computing devices 328 (FIG. 3), 328A (FIG. 4), 328B (FIG. 4), as well as the walls (s) of the room in which the described method is carried out or at least one part of it, or in which at least one device or module is located, which are parts described system, etc.

It is worth noting that such methods for determining the location of at least one sensor 117, a module and / or device associated with the sensor (s) 117 are, for example, cameras (webcams, cameras of mobile devices, cameras, video cameras, including infrared cameras, etc.).

It should be noted that tracking the position of the sensor (s) 117 can be carried out, as in the process of registering a signal, in particular, at least one bioelectric signal (or any other signal (s), including those described in the framework of the present invention), and before the start of the registration of the signal, as well as before or during a possible re-registration of the signal (for example, in the case when the control 630, FIG. 6) was not passed, as described in more detail below.

It is worth noting that in the particular case of the implementation of the present invention, said sensors (or at least one sensor) fit snugly against the surface of the user's head, in particular to the skin of the user's head. It is also worth noting that in order to increase the accuracy of the recorded signal, in some cases (for example, with insufficient signal power, low accuracy of the recorded parameters (in particular, after the processing described below related to HF), significant noise accompanying the signal, insufficient noise immunity of the sensors, or NKI, the absence or poor ability to remove (eliminate) interference in the recorded signal and / or determination of the signal, in the particular case relating to HFF), various means can be used for I provide stability, noise immunity, etc., for example, a conductive gel, saline, etc.

It is also worth noting that at least one sensor 117 can be connected to an NKI (and / or at least one other user state (characteristics) recording module 119, in particular, converted to the described signals) by any currently known time either invented in the future by a direct method or an indirect method. The aforementioned direct method of communication (including connection) may include, for example, communication via terminals and / or wires, soldering, riveting, etc. The indirect method of communication (including connection) mentioned above may include communication by means of intermediate devices, including various types of converters, in particular information converters (converters), in particular, data converters in analog form, digital form or in any other known form. So, such communication (in the particular case, connection) can be carried out using wired communication, wireless communication, through various types of connectors, wires, and other contact and / or contactless connections, etc.

Elements, in a particular case, components, components, modules, devices, etc. of the described system that implements the method described in the framework of the present invention can be interconnected (and with any other modules, devices and services that can receive information from the modules of the described system and / or transmit data to the described modules of the system) through a local area network ( LAN), the Internet (463, FIG. 4), (by means of) mobile / satellite communications and / or via any other type / method of wired communication (for example, via a USB interface, an RS-232 interface / COM port, etc. .d.) and / or wireless communications, for example, Bluetooth, Wi-Fi, mobile cellular (GSM), including 3G, 4G, LTE, in particular in the bands 850/900/1800/1900 MHz, satellite communications, trunking and ultra-low power data transmission channels forming complex wireless networks with mesh topology (ZigBee), etc.

It is worth noting that at least one part of the method described in the framework of the present invention (or the whole method) can be carried out on at least one computing device (or in at least one of the described modules), for example, server, mobile device, personal (computing) device, etc., including, after establishing the connection of such devices (or modules) with each other, for example, through at least one data transfer protocol, including network protocol, e.g. TCP / IP, HTTP / HTTPs (Hyper Text Transfer Protocol, Hypertext Transfer Protocol), FTP (File Transfer Protocol, File Transfer Protocol), POP3 (Post Office Protocol, standard mail connection protocol), SMTP (Simple Mail Transfer Protocol, protocol that defines a set of rules for sending mail), TELNET (remote access protocol), XMP (from the English Extensible Messaging and Presence Protocol - an extensible protocol for exchanging messages and information about presence, formerly known as Jabber - an open, XML-based protocol for instant messaging and presence information in near p mode real-time mode), DTN, etc., including protocols regulated by the NEE 802.15.4 and ZigBee standards, including existing add-ons, for example, specifications of top-level network protocols, in particular, the APS application level (from English application support sublayer), and the NWK network layer, using, in a particular case, the services of the lower layers, the MAC access control layer and the PHY physical layer, etc.

So, in the particular case, the server can be implemented by a web server and / or specialized computing device (in particular, a computer) and / or specialized equipment for performing service software on it (including services of certain tasks).

In one implementation of the present invention, at least one described signal from at least one sensor transmitted to at least one recording module, in particular an NKI, contains several components, in particular channels, for example, Channel 1, Channel 2, Channel 3, Channel 4, Channel 5, etc.), which will be described in more detail below.

In one implementation of the present invention, the described system may include a data display module (display means) 475. Such a display module 475 may include a monitor, a display (eg, a display of a computing device), a screen (eg, a television), an indicator, etc. It is worth noting that the display module 475 may include a touch screen that allows you to enter data by touching with your finger, pen (stylus).

The described system may also include various data input modules 455, in a particular case, implemented by information (data) input devices, for example, a keyboard, mouse, touchpad (touchpad / touchpad), trackball (trackball), tablet ( tablet, pad), including a graphic tablet and / or pen (stylus), microphone, camera, touch screens, voice recorder, etc., which may be an integral part of a computing device and / or may be connected (in a particular case, connected) with the described modules (for example, with the first module data processing 130 and / or the secondary data processing module 140, and / or the results presentation module 150, the display module 475, etc.) in various ways, for example, by wire and / or wireless methods. It is worth noting that in one embodiment of the present invention, the data input unit 455 may be part of a computing device (e.g., computing device 1 (328A)) or connected to such a computing device (e.g., computing device 2 (328B)). It is also worth noting that, in one embodiment of the present invention, the display module 475 may be part of a computing device (e.g., computing device 1 (328A)) or associated with such a computing device (e.g., computing device 2 (328B)).

In one of the implementations of the present invention, the module NKI and / or at least one of any other module registration and / or data processing, in particular, implemented by the registration module 119) may be a device 115 or be an integral part of the device 115.

It is worth noting that said data entry may be carried out by an operator 120, wherein the data entered may include at least control and / or marking, which are described in more detail below.

It is also worth noting that control and / or marking can be carried out by both the operator 120 and at least one module of the described system, for example, the control and marking module 320 (FIG. 3). Moreover, it is worth noting that the control and marking module can be two modules: marking module and control module. In the particular case of the implementation of the present invention, said monitoring and marking is carried out by the operator 120 by means of the monitoring and marking module 320. It should also be noted that the monitoring and marking module 320 (FIG. 3) may be part of the primary data processing module 130 or part of the secondary data processing module 140 .

It is worth noting that the loads described in the present invention can be presented to the user 110 by the operator 120 through the use of any storage medium known or invented later, as well as through various devices, for example, said display modules (in particular, implemented by devices, for example, by means of a display device, such as a projector, monitor, screen / display device, such as a personal computer, smartphone, etc.). It is worth noting that the presentation of the load can be carried out by means of a demonstration (by an operator 120 or in an automatic mode, for example, using a load module (load presentation module) 310, FIG. 3) to a user of drawings with loads, for example, on a sheet of paper, on the screen of a computing device , for example, the monitor of a personal computer, mobile device, projector, etc., by reading the loads to the user, reproducing the loads by means of a sound reproducing device, for example, via tion of the speakers connected to a computing device, jobs movement, etc.

In the particular case of the invention, the display module 475 may be part of a computing device (for example, 328, 328A, 328B) or may be part of device 115.

Said load module 310 may be part of a computing device, for example, computing device 1 328A. The load module 310 can be implemented as a software module, in particular, implemented by software (an application for a computing device, a script, instructions for a computing device, etc.) and carry out the presentation of loads to the user 110, for example, by displaying on the said computing device (328A, etc.), in particular by means of at least one display module 475, which is part of or associated with such a computing device th device. Also, the load module 310 can carry out the presentation of loads to the user 110 by reproducing previously recorded loads (through software and hardware, for example, a computing device and speakers associated with or including a part of such a computing device), in particular when such loads are audio loads . Also, the load module 310 can transmit (in the particular case, transfer) loads from the operator 120 to the user 110, for example, by transferring the loads pronounced by the operator to the microphone and then reproducing the means of reproducing sounds (in particular, audio), for example, by speakers. It is also worth noting that the load module 310 can broadcast the loads selected (or displayed) by the operator 120 to the user 110, for example, by sharing a camera (recording the loads presented by the operator 120) and a display module 475 that displays the loads to the user 110.

It is worth noting that the loads presented to the user (in the particular case, containing tests, tasks) can be presented to the user by the operator 120 either by means of the load module 310 (FIG. 3), or using such a load module 310 (FIG. 3). In the particular case of the implementation of the present invention, the loads may include audio tests (audio tasks), video tests (video tasks), graphic tests (graphic tasks), text tests (text tasks), speech tests (speech tasks) and auxiliary materials related to verbally -linguistic types of load or logical-mathematical types of load, or musical types of load, or spatial types of load, or bodily-kinesthetic types of load, or interpersonal types of load, or intrapersonal t load types and, in a particular case, corresponding to the age groups of the tested (users), for example, age groups (users) in the range from 3 years to 7 years, from 7 years to 11 years, from 11 years to 13 years, from 13 years to 15 years, from 15 years to 17 years, from 18 years and older. It is also worth noting that the tasks of the loads (in the particular case, tests) may differ from each other in complexity depending on the age groups to which such tasks (tests) are presented. So, for example, the age group from 3 years to 7 years may be presented with loads (in particular, tests) less difficult for the user to perform such an age group than loads presented to other age groups, the age of users in which is more than 7 years. So, for example, for users of the age group from 3 to 7 years, the simplest tasks (tests) of loads can be presented, and users of the age group from 18 years older can be presented with the most difficult tasks (tests) of loads. In the particular case of the implementation of the present invention, the choice of the presented loads (carried out by the operator 120 or the load module 310, or the operator by using the load module, in particular by using the software which is the load module 310 or part of the load module 310) for each age group is determined by the need to obtain the described signal satisfying the criterion of sensitivity, that is, in the particular case, upon presentation of such a load, said signal differs from c the state of calm wakefulness by an amount not less than the established threshold value (that is, in particular, it is quite difficult for the test person in this age group).

It is worth noting that the aforementioned auxiliary materials are materials that can be used, for example, by the operator 120 (in particular, when presenting loads to the user), or by the user 110 when responding to the loads presented to him. Examples of such auxiliary materials can be clay, a tactile screen for bodily-kinesthetic loads, a musical instrument for musical loads (audio loads), etc.

It is also worth noting that audio tests (audio tasks), in particular, which are part of audio loads, video tests (video tasks), in particular, which are part of video loads, graphic tests (graphic tasks), in particular, which are part of graphic loads, text tests (text tasks), in particular, which are part of the text loads, speech tests (speech tasks), in particular, which are part of the speech loads. For example, examples of test loads are: images presented to the user with information for the testee's story, texts for reading or retelling from memory by the user 110, anagrams, audio recordings (for example, 5-10 seconds) for remembering and recognition by re-presentation to (tested) users, addressed questions of the operator (or by means of a presentation made by the load module 310) about the personality and character of the (tested) user 110, speech tasks of the operator (or by means of a presentation made by m by loading module 310) draw certain (for example, as described in the framework of the present invention) objects (including figures, drawings, images, etc.) or reproduce a copy from the original (object), and others.

After receiving at least one signal by the registration module 119, in particular, the NKI, the signal (in particular, the signal data) can be processed by such registration module 119, as described in more detail below. Such signal processing may include eliminating signal noise, isolating signal channels (in particular, dividing the signal into channels), etc.

In the particular case of the implementation of the present invention, the registration module 119 performs registration through the associated with the recording module of at least one sensor 117, the signals of the bioelectric activity of the brain of the user in various conditions, corresponding to the user performing loads related to the studied higher mental functions, as described in the framework of the present invention.

Next, the signal from the registration module 119 is transmitted to at least one data processing module, for example, a primary data processing module 130 that performs primary data processing, respectively, as will be described later. Thus, in the primary data processing module 130, the signals of brain bioelectric activity can be marked with signs of a load being performed by a user at a given moment, as well as a mark of the type of load being presented to a user, or marking that a user has not completed a load, as described in the framework of the present invention. Also, in the primary data processing module 130, a recorded signal can be checked (checked) against a sufficiency criterion, a purity criterion, and a sensitivity criterion, as described in the framework of the present invention. It is worth noting that in the primary data processing module 130, the signal can be smoothed using the moving average method at a given epoch depth to determine the average characteristics of the user's signal and brain in each of the execution states of the loads presented to the user, as described in the framework of the present invention.

Further, after the primary data processing, in particular in the module 130, the processed signal (s) data is transmitted to the secondary data processing module 140, in which the secondary data processing is performed, as described in more detail below. Thus, in the secondary data processing module 140, the deviations of the average characteristics in each of the states of the user performing the displayed loads from the characteristics of the state of calm wakefulness can be calculated, as described in the framework of the present invention. Also, in the secondary data processing module 140, calculation and storage of a matrix of coefficients of average characteristics for users can be carried out with success in one or more faces of intelligence that is reliably confirmed by an alternative method, as described in the framework of the present invention. Also, in the secondary data processing module 140, the use of the previously calculated matrix of average characteristics coefficients for calculating the mentioned deviations of the average characteristics as an element of determining the scalar distance, as described in the framework of the present invention. It is also worth noting that the aforementioned storage of the matrix of coefficients of average characteristics can be carried out at least in one data warehouse 470, for example, by means of the secondary data processing module 140.

After secondary processing of the data in the module 140, the processed data can be transmitted to the module providing results 150. In the particular case of the implementation of the present invention, the module providing results can be, for example, at least one output device, for example, a printer, display module 475. In the particular case of the implementation of the present invention, the module providing results 150 provides the user with the results (in particular, deviations of the average characteristics to in each of the states of execution by the user of the presented loads from the characteristics of the state of calm wakefulness) in the form (in the form) of diagrams of values (and comments) along the edges of the intellect (for example, as shown in FIG. 8), as described in the framework of the present invention. In the particular case of the implementation of the present invention, the said comments may contain conclusions, for example, of the operator 120, based on the results shown by the user 110 (and / or) of the analysis and processing of the recorded signals, as described in the framework of the present invention.

Also, the data after processing by the secondary data processing module 140 or the data processing module 210 can be transmitted via wired or wireless communication to at least one computing device, for example, a server (on which data (signal) processing results can be collected) , user devices (smartphones, telephones, personal computers, etc.), to data warehouses 470, etc., from where the results of data processing can be obtained by user 110, user representatives, operator 120, etc. by other persons (or systems, including automatic data collection and processing systems) authorized to gain access to such data.

It is worth noting that in the particular case, the primary data processing module 130 and the secondary data processing module 140 can be combined into one data processing module 210 (FIG. 2) or can be parts of one data processing module, for example, data processing module 210 (FIG. 2). In the particular case of the invention, at least one data processing module, for example, a primary data processing module 130, a secondary data processing module 140, or a data processing module 210 (FIG. 2) can be located on at least one computing device, or can be implemented by at least one computing device or component (software and / or hardware components) of the computing device. So, for example, the primary data processing module 130 may be located on the computing device 1 (328A, FIG 4), the secondary data processing module 140 may be located on the computing device 2 328B, FIG 4 (or be implemented by such a computing device), the data processing module 210 can be located on the computing device 1 328A, FIG 4 (or be implemented by such a computing device) and / or on the computing device 2 328B, FIG 4 (or can be implemented by such a computing device) or be implemented in computing devices.

It is also worth noting that at least one module of the described system (for example, module 119, module 130, module 140, module 455, FIG. 4, module 475, FIG. 4) may be part of or be such a device 115 , or located on such a device 115.

In FIG. 2 shows another exemplary embodiment of a system that implements the method described in the framework of the present invention. In a particular case, the described system may include a data processing module 210. In a particular case, the data processing module 210 may include a primary data processing module 130 and a secondary data processing module 140. It should be noted that the described control and / or marking can be carried out by the operator 120 through interaction them with data processing module 210.

In FIG. 3 shows yet another exemplary embodiment of a system that implements the method described in the framework of the present invention. In the particular case, the described system may include a load module 310, a monitoring and marking module 320. Also, the described system may include a computing device on which the data processing module 210 may be implemented, in the particular case implemented by software (in particular, program code, application, part of the application, etc.).

In FIG. 4 shows yet another exemplary embodiment of a system that implements the method described in the framework of the present invention. In a particular case, the described system may include a computing device, for example, computing device 328A, on which primary data processing module 130, data input module 455, display module 475, and other modules of the described system can be implemented, and also include at least one data storage 470 (in the particular case, implemented by at least one hard disk drive 32, FIG. 9). Also, the described system may include a computing device 328B, on which a secondary data processing module 140 and a reporting module 150 may be implemented. It should be noted that the computing device, in particular computing device 328B, can be connected to data input module 455 (for example implemented by the keyboard 40, FIG. 9, a mouse-type manipulator 42, FIG. 9, a touch screen, etc.), for example, via an interface 46 (FIG. 9). Also, a computing device, in particular, computing device 328B and / or computing device 328A, may be associated with a display module 475, in particular, a monitor 47 (FIG. 9), and other modules of the described system. It should be noted that a computing device, in particular, computing device 328B and / or computing device 328A, may be associated with a data store 470, for example, implemented storage device 57 (FIG. 9).

It is also worth noting that computing device 328A and computing device 328B can be interconnected via the Internet (463), a local area network (LAN), a global computer network (GCS), etc.

In FIG. 5 shows another exemplary embodiment of a system that implements the method described in the framework of the present invention. In the particular case of implementation, the described system may include a device 115, which may include a display module 475, implemented by at least one screen (display, etc.), and one screen (including a touch screen) 525 can be turned into from the user 110, and the second screen 527 may be facing the user 110. In the particular case of the present invention, at least one signal registered by the registration module 119 can be displayed on the screen 525. Also, the screen 525 can be used called by operator 120 to perform the described control and / or labeling. In the particular case of the present invention, at least one load, in particular at least one load task, can be displayed on screen 527. Also, the device 115 may include a primary data processing module 130, a secondary data processing module 140, a data warehouse 470, a sensor (s) 117, a registration module 119. In FIG. 6 shows an exemplary flowchart of a process for determining the development potential of higher mental functions and skills of a person, according to one embodiment of the present invention. The process begins at step 610, in which the user 110 is presenting loads, as described in the framework of the present invention. As mentioned above, the presentation of loads to the user 110 can be carried out by the operator 120 or by automated means, devices, modules and / or methods (for example, the load module 310, in the particular case, implemented by a computing device, for example, 328A, including through the display module 475 , for example, the screen of computing device 328 or a display module associated with such a computing device or devices) with or without operator 120. For example, operator 120 may be presented l to the user 110 loads printed on paper (which is a special case of the storage medium), and the loads shown may also contain loads in audio form, that is, in the particular case, loads that the user 110 can perceive by the hearing organs of the user 110. Also, the operator may present the user with a load through a computing device (for example, computing device 328A or any other computing device, including one equipped with a display module 475), and presented e loads can include demonstration of loads (for example, through a display module, in particular, module 475) and / or playback of loads through audio modules (audio loads), (in particular, implemented devices capable of playing sounds, audio recordings, etc. note that the presentation of audio loads can be carried out by reproducing previously recorded loads and / or instructions of the operator 120, including the user 110, and / or other information, including service information formation that can be used in the process of signal registration (and / or signal processing). So, for example, such service information or operator’s instructions 120 may be at least one keyword, for example, “Load”, “Start”, “Start of load”, “End of load”, etc. Such overhead information or operator’s instructions 120, in particular keywords, can be automatically (and unambiguously) recognized by sensors of used modules (devices, tools, etc.) described within the framework of the present invention.

It is worth noting that the loads presented to the user 110, as described above, can include various types of loads (k, where k is from 1 to K), which are a particular case: the type of load is “calm awake, eyes open” (k = 0) , type of load verbal-linguistic (k = 1), type of load logical-mathematical (k = 2), type of load musical (k = 3), type of load spatial (k = 4), type of load bodily-kinesthetic (k = 5 ), the type of load is interpersonal (k = 6) and / or the type of load is intrapersonal (k = 7), etc.

It is worth noting that at least one load presented to user 110 may comprise a set of loads. So, for example, a set of loads can contain several loads of the same type of load, for example, two or more loads of verbal-linguistic type or logical-mathematical type of loads, etc. It is also worth noting that a set of loads can contain at least one load of one type and at least one load of another type.

In one implementation of the present invention, loads may be presented to user 110 as a series of loads, the series of loads may include at least one set of loads or at least one load of one type and / or at least one load of another type of loads. So, for example, a series of loads can include at least one load of the logical-mathematical type (logical-mathematical load) and / or at least one load of the musical type (musical type), etc.

It is worth noting that presenting to user 110 at least one set of loads or at least one series of loads can be interrupted (for example, by operator 120 and / or recording module 119, and / or load module 310, etc.) for example, in order to concentrate the attention of the user 110 (if the registration module 119 and / or the operator 120 established the fact that the user's attention is not focused on the load, in particular, is not focused on the task contained in the load presented to him). In the particular case, the user's concentration of attention is the return of the concentration of the user 110 to the loads presented to him, in particular, with the direct participation of the operator 120 and / or using the load module 310 of at least one computing device 328, etc., including at least one part of the described system, in particular, implemented by at least one part of the described system. The mentioned interruption may be accompanied by the presentation of loads to the user, the type of which differs from those currently presented to him, these may be relaxing loads, demonstration of images on abstract topics (nature, relaxation), playing relaxing and / or soothing music, operator dialogue 120 with user 110 and etc.

It is worth noting that the presentation of loads, in particular, a series of loads, including sets of loads can be carried out (for example, by operator 120, load module 310, at least one computing device, etc.) continuously. In a particular case, the presentation of the set of loads can be continuous, and the delay in presenting at least one of the loads, which is part of the set of loads and / or series of loads, to the user can be delayed.

Also, the load can be presented to the user in the form of pre-placed on at least one described, the storage medium (in the particular case, the information storage medium). So, for example, loads can be presented to the user through a paper data carrier and / or display module, and / or by an operator 120, etc.

It is also worth noting that the loads, in the particular case of the implementation of the present invention, the loads can be selected by the user 110, for example, by means of at least one computing device (for example 328), to the extent, but not limited to, using a touch screen or any other means, devices, in particular, using a data input module (e.g., module 455).

It should be noted that during the presentation of loads to the user, marking of the end of the presentation of the load 110 to the user can be carried out, for example, by using operator 120 of computing device 328A. In the particular case of the implementation of the present invention, marking is also carried out marking the completion by the user 110 of the load shown by the operator 120 or registered by at least one module of the described system, in particular the monitoring and marking module 320 (FIG. 3) signs (for example, pronouncing the answer, switching the attention of the user 110, attenuation of the recorded signal of the bioelectric activity of the brain, etc.). In the particular case of the implementation of the present invention, the end marker of at least one of the loads can be set by the operator 120 (or, for example, by means of the monitoring and marking module 320, including those implemented on at least one computing device, for example, computing device 328, including a implemented software component, application, part of the application, program code, etc.) at the time of the start of receiving a response from the user 110 (for example, at the time of the start of pronouncing the response). Further, the marker can be shifted (by a predefined time value) when the user 110 completes the load on the timeline corresponding to the signal being registered, in particular, the marker can be shifted backward by the preset time value, i.e. minus the predefined time value. A preset time value may include a range, for example, from 0.01 second to 10 seconds. In the particular case of the implementation of the present invention, the marker offset determined by the predefined time value is the delay time of the user providing 110 the answer, for example, associated with the preparation of the response by the user, the selection of words necessary for the answer, etc. It is worth noting that the end of the execution of the load 110 by the user 110 is the beginning of the implementation by the user 110 of the intention to provide (in a particular case, inform) the answer. The completion of the user load 110 by the user 110 may be determined by the operator 120 or may be detected by at least one sensor 117, a recording device, etc. It is also worth noting that the shifted marker determines the “calculated” (“clean”) termination of the user’s performed load 110, that is, in the particular case, moving the marker excludes from the recorded signal that part of the signal that is not related to the activity of the user's brain 110, registered in the process of implementation by the user of an arbitrary at least one higher mental function (HFF). In the particular case, according to the available registration (determination, understanding, etc.) of delays between the decision of the user's brain and the issuance of the answer, the moment of "thinking" (the user's decision, the user's answer, etc.) is calculated.

It is worth noting that in a private implementation of the present invention, marking the beginning of the presentation of the load set to the user 110 and / or marking the end of the user performing the load set to the user 110 can also be carried out. Also, in one embodiment of the present invention, the start of the presentation of the series of loads to the user 110 can be marked and marked the end of the load by the user 110. The described marking can be carried out by recording the aforementioned markers for example, in the form of moments in time at which the presentation of loads to the user and moments in time at which the end of the execution of the loads by the user is determined ends.

After step 610, the process (of determining the potential for the development of higher mental functions and skills of a person (at least one user 110)) proceeds to step 620, in which it is carried out (in particular, by at least one recording module 119, for example, using NKI ) measurement (recording) of data obtained from at least one sensor 117. It should be noted that the measurement can be implemented in at least one step, which may include the presentation of at least one set of loads or, around the edge at least one series of loads, as well as the presentation of any other loads (including loads that contribute to the "return" of the user's attention 120, relaxing loads, etc.). It is worth noting that, as described above, in step 620, by means of measuring 119, decomposition (separation) of at least one signal into components, in particular, into signal channels, can be performed.

It is also worth noting that the result of the measurements in step 620, in particular, signal processing, as described above, is the vector ω _i , which is the vector of values at the i-th moment in time. In the particular case, the components of the vector ω _i are the values of the recorded (in the particular case, recorded) signal (s) at each time instant, in particular, the values of the components of the recorded channels of at least one signal at each time moment. Moreover, in the particular case of the present invention, this vector is passed to step 630.

Next, at step 630, at least one signal is monitored, in particular, data that contains such a signal, including data that contains at least one signal channel. In one of the implementations of the present invention, the control of the parameters of the signal, in the particular case, represented by the vector ω _i .

It is worth noting that when registering at least one signal, data of the signal (s) can also be stored in at least one data store 470. It should be noted that when registering at least one signal, it can at least one channel of the signal is stored in the data warehouse 470.

It is also worth noting that in the process of presenting loads, including in the process of recording these signals, marking (and / or marking recording, for example) described in the framework of the present invention can be carried out in the form of time stamps. So, for example, the time of the start of registration (the time of the beginning of the presentation of loads to the user, the time of the start of the registration of the signal, the time of placement of the sensors, etc.) can be the time Tn (for example, 18 hours 18 minutes 18 seconds local time), the time of presentation of the first load it can be time t0-1 (for example, 18 hours 20 minutes 00 seconds), the end time for recording the first load is time t'0-1 (for example, 18 hours 23 minutes 23 seconds), the time for presenting the second load can be time t1-1 (e.g. 18 hours 25 minutes t 00 seconds), the end time for recording the first set of loads is t'N-1 time (for example, 18 hours 36 minutes 02 seconds), the end time for recording a series of load sets is t''N-1 time (for example, 18 hours 56 minutes 02 seconds) etc. Mentioned marking, in particular, the time of the beginning of the presentation of the loads, the time of the end of the presentation of the loads, the end time of the user completing tasks contained in the loads, etc. can be stored in the data warehouse 470, and such marking can be stored together with the stored signal or separately from it.

It is worth noting that the described control, as well as marking, can be carried out by the operator 120 and / or the monitoring and marking module 320. Thus, in the particular case of the implementation of the present invention, marking can be carried out automatically, for example, when analyzing the activity of the user's brain (in particular, the recorded signal), or a key phrase spoken by the operator (for example, “the beginning of the presentation of the load”, “the end of the presentation of the load”) or at least one action of the operator 1 20, including that recorded by the camera (for example, a gesture, movement of the operator’s face 120), using tracking devices for facial expressions of the operator’s face, etc.

It should be noted that in the process of presenting the user with 110 loads and / or in the process of registering the signal (s), the data of the recorded signals (in particular, the recorded signals) can be displayed to the user 110 and / or to the operator 120, for example, by means of the display module 475. Display of the data recorded signals can be used, for example, by the operator 120, to establish the beginning and / or end of the execution by the user 110 of the task of the load presented to him. So, for example, the described marking can be carried out by the operator 120, which monitors the strength of the recorded signal, in particular, when the signal strength increases at the time or after the user is subjected to a load 110, the operator can perform the described marking, in a particular case characterizing the beginning of the task 110, which is contained in load. Also, when reducing the strength of the recorded signal, the operator 120 can carry out marking characterizing the completion of the task in the load presented by the user 110.

It is worth noting that marking can be carried out by the control and marking module 310, in particular, on the basis of analysis (in the particular case of measuring software) signal strength (s). It should be noted that the control and marking module may be part of a computing device (device 328), including being part of data processing module 210, in particular, being part of primary data processing module 130.

In the particular case of the implementation of the present invention, the control in step 630 may include the implementation of sufficiency control and / or sensitivity control, and / or purity control.

This way control procedures can be described:

(a) sufficiency: the recorded data is deemed sufficient if the tasks of each type are recorded at least the amount of time set by the operator;

(b) sensitivity: recorded data is considered sensitive if the standardly defined scalar distance between data vectors corresponding to moments of load execution and vectors corresponding to moments of calm wakefulness is less than the threshold sensitivity value set by the operator (always less than 1 by the definition of a scalar product);

(c) purity: recorded data is considered clean if the number of vectors containing zero values is less than the purity threshold set by the operator.

In the particular case of the implementation of the present invention, step 610 can be carried out both after recording the signal (or signal component, in the particular case, the load channel) of at least one load, one set of loads, one series of loads, etc., and upon presentation of all loads (types of loads) to user 110.

Next, the process proceeds to step 640, in which the verification of control is carried out. If the control is passed in step 640, then the process proceeds to step 650, otherwise the process returns to step 610 to present the user with at least one load (in particular, the registered signal which did not pass the control in step 630), followed by repeated signal registration for such a load. It is worth noting that re-registration can be carried out with re-presentation of loads to the user 110.

In step 650, for each vector ω _i , the at least one signal is smoothed by the moving average method, in particular, the channels of such a signal to a given depth of the epoch of N measurements separated from the smoothed one, and the resulting set of values is instantaneous neurometry (μ _i ) higher mental functions. In the particular case of the implementation of the present invention, the instantaneous neurometry μ _i at time i (i-th moment of time), as well as resting neurometry (μ ⁺ ) are calculated by the formula:

, где

where

j is the summation index of the external operand of the denominator (takes values from zero to a given depth of the era N);

M is the number of channels of at least one signal or two signals;

k is the number of types of loads, in the particular case of tasks that contain loads;

T - time of presentation of loads;

N is the smoothing depth (epoch depth), with N <T (N less than T);

i ∈ [0, T] (i belongs to the time interval from 0 to T);

, если в момент времени i пользователь выполнил задание, содержащееся в нагрузке k;μ _i is defined as

if at time i the user completed the task contained in the load k;

- пространство векторов действительных чисел размерности М;

- the space of vectors of real numbers of dimension M;

является силой сигнала в момент времени t=i-j;moreover

is the signal strength at time t = ij;

является общей силой сигналов в интервале t∈(i-N, …, i).

is the total signal strength in the interval t∈ (iN, ..., i).

It should be noted that the resting neurometry (μ ₀ ) is a normalizing vector corresponding to instantaneous neurometry in the functional state of the user 110 “calm awake, eyes open”.

, причем μ^k - такой вектор, что выполняется условие

, т.е. осуществляется вычисление вектора, сумма скалярных расстояний от которого до всех векторов в k-том типе нагрузки - минимально, где

является средним значением, которое принимается числовой характеристикой состояния мозга в k-том типе нагрузки. В частном случае осуществляется поиск такого вектора, который удовлетворяет указанному условию по минимальной сумме расстояния от него (искомого) до данных

.After step 650, the process proceeds to step 660, in particular, the calculation of vectors

, and μ ^k is a vector such that the condition

, i.e. the vector is calculated, the sum of the scalar distances from which to all vectors in the k-th type of load is minimal, where

is the average value, which is taken by the numerical characteristic of the state of the brain in the k-th type of load. In a particular case, a vector is searched that satisfies the specified condition by the minimum sum of the distance from it (the desired one) to the data

.

Next, the set of instant neurometry (μ _i ) of higher mental functions calculated in step 660 is passed to step 670, in which the set of instant neurometry (μ _i ) is normalized to the resting neurometry (μ ⁰ ). In particular, the HFF Ω neurometry is calculated, which is the result of calculating the average scalar distance over the entire recording time between the instantaneous neurometrics and the resting neurometry, and the final value is the neurometry of the highest mental function and can take values from zero to one:

для любой k принадлежащей диапазону значений от 1 до K, где (μ^k, μ⁰) является стандартно определенным скалярным произведением.

for any k belonging to the range of values from 1 to K, where (μ ^k , μ ⁰ ) is a standard-defined scalar product.

where Ω ^k is the neurometry of the k-th higher mental function.

, в частности, матрицу коэффициентов средних характеристик для пользователей с достоверно подтвержденной альтернативным способом успешностью в одной или нескольких гранях интеллекта. Стоит отметить, что самообучение алгоритма осуществляется посредством введения растяжения пространства матрицей (самообучения)

, которая определяется как среднее нормальное ожидаемое отклонение в любой момент времени реализации пользователем ВПФ (в частности в процессе выполнения пользователем нагрузок, включая процесс регистрации сигналов) мгновенной нейрометрики от нейрометрики покоя. Полученное значение ВПФ является нейрометрикой высшей психической функции, причем на таким образом определенном пространстве (Ω принадлежит области значений от нуля до единицы) высшая психическая функция имеет больший потенциал развития тогда (и только тогда), когда ее нейрометрика ВПФ стремится к единице, и меньший потенциал, когда ее нейрометрика ВПФ стремится к нулю.Next, the process proceeds to optional step 680, in which the described algorithm for computing the VPF neurometry, in particular, the self-learning algorithm, is trained. In the particular case, in this step, a transition is made from a standardly defined scalar product to statistical distortion through a self-learning matrix

in particular, a matrix of coefficients of average characteristics for users with reliably confirmed by an alternative method success in one or several facets of intelligence. It should be noted that the self-learning of the algorithm is carried out by introducing the expansion of space by a matrix (self-learning)

, which is defined as the average normal expected deviation at any moment in time when the user implements the HMF (in particular, during the user's execution of loads, including the process of recording signals) of instant neurometry from resting neurometry. The obtained value of HFF is a neurometry of higher mental function, and in this way defined space (Ω belongs to the range of values from zero to one), higher mental function has a greater development potential if (and only if) its HFF neurometry tends to unity, and less potential when her HPF neurometry tends to zero.

, причем в опциональном шаге 680 вычисление Ω^k с использованием вычисленного (μ^k, μ⁰) осуществляется как вычисление нейрометрики высшей психической функции

, принадлежащей

, как

, причем

при первом измерении. Стоит отметить, что в приведенной выше формуле I является квадратной единичной матрицей размера K:K.In the particular case of the implementation of the present invention in the normalization step 670 (μ ^k , μ ⁰ ) is calculated by the formula =

moreover, in optional step 680, the calculation of Ω ^k using the calculated (μ ^k , μ ⁰ ) is carried out as the calculation of the neurometry of the higher mental function

owned by

, as

, and

at the first measurement. It is worth noting that in the above formula, I is a square unit matrix of size K: K.

Next, in step 690, the calculation results are provided by the described method, as described in the framework of the present invention. One of the options for providing results is shown in FIG. 8.

. Стоит отметить, что в каждый записываемый вектор записывается, "1" если это время являлось временем работы пользователя (тестируемого) над заданием (являлось выполнением пользователем) k-ого типа, и записывается "0" если это любой другой момент (например, когда пользователь не выполняет умственных нагрузок типа к).In FIG. 7 shows an exemplary marking option, in accordance with one embodiment of the present invention. In the particular case of the implementation of the present invention, the user is presented with a load of type k 110: k = 0 - “calm wakefulness, eyes open”, k = 1 - verbal-linguistic type of load (712), k = 2 - logical-mathematical type of load (722 ), k = 3 - musical type of load (732), k = 4 - spatial type of load (742), k = 5 - bodily-kinesthetic type of load (752), k = 6 interpersonal type of load (762) or k = 7 - intrapersonal type of load (772). At the time point (t _k ⁰ ) corresponding to the completion of the presentation of one of the loads to the user 110 (in particular, at least one task that contains the load), the marking described in the framework of the present invention is carried out. Until the next mark, all vectors in the log have a marking mark.

. It is worth noting that in each written vector it is written, “1” if this time was the time the user (tested) worked on the task (was the user's execution) of the kth type, and “0” is written if this is any other moment (for example, when the user does not perform mental stress type k).

It is worth noting that the described marking can be carried out by the operator 120 and / or by means of the control and marking module 320, the primary data processing module 130, the data processing module 210, etc. For example, on a computing device, for example, computing device 328A, the described marking by the operator 120 may be carried out by using software installed on such a computing device. So, for example, the operator 120 can carry out marking through the interaction of the data input module (455, FIG. 4) and software components, such as elements of a graphical software interface. It is worth noting that such software allows recording recorded signals, as well as the results of marking (in the particular case, markers). It is also worth noting that such software can be associated with the control and marking module 320, implement at least one part of the control and marking module 320, for example, implement marking. As described above, such marking can be carried out by the operator 120 or in automatic mode. In automatic mode, said software may, for example, by processing overhead information or instructions of an operator 120, in particular keywords, perform the described marking. In a particular case, said processing of service information or instructions of an operator 120 may include the conversion of service information or instructions of an operator 120, in particular, speech recognition (in particular, keywords). For example, the keywords “First load”, “End of the first load”, “End of the load”, etc. can be recognized by the mentioned software and converted into a corresponding marker, for example, a marker corresponding to the completion of the presentation of one of the loads to the user 110. It should be noted that the software in a particular case may be an application for a computing device, including a mobile application, program code ( in compiled or not compiled form), running on a computing device, etc.

In FIG. 8 illustrates an exemplary embodiment of providing results, according to one embodiment of the present invention.

In the particular case of the implementation of the present invention, the neurometry Ω ¹ is the VPF of the verbal-linguistic type of load (speech / verbally linguistic skills), Ω ² is the VPF of the logical-mathematical type of load (mathematics / logic / count), Ω ³ is the VPF of the musical type of load (music / rhythm), Ω ⁴ is the HFF of the spatial type of load (spatial memory / imagination / creativity), Ω ⁵ is the HFF of the bodily-kinesthetic type of load (bodily-kinesthetic / sport / craft / movement and gestures), Ω ⁶ corresponds to the HFF of the inter chnostnomu load type, Ω ⁷ corresponds VPF intrapersonal load type.

In the particular case of the implementation of the present invention, at least one part of the described method and system may be (or may be implemented) software, application or part of the application. So, in a particular case, the application can be used to control (including tracking parameters, control, data transfer, etc., including their constituent elements (in particular, modules)) of at least one described module of the system, recording devices. In the particular case, the described modules (or devices) can be computing devices or contain at least one part of computing devices. Also, at least one part of the described method and system may be at least one service implemented, for example, by program code, a script (s), and also be executable components of various systems (in particular, computer systems) and subsystems ( for example, subsystems, input, output subsystems (in particular, the graphics subsystem), storage (in particular, the disk subsystem), data transfer, etc.).

The described modules, devices, software, services and / or other methods, in particular, implemented in software or hardware, as well as any combination of them, or any part thereof, are capable of controlling, monitoring, informing, controlling, including via data transmission, etc., at least one module (device, etc.) of the described system or other software, services, hardware and / or modules (software or hardware).

It is worth noting that the above-mentioned modules can include the execution (execution) of program code, executable application code, machine code, etc., including in compiled and / or non-compiled form, for example, in the form of javascript (java scripts ), as well as in the form of one of the intermediate languages (programming), for example, the intermediate language CIL, from English. Common Intermediate Language.

Also, the described modules (and / or devices, etc.) can be implemented as two or more modules (or devices), and one module (or device). Also, in the particular case, at least one module or at least one component thereof can be implemented by at least one executable file. Moreover, in the particular case, the module can be implemented (be) one executable file, which in turn can be associated with at least one program library, for example, implemented as a dll file, which is a compiled form of a dynamic link library ( from the English Dynamic Link Library), as well as with at least one file, for example, containing application service data, application metadata, data necessary for the application to function, and / or a service (local and / or remote, for example, the web server ISOM / web service), including applications and services built on a service-oriented architecture (from the English service-oriented architecture / SOA), including, but not limited to, REST technologies (from the English Representational State Transfer - “transfer of presentation state »), Remote procedure call (from the English. Remote Procedure Call, RPC) and others.

In FIG. 9 shows an example of a general-purpose computer system that includes a multi-purpose computing device in the form of a computer 20 or a server, or a module of the system described in the present invention, including a processor 21, system memory 22, and system bus 23 that couples various system components, including system memory with 21 processor.

The system bus 23 may be any of various types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. System memory includes read-only memory (ROM) 24 and random access memory (RAM) 25. The ROM 24 stores the basic input / output system 26 (BIOS), consisting of basic routines that help exchange information between elements within the computer 20, for example, at the time of launch.

Computer 20 may also include a hard disk drive 27 for reading from and writing to a hard disk, not shown, a magnetic disk drive 28 for reading from or writing to a removable magnetic disk 29, and an optical drive 30 for reading from or recording to a removable optical disc 31 such as a CD, a digital video disc, and other optical means. The hard disk drive 27, the magnetic disk drive 28, and the optical disk drive 30 are connected to the system bus 23 by means of the hard disk drive interface 32, the magnetic disk drive interface 33, and the optical drive interface 34, respectively. Storage devices and their respective computer-readable means provide non-volatile storage of computer-readable instructions, data structures, program modules and other data for computer 20.

Although the typical configuration described here uses a hard disk, a removable magnetic disk 29, and a removable optical disk 31, one skilled in the art will appreciate that other types of computer-readable means that can store data that are accessible by a computer may also be used in a typical operating environment. such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, random access memory (RAM), read-only memory (ROM), etc.

Various software modules, including operating system 35, may be stored on a hard disk, magnetic disk 29, optical disk 31, ROM 24, or RAM 25. Computer 20 includes a file system 36 associated with or included with the operating system 35 or more software application 37, other program modules 38, and program data 39. A user may enter commands and information into computer 20 using input devices such as a keyboard 40 and pointing device 42. Other input devices (not shown) may include be a microphone, joystick, gamepad, satellite dish, scanner, or any other.

These and other input devices are connected to the processor 21 often through a serial port interface 46 that is connected to the system bus, but can be connected via other interfaces, such as a parallel port, game port, or universal serial bus (USB). A monitor 47 or other type of visual display device is also connected to the system bus 23 via an interface, for example, a video adapter 48. In addition to the monitor 47, personal computers typically include other peripheral output devices (not shown), such as speakers and printers.

Computer 20 may operate in a networked environment through logical connections to one or more remote computers 49. The remote computer (or computers) 49 may be another computer, server, router, network PC, peer to peer device, or other node on a single network, and typically also includes includes most or all of the elements described above with respect to computer 20, although only an information storage device 50 is shown. Logical connections include a local area network (LAN) 51 and a global computer network (GC) ) 52. Such networking environments are usually common in offices, enterprise-wide computer networks, the Internet.

The computer 20 used in the LAN network environment is connected to the local area network 51 via a network interface or adapter 53. The computer 20 used in the GC network environment typically uses a modem 54 or other means to establish communication with the global computer network 52, such as the Internet.

The modem 54, which may be internal or external, is connected to the system bus 23 via the serial port interface 46. In a networked environment, program modules or parts thereof described with reference to computer 20 may be stored on a remote information storage device. It should be noted that the network connections shown are typical, and other means may be used to establish communication communication between computers.

In conclusion, it should be noted that the information provided in the description are examples that do not limit the scope of the present invention defined by the claims. One skilled in the art will recognize that there may be other embodiments of the present invention consistent with the spirit and scope of the present invention.

Claims (20)

1. A method for determining the development potential of higher mental functions and human skills, which includes: (A) presenting to the user at least one load, where the type of each load corresponds to one facet of intelligence and the load corresponds to the age group of users; (B) recording, using at least one sensor for detecting signals of brain bioelectrical activity, at least one signal of bioelectric activity of the brain of the user in various psychophysiological conditions corresponding to the user's performance of the load related to the studied higher mental functions; (B) smoothing at least one signal recorded by at least one of the sensors using the moving average method, and the resultant signal obtained by smoothing the said signal using the moving average method is an instant neurometry of higher mental functions, and instant neurometry of higher mental functions μ _i at time i, and μ ⁰ neyrometrika rest which is a normalizing vector corresponding instantaneous neyrometrike in the functional state polzovatsja I quiet wakefulness, calculated by the formula:

moreover, j is the summation index of the external operand of the denominator and takes values from zero to N, and N is the smoothing depth, where N <T, M is the number of channels of the neurocomputer interface, k is the number of types of loads applied,

is the space of vectors of real numbers of dimension M, and T is the time of presentation of loads, where i belongs to the time interval from 0 to T, and μ _i is defined as

if at time i the user completed the task contained in the load k, and

is the strength of the signal recorded by at least one of the sensors at time t = ij and

is the total strength of the signals recorded by at least one of the mentioned sensors, in the range from iN to i, where the set of instantaneous neurometry μ _{i is} normalized to μ ⁰ , which is a resting neurometry; (D) the implementation of the calculation of the neurometry of the highest mental function (Ω), which is the result of calculating the average scalar distance over the entire recording time between the obtained instantaneous neurometrics and resting neurometry, as

which is the average value, which is a numerical characteristic of the state of the user's brain during the bioelectrical activity of the user's brain, in the kth type of load, and the final value of Ω is the neurometry of the highest mental function, which takes values from zero to one, and the neurometry of the kth higher mental function calculated by the formula

for any k belonging to the range of values from 1 to the number of presented loads K, where (μ ^k , μ ⁰ ) is a standard defined scalar product, and μ ^k is a vector such that the condition

moreover

is the average value that is used as a numerical characteristic of the state of the brain in the k-th type of load; moreover, if the neurometric of higher mental function belongs to the range of values from zero to unity, the higher mental function has a greater development potential than when the neurometric of higher mental function tends to unity, and less potential when the neurometric of higher mental function tends to zero. 2. The method according to p. 1, characterized in that the loads presented to the user contain sets of audio tests, video tests, graphic tests, text tests, speech tests. 3. The method according to claim 1, characterized in that the loads imposed on the user correspond to age groups of users in the range from 3 years to 7 years, from 7 years to 11 years, from 11 years to 13 years, from 13 years to 15 years, from 15 years to 17 years, from 18 years and older. 4. The method according to p. 1, characterized in that the load presented to the user contains at least one task for the user to perform, each of the loads presented to the user is one of the types of loads and is a verbal-linguistic type of load or logical-mathematical type of load, or musical type of load, or spatial type of load, or bodily-kinesthetic type of load, which includes an oral task for the user to draw a geometric figure or includes presenting to the user an image of a figure made up of fingers, and the user must repeat the orally or folded figures, or an interpersonal type of load, which includes presenting to the user an image containing a situation image in order to receive a situation analysis from the user with a request to tell about what happened or occurs in the image. 5. A system for determining the development potential of higher mental functions and human skills, and the system contains: - a registration module in which registration is carried out by means of at least one sensor associated with the recording module of at least one signal of the bioelectric activity of the brain of the user in various conditions corresponding to the user performing at least one load related to it investigated higher mental functions, where the type of each load corresponds to one facet of intelligence and the load corresponds to the age group of users; - primary data processing module, in which: - smoothing of at least one registered signal by the moving average method, and the resultant set of values obtained by smoothing the signal recorded by at least one of the mentioned sensors by the moving average method is instantaneous neurometry of higher mental functions, and instant neurometry of higher mental functions μ _i at time i, and μ ⁰ neyrometrika rest which is a normalizing vector corresponding instantaneous neyrometrike in the functional state floor zovatelya quiet wakefulness, calculated by the formula:

moreover, j is the summation index of the external operand of the denominator and takes values from zero to N, and N is the smoothing depth, where N <T, M is the number of channels of the neurocomputer interface, k is the number of types of loads applied,

is the space of vectors of real numbers of dimension M, T is the time of presentation of loads, where i belongs to the time interval from 0 to T, and μ _i is defined as

if at time i the user completed the task contained in the load k, and

is the strength of the signal recorded by at least one of the sensors at time t = ij and

is the total strength of the signals recorded by at least one of the mentioned sensors, in the range from iN to i, where the set of instantaneous neurometry μ _{i is} normalized to μ ⁰ , which is a resting neurometry; - a secondary data processing module, in which: - calculation of the neurometry of the highest mental function (Ω), which is the result of calculating the average scalar distance over the entire recording time between the instantaneous peyrometrics and resting neurometry, as

which is the average value, which is a numerical characteristic of the state of the user's brain during the bioelectrical activity of the user's brain, in the kth type of load, and the final value of Ω is the neurometry of the highest mental function, which takes values from zero to one, and the neurometry of the kth higher mental function calculated by the formula

for any k belonging to the range of values from 1 to the number of presented loads K, where (μ ^k , μ ⁰ ) is a standard defined scalar product, and μ ^k is a vector such that the condition

moreover

is the average value that is used as a numerical characteristic of the state of the brain in the k-th type of load, moreover, if the neurometric of the higher mental function belongs to the range of values from zero to unity, the higher mental function has a greater development potential then when the neurometric of the higher mental function tends to unity, and the lower potential when the neurometric of the higher mental function tends to zero. 6. The system according to claim 5, characterized in that the loads imposed on the user contain sets of audio tasks, video tasks, graphic tasks, text tasks, speech tasks. 7. The system according to claim 5, characterized in that the loads imposed on the user correspond to age groups of users ranging from 3 years to 7 years, from 7 years to 11 years, from 11 years to 13 years, from 13 years to 15 years, from 15 years to 17 years, from 18 years and older. 7. The system according to claim 5, characterized in that the load presented to the user contains at least one task for the user to perform, each of the loads presented to the user is one of the types of loads and is a verbal-linguistic type of load or logical-mathematical type of load, or musical type of load, or spatial type of load, or bodily-kinesthetic type of load, which includes an oral task for the user to draw a geometric figure or including presenting to the user an image of the figure folded from the fingers, and the user must repeat the orally or folded figures, or the interpersonal type of load, which includes presenting to the user an image containing the image of the situation in order to receive from the user an analysis of the situation with a request to tell you that happened or is happening on the image. 9. The system according to claim 5, characterized in that the registration module is a neuro-computer interface (NCI).

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