KR101699623B1 - Game application system for improving the symmetry of the left/light brain activity - Google Patents

Abstract

The present invention relates to a system and a method for optimizing brain function by improving symmetry of left/right brain activity. According to an embodiment, the system for measuring brain wave comprises: an analyzing unit which is connected to a brain wave measuring device and performs a preprocessing process on brain wave data associated with the brain wave received in real time; a storage unit which classifies the brain wave data obtained by the pre-processing process on a predetermined standard to store the brain wave data in database; and a result unit which provides a training result related to the measured brain wave measured from the user by each section and provides the increase rate of the brain wave based on the cumulative average of the brain wave data stored in the database.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a game application system for enhancing left / right brain activity symmetry,

The following description relates to neurofeedback techniques and relates to systems and methods for improving left and right brain activity.

Research on the human brain has been rapidly advancing into brain science, brain engineering, and brain medicine since 1990. In particular, the human cerebrum is divided into the left and right hemispheres and performs different functions in each hemisphere. The reason is that when processing the input information, the left brain will solve the logical problems such as reasoning and calculation, and the right brain will solve the sensuous problems such as painting, music appreciation and sports. . The difference between left and right brain is called brain differentiation in brain science. In general, it is possible to grasp the degree of asymmetry of left and right brain hemispheres by EEG analysis. The human brain has different characteristics, and the overall development of the brain and the balance of the left and right brain are essential for optimizing brain function.

Anxiety and depression are symptoms that can be seen in the aforementioned left and right brain imbalances. The emotional tendency is changed by the asymmetry of left and right brain. The bigger the difference, the more abnormal symptoms such as anxiety and depression are seen.

Recently, attention has been paid to interface through EEG for the purpose of improving these mental disorders in various business fields such as medical, game, and u-Health. It is used mainly in programs to treat and prevent through the training to control the condition of the patients with mental or physical illness and to control EEG. A typical example is neurofeedback. Neurofeedback, a type of biofeedback using brain waves, has been used as a game interface to improve potentiality and emotional state through training and to improve clinical symptoms in patients. Currently, various clinical studies have been published with various effects confirmed in various areas related to brain function such as ADHD and brain damage.

Studies have shown that children with asymmetry in the left / right brain, particularly those with predominance in the right brain, are more vulnerable to higher levels of schooling, A technique for enhancing the left / right ratio and enhancing the utilization of the left / right ratio needs to be proposed.

Korean Patent Laid-Open No. 10-2011-0024493 discloses a learning apparatus and a learning method using brain waves.

A problem to be solved by the present invention is to provide a system and method for improving emotional disorders such as anxiety and depression by improving the symmetry of left and right brain activity through optimization of brain function.

The problem to be solved by the present invention is that, in general, the characteristics of patients showing emotional disturbance are helpless and have a low commitment to the treatment, so that the game-based contents of interesting inducements and customized games are provided through users to provide strong motivation to the patients System and method.

According to an embodiment of the present invention, a system for measuring brain waves includes an analyzer connected to an EEG device and performing a preprocessing process on brain wave data related to EEG received in real time; A storage unit for classifying the EEG data subjected to the preprocessing process according to predetermined criteria and storing the data in a database; And a result section for providing training results related to the EEG measured by the user by predetermined intervals and providing an increase rate of brain waves based on the cumulative average of EEG data stored in the database.

According to one aspect of the present invention, the EEG measurement system includes: a connection unit for extracting contents based on profile information of the user and the training result; And a providing unit for providing the extracted contents to the user as EEG training contents.

According to another aspect of the present invention, the analyzing unit may perform fast Fourier transform analysis of analyzing EEG data measured from the EEG apparatuses by a predetermined category, and separating the EEG data into a plurality of waves according to the Fourier theory .

According to another aspect of the present invention, the analyzing unit classifies the EEG data into frequency components by using a method of converting a time-series signal into a frequency domain and determining an aspect of a signal according to a frequency change, The distribution can be grasped and indicators corresponding to the respective categories can be applied based on the density and the distribution.

According to another aspect of the present invention, the analysis unit determines the user's concentration by applying the attention index in the cognitive ability development section, derives the average of the alpha wave and the beta wave of the EEG data measured in real time in the alpha wave and beta wave training, , The rate of increase is determined by comparing the average of the cumulative data measured through training before the predetermined period, and the AIM (Alpha Inactivity Mechanism) based on the theory that the activity given to the brain in the left and right brain activation is inversely proportional to the alpha wave The left and right brain activity can be determined.

According to another aspect, the providing unit may provide at least one EEG training content for cognitive function development, left brain activation, right brain activation, alpha wave training, and beta wave training for the development of the function of the frontal lobe.

According to another aspect of the present invention, the storage unit stores the measured EEG data classified into Theta wave, Alpha wave, Beta wave and SMR (Sensory Motor Rhythm).

According to an embodiment of the present invention, there is provided a method of measuring an EEG comprising: performing a preprocessing process on EEG data related to EEG received in real time in connection with an EEG instrument; Classifying the brain wave data that has undergone the preprocessing process on a predetermined basis and storing the classified brain wave data in a database; And providing training results related to EEG measured by the user for each predetermined interval and providing an increase rate of brain waves based on a cumulative average of EEG data stored in the database.

According to one aspect of the present invention, the EEG measurement method includes: extracting a content based on the profile information of the user and the training result; And providing the extracted contents to the user as EEG training contents.

According to another aspect of the present invention, the step of performing a preprocessing process on EEG data related to an EEG received in real time in connection with the EEG measurement device includes analyzing EEG data measured by the EEG measurement device by predetermined categories, And performing fast Fourier transform analysis for separating the brain wave data into a plurality of waves according to the Fourier theory.

According to another aspect of the present invention, the step of performing a preprocessing process on EEG data related to an EEG received in real time in connection with the EEG measurement device includes the steps of converting a time series signal into a frequency domain and determining an aspect of a signal according to a frequency change Analyzing the EEG data by frequency components, determining the density and distribution of the classified frequency components, and applying indicators corresponding to the respective categories based on the density and the distribution .

According to another aspect of the present invention, the step of performing a preprocessing process on EEG data related to an EEG received in real time in connection with the EEG measurement device includes the steps of: The averages of the alpha and beta waves of EEG data measured in real time in Papa and Beta wave training were determined and the rate of increase was determined by comparing the average of the measured data measured through training before the predetermined period, And determining the activity of the left and right brain by applying the activity given in the brain to Alpha Inactivity Mechanism (AIM) based on a theory that is inversely proportional to alpha waves.

According to another aspect of the present invention, the step of providing the extracted contents as the EEG training contents to the user includes at least one of cognitive ability development, left brain activation, right brain activation, alpha wave training and beta wave training for functional development of the frontal lobe And providing EEG training content.

According to another aspect of the present invention, the step of classifying the EEG data subjected to the preprocessing process according to a preset reference and storing the EEG data in a database may further include a step of classifying the measured EEG data into theta wave, Alpha wave, Wave, and SMR (Sensory Motor Rhythm).

The EEG measurement system according to one embodiment can improve the symmetry of the left and right brain activities, thereby improving the emotional disorder such as optimization of the brain function and anxiety.

1 is a view for explaining the operation and configuration of an EEG measurement system according to an embodiment.
FIGS. 2 to 6 are diagrams for explaining an example of providing EEG training contents of an EEG measurement system according to an embodiment.
FIG. 7 is a diagram for explaining the EEG measurement result of the EEG measurement system according to the embodiment.
FIG. 8 is a diagram for explaining an EEG measurement device of an EEG measurement system according to an embodiment.
FIG. 9 is a flowchart for explaining an EEG measurement method of an EEG measurement system according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

1 is a view for explaining the operation and configuration of an EEG measurement system according to an embodiment.

The EEG measurement system may include an analysis unit 110, a storage unit 120, a result unit 130, a connection unit 140, and a data providing unit 150 to improve the symmetry of left and right brain activity.

The EEG measurement system can provide the user with the EEG training contents, and the user can perform the training through the EEG training contents. The analysis unit 110 may perform a preprocessing process on EEG data related to an EEG received in real time in connection with an EEG measurement device. At this time, the brain wave measuring device can transmit the EEG data to the analyzer 110 through Wi-Fi or local connect. The analyzer 110 analyzes the EEG data measured by the EEG apparatuses according to predetermined categories and performs Fast Fourier Transform (FFT) analysis for separating EEG data into a plurality of waves according to the Fourier theory. have.

Fast Fourier transform analysis can be expressed as Equation (1).

Equation 1:

The analysis unit 110 classifies the EEG data into frequency components by using a method of converting a time series signal into a frequency domain and determining an aspect of a signal according to the frequency transformation, grasps the density and distribution of the classified frequency components, And indices corresponding to respective categories can be applied based on the distribution.

The analysis unit 110 can grasp the concentration of the user by applying the attention index in the cognitive ability development section. At this time, the attention index is expressed by Equation (2).

Equation 2:

The analyzer 110 can derive the average of the alpha wave and the beta wave of the EEG data measured in real time in the interval in the alpha wave and beta wave training interval. The analyzer 110 may determine the rate of increase by comparing with the average of the measured cumulative data previously trained. In other words, the analyzer 110 can determine the rate of increase by deriving the averages of the alpha and beta waves of the EEG data measured in real time, and comparing the average of the measured cumulative data through training before the preset period.

The analysis unit 110 can apply the Alpha Inactivity Mechanism (AIM) based on the existing theory that the left and right brain activity is generally inversely proportional to the activity of the brain in the brain. The equation 3 is as follows.

Equation (3)

과

은 각각 왼쪽과 오른쪽의 알파파의 크기를 의미할 수 있다. 좌뇌와 우뇌의 활성도를 비교하기 위하여 수학식 3을 통하여 획득된 수치를 백분율로 결과부(130)에 나타낼 수 있다. here,

and

Can mean the size of the left and right alpha waves, respectively. In order to compare the activity of the left brain and the right brain, the numerical value obtained through Equation (3) can be expressed as a percentage in the result portion (130).

The storage unit 120 may classify the EEG data that has undergone the preprocessing process on a preset basis and store the EEG data in a database. The storage unit 120 stores the measured EEG data classified into Theta wave, Alpha wave, Beta wave and SMR (Sensory Motor Rhythm).

The result unit 130 may provide the training results related to the EEG measured by the user by predetermined intervals and provide the rate of increase of the EEG based on the cumulative average of the EEG data stored in the database. For example, the result unit 130 may provide EEG data measured through the training contents to be displayed for each section.

The connection unit 140 can extract the content based on the profile information of the user and the training result. At this time, the profile information of the user may include identification data of the user, for example, the name of the user, the age of the user, the gender of the user, etc., and may mean EEG information analyzed by the user. The connection unit 140 can automatically provide the user with optimized scheduling by extracting suitable content based on the training results and the user's profile information.

The providing unit 150 may provide the extracted contents to the user as EEG training contents. The providing unit 150 may provide at least one EEG training content for cognitive function development, left brain activation, right brain activation, alpha wave training, and beta wave training for functional development of the frontal lobe.

FIGS. 2 to 6 are diagrams for explaining an example of providing EEG training contents of an EEG measurement system according to an embodiment. The EEG measurement system can be designed so that the EEG can be induced naturally by each category during training. EEG systems can provide interesting game-like content to provide motivation to users with emotional disturbances. The EEG measurement system can provide the user with output of EEG training contents, for example, through a terminal such as a smart phone or a PC. At this time, the EEG measurement system provides the EEG training contents, and by providing the interface, the user can receive the input in real time.

Referring to FIG. 2, an execution screen of the content 210 for cognitive capability development is shown. The providing unit 150 can develop the left and right brain in a balanced manner by providing at least one of the contents of the name memorization and the picture matching for the development of the cognitive ability and the brain activity in the information processing is increased, And may be effective in improving and improving attention.

Referring to FIG. 3, an execution screen of contents 310 for alpha-wave and beta-wave training is shown. The classifier 150 may provide at least one of the classical music appreciation and meditation 310 for alpha wave training to elevate the user's alpha wave through brain relaxation. Referring to FIG. 6, the providing unit 150 may increase the beta wave by providing the focused archery content 610 for beta wave training. Each waveform training can provide the user with the right EEG distribution.

Referring to FIGS. 4 and 5, an execution screen of contents for left and right brain activity is shown. The providing unit 150 provides at least one content 510 of associative training-based computation-sums and 1 to 50 to activate the left brain, and searches for a card, which is a training to process spatial and overall information to activate the right brain Content can be provided. Through this, the deliverer 150 can provide a balanced development of the left and right hemispheres by training the left and right hemispheres of the patients having the left and right hemispheric asymmetry.

FIG. 7 is a diagram for explaining the EEG measurement result of the EEG measurement system according to the embodiment.

The brainwave measurement system shows the results of the training using the EEG generated by each training interval and the whole training.

The EEG measurement system can provide the training results related to the EEG measured by the user by predetermined intervals and can provide the EEG increase rate based on the cumulative average of the EEG data stored in the database.

For example, EEG systems can score points on training results in advance. The training result measured by the user can be scored and the user's state can be grasped by correlating the scored EEG data with the index scored by intervals.

For example, an EEG measurement system can compare the rate of increase in user training results. The EEG system can provide scheduling to the user preferentially in the next training from the training with low rate of increase in the training result of the user. EEG systems can automatically provide users with optimized scheduling by increasing the number of training sessions without increasing the number of training sessions and increasing the number of training sessions.

The EEG measurement system can provide training results through a plurality of graphs, for example. The EEG measurement system can provide the training result and the EEG increase rate to the display of the terminal.

The brainwave measurement system can be provided with graphs for each region, left brain and right brain activity, average of brain wave waveform, training results, alpha wave training, and beta wave training.

The user can check his / her brain wave in real time and compare with the average of brain waves derived from previous training. In addition, the user can confirm the degree of symmetry of the left and right brain and the average of brain waves in the whole section at a glance.

FIG. 8 is a diagram for explaining an EEG measurement device of an EEG measurement system according to an embodiment.

For example, Emotive EPOC is composed of 14 channels and 2 ground sensors and can receive data of 128Hz per second. EEGLAB was used to analyze the measured data.

The choice of EEG location can be based on the theory that emotional disturbances such as depression and anxiety are closely related to the frontal lobe, as in 810, four channels of the frontal lobe can be used.

The user can perform the training by wearing the EEG device. The user can confirm the input of the EEG data during the training through the training contents, and leave the marker at each training interval so that each section can be classified more accurately can do. At this time, the brain-wave measuring system can modify and provide the training contents so as to memorize and solve the problem.

The EEG measurement system can store the average value of alpha, theta, beta, and SMR based on the relative power value using EEGLAB. The EEG measurement system measures the EEG activity of each section with four EEG frequency bands and calculates the index to obtain accurate figures. In addition, the EEG data during the training period can be compared to determine the accuracy and the amount of increase.

The EEG measuring system according to one embodiment can be applied to various fields such as helping students to learn more by improving the concentration of students or improving emotional disorders such as anxiety.

FIG. 9 is a flowchart for explaining an EEG measurement method of an EEG measurement system according to an embodiment.

The EEG measurement system is designed to optimize the brain function by symmetrically improving the left and right brain activity. After analyzing the EEG, it can improve the emotional problems such as depression and anxiety through regular training.

The EEG measurement method can be performed by the EEG measurement system, and the EEG measurement system will be described with reference to FIGS. 1 to 8 as described above.

In step 910, the EEG measurement system extracts the content based on the profile information of the user and the training result, and provides the extracted content to the user as the EEG training content. At this time, the EEG training contents can be produced so as to generate EEG induced by each category.

In step 920, the EEG measurement system may perform a preprocessing process on the EEG received in real time in connection with the EEG measurement device. The brain wave measurement system can construct the user 's profile by separating and storing the EEG waves into the alpha wave, beta wave, theta wave, and SMR wave through the FFT analysis process.

In step 930, the EEG measurement system may classify the EEG that has undergone the preprocessing process into predetermined criteria and store the EEG in a database.

In step 940, the EEG measurement system can provide an increase rate of EEG through the training result measured from the user and the cumulative average of stored EEP data. At this time, the EEG system can show the result of the training by each category, and can calculate the rate of increase by comparing the average of the training EEG data stored in the database with the EEG data of the current training. The EEG measurement system can provide the output of the training through the EEG training contents.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (14)

A system for measuring brain waves,
An analysis unit connected to the EEG device and performing a preprocessing process on EEG data related to the EEG received in real time;
A storage unit for classifying the EEG data subjected to the preprocessing process according to predetermined criteria and storing the data in a database; And
A training result related to the EEG measured by the user is provided for each predetermined interval and a result of providing a rate of increase of brain waves based on the cumulative average of EEG data stored in the database
Lt; / RTI >
A connection unit for extracting contents based on the profile information of the user and the training result; And
And providing the extracted content to the user as an EEG training content
Further comprising:
Wherein the profile information of the user includes identification data including at least one of a name, an age, and a gender of the user, and EEG information analyzed by the user,
The connecting portion
Extracting personalized content based on profile information including the user's identification data, brain wave information, and training results,
Wherein the providing unit comprises:
Providing at least one EEG training content for cognitive development, left brain activation, right brain activation, alpha wave training, and beta wave training for functional development of the frontal lobe
Wherein the EEG measurement system comprises: delete The method according to claim 1,
The analyzing unit,
The EEG data measured by the EEG device are divided into a predetermined category and subjected to fast Fourier transform analysis for separating the EEG data into a plurality of waves according to the Fourier theory
Wherein the EEG measurement system comprises: The method of claim 3,
The analyzing unit,
The EEG data is classified into frequency components by using an analysis method that converts a time series signal into a frequency domain and determines an aspect of a signal according to a frequency change. The density and distribution of the classified frequency components are grasped, Based on the indicators applicable to each category
Wherein the EEG measurement system comprises: 5. The method of claim 4,
The analyzing unit,
In the cognitive ability development section, the attention index is applied to determine the user's concentration,
The averages of alpha and beta waves of EEG data measured in real time in the alpha wave and beta wave training are determined and the rate of increase is determined by comparing the average of the measured data measured through training before the predetermined period,
The left brain and right brain activity is determined by applying the brain activity to the Alpha Inactivity Mechanism (AIM) based on the theory that the activity in the brain is inversely proportional to the alpha wave
Wherein the EEG measurement system comprises: delete The method according to claim 1,
Wherein,
The measured EEG data is classified into the Theta wave, Alpha wave, Beta wave and SMR (Sensory Motor Rhythm)
Wherein the EEG measurement system comprises: In a method for measuring EEG,
Performing a preprocessing process on EEG data related to an EEG received in real time in connection with an EEG measurement device;
Classifying the brain wave data that has undergone the preprocessing process on a predetermined basis and storing the classified brain wave data in a database; And
Providing a training result related to the EEG measured by the user for each predetermined interval and providing an increase rate of brain waves based on a cumulative average of EEG data stored in the database
Lt; / RTI >
Extracting contents based on the profile information of the user and the training result; And
Providing the extracted content to the user as EEG training content
Further comprising:
Wherein the profile information of the user includes identification data including at least one of a name, an age, and a gender of the user, and EEG information analyzed by the user,
Wherein the step of extracting the content based on the profile information of the user and the training result comprises:
Extracting personalized content based on profile information including the user's identification data, brain wave information, and training results,
The step of providing the extracted contents to the user as EEG training contents includes:
Providing at least one EEG training content for cognitive development, left brain activation, right brain activation, alpha wave training, and beta wave training for functional development of the frontal lobe
Wherein the EEG measurement method comprises the steps of: delete 9. The method of claim 8,
The step of performing pre-processing on EEG data related to an EEG received in real time in connection with the EEG instrument,
Analyzing EEG data measured by the EEG apparatuses according to predetermined categories, and performing fast Fourier transform analysis of separating the EEG data into a plurality of waves according to Fourier theory
/ RTI > 11. The method of claim 10,
The step of performing pre-processing on EEG data related to an EEG received in real time in connection with the EEG instrument,
The EEG data is classified into frequency components by using an analysis method that converts a time series signal into a frequency domain and determines an aspect of a signal according to a frequency change. The density and distribution of the classified frequency components are grasped, Applying indicators corresponding to respective categories on the basis of
/ RTI > 12. The method of claim 11,
The step of performing pre-processing on EEG data related to an EEG received in real time in connection with the EEG instrument,
In the cognitive ability development section, the attention index is applied to determine the user's concentration,
The averages of alpha and beta waves of EEG data measured in real time in the alpha wave and beta wave training are determined and the rate of increase is determined by comparing the average of the measured data measured through training before the predetermined period,
Determining the activity of the left and right brain by applying to the Alpha Inactivity Mechanism (AIM) based on the theory that the activity given to the brain in the activation of the left brain and right brain is inversely proportional to alpha waves
/ RTI > delete 9. The method of claim 8,
The step of classifying the electroencephalogram data subjected to the preprocessing process according to predetermined criteria and storing the data in a database may include:
Classifying and storing the measured EEG data into Theta wave, Alpha wave, Beta wave and SMR (Sensory Motor Rhythm)
/ RTI >

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