WO2010123275A2

WO2010123275A2 - Device and method for developing software applications using brainwaves

Info

Publication number: WO2010123275A2
Application number: PCT/KR2010/002497
Authority: WO
Inventors: 조경은; 엄기현; 성연식
Original assignee: 동국대학교 산학협력단
Priority date: 2009-04-24
Filing date: 2010-04-21
Publication date: 2010-10-28
Also published as: WO2010123275A3; KR100951159B1

Abstract

The present invention relates to a technique for developing software applications using brainwaves. In one aspect of the present invention, brainwaves received from a user can be converted to standard brainwaves having a standard normal distribution phase, and predetermined commands can be mapped for various sections in the brainwave data obtained by the conversion to the standard brainwaves having the standard normal distribution phase. Further, in order to convert the received brainwaves to a standard normal distribution, a brainwave normal distribution can be prepared for each user and each stimulus, and conversion parameters for converting the brainwave normal distribution to a standard normal distribution can be determined. There is therefore an advantage in software applications development, in that user-based brainwave pattern classification and event processing can be effected in a straightforward fashion.

Description

Application program development device and method using EEG

Related to EEG processing technology and application development technology.

EEG generally refers to the electric current generated by the activity of the brain. EEG is also called an electroencephalogram (EEG), just like an electrocardiogram or an EMG, because it usually appears in the form of waves.

EEG can be classified into several types according to its frequency and amplitude.

Alpha waves are regular waves, usually around 10 Hz, appearing continuously. Alpha waves usually appear at a steady state. Alpha waves are suppressed when you open your eyes, look at objects, or mentally excite.

Beta (β) waves are brain waves when consciousness is awake and appears in the cerebrum during active brain function. If this condition persists, you are in a state of tension and anxiety.

Gamma (γ) waves perform at high levels of complex mental function or appear in extreme conditions of excitement.

In addition, there are delta (δ) waves and theta (θ) waves appearing on the surface of the water.

As such, it is possible to infer the mental state of the person according to the waveform of the EEG, and thus, various applications that depend on the mental state have recently appeared.

For example, a game using an EEG is typical. For example, in a game based on EEG, the movement of a character in the game may be controlled according to mental concentration.

However, since EEG has a different pattern for each user, it is difficult to develop an application program using EEG.

For example, if an event in the game is to be generated depending on the concentration, the EEG information corresponding to the concentration should be defined, and the event should be mapped to the EEG information.

However, since the EEG information corresponding to the concentration can be different for each person, it is inconvenient for the developer to manually correct the user EEG and check whether an event occurs.

In the present specification, an apparatus and method for developing an application program using an EEG for converting an EEG into a standard EEG on a standard normal distribution and automatically mapping a specific command based on the EEG information converted into a standard EEG on a standard normal distribution are disclosed.

Specifically, the application development apparatus using the EEG according to an aspect of the present invention, the EEG conversion unit and the standard EEG for converting the measured EEG to the standard EEG on the standard normal distribution for each section, and the command for each section It may include a command mapping unit for mapping.

The apparatus may further include a profile storage unit for storing a plurality of EEG normal distributions and a parameter generator for generating conversion parameters for converting each EEG normal distribution into a standard normal distribution.

According to an aspect of the present invention, the EEG converting unit may convert the EEG measured using the conversion parameter and the user information into a standard EEG on the standard normal distribution.

According to an aspect of the present invention, the EEG normal distribution may be defined as EEG information normalized to a plurality of EEGs generated by giving a specific stimulus to a specific user several times.

According to an aspect of the present invention, a plurality of conversion parameters may be generated and stored by reflecting characteristics of each user and each stimulus.

On the other hand, the application program development method using the EEG according to an aspect of the present invention, comprising the steps of constructing a profile consisting of a plurality of EEG normal distribution, generating a conversion parameter for converting each EEG normal distribution to a standard normal distribution, The method may include converting the measured EEG into a standard EEG on a standard normal distribution by using the conversion parameter and user information, and dividing the standard EEG into sections and mapping a command to each section.

In case of developing an application program using EEG, it is necessary to execute a specific command in a predetermined waveform section. Since there are variations in brain waves for each individual, it is not easy to detect a predetermined waveform section or classify an EEG pattern. However, according to the disclosed contents, the received brain waves are converted into the standard EEG on the standard normal distribution based on the EEG normal distribution and the conversion parameters, and the instructions are mapped based on the EEG information having the standard normal distribution, so that the application can be easily developed. .

1 is a block diagram of an application development apparatus using an EEG according to an embodiment of the present invention.

2 and 3 show the EEG normal distribution according to an embodiment of the present invention.

4 illustrates a conversion parameter according to an embodiment of the present invention.

5 illustrates an EEG converter according to an embodiment of the present invention.

6 illustrates a method of mapping instructions according to an embodiment of the present invention.

Figure 7 shows the overall flow of the application development method using the EEG according to an embodiment of the present invention.

Application development method using an EEG according to an embodiment of the present invention comprises the steps of constructing a profile consisting of a plurality of EEG normal distribution; Generating conversion parameters for converting each EEG normal distribution into a standard normal distribution; Converting the measured EEG into a standard EEG on a standard normal distribution using the conversion parameter and user information; And dividing the standard EEG into sections and mapping a command to each section.

Hereinafter, with reference to the accompanying drawings will be described in detail a specific example for the practice of the present invention.

Referring to FIG. 1, the apparatus 100 for developing an application program using EEG according to the present embodiment may include a profile storage unit 101, a parameter generator 102, an EEG converter 103, and an instruction mapping unit 104. It may include.

The profile storage unit 101 stores a plurality of EEG normal distributions.

The EEG normal distribution may be defined as EEG information that is normalized to a plurality of EEGs generated by giving a specific user a specific stimulus several times. For example, an EEG induced by a stimulus can be expressed as a function of current value over time. At this time, it is possible to obtain a predetermined EEG value by sampling and quantizing the continuous current value. Here, the EEG values are set on the X axis, and the Z value of the normal distribution corresponding to the frequency of occurrence of each EEG value is set on the Y axis, thereby obtaining the EEG normal distribution.

The parameter generator 102 generates a conversion parameter for converting each EEG normal distribution stored in the profile storage 101 into a standard normal distribution. In addition, the parameter generator 102 may store conversion parameters for each user and each stimulus.

Normal distribution refers to the distribution that appears when some data is collected in the natural state, and standard normal distribution refers to a normal distribution with a mean of 0 and a standard deviation of 1. In other words, the conversion parameter may mean an adjustment value for converting each EEG normal distribution given for each user and stimulus into a standard normal distribution. Here, the adjustment may be, for example, extending or decreasing the normal distribution curve to both sides, or parallelizing the normal distribution curve as a whole.

The brain wave converter 103 receives user information and a user brain wave. The brain wave converter 103 may convert the received user brain wave into a standard brain wave on a standard normal distribution.

For example, the EEG converter 103 may receive user information, and may inquire and select a conversion parameter corresponding to the received user information from the parameter generator 102. In this case, the user information may include a user ID and a kind of stimulus given to the user. In addition, the EEG converting unit 103 may apply the selected conversion parameter to the received user EEG to convert the received user EEG into a standard EEG on the standard normal distribution.

The command mapping unit 104 may map commands according to the converted standard brain waves. For example, the transformed standard EEG may be represented by a standard normal distribution curve, and the standard normal distribution curve may be divided into sections and the command may be mapped to each section.

Here, the interval may be a converted standard EEG value on the standard normal distribution. The command mapped to each section may be various commands or events to be executed in an application program.

According to this configuration, since the measured EEG values are converted into standard EEG values on the standard normal distribution, it is easy to classify or analyze patterns of EEG information. In addition, since various commands are mapped based on EEG information following a standard normal distribution, it is possible to develop an application program using only the mapped commands without considering individual characteristics of the user.

Next, referring to FIGS. 2 and 3, the EEG normal distribution stored in the profile storage unit 101 will be described in more detail.

In FIG. 2, the EEG 201 may be measured by giving a specific user (eg, A, B, etc.) a specific stimulus (eg, α, β, γ, etc.) several times.

In addition, the EEG normal distribution 202 may be generated by user and stimulus by normalizing the plurality of EEG 201 measured in this way.

For example, as shown in FIG. 3, it is possible to apply the stimulus α to user A 100 times to obtain the frequency of occurrence of each EEG value. The EEG value may be a value obtained by sampling and quantizing a continuous current value of the EEG, and the occurrence frequency may be a value obtained by dividing the number of occurrences of the EEG value by the total number of EEG values.

As shown in FIG. 2, a plurality of EEG normal distributions 202 may be generated for each user and stimulus, and the generated EEG normal distributions are stored in the profile storage unit 101 in a table form as shown in FIG. 3. It is possible.

Next, the conversion parameter according to an embodiment of the present invention will be described in more detail with reference to FIG. 4.

In FIG. 4, 401 represents an EEG normal distribution, and 402 represents an EEG standard normal distribution. At 402, the output value is a portion corresponding to the EEG value of 401, and has a value from 1 to 100. In addition, the probability of occurrence of each output value at 402 may be obtained in advance through a probability density function following a standard normal distribution table or a standard normal distribution.

According to an embodiment of the present invention, converting the EEG normal distribution to the standard normal distribution may be to convert the EEG value of 401 to the output value of 402. However, since the EEG value and the output value do not have the same range as shown in FIG. 4, it is necessary to adjust them, and the degree of adjustment serves as a conversion parameter.

For example, when the frequency of occurrence of the EEG value and the probability of generating the output value are compared with each other, it is possible to use the EEG value as it is. In addition, when there is a difference between the frequency of occurrence of the EEG value and the probability of the output value, random processing is performed after one EEG value corresponds to two or more output values, or two or more EEG values are output values. It is possible to adjust to correspond to.

In FIG. 4, P1 represents such an adjustment value, that is, a conversion parameter, and there may be a plurality of conversion parameters for each user and stimulus. For example, the range of EEG values may vary according to a given EEG even for each user or the same user, and thus the degree of control must also vary. Such conversion parameters are generated and stored by the parameter generator 102 as described above.

Next, with reference to FIG. 5, the EEG conversion unit 103 will be described in more detail how to convert the EEG into the standard EEG on the standard normal distribution.

For example, in FIG. 5, the EEG converting unit 103 may receive user information and EEG. The user information may be a kind of user ID and stimulus.

The EEG converter 103 inquires a corresponding conversion parameter by using the received user information. For example, in FIG. 5, when receiving a brain wave by giving a stimulus α to a user A, the brain wave converter 103 may select a P1 conversion parameter.

The EEG converter 103 may select a corresponding conversion parameter in the parameter generator 102 and apply the selected conversion parameter to the received EEG to convert the received EEG into a standard EEG on the standard normal distribution.

Next, referring to FIG. 6, a method of mapping the command by the command mapping unit 104 will be described in more detail.

The command mapping unit 104 may classify the standard normal distribution curve of the EEG converted by the EEG converter unit by section, and map the command to each section.

In Figure 6, 601 shows an example of such a standard normal distribution curve. 602 and 603 show respective sections of the curve according to the present embodiment. In the present embodiment, the interval may be a converted standard EEG value on the standard normal distribution. For example, the output value of 402 in FIG. 4 may be each section.

In addition, commands may be mapped to each section. For example, the command mapping unit 104 may map command 1 to section 1 602 and map command 2 to section 2 603.

In this case, the commands may be various commands or events to be executed in an application program, and the size or width of each

section

602 and 603 may also be variously set.

7 illustrates an application program development method using an EEG according to an embodiment of the present invention.

In FIG. 7, in the method for developing an application program using the EEG according to the present embodiment, first, a profile consisting of a plurality of EEG normal distributions is configured (701).

For example, it is possible for the profile storage unit 101 to store the EEG normal distribution in which a specific stimulus is given to a specific user several times and normalized to a plurality of EEGs each generated.

Then, a conversion parameter for converting each EEG normal distribution into a standard normal distribution is generated (702).

For example, in order to convert the EEG normal distribution of each user and stimulus into a standard normal distribution, the parameter generator 102 grasps the degree of adjustment of each EEG normal distribution and sets the degree of adjustment of each EEG normal distribution as a conversion parameter. It is possible to save by user and by stimulus.

The measured EEG is converted into a standard EEG on the standard normal distribution using the conversion parameter and the user information (703).

For example, the EEG converting unit 103 may receive the user information and the user EEG and inquire and select a corresponding conversion parameter based on the received user information. It is also possible to apply the selected conversion parameter to the received user EEG to convert the measured EEG into a standard EEG on the standard normal distribution.

Then, the standard normal distribution curve is divided for each section, and commands are mapped to each section (704).

For example, as illustrated in FIG. 6, the command mapping unit 104 may divide the standard normal distribution into sections, and map commands to each section.

In the above, the specific example for the implementation of the present invention has been described. The foregoing embodiments are intended to illustrate the present invention by way of example and the scope of the present invention is not limited to the specific embodiments.

Claims

EEG conversion unit for converting the measured EEG to the standard EEG on the standard normal distribution; And

Apparatus for developing an application program using an EEG comprising a command mapping unit for classifying the standard EEG for each section, and the command for each section.
The method of claim 1,

A profile storage unit for storing a plurality of EEG normal distributions; And

A parameter generator for generating conversion parameters for converting each EEG normal distribution into a standard normal distribution; Application development apparatus using a brain wave further comprising.
The method of claim 2,

The brain wave converter, the application program development apparatus using the brain wave, characterized in that for converting the measured brain wave to the standard brain wave using the conversion parameter and the user information.
The method of claim 2,

The EEG normal distribution of the EEG application program development apparatus, characterized in that stored for each user of the application program using the EEG or the type of stimulation given to the user of the application program using the EEG.
Constructing a profile consisting of a plurality of EEG normal distributions;

Generating conversion parameters for converting each EEG normal distribution into a standard normal distribution;

Converting the measured EEG into a standard EEG on a standard normal distribution using the conversion parameter and user information; And

Dividing the standard EEG by section and mapping a command to each section; Application development method using a brain wave comprising a.
The method of claim 5,

The EEG normal distribution, the EEG-based application program development method, characterized in that stored for each user of the application program using the EEG or the type of stimulation given to the user of the application program using the EEG.