KR101432044B1 - Apparatus and method for measuring visual fatigue based on brain wave - Google Patents

Abstract

An EEG information acquiring unit for acquiring EEG information at different positions on the scalp; And a controller for acquiring causal relationship information between brain wave information at different positions on the scalp and acquiring visual fatigue information based on the causal relationship information, and an apparatus and method for measuring visual fatigue based on brain waves are provided .
According to the present invention, it is possible to provide an objective index of visual fatigue caused by watching 3D contents.

Description

[0001] APPARATUS AND METHOD FOR MEASURING VISUAL FATIGUE BASED ON BRAIN WAVE [0002]

The present invention relates to an apparatus and method for measuring visual fatigue based on brain waves. And more particularly, to a device and a method for measuring visual fatigue by quantifying the degree of interaction between EEG information of a specific part of the brain.

As the 3D industry is emerging, there is a very active movement to establish human safety standards in the production of contents such as movie game broadcasts. Therefore, manufacturing standards that do not cause visual fatigue due to excessive stereoscopic vision should be prepared.

According to Patent Document 1, the visual fatigue is measured by perceiving the subjective report of the subject before and after viewing of the three-dimensional image or the correlation between the congestion distance of the user's eyes and the focal distance. However, there is a problem in that the measured data can not be applied to the video industry or the medical industry because it is difficult to obtain objective and reliable fatigue by the measurement method of Patent Document 1.

According to Patent Document 2, a method of measuring visual fatigue using an ERP (Event Related Potential) reaction based on the oddball paradigm is proposed. However, there is a limitation that the visual fatigue can not be measured at the same time as viewing the 3D contents. Further, since the additional ERP measurement itself may cause fatigue after the content is viewed, the time required for the ERP measurement is equal to the viewing time of the 3D content But there is a limit to the practical fatigue measurement method.

Patent Document 3 attempts to quantify visual fatigue through VEP (Visual Evoked Potential) measurement, but has a limit similar to that of Patent Document 2 above.

Korean Laid-Open Patent No. 2012-0125701 Korean Patent Publication No. 2010-0104330 Japanese Patent Application Laid-Open No. 10-52402

One aspect of the present invention relates to a visual fatigue measuring device that can estimate visual fatigue of a user caused by viewing 3D contents by objectively and reliably measuring the degree of interaction between brain wave waveforms of major parts of the brain, .

However, the problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided an EEG information acquiring unit for acquiring EEG information at different positions on a scalp; And a controller for acquiring causal relationship information between brain wave information at different positions on the scalp and acquiring visual fatigue information based on the causal relationship information.

According to another aspect of the present invention, there is provided a method for generating EEG information, the method comprising: acquiring brain wave information at different positions on a scalp; Obtaining causal relationship information of mutual EEG information at different positions on the scalp; And acquiring visual fatigue information based on the causal relationship.

According to the present invention, it is possible to provide an objective index of visual fatigue caused by watching 3D contents.

1 is a block diagram showing an apparatus for measuring visual fatigue according to an embodiment of the present invention.
2 is a flowchart illustrating a method of measuring visual fatigue according to an embodiment of the present invention.
FIG. 3 is a graph showing statistical validity of a visual fatigue measurement method according to an embodiment of the present invention.
4 is a view showing a standard measurement position on the scalp for EEG measurement according to an embodiment of the present invention.
FIG. 5 is a graph showing a causal relationship between EEG waveforms at a plurality of EEG measurement positions on a scalp according to an embodiment of the present invention.
6 is a diagram illustrating an example of an EEG waveform used to derive a cause-and-effect relationship between EEG waveforms according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating temporal fatigue predicted based on causality between EEG waveforms according to an embodiment of the present invention with time.
FIG. 8 is a diagram illustrating an example of an apparatus for measuring visual fatigue based on an EEG according to an embodiment of the present invention.

It is noted that the technical terms used herein are used only to describe specific embodiments and are not intended to limit the invention. It is also to be understood that the technical terms used herein are to be interpreted in a sense generally understood by a person skilled in the art to which the present invention belongs, Should not be construed to mean, or be interpreted in an excessively reduced sense. Further, when a technical term used herein is an erroneous technical term that does not accurately express the spirit of the present invention, it should be understood that technical terms that can be understood by a person skilled in the art are replaced. In addition, the general terms used in the present invention should be interpreted according to a predefined or prior context, and should not be construed as being excessively reduced.

Also, the singular forms "as used herein include plural referents unless the context clearly dictates otherwise. In the present application, the term "comprising" or "comprising" or the like should not be construed as necessarily including the various elements or steps described in the specification, Or may be further comprised of additional components or steps.

Further, the suffix "module" and "part" for the components used in the present specification are given or mixed in consideration of ease of description, and do not have their own meaning or role.

Furthermore, terms including ordinals such as first, second, etc. used in this specification can be used to describe various elements, but the elements should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals refer to like or similar elements throughout the several views, and redundant description thereof will be omitted.

In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. It is to be noted that the accompanying drawings are only for the purpose of facilitating understanding of the present invention, and should not be construed as limiting the scope of the present invention with reference to the accompanying drawings.

Hereinafter, an apparatus and method for measuring visual fatigue based on the brain waves of the present invention will be described in detail so that those skilled in the art can easily carry out the present invention.

1 is a block diagram illustrating an apparatus 100 for measuring visual fatigue according to an embodiment disclosed herein.

The visual fatigue measuring apparatus 100 includes a biometric information acquiring unit 140 and a control unit 180. And may include at least one of a communication unit 110, an EEG measuring unit 121, a user input unit 130, an output unit 150, a memory 160, and an interface unit 170. However, no. The components shown in FIG. 1 are illustrative, and a visual fatigue measurement device having more or fewer components may be implemented.

The communication unit 110 may include one or more modules that enable network communication between the visual fatigue measurement apparatus 100 and the communication system, and between the visual fatigue measurement apparatus 100 and the EEG measurement unit.

The communication unit 110 includes a function for allowing the visual fatigue measuring apparatus to connect to the network and includes a visual fatigue measuring apparatus 100, a communication system, a visual fatigue measuring apparatus 100 and an output apparatus or a visual fatigue measuring apparatus 100) and the EEG measurement unit.

For example, the visual fatigue measuring apparatus 100 may be connected to an output device, or a separate device such as an EEPROM, by the communication unit 110. The communication unit 110 may include an Internet module 113, a short distance communication module 114, and other communication means.

The Internet module 113 refers to a module for accessing the Internet and may be built in or enclosed in the visual fatigue measurement apparatus 100. WLAN (Wi-Fi), Wibro (Wireless broadband), Wimax (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access) and the like can be used as Internet technologies.

The short-range communication module 114 refers to a module for short-range communication. Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and the like can be used as a short range communication technology.

The EEG measuring unit 121 may measure the EEG on the scalp as shown in FIG. 4, for example, Fp1, Fp2, F3, Fz, F4, F7, F8, T3, C3, Cz, C4, T4, T5, P3, P4, T6, O1, and O2, but the present invention is not limited thereto. At this time, the voltage difference between each standard measurement position and the reference electrode connecting both earbuds can be measured and utilized as brain wave information. This is an example, and a more various apparatus for measuring visual fatigue can be implemented based on various combinations of reference electrodes and measurement electrodes used for extracting EEG information.

The user input unit 130 generates input data for controlling the operation of the visual fatigue measurement apparatus 100 by a user. The user input unit 130 may include a key pad, a dome switch, a touch pad (static / static), a jog wheel, a jog switch, and the like.

The bio-information acquiring unit 140 may include an EEG information acquiring unit 141.

EEG is a waveform that draws or amplifies the current that occurs in response to brain activity. The types of brain waves that occur in the human brain are divided into gamma, alpha, beta, cetapha, delta, ILF (infra low fluctuation), and DC depending on the frequency band. Gamma waves can have frequencies above 30 Hz. Alpha waves are brain waves that occur when a person closes his or her eyes and relaxes, and can have frequencies between 8 Hz and 12 Hz. Beta waves can have frequencies between 13 Hz and 32 Hz, most of which occur when consciousness is awake. Theta waves occur in shallow sleep and can have frequencies lower than that of alpha waves (4 Hz to 8 Hz) and are produced at the boundary between the perception and the dream. Delta waves are frequencies of frequencies below 4 Hz that are lower than those of theta waves and are the most commonly measured EEGs in the sleep or unconscious state. The SCP (slow cortical potential) may have a frequency of less than 1 Hz.

The EEG information obtaining unit 141 may obtain EEG information at different positions on the scalp measured through the EEG measuring unit 121. [ The EEG information may include at least one of amplitude information of the EEG waveform, frequency information of the EEG waveform, amplitude information of the EEG waveform of the specific frequency band, and frequency information of the EEG waveform of the specific frequency band.

The EEG measuring unit 121 may be provided in the visual fatigue measuring apparatus 100 or may be provided separately from the visual fatigue measuring apparatus 100. When the brain wave measuring unit 121 is provided in the visual fatigue measuring apparatus 100, the brain wave information obtaining unit 141 can directly obtain brain wave information measured by the brain wave measuring unit 121, In the case where it is provided separately from the visual fatigue measuring apparatus 100, the brain wave information measured by an EEG measuring unit such as an EEG may be received using the Internet module 113, the short distance communication module 114, and other communication means.

The display unit 151, the sound output module 152, the haptic module 154, and the like may be included in the output unit 150.

The output unit 150 may be provided in the visual fatigue measurement apparatus 100.

Alternatively, the output unit 150 may be provided separately from the visual fatigue measurement apparatus 100.

For example, the visual fatigue measuring apparatus 100 may output information processed by the visual fatigue measuring apparatus through a separate output unit using the Internet module 113, the short-range communication module 114, and other communication means.

The display unit 151 displays (outputs) information processed by the visual fatigue measurement apparatus 100. [ For example, it is possible to display brain wave information acquired by the visual fatigue measuring apparatus. In addition, visual fatigue information of a user derived by combining effective brain wave information can be displayed.

The display unit 151 may be a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display display, a 3D display, and an e-ink display.

The sound output module 152 also outputs an acoustic signal related to information (for example, brain wave information, visual fatigue information, etc.) processed by the visual fatigue measuring apparatus 100. [ The audio output module 152 may include a receiver, a speaker, a buzzer, and the like.

The haptic module 154 generates various tactile effects that the user can feel. A typical example of the haptic effect generated by the haptic module 154 is vibration. The intensity and pattern of the vibration generated by the tactile module 154 are controllable. For example, different vibrations may be synthesized and output or sequentially output.

In addition to the vibration, the haptic module 154 may include a pin arrangement vertically moving with respect to the contact skin surface, a spraying force or suction force of the air through the injection port or the suction port, a touch on the skin surface, contact with an electrode, And various tactile effects such as an effect of reproducing a cold sensation using an endothermic or exothermic element can be generated.

The haptic module 154 can transmit the tactile effect through direct contact, and can be implemented so that the user can feel the tactile effect through the muscular sense of the finger or arm. The haptic module 154 may include two or more haptic modules 154 according to the configuration of the visual fatigue measurement device.

The haptic module 154 can generate a haptic effect associated with information (e.g., real-time brain wave information, real-time predicted visual fatigue information, and the like) processed in the visual fatigue measuring apparatus 100. [

The memory 160 may store a program for the operation of the controller 180 (for example, a causal relation calculating method between brain wave information, a visual fatigue calculating method based on a causal relationship), and may store input / output data For example, brain wave information, visual fatigue information, etc.) may be temporarily stored. The memory 160 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory), a RAM (Random Access Memory), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read- A disk, and / or an optical disk.

The interface unit 170 may serve as a path to an external device connected to the visual fatigue measuring apparatus 100. The interface unit 170 receives data from an external device or supplies power to each component in the visual fatigue measurement device 100 or transmits data to the external device in the visual fatigue measurement device 100 do.

The controller 180 typically controls the overall operation of the visual fatigue measuring apparatus 100. Particularly, the controller 180 can obtain causal relation information of mutual EEG information at different positions on the scalp, and obtain visual fatigue information based on the causal relationship information.

Specifically, the control unit 180 amplifies the acquired EEG, removes a spurious component from the amplified signal, and converts the signal from which the spurious component is removed to a digital signal. Then, the amplified EEG is fourier transformed, and the EEG output value is calculated to analyze the EEG. Further, the first brain wave signal generated at a specific position on the scalp may function to calculate the degree of influence on the second brain wave signal generated at another position. Visual fatigue information can be obtained based on the causal relationship between brain wave information.

The EEG information may include at least one of amplitude information of the EEG waveform, frequency information of the EEG waveform, amplitude information of the EEG waveform of the specific frequency band, and frequency information of the EEG waveform of the specific frequency band. The causal relationship information may include at least one of causal relationship information between EEG waves and causal relationship information between EEG waves of a specific frequency band.

Hereinafter, a method of measuring the visual fatigue of the present invention will be described with reference to the accompanying drawings.

2 is a flowchart illustrating a method of measuring visual fatigue according to an embodiment of the present invention.

According to an embodiment of the present invention, the visual fatigue measuring apparatus can acquire EEG information at different positions on the scalp (S210).

Fz, F4, F7, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 on the scalp as shown in Fig. , And O2, can be measured at standard measurement positions. At this time, it is possible to acquire brain wave information by measuring a voltage difference between each standard measurement position and a reference electrode connecting both earlobes.

According to an embodiment of the present invention, a plurality of brain wave information can be obtained. The plurality of EEG information refers to a plurality of EEG information measured at different positions on the scalp in the same time zone. When the brain wave information acquired at one position in the same time zone is defined as the first brain wave information, the brain wave information acquired at a position different from the one position can be defined as the second brain wave information (see FIG. 6).

When the brain wave measuring unit 121 is provided in the visual fatigue measuring apparatus 100, the visual fatigue measuring apparatus 100 can directly acquire brain wave information.

When the EEG measuring unit 121 is provided separately from the visual fatigue measuring apparatus 100, the visual fatigue measuring apparatus 100 transmits the cardiac information measured by the EEG measuring unit 121 to the Internet module 113, , The short-range communication module 114, or the like.

Then, the controller 180 may obtain a causal relation between brain wave information at different positions on the scalp (S220). The causal relationship information may include at least one of causal relationship information between the EEG waveforms and causal relationship information between the EEG waveforms of the specific frequency band.

An example of a method of acquiring the causal relationship between the EEG information is as follows, but is not limited thereto.

For example, the controller 180 may convert the first brain wave information into a frequency domain and the second brain wave information into a frequency domain. At this time, the controller 180 may calculate a causal relation coefficient between the first EEG information and the second EEG information.

For example, assuming that EEG waveforms X and Y are obtained at two standard measurement positions, the procedure for obtaining the causality coefficient for each waveform can be performed according to the following procedure.

,

)를 획득할 수 있다. 이하, 별다른 언급이 없는 한, 파형의 측정치와 예측치의 단위는 전압으로 표시될 수 있다.
First, the difference between the measured value and the predicted value of X and Y at the time t according to the following equations (1) and (2)

,

Can be obtained. Hereinafter, unless otherwise specified, the measured values of the waveform and the unit of the predicted value may be expressed by a voltage.

(1)

,

,

: t시점에서의 X의 측정치,

: t시점 이전의 모든 X 값으로부터 환산한 t시점에서의 X의 예측치,

: 자기상관계수,

: X의 측정치와 예측치의 차이]
[

: a measurement value of X at time t,

: predicted value of X at time t converted from all X values before time t,

: Auto correlation coefficient,

: Difference between measured and predicted value of X]

(2)

,

,

: t시점에서의 Y의 측정치,

: t시점 이전의 모든 Y 값으로부터 환산한 t시점에서의 Y의 예측치,

: 자기상관계수,

: Y의 측정치와 예측치의 차이]
[

: a measurement value of Y at time t,

: predicted value of Y at time t converted from all Y values before time t,

: Auto correlation coefficient,

: Difference between measured and predicted value of Y]

를 얻을 수 있다. 또한, 상기 수학식 2에서는 Y의 과거값으로부터 환산한 t시점에서의 Y의 예측치와 실제 t 시점에서 측정한 Y의 측정치와의 차이를 통해 오차

를 얻을 수 있다.
In Equation (1), the difference between the predicted value of X at the time t converted from the past value of X and the measured value of X measured at the actual time t,

Can be obtained. In Equation (2), the difference between the predicted value of Y at time t converted from the past value of Y and the measured value of Y measured at actual time t,

Can be obtained.

,

)를 획득할 수 있다
If all the X values and all the Y values before the time t are considered together, the difference between the measured values of X and Y at the time t and the predicted value (

,

) ≪ / RTI >

(3)

:t시점에서의 X의 측정치

: t시점 이전의 모든 X 값으로부터 환산한 t시점에서의 X기반 X의 예측치 성분,

: t시점 이전의 모든 Y 값으로부터 환산한 t시점에서의 Y기반 X의 예측치 성분,

: X의 측정치와 예측치의 차이]
[

: a measure of X at time t

: predicted component of X-based X at time t converted from all X values before time t,

: predicted component of Y-based X at time t converted from all Y values before time t,

: Difference between measured and predicted value of X]

(4)

:t시점에서의 Y의 측정치

: t시점 이전의 모든 X 값으로부터 환산한 t시점에서의 X기반 Y의 예측치 성분,

: t시점 이전의 모든 Y 값으로부터 환산한 t시점에서의 Y기반 Y의 예측치 성분,

: Y의 측정치와 예측치의 차이]
[

: a measure of Y at time t

: predicted component of X-based Y at time t converted from all X values before time t,

: predicted component of Y-based Y at time t converted from all Y values before time t,

: Difference between measured and predicted value of Y]

와 수학식 3에서 얻은

사이에,

>

의 관계가 성립하면 Y가 X에 영향을 미친다는 것을 의미한다. 또한, 상기 수학식 2에서 얻은

와 수학식 4에서 얻은

사이에,

>

의 관계가 성립하면 X가 Y에 영향을 미친다는 것을 의미한다.
In the formula (1)

And the equation

Between,

>

, Y implies that it affects X. In addition, the

And the equation

Between,

>

, It means that X affects Y. In other words,

이며,

,

로 정의할 수 있다.
If the terms expressing the error are not correlated with each other over time, the Covariance matrix

Lt;

,

.

At this time, Y _t has a causal relation coefficient as shown in Equation (5) with respect to X _t .

(5)

,

,

: Y에서 X로의 인과관계 계수 ,

: X에서 Y로의 인과관계 계수][

: Causality coefficient from Y to X,

: Causality coefficient from X to Y]

According to Equation (5), the controller 180 can obtain the brain-wave causality coefficient F _{Y ? X} or F _{X ? Y} based on the brain wave information.

The greater the causality coefficient, the greater the degree to which the EEG signal occurring at a particular location affects the EEG signal occurring at another location.

FIG. 5 shows an example of a causal relationship between EEG waveforms at a plurality of EEG measurement positions on the scalp, based on a causality called Granger causality composed of a plurality of EEG waves. Among the combinations of the interconnections between the measurement positions, the combination of the interconnections having a relatively high visibility and close fatigue is shown. The degree of influence on fatigue is proportional to the thickness of the connecting line. As the degree of fatigue increases as the degree of fatigue increases, the interior is marked as a fully filled line. When the fatigue increases as the degree of fatigue increases, 5, the degree of fatigue increases as the degree of connection increases in the case of O1? F3, F3? C4, F3? P3, P3? O2 and P4? O1. In the case of O2? F3 and O2? F4, It can be seen that fatigue increases as the temperature decreases. Also, in the case of O1 → F3 in these networks, the degree of influence on fatigue is relatively large.

It is possible to calculate the degree of the influence of the EEG signal generated at the first position on the scalp on the EEG signal generated at the second position, that is, the causal relationship between EEGs occurring at each position based on the Granger causality principle. The cause and effect relationship can be quantified by quantification. For example, the causality coefficient calculated through the processes of Equations (1) to (5) is an example.

The visual fatigue information may be obtained based on the causal relationship (S230).

A visual fatigue prediction function can be derived by combining coefficients having a high specific gravity reflecting the visual fatigue among the causal relation coefficients. These functions may appear differently depending on the user's personal characteristics or may appear as normal functions. In the case of a person whose prediction function deviates much from the general function, the function optimized for the individual can be derived first, and the visual fatigue based on the EEG can be presented in real time by the procedure proposed in the present invention.

According to one embodiment of the present invention, the visual fatigue based on the actual EEG can be derived as follows using the result of Equation (5).

(6)

,

,

은 각각 O1→F3, F3→P3, O2→F3의 인과관계 계수를 의미한다. O1은 좌측 후두엽의 표준 측정위치를, F3은 좌측 전두엽의 표준 측정위치를, P3는 좌측 두정엽의 표준 측정위치를, O2는 우측 후두엽의 표준 측정위치를 각각 나타낸다. here,

,

,

Denote causality coefficients of O1? F3, F3? P3, and O2? F3, respectively. O1 is the standard measurement position of the left occipital lobe, F3 is the standard measurement position of the left frontal lobe, P3 is the standard measurement position of the left parietal lobe, and O2 is the standard measurement position of the right occipital lobe.

The visual fatigue was expressed as a value between 0 and 10, and the statistical validity was confirmed through a data plot as shown in FIG. Referring to FIG. 3, a visual analogue scale (VAS) reported at intervals of 10 minutes during 20 hours of viewing of 3D contents by one of the subjects and a prediction fatigue based on the EEG predicted according to Equation (6) (Predicted VAS). The relationship between the subjective fatigue and the predicted fatigue is R = 0.58, and the p value indicating statistical significance is p = 0.04.

As described above, it is possible to derive the fatigue function that best predicts the fatigue that the user feels subjective by combining the degree of connection between the pairs of brain wave information based on the EEG and the correlation coefficient.

The visual fatigue information obtained through this process is output through the display unit 151. [ In the present invention, the visual fatigue information may be obtained and transmitted to a separate device so that the information may be used. Alternatively, the visual fatigue information may be directly outputted and confirmed, so that the step of outputting the visual fatigue may be selectively added .

7 is an example of a diagram expressing visual fatigue predicted in real time on the basis of a causal relationship between EEG waveforms. The control unit 180 can calculate the fatigue index in real time, thereby enabling to grasp an environment and conditions in which the fatigue index is excessively induced. Such information can be useful not only for visual fatigue measurement devices but also for electronic devices, 3D content appreciation devices or 3D contents which can mount 3D contents including images.

FIG. 8 shows an example of a visual fatigue measuring apparatus for displaying visual fatigue information measured by the above method. The brain wave-based visual fatigue acquired by the present invention can be continuously updated and displayed on the display unit 151 in real time as time passes.

In addition, the acoustic output module 152 and the haptic module 154 may separately output the visual fatigue.

Through the information thus output, the user can intuitively confirm the objective fatigue index based on the brain wave. In this manner, by enabling the fatigue information based on the bio-signal to be confirmed in real time, the state of the environment and the body can be improved so that the fatigue index does not exceed the preferable range.

The visual fatigue measurement method according to the embodiment of the present invention described above can be used individually or in combination with each other. Further, the steps constituting each embodiment can be used individually or in combination with the steps constituting the other embodiments.

In addition, the above-described method can be implemented in a recording medium readable by a computer or the like using, for example, software, hardware, or a combination thereof.

According to a hardware implementation, the methods described so far can be applied to various types of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays processors, controllers, micro-controllers, microprocessors, and other electronic units for performing other functions.

According to a software implementation, the procedures and functions described herein may be implemented in separate software modules. The software modules may be implemented in software code written in a suitable programming language. The software code may be stored in a memory and executed by a processor.

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, but, on the contrary, It falls within the scope of the invention.

Claims (8)

An EEG information acquiring unit for acquiring EEG information at different positions on the scalp; And
And a control unit for acquiring causal relationship information of mutual EEG information at different positions on the scalp and acquiring visual fatigue information based on the causal relationship information,
Wherein the causal relationship information includes at least one of causal relationship information between the EEG waveforms and causal relationship information between EEG waves of a specific frequency band. The method according to claim 1,
An amplitude information of an EEG waveform, frequency information of an EEG waveform, amplitude information of an EEG waveform of a specific frequency band, and frequency information of an EEG waveform of a specific frequency band. delete The method according to claim 1,
The visual fatigue measuring apparatus may further include at least one of an output unit for outputting the visual fatigue information, an interface unit for transmitting the visual fatigue information to an external device, a communication unit for network connection, a memory, a user input unit, Visual fatigue measuring device based on EEG. Acquiring brain wave information at different positions on the scalp;
Obtaining causal relationship information of mutual EEG information at different positions on the scalp; And
And obtaining visual fatigue information based on the causal relationship,
Wherein the causal relationship information includes at least one of causal relationship information between EEG waveforms and causal relationship information between EEG waves of a specific frequency band. delete 6. The method of claim 5,
Wherein the visual fatigue measuring method further includes outputting the visual fatigue information. 6. The method of claim 5,
Wherein the visual fatigue measurement method further comprises transmitting the visual fatigue information to a separate apparatus.

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