KR20150030498A - Measurement of depression depth with frontal lobe brain waves - Google Patents

Abstract

A method for measuring the depth of depression using frontal brain waves is disclosed. The method comprises the steps of: obtaining a user′s frontal brain waves from an electroencephalogram (EEG) measurement device; converting data on the frontal brain waves into a frequency band through a fast Fourier transform (FFT); and measuring the depth of depression of the user by analyzing the power of alpha waves using a power spectrum for each frequency of the frontal brain wave data. Therefore, by obtaining brain waves using a minimum number of channels, the method enables a user to easily obtain the user′s brain waves and simply diagnose the user′s mental illness.

Description

TECHNICAL FIELD [0001] The present invention relates to a method for measuring depression depth using frontal lobe brain waves,

Embodiments of the present invention are directed to methods for measuring depression depth using EEG.

Along with the development of HCI (Human-Computer Interaction) field, various research and technology development using bio-signals are being actively performed. Biological signals include electrocardiogram, EMG, EEG (or electroencephalogram), body temperature, and respiratory rate.

In particular, EEG is a record of the currents that occur according to the activity of the brain. It can not only represent the function of the brain but also indirectly estimate the lesion through EEG findings. For example, EEG is clinically used as an indispensable test method for diagnosis of epilepsy, type classification of epilepsy, differential diagnosis of seizure diseases, and is also useful for classification of sleep stages, coma and brain death.

Until now, brain waves have been read depending on the visual analysis of the experts, and the visual readings are visualized by the experts, and the waveform, amplitude, frequency, and overall distribution and reactivity of the brain waves are examined and the state of the subject (awakening, Etc.) and age.

Conventional EEG analysis is difficult to read and only visual analysis by experts is used so that it can be applied immediately after reading. In addition, because of the nature of EEG, any one frequency prevails, but there are limitations in that it can not be used without expertise in the use of EEG due to the lack of objective technology to quantify with the naked eye since waveforms of various frequencies are mixed. For this reason, objective techniques for brain waves are difficult, and the usefulness of the diagnosis of diseases such as evaluation of cognitive function, dementia, and schizophrenia has not been found yet.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide an easy access for an expert as well as an ordinary person to an acquired EEG using the minimum channel.

Another object of the present invention is to evaluate objectively and accurately the depth of depression through quantitative EEG analysis rather than visual EEG analysis.

According to an embodiment of the present invention, a method for measuring a depression depth includes the steps of: obtaining frontal EEG data of a user from an EEG (electroencephalogram) measuring apparatus; Converting the frontal brain EEG data into a frequency band through Fast Fourier Transform; And measuring the depth of depression of the user through power analysis of an alpha wave using a power spectrum of each frequency of the frontal brain EEG data.

According to one aspect, the frontal brain EEG data can be measured using two channels of electrodes.

According to another aspect, the electrode can be attached to the left hemisphere (FP1) and the right hemisphere (FP2) of the frontal lobe based on the ten-twenty electrode system of the international standard.

According to another aspect of the present invention, the step of acquiring the frontal brain EEG data may be performed by separating left brain EEG data measured in the left hemisphere of the frontal lobe and right brain EEG data measured in the right hemisphere of the frontal lobe.

According to another aspect, the method for measuring the depth of depression may further include a preprocessing step of removing the noise signal included in the frontal brain EEP data before measuring the depth of depression.

According to another aspect, a method for measuring a depression depth further includes a preprocessing step of removing a noise signal included in the frontal brain EEG data using an Independent Component Analysis algorithm before measuring the depression depth can do.

According to another aspect, the method for measuring the depth of depression may further include a preprocessing step of removing a noise signal due to blinking in the frontal brain EEP data before measuring the depth of depression.

According to another aspect of the present invention, the step of converting into the frequency band comprises: dividing the frontal brain EEG data into at least one of a delta, a delta, ata, alpha, alpha, , β), and gamma wave (gamma, γ).

According to another aspect of the present invention, the step of acquiring the frontal brain EEG data comprises the steps of: obtaining left EEG data measured in the left hemisphere of the frontal lobe and right brain EEG data measured in the right hemisphere of the frontal lobe, Measuring the activation level of the left brain wave data and the right brain wave data; And measuring a degree of asymmetry in accordance with the difference in activation of the alpha wave between the left brain wave data and the right brain wave data.

According to another aspect, the degree of activation of the alpha-waves can be measured as the amount of alpha-wave generating power relative to the amount of frequency generating power in the entire band of EEG.

According to another aspect, the degree of asymmetry can be quantified to a numerical value through equation (1).

Equation 1:

(Wherein, P is _FP1a alpha activation degree, P _FP2a the left brain wave data indicates the degree of alpha-wave activation of the right brain wave data).

According to another aspect, the method for measuring the depth of depression may further include providing a value corresponding to the degree of asymmetry as a result of the measurement of the depth of depression.

According to an embodiment of the present invention, an apparatus for measuring a depression depth includes an acquiring unit for acquiring a frontal brain EEG data of a user from an EEG (electroencephalogram) measuring apparatus; A converting unit for converting the frontal brain EEG data into a frequency band through Fast Fourier Transform; And a measurement unit for measuring the depth of depression of the user through power analysis of an alpha wave using a power spectrum of each frequency of the frontal brain EEG data.

According to the embodiment of the present invention, the user can easily acquire brain waves by acquiring the brain waves using the minimum channel, and can easily diagnose the mental disease of the user.

According to the embodiment of the present invention, by self-diagnosis by the user himself or herself prior to diagnosis by experts through quantitative objective methods in the EEG analysis, the depressed patients are grasped by their own state, can do.

FIG. 1 is a flowchart illustrating a method for measuring a depression depth using a frontal brain EEG according to an embodiment of the present invention.
2 illustrates an electrode attachment position for EEG measurement according to an embodiment of the present invention.
FIG. 3 shows waveforms of the EEG before (eliminating) and after (eliminating) blink signals in an embodiment of the present invention.
4 is a flowchart illustrating a detailed procedure of depression depth calculation step in an embodiment of the present invention.
FIG. 5 is a block diagram illustrating an internal configuration of a device for measuring depth of depression using frontal brain EEG according to an embodiment of the present invention. Referring to FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

More particularly, the present invention relates to a method for evaluating the depth of depression using brain waves, and more particularly, to a method and apparatus for measuring the brain waves of the brain hemisphere frontal lobe (front part) And to measure the depth of depression at the end.

FIG. 1 is a flowchart illustrating a method for measuring a depression depth using a frontal brain EEG according to an embodiment of the present invention. The depression depth measurement method according to one embodiment may be performed by the depression depth measurement apparatus described with reference to FIG.

Referring to FIG. 1, a depression depth measurement method according to an exemplary embodiment includes an EEG acquisition step 100, a noise removal step 200, a digital conversion step 300, a depression depth calculation step 400, 500).

In the EEG acquiring step 100, the depression depth measuring device can acquire an EEG signal, which is an EEG signal measured from the EEG measuring device. In this embodiment, instead of multi-channel EEG analysis through multi-channel EEG acquisition by a specialist, EEG can be acquired using only the minimum channel necessary for EEG analysis. For example, the depression depth measuring apparatus can acquire a user's brain wave signal measured through two channel electrodes.

2 illustrates an electrode attachment position for EEG measurement according to an embodiment of the present invention.

The depression depth measurement device attaches an electrode to a user's brain and acquires EEG data measured through the electrode. At this time, the position of the electrode attachment for measuring the EEG is 10-20 electrode system Based on the location of the scalp.

In this embodiment, the apparatus for measuring depression depth can acquire brain wave data from two channels of FP1 and FP2, which are signal positions of the frontal lobe, and can separately store brain wave data acquired for each channel at this time.

Referring again to FIG. 1, in the noise removal step 200, the apparatus for measuring depth of depression may remove a noise signal included in brain wave data at the time of EEG measurement so as to increase the accuracy of the result on the depth of depression.

EEG, EKG, EMG, pulse, skin potential, line noise (EEG), and electroencephalogram (EEG) were measured because the electroencephalogram data acquired for each channel was microscopic. ), And so on. In order to increase the measurement accuracy of depression depth, the noise involved in EEG measurement should be removed before signal conversion.

For example, the depression depth measuring device classifies noise by applying an Independent Component Analysis (ICA) algorithm, and stores the signal characteristics of the most common eye flicker in the EEG data in advance to remove the corresponding signal have.

At this time, the detailed procedure of removing the blink signal is as follows.

(1) In order to remove eye flicker, which is the largest artifact in an EEG signal, a threshold is set to discriminate a blink signal from a pure EEG. At this time, the threshold value can be determined by calculating its average over the entire length of the read data, and then multiplying the average value by 4 times.

(2) First, to find the blinking part of the eye, the peak value of the blinking part of the eye is detected. To do this, change the read data to an absolute value, and then search the slope code at each point to find a slope at two points backward with respect to the current point +, and a slope at two points ahead of the current point.

(3) The peak value is compared with the threshold value to distinguish whether the peak of the point is pure EEG or a peak of the eye flicker signal. If the peak value at that point is greater than the threshold value, it is regarded as an eye flicker signal. At this time, to find the point where the blink signal ends, find the point where the sign changes twice from the blink peak point. This position can be seen as the end of the blinking of the eye, but considering the influence of the remaining components on the back, we can set the point where we skipped a certain section as the starting point for new EEG processing.

FIG. 3 shows the waveforms before (blinking) and after (blinking) with respect to EEG signals. As shown in FIG. 3, a pure EEG waveform in which an eye flicker signal is removed from an EEG signal can be obtained through the noise removal process.

The depression depth measurement device needs a process to obtain quantitative EEG (QEEG) through the process of acquiring specific information or characteristics through the mathematical process of the EEG data stored as a digital signal for EEG after noise cancellation. The process of obtaining quantitative brain waves includes signal analysis including detection of seizure waves, signal position analysis and frequency analysis, brain mapping indicating topographical distribution, and statistical analysis.

Referring again to FIG. 1, in the digital conversion step 300, a device for measuring depression depth can convert frequency-based classification of EEG through the FFT (Fast Fourier Transform).

The FFT method transforms a time-series signal, which varies with time, into a frequency domain to determine the shape of the signal according to the frequency change. The following mathematical processing is performed to transform the brain wave data.

Mathematical processing of FFT:

을 취해 모두 합하면 수학식 3이 유도되고 원 신호의 제곱의 합과 FFT를 거친 신호의 제곱의 합은 총 파워 값(total power)(수학식 3)이 된다.The absolute value is taken and squared on both sides of the equation (2)

(3) is derived, and the sum of the squares of the original signals and the sum of the squares of the signals after FFT becomes the total power (Equation 3).

The power spectrum satisfying the above theorem can be defined as Equation (4).

The EEG measured at the frequency of the cerebral epidermis transformed through the above mathematical processing is converted into a frequency band through an FFT process, and a delta (delta, delta: 0 to 4 Hz), ata ), Alpha (alpha: 8 to 12 Hz), beta (beta: 13 to 30 Hz) and gamma (gamma: 30 to 50 Hz).

Table 1 below shows frequency values and conscious state according to each EEG.

Electroencephalogram Frequency range Characteristic delta wave 0 to 4 Hz Sleep state θ (Theta) wave 4 to 8 Hz Sleepy or meditative alpha (alpha) wave 8-12Hz Relaxed state SMR wave 12 to 15 Hz Attention state Mid-β wave 15-20 Hz Activity status High-β wave 20-30Hz Stress excited or stressed γ (Gamma) wave 30 to 50 Hz Anxiety, excitement, stressful state

Referring again to FIG. 1, in the depression depth calculation step 400, the device for measuring depression depth can measure the depth of depression using frequency information of characteristics related to depression among the frequency components of the frontal brain EEG. In other words, the depression depth measuring apparatus can measure the depression depth of the user through the power analysis of the alpha wave using the frequency spectrum of power calculated by the channel-specific FFT. The characteristics of depression differ between the left and right alpha waves of the frontal lobe. When the left brain is activated, it is evaluated positively, and when the right brain is activated, it is evaluated negatively. In this embodiment, the device for measuring depression depth measures the depth of depression based on the above-described characteristics of the frontal EEG.

Then, in the result providing step 500, the depression depth measuring apparatus displays the result of the depression depth measured in the depression depth calculating step 400 through the display means, thereby providing the user or the examiner with the result of the depression depth of the user .

4 is a flowchart illustrating a detailed process of depression depth calculation step 400 according to an exemplary embodiment of the present invention.

At the time of brain activity, the right frontal sphere in the frontal lobe is associated with the negative part, but in contrast, the highest activity is associated with the positive part of the left sphere. In particular, these characteristics are highly related to the activities of ALPAPA. Giant alpha waves have the bandwidth power of the brain's inactive state or arousal state, reducing specific areas and alpha waves when more brain activity occurs.

Since the activity given to the brain is generally inversely proportional to the alpha wave, the depression depth measuring device measures the power of the whole brain (1-50 Hz) and the power of the alpha wave (8-12 Hz) (410) by measuring the power of the relative alpha wave by measuring the amount of the alpha power relative to the total amount of the EEG power.

In order to check how much the alpha waves are activated in the left and right hemispheres of the signals obtained from the frontal brain EEG, the relative powers of the alpha waves in the EEG data appearing in the respective channels are calculated in accordance with Equation 5 .

Using the EEG measured by the channels FP1 and FP2, the activity of the alpha waves can be quantified with respect to each of the left and right hemispheres at the frontal position through the relative power of the alpha waves.

Next, the device for measuring depression depth can calculate the degree of asymmetry activation of the left / brain as shown in Equation (6) using the quantified activation level of alpha wave for each hemisphere (420).

Here, P _FP1a represents the alpha wave relative power of the left brain wave (FP1), and P _FP2a represents the alpha wave relative power of the right brain wave (FP2).

The degree of activation of the aspheric asphericity calculated by Equation (6) is close to 100 when the asymmetrical increase of the right side of the right side is increased with 0 as the reference. In other words, depression depths can range from 0 to 100, and these values can be displayed as a result of depression depth through a variety of display methods.

Therefore, the method of measuring depression depth according to the present invention is a method of measuring depression depth by quantifying the alpha wave difference of left and right brain using frequency information of acquired brain waves by acquiring brain waves using two necessary minimum channels, The depth of depression can be measured.

The depression depth measurement method according to the present invention may include at least two operations based on the details described with reference to FIGS. At this time, two or more operations can be combined, and the order or position of operations can be changed.

The methods according to embodiments of the present invention may be implemented in the form of a program instruction that can be executed through various computer systems and recorded in a computer-readable medium. Particularly, in the present embodiment, the step of acquiring the frontal brain EEG data of the user from the EEG measuring device; Converting the frontal lobe brain wave data into a frequency band through fast Fourier transform; And measuring the depression depth of the user through power analysis of the alpha wave using frequency-specific power spectrum of the frontal brain EEG data.

FIG. 5 is a block diagram illustrating an internal configuration of a device for measuring depth of depression using frontal brain EEG according to an embodiment of the present invention. Referring to FIG.

 5, the apparatus for measuring depth of depression according to an embodiment includes an acquisition unit 510, a preprocessor 520, a conversion unit 530, a measurement unit 540, and a provision unit 550 . The apparatus for measuring depression depth according to one embodiment may further include components or may further include additional components based on the details of the method for measuring the depression depth described with reference to FIGS.

The acquiring unit 510 can acquire the EEG signal which is the user's brain wave signal measured from the EEG measuring apparatus. The acquisition unit 510 may acquire EEG data measured through an electrode attached to the user's brain. At this time, the position of the electrode attachment for measuring the EEG is determined based on the 10-20 electrode system of the International Standard Corporation, Position FP1 and right-brain position FP2.

The preprocessing unit 520 may remove noise included in the EEG signal to refine the EEG signal acquired through the acquisition unit 510. [ For example, the preprocessor 520 may classify the noise by applying an independent component analysis (ICA) algorithm, and then remove the blinking noise signal, which is most common in the EEG data, from the EEG signal. At this time, the preprocessor 520 searches the slope code at each point after changing the read EEG data to an absolute value. Then, the preprocessing unit 520 calculates a slope at two points backward with respect to the current point, The peak value of the eye blinking portion can be detected by comparing the peak value of the eye blinking portion with the predetermined threshold value to distinguish whether the peak of the blinking portion is a pure EEG peak or a blinking eye peak, If the peak value at that point is greater than the threshold value, it is regarded as an eye flicker signal.

The transforming unit 420 transforms the noise-canceled EEG into frequency units through FFT, and can classify the EEG into frequencies. In other words, in order to convert an analog type signal having a continuous time and amplitude into a digital signal that can be analyzed, an FFT method is used to convert a time domain signal into a frequency domain to identify frequency components of the signal have. At this time, the EEG transformed by the transforming unit 420 is transformed into a frequency band through an FFT process, and then converted into a frequency band by a delta (delta, delta, 0-4 Hz), ata (θ, 4-8 Hz) (beta: 13 to 30 Hz) and gamma (gamma: 30 to 50 Hz).

The measuring unit 540 can measure the depression depth of the user through the power analysis of the alpha wave using the frequency spectrum of each frequency calculated through the channel-by-channel FFT. For example, the measuring unit 540 measures the amount of power of the entire EEG (1 to 50 Hz) and the amount of power of the ALPHA wave (8 to 12 Hz) The power analysis of the relative alpha waves can be performed. At this time, the measuring unit 540 measures the relative power of the alpha waves in the EEG data displayed on each channel to determine how much the alpha waves are activated in the left / right half of the signals acquired from the frontal brain EEG, . Next, the measuring unit 540 can calculate the degree of activation of the alpha _wave asymmetry between the left brain and the right brain as shown in Equation (6) using the activation degree (P _FP1a , P _FP2a ) of the left / have.

The providing unit 550 may display the result of the depression depth measured by the measuring unit 540 through the display unit, thereby providing the user or the examiner with the result of the depression depth of the user. The degree of alpha-asymmetric activation calculated through the measuring unit 540 may be a value ranging from 0 to 100, and the providing unit 550 may display the numerical value of the calculation result as a result of the depression depth through various display methods .

According to the above-described configuration, the apparatus for measuring depression depth stores data by dividing brain waves of each channel with respect to brain waves acquired from two frontal lobes, removes noise other than brain waves such as eye flickering on the acquired data, And the depth of the user's depression can be measured through the degree of asymmetry of the brain wave by quantifying the alpha wave difference of the left and right brain through the acquired brain wave frequency information of each channel.

As described above, according to the embodiment of the present invention, the user can easily acquire brain waves by acquiring brain waves using the minimum channel, and diagnosis of the user's mental illness can be simplified. In addition, according to the embodiment of the present invention, it is possible to diagnose depression patients by self-diagnosis before the diagnosis by experts through quantitative objective methods in EEG analysis, . ≪ / RTI >

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.

510:
520:
530:
540:
550: Offering

Claims (25)

Obtaining frontal EEG data of a user from an EEG (electroencephalogram) measuring device;
Converting the frontal brain EEG data into a frequency band through Fast Fourier Transform; And
Measuring the depth of depression of the user through power analysis of an alpha wave using a frequency spectrum of the frontal brain EEG data;
/ RTI > wherein the depression depth is measured by the method. The method according to claim 1,
The frontal brain EEG data are measured using two channels of electrodes
Wherein the depression depth measurement method comprises the steps of: 3. The method of claim 2,
The electrode is attached to the left hemisphere (FP1) and the right hemisphere (FP2) of the frontal lobe based on the ten-twenty electrode system of the international standard
Wherein the depression depth measurement method comprises the steps of: The method according to claim 1,
The step of acquiring the frontal brain EEG data comprises:
The left brain EEG data measured in the left hemisphere of the frontal lobe and the right brain EEG data measured in the right hemisphere of the frontal lobe
Wherein the depression depth measurement method comprises the steps of: The method according to claim 1,
A pre-processing step of removing a noise signal included in the frontal brain EEG data before measuring the depth of depression
Wherein the depression depth measurement method further comprises: The method according to claim 1,
Before the measurement of the depression depth, a preprocessing step of removing the noise signal included in the frontal brain EEG data using an Independent Component Analysis algorithm
Wherein the depression depth measurement method further comprises: The method according to claim 1,
A pre-processing step of removing a noise signal due to blinking in the frontal brain EEP data before measuring the depth of depression
Wherein the depression depth measurement method further comprises: The method according to claim 1,
The step of converting into the frequency band includes:
Classifying the prefrontal EEG data into delta, delta, theta, alpha, alpha, beta, beta and gamma according to the frequency
Wherein the depression depth measurement method comprises the steps of: The method according to claim 1,
The step of acquiring the frontal brain EEG data comprises:
The left brain EEG data measured in the left hemisphere of the frontal lobe and the right brain EEG data measured in the right hemisphere of the frontal lobe are separately obtained,
Wherein the step of measuring the depression depth comprises:
Measuring the activation level of the left brain wave data and the right brain wave data; And
Measuring a degree of asymmetry according to a difference in activation of the alpha wave between the left brain wave data and the right brain wave data
/ RTI > wherein the depression depth is measured by the method. 10. The method of claim 9,
The degree of activation of the alpha-wave is measured by the amount of alpha-wave generating power relative to the amount of frequency generating power in the entire band of EEG
Wherein the depression depth measurement method comprises the steps of: 10. The method of claim 9,
The degree of asymmetry is quantified as a numerical value through Equation (1)
Wherein the depression depth measurement method comprises the steps of:
Equation 1:

(Wherein, P is _FP1a alpha activation degree, P _FP2a the left brain wave data indicates the degree of alpha-wave activation of the right brain wave data). 12. The method of claim 11,
Providing a numerical value corresponding to the degree of asymmetry as a result of the measurement of depression depth
Wherein the depression depth measurement method further comprises: 21. A computer readable medium comprising instructions for controlling a computer system to measure a depression depth,
The command includes:
Obtaining frontal EEG data of a user from an EEG (electroencephalogram) measuring device;
Converting the frontal brain EEG data into a frequency band through Fast Fourier Transform; And
Measuring the depth of depression of the user through power analysis of an alpha wave using a frequency spectrum of the frontal brain EEG data;
≪ / RTI > wherein said computer system is controllable by said computer system. An acquisition unit for acquiring user's frontal brain wave data from an EEG (electroencephalogram) measurement device;
A converting unit for converting the frontal brain EEG data into a frequency band through Fast Fourier Transform; And
A measurement unit for measuring the depth of depression of the user through power analysis of an alpha wave using a frequency spectrum of the frontal brain EEG data,
A depression depth measuring device. 15. The method of claim 14,
The frontal brain EEG data are measured using two channels of electrodes
Wherein said depression depth measuring device is a depression depth measuring device. 16. The method of claim 15,
The electrode is attached to the left hemisphere (FP1) and the right hemisphere (FP2) of the frontal lobe based on the ten-twenty electrode system of the international standard
Wherein said depression depth measuring device is a depression depth measuring device. 15. The method of claim 14,
Wherein,
The left brain EEG data measured in the left hemisphere of the frontal lobe and the right brain EEG data measured in the right hemisphere of the frontal lobe
Wherein said depression depth measuring device is a depression depth measuring device. 15. The method of claim 14,
Before measuring the depth of depression, a pre-processing unit for removing a noise signal included in the frontal brain EEG data,
Wherein the depression depth measurement device further comprises: 15. The method of claim 14,
Before the measurement of the depression depth, a preprocessing unit for removing a noise signal included in the frontal brain EEG data using an Independent Component Analysis
Wherein the depression depth measurement device further comprises: 15. The method of claim 14,
A pre-processing unit for removing a noise signal due to blinking in the frontal brain EEG data before measuring the depth of depression,
Wherein the depression depth measurement device further comprises: 15. The method of claim 14,
Wherein,
Classifying the prefrontal EEG data into delta, delta, theta, alpha, alpha, beta, beta and gamma according to the frequency
Wherein said depression depth measuring device is a depression depth measuring device. 15. The method of claim 14,
Wherein,
The left brain EEG data measured in the left hemisphere of the frontal lobe and the right brain EEG data measured in the right hemisphere of the frontal lobe are separately obtained,
Wherein the measuring unit comprises:
Measuring the degree of activation of the left and right brain wave data and the degree of activation of the left and right brain wave data and then measuring the asymmetry of the left and right brain wave data according to the activation difference
Wherein said depression depth measuring device is a depression depth measuring device. 23. The method of claim 22,
The degree of activation of the alpha-wave is measured by the amount of alpha-wave generating power relative to the amount of frequency generating power in the entire band of EEG
Wherein said depression depth measuring device is a depression depth measuring device. 23. The method of claim 22,
The degree of asymmetry is quantified as a numerical value through equation (2)
Wherein said depression depth measuring device is a depression depth measuring device.
Equation 2:

(Wherein, P is _FP1a alpha activation degree, P _FP2a the left brain wave data indicates the degree of alpha-wave activation of the right brain wave data). 25. The method of claim 24,
And providing a numerical value corresponding to the degree of asymmetry as a result of the measurement of depression depth;
Wherein the depression depth measurement device further comprises:

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