KR20220153148A - Apparatus and method for analysis of neural signals based on variance analysis - Google Patents

Abstract

Disclosed are a device and a method for analyzing electroencephalogram based on variance analysis. According to an embodiment of the present invention, an EEG analysis device includes: an EEG acquisition unit which acquires a plurality of EEG signals measured under various conditions; a spectrum generating unit for generating a plurality of time-frequency spectrums from the plurality of acquired EEG signals; and a variance analysis unit for calculating an F value of the signal intensity for each time-frequency from the plurality of time-frequency spectrums using a variance analysis method. According to the present invention, it is possible to easily identify EEG characteristics which differ depending on EEG measurement conditions.

Description

Apparatus and method for analysis of neural signals based on variance analysis}

It relates to an apparatus and method for analyzing EEG based on analysis of variance.

Understanding brain mechanisms through EEG analysis is very important. This not only has an academic meaning, but also directly affects the treatment of brain-related diseases, and can be used in various ways in real life. For example, by analyzing which area of the brain epilepsy occurs, it can pinpoint the surgical area. In addition, by analyzing brain waves, movement intentions can be predicted, and accordingly, quadriplegic patients can control the robot according to their intentions. In addition, electrical stimulation to the brain can give a person who is blind or unable to feel sensations or see ahead.

EEG has different characteristics depending on the frequency domain, and different signals are generated for each brain region over time. For example, when the left hand is moved, the power of 8-13Hz alpha waves and 13-30Hz beta waves in the right motor cortex decreases, and when the movement ends, the power of alpha and beta waves increases. These are called event-related desynchronization (ERD) and event-related synchronization (ERS), respectively. In addition, when the right hand is moved, the power of alpha and beta waves in the left motor cortex decreases, and when the foot or face is moved, the EEG in the corresponding brain area changes.

The time-frequency analysis method is a very useful method for analyzing these EEG characteristics. The time-frequency analysis converts the brain waves expressed in the time domain into the frequency domain to show how the intensity of each frequency changes over time. In time-frequency analysis, the row indicates the time in which the frequency column is the column, and the color indicates the signal strength. This time-frequency analysis is very useful and has been used as a basic analysis in various brain studies.

However, it is difficult to directly know which characteristics change according to different conditions in the existing time-frequency analysis method.

Republic of Korea Patent Registration No. 10-1737930

An object of the present invention is to provide an EEG analysis apparatus and method based on analysis of variance in order to solve the above problems.

In order to achieve the above objects, an apparatus and method for analyzing EEG based on analysis of variance are disclosed. An EEG analysis device according to an aspect includes an EEG acquisition unit that acquires a plurality of EEG signals measured under various conditions; a spectrum generating unit generating a plurality of time-frequency spectra from the plurality of acquired EEG signals; and a variance analysis unit calculating an F value of signal intensity for each time-frequency from the plurality of time-frequency spectra using a variance analysis technique; can include

The EEG analysis apparatus may further include a grouping unit for grouping the obtained plurality of EEG signals or the generated plurality of time-frequency spectra according to the condition.

The time-frequency image generator may use a short-time Fourier transform, a wavelet transform, a bilinear time-frequency distribution, and a Wigner-Ville distribution. , the Hilbert-Huang transform, and the Gabor-Wigner distribution may be used to generate the plurality of temporal frequency images.

The F value may be a ratio of inter-group variance to intra-group variance.

The variance analysis unit may calculate the intra-group variance using the following equation.

[mathematical expression]

는 i번째 그룹의 Y의 평균을 나타냄.where i is the group index, j is the time-frequency spectrum index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, N is the total number of time-frequency spectra, and Y is the number of time-frequency spectra at that time and at that frequency. signal strength,

denotes the mean of Y of the ith group.

The variance analysis unit may calculate the variance between the groups using the following equation.

[mathematical expression]

는 Y의 전체 평균,

는 i번째 그룹의 Y의 평균을 나타냄.where i is the group index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, Y is the signal strength at the corresponding time and frequency,

is the overall mean of Y,

denotes the mean of Y of the ith group.

The condition may include the state of the subject and the measurement location.

The state of the subject may include mood, movement, movement imagination, and health state.

An EEG analysis method according to another aspect includes acquiring a plurality of EEG signals measured under various conditions; generating a plurality of time-frequency spectra from the plurality of acquired EEG signals; and calculating an F value of signal intensity for each time-frequency from the grouped plurality of time-frequency spectra using an analysis of variance technique; can include

The EEG analysis method may include grouping the obtained plurality of EEG signals or the generated plurality of time-frequency spectra according to the condition; may further include.

The generating of the plurality of time-frequency spectra may include a short-time Fourier transform, a wavelet transform, a bilinear time-frequency distribution, and a Wigner-Ville distribution. The plurality of temporal frequency images may be generated using one of a Wigner-Ville distribution, a Hilbert-Huang transform, and a Gabor-Wigner distribution.

The F value may be a ratio of inter-group variance to intra-group variance.

Calculating the F value may include calculating the intra-group variance using the following equation; can include

[mathematical expression]

는 i번째 그룹의 Y의 평균을 나타냄.where i is the group index, j is the time-frequency spectrum index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, N is the total number of time-frequency spectra, and Y is the number of time-frequency spectra at that time and at that frequency. signal strength,

denotes the mean of Y of the ith group.

Calculating the F value may include calculating the variance between groups using the following equation; can include

[mathematical expression]

는 Y의 전체 평균,

는 i번째 그룹의 Y의 평균을 나타냄.where i is the group index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, Y is the signal strength at the corresponding time and frequency,

is the overall mean of Y,

denotes the mean of Y of the ith group.

The condition may include the state of the subject and the measurement location.

The state of the subject may include mood, movement, movement imagination, and health state.

According to the EEG analysis device and method according to an embodiment, it is possible to easily check EEG characteristics that differ according to EEG measurement conditions, and can be used for determining a user's mood, diagnosing and treating diseases, predicting the user's movement intention, and controlling a robot. can

1 is a diagram illustrating an EEG analysis device according to an exemplary embodiment.
2 is an exemplary diagram for explaining a method of calculating an F value of signal strength for each time-frequency.
3 is an exemplary view of a time-frequency spectrum of an EEG signal for each motion image and a time-frequency spectrum of an F value.
4 is an exemplary view of F value time-frequency spectrum for each EEG measurement location.
FIG. 5 is an exemplary view of an F value time-frequency spectrum obtained by averaging F value time-frequency spectra for all EEG measurement locations of FIG. 4 .
6 is an exemplary diagram of a topography of F values over time.
7 is a diagram illustrating an EEG analysis device according to an exemplary embodiment.
8 is a diagram for illustrating and describing a computing environment including a computing device suitable for use in example embodiments.
9 is a diagram illustrating an EEG analysis method according to an exemplary embodiment.
10 is a diagram illustrating an EEG analysis method according to an exemplary embodiment.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. In adding reference numerals to components of each drawing, it should be noted that the same components have the same numerals as much as possible even if they are displayed on different drawings. In addition, in describing the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description will be omitted.

Meanwhile, in each step, each step may occur in a different order from the specified order unless a specific order is clearly described in context. That is, each step may be performed in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

Terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to the intention or custom of a user or operator. Therefore, the definition should be made based on the contents throughout this specification.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. Terms are only used to distinguish one component from another. Singular expressions include plural expressions unless the context clearly dictates otherwise, and terms such as 'include' or 'have' refer to features, numbers, steps, operations, components, parts, or combinations thereof described in the specification. It is intended to specify that something exists, but it should be understood that it does not preclude the possibility of the existence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

In addition, the division of components in the present specification is merely a classification for each main function in charge of each component. That is, two or more components may be combined into one component, or one component may be divided into two or more for each more subdivided function. In addition, each component may additionally perform some or all of the functions of other components in addition to its main function, and some of the main functions of each component are dedicated to other components. may be performed. Each component may be implemented as hardware or software, or as a combination of hardware and software.

FIG. 1 is a diagram illustrating an EEG analyzer according to an exemplary embodiment, and FIG. 2 is an exemplary diagram for explaining a method of calculating an F value of signal intensity for each time-frequency.

Referring to FIGS. 1 and 2 , an EEG analysis device 100 according to an exemplary embodiment includes an EEG acquisition unit 110, a spectrum generator 120, a grouping unit 130, and a variance analysis unit 140. can do.

The EEG acquisition unit 110 may acquire a plurality of EEG signals measured under various conditions. Here, the condition includes the state of the subject and the measurement position, etc., the state includes mood state, motion state, motion imagination state, health state, etc., and the measurement position is the EEG measurement position, FP1, FP2, FPz, F3 , F4, F7, F8, Fz, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1, O2, Oz, and the like. Mood can include sadness, joy, anger, worry, anxiety, fear, satisfaction, disappointment, depression, peace, happiness, excitement, and the like. The movement may include a finger movement, a toe movement, a hand movement, a foot movement, an arm movement, a leg movement, a head movement, a neck movement, an eye movement, a tongue movement, and the like. Imagining motion may include imagining finger motion, imagining toe motion, imagining hand motion, imagining foot motion, imagining arm motion, imagining leg motion, imagining head motion, imagining neck motion, imagining eye movement, imagining tongue movement, and the like. Health conditions may include normal, convulsive disease, headache, cerebrovascular disease, brain tumor, impaired consciousness, meningitis, dementia, schizophrenia, and the like.

For example, the EEG acquisition unit 110 includes an EEG measuring device, and a plurality of EEG signals may be obtained by measuring EEG signals of one or more subjects under various conditions using the EEG measuring device. .

For another example, the EEG acquisition unit 110 may acquire a plurality of EEG signals by receiving a plurality of EEG signals measured under various conditions from an external device that measures and/or stores EEG signals. At this time, the brain wave acquisition unit 110 may use wired or wireless communication technology. Here, wireless communication technologies include Bluetooth communication, Bluetooth Low Energy (BLE) communication, Near Field Communication (NFC), WLAN communication, Zigbee communication, Infrared Data Association (IrDA) communication, and WFD. (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant + communication, WIFI communication, RFID (Radio Frequency Identification) communication, 3G communication, 4G communication and 5G communication, etc. may be included, but is not limited thereto.

The spectrum generator 120 may generate a plurality of time-frequency spectra from a plurality of EEG signals acquired through the EEG acquisition unit 110 . Here, the time-frequency spectrum may indicate each time and the signal strength of each frequency at the corresponding time. Therefore, it is possible to know how the signal intensity changes for each frequency with time through the time-frequency spectrum.

For example, the spectrum generator 120 uses a time-frequency transform technique, such as short-time Fourier transform, wavelet transform, bilinear time-frequency distribution, The time-frequency spectrum of each EEG signal can be generated using the Wigner-Ville distribution, Hilbert-Huang transform, Gabor-Wigner distribution, etc. have.

The grouping unit 130 may group the plurality of time-frequency spectra generated by the spectrum generation unit 120 according to conditions in which the EEG signals are measured.

For example, if a plurality of acquired EEG signals are EEG signals measured under various motion images, the grouping unit 130 may group a plurality of time-frequency spectra according to the type of motion images in which corresponding EEG signals are measured. .

The variance analysis unit 140 may calculate the F value of the signal intensity for each time and for each frequency (hereinafter, for each time-frequency) from a plurality of time-frequency spectra grouped using a variance analysis technique. Here, the F value may be the ratio of variance between groups to variance within groups. Therefore, the larger the F value, the smaller the change in the same group and the larger the change in different groups.

For example, referring to FIG. 2 , the variance analysis unit 140 may calculate the F value of the signal strength for each time-frequency using Equations 1 to 3.

는 Y의 전체 평균,

는 i번째 그룹의 Y의 평균을 나타낼 수 있다.where f is frequency, t is time, i is group index, j is time-frequency spectrum index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, N is the total number of time-frequency spectra, Y is the signal strength at that time and at that frequency,

is the overall mean of Y,

May represent the average of Y of the i-th group.

The variance analysis unit 140 may generate an F value time-frequency spectrum based on the calculated F value of the signal intensity for each time-frequency. In this case, the F value time-frequency spectrum may indicate each time and the F value of each frequency of the corresponding time.

The variance analysis unit 140 determines a frequency or frequency band having an F value equal to or greater than a predetermined value based on the calculated F value of the signal strength for each time-frequency, and determines F over time in the determined frequency or frequency band. You can create a topography of values.

As described above, the larger the F value, the smaller the change in the same group and the larger the change in different groups. Therefore, the EEG characteristics that differ depending on the conditions can be easily confirmed through the F value.

Accordingly, EEG characteristics, that is, time, frequency, and signal magnitude, in which the F value is greater than or equal to a predetermined value, can be used for mood determination, disease diagnosis and treatment, motion intention prediction, and robot control.

3 is an exemplary view of a time-frequency spectrum of an EEG signal for each motion image and a time-frequency spectrum of an F value. Specifically, in FIG. 3 (a) is a time-frequency spectrum of an EEG signal measured during left hand movement imagination, (b) is a time-frequency spectrum of an EEG signal measured during right hand movement imagination, and (c) is a foot movement imagination. (d) is the time-frequency spectrum of the EEG signal measured during tongue movement imagination, (e) is the time-frequency spectrum ((a), (b), ( c), F value time-frequency spectrum generated from (d)). The EEG signal used in (a) to (d) may be one measured at the C3 location of the same subject.

Referring to (a) to (d) of FIG. 3 , it is easy to know in which frequency domain a change in signal intensity occurs for each motion image over time. However, it is difficult to know at which time zone and at which frequency band the difference in signal strength occurs according to the four different motion imaginations using only (a) to (d).

However, in the F value time-frequency spectrum shown in (e) of FIG. 3, it is easy to know at which time zone and which frequency band the difference in signal strength occurs according to four different motion images. In the example of FIG. 3 , it can be seen that four different motion images have a difference between 0.55 seconds and 3.36 seconds at 8 to 16 Hz and 19 to 42 Hz.

4 is an exemplary diagram of an F value time-frequency spectrum for each EEG measurement location, and FIG. 5 is an exemplary diagram of an F value time-frequency spectrum obtained by averaging F value time-frequency spectra for each EEG measurement location in FIG. 4 . 6 is an exemplary view of the topography of F values over time. FIG. 4 measures EEG signals at 22 locations of each of 9 subjects during left hand motion imagination, right hand motion imagination, foot motion imagination, and tongue motion imagination, and generates F value time-frequency spectra for each subject at each measurement location. And, the generated F value time-frequency spectrum for each subject at each measurement position is averaged for all subjects at each measurement position, and the F value time-frequency spectrum shown at the corresponding measurement position is shown. (a) of FIG. 6 is an example of calculating the average of F values of 17 to 27 Hz in the F value time-frequency spectrum for each EEG measurement location in FIG. 4 and displaying the topography displayed at the corresponding EEG measurement location according to time, (b) is an example in which the average F value of 9 to 15 Hz is calculated in the F value time-frequency spectrum for each EEG measurement location in FIG. 4 and the topography displayed at the corresponding EEG measurement location is displayed according to time.

Referring to FIG. 4 , overall characteristics of each measurement location can be easily checked at a glance. In the case of FIG. 4, it can be seen that high F values are exhibited at positions C3, Cz, and C4.

Referring to FIG. 5 , important characteristics of frequency and time at which a difference occurs for each motion image can be easily confirmed. Referring to FIG. 5 , it can be confirmed that a difference occurs between 17 to 27 Hz and 9 to 15 Hz in four different motion images.

Referring to FIG. 6, it can be confirmed that the hand and foot have a high F value.

7 is a diagram illustrating an EEG analysis device according to an exemplary embodiment.

Unlike the EEG analyzer 100 of FIG. 1 , the EEG analyzer 200 of FIG. 7 may perform grouping first and generate a time-frequency spectrum.

That is, the grouping unit 210 groups a plurality of EEG signals acquired through the EEG acquisition unit 110 according to conditions in which the EEG signals are measured, and the spectrum generator 220 groups the plurality of EEG signals acquired through the grouping unit 210. A plurality of time-frequency spectra can be generated from the EEG signal.

8 is a diagram for illustrating and describing a computing environment including a computing device suitable for use in example embodiments. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and the computing environment may include additional components other than those described below.

The illustrated computing environment 700 may include a computing device 710 . According to an embodiment, the computing device 710 includes, for example, the EEG acquisition unit 110 shown in FIGS. 1 and 6, the spectrum generators 120 and 220, the grouping units 130 and 210, and the analysis of variance. Like the unit 140, one or more components included in the EEG analysis devices 100 and 200 may be included.

The computing device 710 may include at least one processor 711 , a computer readable storage medium 712 and a communication bus 713 . Processor 711 may cause computing device 710 to operate according to the above-mentioned example embodiments. For example, processor 711 may execute one or more programs 714 stored on computer readable storage medium 712 . One or more programs 714 may include one or more computer-executable instructions, which when executed by processor 711 cause computing device 710 to perform operations in accordance with an illustrative embodiment. can be configured to

Computer-readable storage medium 712 may store computer-executable instructions or program code, program data, and/or other suitable forms of information. Program 714 stored on computer readable storage medium 712 may include a set of instructions executable by processor 711 . According to one embodiment, computer readable storage medium 712 includes memory (volatile memory such as random access memory, non-volatile memory, or suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, other forms of storage media that can be accessed by computing device 710 and store desired information, or a suitable combination thereof.

The communication bus 713 may interconnect various other components of the computing device 710, including the processor 711 and the computer readable storage medium 712.

Computing device 710 may also include one or more input/output interfaces 715 and one or more network communication interfaces 716 that provide interfaces for one or more input/output devices 720 . Input/output interface 715 and network communication interface 716 may be connected to communication bus 713 . The input/output device 720 may be connected to other components of the computing device 710 through an input/output interface 715 . The input/output device 720 may include, for example, a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices, and/or a photographing device. and/or output devices such as display devices, printers, speakers, and/or network cards. The input/output device 720 may be included inside the computing device 710 as a component constituting the computing device 710, or may be connected to the computing device 710 as a separate device distinct from the computing device 710. have.

9 is a diagram illustrating an EEG analysis method according to an exemplary embodiment. The EEG analysis method of FIG. 9 may be performed by the EEG analysis device 100 of FIG. 1 .

Referring to FIG. 9 , the EEG analysis device may acquire a plurality of EEG signals measured under various conditions (910). Here, the condition includes the state of the subject and the measurement position, etc., the state includes mood state, motion state, motion imagination state, health state, etc., and the measurement position is the EEG measurement position, FP1, FP2, FPz, F3 , F4, F7, F8, Fz, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1, O2, Oz, and the like. Mood can include sadness, joy, anger, worry, anxiety, fear, satisfaction, disappointment, depression, peace, happiness, excitement, and the like. The movement may include a finger movement, a toe movement, a hand movement, a foot movement, an arm movement, a leg movement, a head movement, a neck movement, an eye movement, a tongue movement, and the like. Imagining motion may include imagining finger motion, imagining toe motion, imagining hand motion, imagining foot motion, imagining arm motion, imagining leg motion, imagining head motion, imagining neck motion, imagining eye movement, imagining tongue movement, and the like. Health conditions may include normal, convulsive disease, headache, cerebrovascular disease, brain tumor, impaired consciousness, meningitis, dementia, schizophrenia, and the like.

For example, the EEG analysis device includes an EEG measuring device, and a plurality of EEG signals may be obtained by measuring EEG signals of one or more subjects under various conditions using the EEG measuring device. Alternatively, the EEG analysis device may acquire a plurality of EEG signals by receiving a plurality of EEG signals measured under various conditions from an external device that measures and/or stores the EEG signals.

The EEG analysis device may generate a plurality of time-frequency spectra from a plurality of acquired EEG signals (920). For example, the brain wave analysis device uses a time-frequency transform technique, such as a short-time Fourier transform, a wavelet transform, a bilinear time-frequency distribution, a wigner- A time-frequency spectrum of each EEG signal may be generated using a Wigner-Ville distribution, a Hilbert-Huang transform, a Gabor-Wigner distribution, or the like.

The EEG analysis apparatus may group the generated plurality of time-frequency spectra for each EEG signal measured condition (930).

The EEG analysis apparatus may calculate the F value of the signal intensity for each time-frequency from a plurality of time-frequency spectra grouped using the variance analysis technique (940). Here, the F value may be the ratio of variance between groups to variance within groups.

For example, the EEG analyzer may calculate the F value of the signal intensity for each time-frequency using Equations 1 to 3. For example, the EEG analyzer calculates the variance between groups using Equation 2, calculates the variance within groups using Equation 3, and calculates the F value of the signal intensity for each time-frequency using Equation 1. can be calculated

The EEG analysis apparatus may generate an F value time-frequency spectrum based on the calculated F value of the signal intensity for each time-frequency (950). In this case, the F value time-frequency spectrum may indicate each time and the F value of each frequency of the corresponding time.

10 is a diagram illustrating an EEG analysis method according to an exemplary embodiment. The EEG analysis method of FIG. 10 may be performed by the EEG analysis device 200 of FIG. 7 .

Referring to FIG. 10 , a plurality of EEG signals measured under various conditions of the EEG analyzer may be acquired (1010).

The EEG analysis device may group the acquired EEG signals according to conditions in which the EEG signals were measured (1020).

The EEG analysis apparatus may generate a plurality of time-frequency spectra from a plurality of grouped EEG signals (1030).

The EEG analysis apparatus may calculate the F value of the signal intensity for each time-frequency from a plurality of time-frequency spectra grouped using the variance analysis technique (1040).

The EEG analysis apparatus may generate an F value time-frequency spectrum based on the calculated F value of the signal intensity for each time-frequency (1050).

The above-described embodiments may be implemented as computer readable codes on a computer readable recording medium. A computer-readable recording medium may include all types of recording devices storing data that can be read by a computer system. Examples of computer-readable recording media may include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, and the like. In addition, the computer-readable recording medium may be distributed among computer systems connected through a network, and may be written and executed as computer-readable codes in a distributed manner.

So far, the present invention has been looked at mainly with its preferred embodiments. Those skilled in the art to which the present invention pertains will be able to understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the scope of the present invention should be construed to include various embodiments within the scope equivalent to those described in the claims without being limited to the above-described embodiments.

100, 200: EEG analysis device 110: EEG acquisition unit
120, 220: spectrum generator 130, 210: grouping unit
140: distributed analysis unit 700: computing environment
710 computing device 711 processor
712: computer readable storage medium 713: communication bus
714 program 715 input/output interface
716: network communication interface 720: input/output device

Claims (16)

an EEG acquisition unit that acquires a plurality of EEG signals measured under various conditions;
a spectrum generating unit generating a plurality of time-frequency spectra from the plurality of acquired EEG signals; and
a variance analysis unit calculating an F value of signal intensity for each time-frequency from the plurality of time-frequency spectra using a variance analysis technique; including,
EEG analysis device. According to claim 1,
a grouping unit grouping the obtained plurality of EEG signals or the generated plurality of time-frequency spectra according to the condition; Including more,
EEG analysis device. According to claim 1,
The time-frequency image generation unit,
Short-time Fourier transform, Wavelet transform, Bilinear time-frequency distribution, Wigner-Ville distribution, Hilbert-Huang transform Generating the plurality of temporal frequency images using one of a -Huang transform) and a Gabor-Wigner distribution,
EEG analysis device. According to claim 1,
The F value is the ratio of the between-group variance to the within-group variance,
EEG analysis device. According to claim 4,
The variance analysis unit,
Calculating the intra-group variance using the following equation,
EEG analysis device.
[mathematical expression]

where i is the group index, j is the time-frequency spectrum index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, N is the total number of time-frequency spectra, and Y is the number of time-frequency spectra at that time and at that frequency. signal strength,

denotes the mean of Y of the ith group. According to claim 4,
The variance analysis unit,
Calculating the variance between the groups using the following equation,
EEG analysis device.
[mathematical expression]

where i is the group index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, Y is the signal strength at the corresponding time and frequency,

is the overall mean of Y,

denotes the mean of Y of the ith group. According to claim 1,
The condition includes the state of the subject and the measurement location,
EEG analysis device. According to claim 7,
The state of the subject includes mood, movement, movement imagination, health state,
EEG analysis device. Acquiring a plurality of EEG signals measured under various conditions;
generating a plurality of time-frequency spectra from the plurality of acquired EEG signals; and
Calculating an F value of signal intensity for each time-frequency from the plurality of time-frequency spectra using an ANOVA technique; including,
EEG analysis method. According to claim 9,
grouping the obtained plurality of EEG signals or the generated plurality of time-frequency spectra according to the conditions; Including more,
EEG analysis method. According to claim 9,
Generating the plurality of time-frequency spectra,
Short-time Fourier transform, Wavelet transform, Bilinear time-frequency distribution, Wigner-Ville distribution, Hilbert-Huang transform Generating the plurality of temporal frequency images using one of a -Huang transform) and a Gabor-Wigner distribution,
EEG analysis method. According to claim 9,
The F value is the ratio of the between-group variance to the within-group variance,
EEG analysis method. According to claim 12,
The step of calculating the F value is,
Calculating the intra-group variance using the following equation; including,
EEG analysis method.
[mathematical expression]

where i is the group index, j is the time-frequency spectrum index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, N is the total number of time-frequency spectra, and Y is the number of time-frequency spectra at that time and at that frequency. signal strength,

denotes the mean of Y of the ith group. According to claim 12,
The step of calculating the F value is,
Calculating the variance between the groups using the following equation; including,
EEG analysis method.
[mathematical expression]

where i is the group index, K is the number of groups, n _i is the number of time-frequency spectra in the ith group, Y is the signal strength at the corresponding time and frequency,

is the overall mean of Y,

denotes the mean of Y of the ith group. According to claim 9,
The condition includes the state of the subject and the measurement location,
EEG analysis method. According to claim 15,
The state of the subject includes mood, movement, movement imagination, health state,
EEG analysis method.

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