KR20160114406A - Information folw measuring appratus - Google Patents

Abstract

The present invention provides an information flow measuring device between brainwave signals capable of simultaneously a direction and strength of an information flow and measuring an information flow having temporal high resolution. According to an embodiment of the present invention, the information flow measuring device between brainwave signals comprises: a data partition unit configured to partition inputted brainwave data and set a frequency width; a phase operation unit configured to wavelet-convert the brainwave data to calculate a phase; and a phase slope measurement unit configured to measure a phase slope of the wavelet-converted brainwave data.

Description

[0002] INFORMATION FOLW MEASURING APPRATUS [0003]

A person uses an electrical signal of the brain to think, judge, and act. Each brain region has a different functional role, but usually the brain's various regions of the brain interact, no matter how simple the task is. Thus, when the brain performs a specific task, it can be seen from the connectivity between the brain regions which brain regions interact with each other.

For example, when a person receives a visual response such as looking at a photograph, etc., the frontal lobe responsible for the onset area of the brain is activated, and the frontal lobe responsible for the accident is activated when the photographer thinks that he is handsome or beautiful . In this case, the electrical signal in the brain may be thought to be transmitted from the occipital lobe responsible for visualization to the frontal lobe responsible for the accident, and there is a connection between the two areas.

Recently, attention has been paid to observing the connectivity between brain regions for purposes such as brain function investigation and brain disease diagnosis. In other words, it is possible to observe the interaction between the brain regions by observing the connectivity between the brain regions related to the subject after giving a specific stimulus to the subject or performing an action, or by observing brain-related diseases such as Alzheimer's disease, dementia, schizophrenia, By comparing the connectivity between specific brain regions of the patient and the normal person, the difference in connectivity between the normal brain region and the normal brain can be used to diagnose the disease.

These studies have been studied by people who do cognitive research related to the brain. However, most of the previous studies used the connectivity between the brain regions to measure the EEG, and then to see the connectivity between the brain regions after post-processing.

In most of the previous studies, we observed the connectivity on the scalp surface using the potential values measured by the electrodes attached to the surface of the subject's scalp. In this case, the volume conduction phenomenon, where one signal source is measured at two or more different electrodes at the same time, interferes with the measurement of connectivity between brain regions.

In addition, since conventional brainwave measurements and connectivity calculations are independent procedures, it is impossible to calculate the connectivity simultaneously with EEG measurements. Therefore, there is a need for a monitoring system capable of observing the connectivity of the brain region in real time while avoiding distortion caused by EEG measurement on the scalp surface when observing the connectivity between brain regions.

The present invention provides an apparatus for measuring information flow between EEG signals capable of simultaneously measuring directionality and intensity of an information flow and measuring an information flow having temporal high resolution.

The present invention also provides an apparatus for measuring information flow between EEG signals capable of overcoming the volume conduction phenomenon, which is a fundamental problem of EEG based on the scalp electrode.

According to an aspect of the present invention, there is provided an apparatus for measuring information flow between EEG signals, comprising: a data divider for dividing input EEG data and setting a frequency width; a phase calculator for calculating a phase by wavelet transforming the EEG data; And a phase slope measuring unit for measuring a phase slope of the data.

Here, the data divider may divide the frequency into a predetermined frequency width around a predetermined central frequency.

The apparatus further includes a noise removing unit for removing noise included in the brain wave data, and a surrogate brain wave data generating unit for generating surrogate brain wave data by removing information flow between signals in the brain wave data .

The phase operation unit may include a wavelet transform unit for wavelet-transforming the surrogate EEG and EEG data, and a conjugate product calculator for obtaining a phase difference through a conjugate product of wavelet coefficients between two EEG signals of two different channels .

Further, the conjugate product calculation unit may calculate the conjugate product Z (t, f) according to Equation (1).

[Equation 1]

(Where f is the frequency, t is the time, and y1 (t, f) and y2 * (t, f)

The phase slope measuring unit may include a phase difference conjugate calculating unit for calculating a phase slope by taking an imaginary part of a phase conjugate product and a coherence calculating unit for obtaining a coherence between data slices of the phase slope, (X (t, f1, f2)) can be calculated according to Equation (2).

&Quot; (2) "

는 허수부 연산자임).(Where f1 and f2 are different frequencies and f1 < f2,

Is an imaginary operator).

Also, the phase slope measuring unit may further include a coherence calculating unit, wherein the coherence calculating unit derives a phase slope coherence, and the coherence calculating unit may calculate a phase slope coherence (PSC) according to Equation (3).

&Quot; (3) &quot;

(Where E [] is the expected value for time and frequency).

Also, the information flow measuring apparatus may include an information flow analyzing unit that analyzes the information flow using the phase slope coherence, and the information flow analyzing unit displays the magnitude of the phase slope coherence as an intensity of the information flow, The sign of the coherence can be displayed in the direction of the information flow. In addition, the statistical significance of the statistical calculation result can be displayed by comparing the phase gradient coherence of the EEG derived by Equation (3) with that of the surrogate EEG.

According to an aspect of the present invention, it is possible to simultaneously measure the direction and intensity of an information flow, and to measure information flow having temporal high resolution in real time.

Also, the present invention can overcome the volume conduction phenomenon which is a fundamental problem of the brain electrode based on the scalp electrode.

1 is a block diagram illustrating an apparatus for measuring information flow between EEG signals according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating an information flow measurement method between EEG signals according to an embodiment of the present invention. Referring to FIG.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art to which the present invention pertains. The present invention may be embodied in many different forms and is not limited to the embodiments described herein.

1 is a block diagram illustrating an apparatus for measuring information flow between EEG signals according to an embodiment of the present invention.

1, an apparatus 100 for measuring information flow between EEG signals according to an embodiment of the present invention includes a data collecting unit 10, a data dividing unit 20, a noise removing unit 30, A phase calculator 50, a phase slope measuring unit 60, and an information flow analyzing unit 70. The data generating unit 40, the phase calculating unit 50,

The data collecting unit 10 collects brain wave data, and attaches brain electrodes to the subject to detect brain waves. The data division unit 20 sets the division length of brain wave data according to the input frequency band of interest and the time resolution. The data division unit sets the frequency width of the brain wave data to be divided.

The noise removing unit 30 removes noises such as eye noise and electromyogram noise from the divided EEG data and applies a band-pass filter to remove signals other than the frequency range of interest.

The surrogate brain wave data generator 40 removes information flow information from the noise-canceled brain wave data to generate surrogate brain wave data. The surrogate EEG data is the same data as the existing EEP data but the data flow between EEG data is removed.

These surrogate EEG data are compared with the information flow obtained from the original EEG data, and the original EEG data can be verified against the surrogate EEG data. The following phase calculator 50 and phase tilt measurer 60 are equally applied to brain waves and surrogate EEG to be displayed in the following information flow analyzer 70.

The phase calculating unit 50 performs wavelet transform on brain wave data to calculate a phase. The phase arithmetic unit 50 includes a wavelet transform unit 51 for wavelet-transforming surrogate EEG data and a conjugate product calculation unit 52 for obtaining a phase through a conjugate product of wavelet coefficients between two EEG signals of two different channels. .

The wavelet transform unit 51 wavelet transforms the surrogate EEG data using a complex number moret function as a wavelet. The wavelet transforms are a wavelet transform of a certain locally existing wavelet as a pattern and are expressed as arbitrary waveforms through a scale of enlargement or reduction. Since the Fourier transform does not have time information, it can not be used for time-frequency analysis. Thus, by introducing the concept of the time window to the Fourier transform, a STFT (Short Time Fourier Transform) which can recognize the time information can be used. However, the biggest problem in STFT time-frequency analysis is that there is no baseline similarity. The original Fourier transform is sensitive to a singular point because it is a base, and is used for anomaly detection and the like. However, in STFT, similarity is broken by giving a time window, and sensitivity to a singular point is lowered. Thus, a method of time-frequency analysis without destroying the similarity of the base is devised, which is wavelet transform. The characteristic of the time-frequency analysis by the wavelet transform is that the time resolution is high in the high frequency region and the frequency resolution is high in the low frequency region. Through this, wavelet transform is more effective in time - frequency analysis than STFT.

The conjugate product calculation unit 52 calculates the conjugate product Z (t, f) in accordance with the following equation (1).

[Equation 1]

Here, f is the frequency, t is the time, and y1 (t, f) and y2 * (t, f) are EEG data with conjugate complex numbers. The wavelet transform generates a wobble complex type brain wave data. The conjugate product calculation unit 52 multiplies the conjugate complex numbers to calculate a conjugate product. Herein, the phase difference can be defined as a value obtained by subtracting the phase of one data from the phase of another data in wavelet-transformed data as in Equation (1) above. That is, since the phase difference is aug (Z (t, f)), Z (t, f) contains phase difference information.

The phase tilt measuring unit 60 measures the phase slope of the wavelet-transformed brain wave data. The phase slope measuring unit 60 includes a phase conjugate product calculating unit 61 and a coherence calculating unit 62.

The phase difference conjugate product calculation unit 61 obtains a phase slope using the phase difference of different frequency components. The phase difference conjugate product calculation unit 61 obtains the phase slope X (t, f1, f2) based on the frequency width? Set per the center frequency fc.

 Here, f1 and f2 are different frequencies, and f1 < f2 and fc -? / 2? F1 and f2? Fc +? / 2. For example, when the center frequency fc is 10 Hz and the frequency width Δ is 4 Hz, the phase slope is obtained within the range of 8 Hz to 12 Hz. A total of 28 phase slopes are derived when the frequency resolution is 0.5 Hz.

For example, if the number of sampling waves is 500 Hz and the length of the divided data is 100 (200 ms), a total of 2800 phase slope ensembles can be obtained in the segmented data.

The phase difference conjugate product calculation unit 62 derives the phase slope X (t, f1, f2) using the following equation (2). The flow direction of the data can be obtained through the phase slope.

&Quot; (2) &quot;

는 허수부 연산자임.Here, f1 and f2 are different frequencies, f1 < f2,

Is an imaginary operator.

The coherence calculator 63 derives the phase slope coherence (PSC) using the following equation (3).

&Quot; (3) &quot;

Where E [] is the expected value for time and frequency.

The information flow analysis unit 70 analyzes the information flow using the phase slope coherence, displays the magnitude of the phase slope coherence as the intensity of the information flow, and displays the sign of the phase slope coherence in the direction of the information flow.

FIG. 2 is a flowchart illustrating an information flow measurement method between EEG signals according to an embodiment of the present invention. Referring to FIG.

The method of measuring information flow between neural signals according to the present embodiment includes a data collecting step S101, a data dividing step S102, a noise removing step S103, a surrogate EEG data generating step S104, a phase calculating step S105, Phase slope measurement step S106, and information flow analysis step S107.

In the data collection step (S101), brain waves are collected by attaching a brain electrode to the subject. The data segmentation step (S102) sets the segmentation length of brain wave data according to the input frequency band of interest and the time resolution. The data division unit sets the frequency width of the brain wave data to be divided.

In the noise removing step (S103), noise such as eye noise and electromyogram noise is removed from the divided EEG data, and a band-pass filter is applied to remove signals other than the frequency band of interest. If it is determined in step S103 that the noise has been removed, the process proceeds to the generation step of the surrogate brain wave data (step S104). If it is determined that the noise removal is unsuccessful, the information flow is performed based on interpolation from five past data Analogy.

The surrogate brain wave data generation step (S104) removes information flow information from the noise-canceled brain wave data to generate surrogate brain wave data. The surrogate EEG data is the same data as the existing EEP data but the data flow between EEG data is removed.

These surrogate EEG data are compared with the information flow obtained from the original EEG data, and the original EEG data can be verified against the surrogate EEG data.

The phase calculation step S105 calculates the phase by wavelet transforming the brain wave data. The phase calculation step S105 includes a wavelet transform step of wavelet-transforming EEG and surrogate EEG data and a conjugate product calculation step of obtaining a phase difference through a conjugate product of wavelet coefficients between two EEG signals of two different channels.

The wavelet transform step wavelet transforms the surrogate EEG data using a complex mollet function as a wavelet. The wavelet transforms are a wavelet transform of a certain locally existing wavelet as a pattern and are expressed as arbitrary waveforms through a scale of enlargement or reduction. Since the Fourier transform does not have time information, it can not be used for time-frequency analysis. Thus, by introducing the concept of the time window to the Fourier transform, a STFT (Short Time Fourier Transform) which can recognize the time information can be used. However, the biggest problem in STFT time-frequency analysis is that there is no baseline similarity. The original Fourier transform is sensitive to a singular point because it is a base, and is used for anomaly detection and the like. However, in STFT, similarity is broken by giving a time window, and sensitivity to a singular point is lowered. Thus, a method of time-frequency analysis without destroying the similarity of the base is devised, which is wavelet transform. The characteristic of the time-frequency analysis by the wavelet transform is that the time resolution is high in the high frequency region and the frequency resolution is high in the low frequency region. Through this, wavelet transform is more effective in time - frequency analysis than STFT.

The conjugate product calculation step calculates the conjugate product (Z (t, f)) according to the following equation (1).

[Equation 1]

Here, f is the frequency, t is the time, and y1 (t, f) and y2 (t, f) * are EEG data with a conjugate complex number relationship. The wavelet transform generates the wake-up complex-type EEG data. The conjugate product calculation step multiplies the conjugate complex number to calculate the conjugate product.

The phase slope measuring step (S106) measures the phase slope of the wavelet-transformed brain wave data. The phase slope measurement step includes a phase difference conjugate product calculation step and a coherence calculation step.

In the phase difference conjugate product calculation step, the phase slope is obtained using the phase difference of different frequency components. The phase difference computing step obtains the phase slope X (t, f1, f2) based on the frequency width? Set per the center frequency fc.

Here, the phase difference can be defined as a value obtained by subtracting the phase of one data from the phase of another data in the wavelet-transformed data as in Equation (2) below.

Here, f1 and f2 are different frequencies, and f1 < f2 and fc -? / 2? F1 and f2? Fc +? / 2. For example, when the center frequency fc is 10 Hz and the frequency width Δ is 4 Hz, the phase slope is obtained within the range of 8 Hz to 12 Hz. A total of 28 phase slopes are derived when the frequency resolution is 0.5 Hz.

For example, if the number of sampling waves is 500 Hz and the length of the divided data is 100 (200 ms), a total of 2800 phase slope ensembles can be obtained in the segmented data.

The phase slope coherence operation step derives the phase slope coherence (PSC) using the following equation (3). The direction of data flow can be obtained through phase slope coherence.

&Quot; (2) &quot;

는 허수부 연산자임.Here, f1 and f2 are different frequencies, f1 < f2,

Is an imaginary operator.

The coherent conjugate operation step derives the phase slope coherence (PSC) using the following equation (3).

&Quot; (3) &quot;

Where E [] is the expected value for time and frequency.

The information flow analysis step (S107) analyzes the information flow using the phase slope coherence. The magnitude of the phase slope coherence is represented by the intensity of the information flow, and the sign of the phase slope coherence is indicated in the direction of the information flow. We also compare the phase slope coherence (PSC) derived from EEG and surrogate EEG to demonstrate its effectiveness.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, Of course.

10: data collecting unit 20: data dividing unit
30: noise removing unit 40: surrogate brain wave data generating unit
50: phase operation unit 51: wavelet transform unit
52: conjugate product calculation unit 60: phase slope measurement unit
61: phase difference conjugate product calculation unit 62: coherence calculation unit
70: information flow analysis section

Claims (8)

A data divider for dividing input brain wave data and setting a frequency width;
A phase arithmetic unit for performing wavelet transformation on the EEG data to calculate a phase; And
A phase tilt measuring unit for measuring a phase tilt of the wavelet transformed EEG data;
And an information flow measuring device for measuring an information flow between the EEG signals. The method according to claim 1,
Wherein the data dividing unit divides the frequency into a predetermined frequency width centered on a predetermined central frequency. The method according to claim 1,
The apparatus for measuring information flow between EEG signals,
A noise removing unit for removing noise included in the brain wave data, and
A surrogate brain wave data generation unit for generating surrogate brain wave data by removing information flow between signals in the brain wave data;
And an EEG-based information flow measuring device. The method according to claim 1,
The phase calculating unit includes a wavelet transform unit for wavelet-transforming the EEG and surrogate EEG data, and a conjugate product calculating unit for obtaining a phase through a conjugate product of wavelet coefficients between two EEG signals of two different channels. Flow measuring device. 5. The method of claim 4,
Wherein the conjugate product calculating unit calculates the conjugate product (Z (t, f)) according to Equation (1)
[Equation 1]

(Where f is frequency, t is time, and y1 (t, f) and y2 * (t, f) are EEG data of different channels with complex conjugate relations). The method according to claim 1,
Wherein the phase slope measuring unit includes a phase difference conjugate product calculating unit for obtaining a phase slope by using a phase difference of different frequency components and a coherence calculating unit for obtaining a phase slope coherence,
Wherein the phase difference conjugate product calculation unit calculates the phase slope according to Equation (2)
&Quot; (2) &quot;

(Where f1 and f2 are different frequencies and f1 &lt; f2,

Is an imaginary operator). The method according to claim 6,
Wherein the coherence operation unit derives a phase slope coherence,
Wherein the coherence calculator calculates the phase slope coherence (PSC) according to Equation (3)
&Quot; (3) &quot;

(Where E [] is the expected value for time and frequency). The method according to claim 6,
Wherein the information flow measuring apparatus includes an information flow analyzing unit that analyzes the information flow using the phase slope coherence,
Wherein the information flow analyzing unit displays the magnitude of the phase slope coherence as an intensity of the information flow and displays the sign of the phase slope coherence in the direction of the information flow.

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