JP7136264B2 - STRESS DETERMINATION DEVICE, PROGRAM AND METHOD - Google Patents

Description

The present invention relates to a stress determination device, program and method, and more particularly to a stress determination device and method for determining a stress state based on brain potential.

Conventionally, as a method of estimating the mental stress of employees in the field of labor management, etc., a method of checking with a questionnaire has been mainly used. However, since the check using the questionnaire has a strong subjective element, there is a problem that an erroneous judgment is made when the stress damage is disguised or concealed due to the calculation of the subject, and it is a more objective evaluation method. and the development of determination methods are desired.

When the brain receives mental stress such as sadness or fear, the limbic system, which is the emotional center, is greatly affected, but the primary emotional response is thought to be generated mainly in the amygdala. For example, in Non-Patent Document 1, 20 subjects who lost a pet were given a verbal task related to the pet and investigated the active site with fMRI. It is disclosed that the dominant activity is observed in the portion where the This primary neural activity acts on the nucleus of the hypothalamus, which is also in the limbic system, and passes through the autonomic nervous system, which consists of sympathetic nerves and parasympathetic nerves, to secondary physiological responses (such as blood pressure, pulse, pupillary response, etc.). It is also known that the secondary response extends to the frontal region and suppresses the activity of the self-reward system, which is the nucleus accumbens and is the center of pleasure. Furthermore, it is known that, in subjects whose feelings of joy are reduced due to mental illness, the activity of the nucleus accumbens is reduced and the delta component of the electroencephalogram (EEG) of the anterior cingulate cortex is increased (Non-Patent Document 2).

If such physiological changes in stress-related brain activity are observed by PET or fMRI, it is expected that stress can be objectively evaluated more accurately. However, PET using an isotope is not suitable for stress testing that requires repeated measurements because not only is the apparatus bulky, but radiation exposure is involved in the measurement. In addition, since the fMRI method exposes the subject to a strong magnetic field and a high-frequency electromagnetic field, it cannot be used for subjects who have a pacemaker or metal prosthesis, etc., and requires a large-scale device to generate a strong magnetic field. This greatly limits the usage environment. The MEG (Magneto-encephalogram) method, which can be used instead of these measurement methods, not only requires a strong magnetic shield, but also requires a cooling device to realize superconductivity. Expensive measuring equipment is required.

On the other hand, measurement of physiological changes in brain activity by electroencephalogram (electroencephalogram) observed from the scalp has been widely used in clinical practice because it does not require large-scale equipment.

In general clinical practice, where the electrical potential distribution on the scalp is observed with a large number of electrodes, brain electrical potentials observed using 19 electrodes in the international 10-20 method should be recorded in a pen recorder or computer memory. has been done. For example, assuming that the brain potential observed on the scalp is generated by an equivalent dipole power source assumed inside the brain, the position, direction, and current value of the equivalent dipole power source are inversely estimated from the brain potential distribution on the scalp. An equivalent expression method has been developed by the "dipole estimation method" (Non-Patent Document 3). However, in this method, in order to inversely estimate the dipoles inside the brain from the electric potential distribution on the scalp, not only does this method require large-scale calculations accompanied by search work in multidimensional space, but it also obtains a stable solution. Therefore, a large number of electrodes were required.

On the other hand, a method of estimating the emotional state of a subject using brain potentials observed from 10 electrodes placed on the scalp has also been performed (Non-Patent Documents 4 and 5). Specifically, the correlation between the potentials observed between each two electrodes is evaluated using ₁₀ C ₂ = 45 cross-correlation functions, which are then evaluated for each band of brain potential θ, α, and β. , and a method of expressing with a total of 45×3=135 parameters has been used. However, even in this method, since at least ten electrodes are required, it is difficult to maintain stable electrode contact during measurement.

Peter J. Freed, Ted K.Yanagihara, Joy Hirsch, and J. John Mann,“Neural mechanism of grief regulation”, Biol. Psychiatry, vol.66, no.1,pp.33-40, July 2009. Jan Wacker, Daniel G. Dillon, Diego A. Pissagalli, “The role of the nucleus accumbens and rostral anterior cingulate cortex in anhedonia: Integration of resting EEG, fMRI, and Volumetric Techniques, Neuroimage, vol.46, no.1, pp. 327-337, May 2009. J. Hara, W. R. Shankle and T. Musha, Cortical Atrophy in Alzheimer'sDisease Unmasks Electrically Silent Sulci and Lowers EEG Dipolarity, IEEETrans. Biomed. Eng., vol.46, no.8, 905-910, 1999. Toshimitsu Musha, "Measuring 'Kokoro'," Nikkei Science, April 20-29 (1996) T. Musha, Y. Terasaki, H. A. Haque, and G. A. Ivamitsky, “Featureextraction from EEGs associated with emotions,” Artif. Life Robot., 1(1):15-19, 1997.

As a device for judging (evaluating) a mental stress state in this way, a device for judging a stress state based on brain potentials that can be constructed relatively easily is preferable. It required a large number of electrodes, which was a heavy burden on the subject.

The present invention has been made to solve such problems, and the main purpose thereof is to provide a stress determination device capable of quantitatively determining stress while further reducing the burden on the subject. and

In order to achieve the above object, a stress determination device as one aspect of the present invention measures the brain potential of a subject using sensors attached to three different positions on the surface of the subject's frontal region or temporal region. a brain potential signal acquisition means for acquiring a signal; time series data in a specific frequency band caused by deep brain activity from each of the acquired brain potential signals; computing means for calculating a correlation value indicating the correlation between the acquired brain potential signals based on the phase relationship, and determining the stress state of the subject based on the correlation value, wherein the computing means , where VA(t), VB(t), and VC(t) are the time-series data extracted from each of the attached sensors A, B, and C, VA(t) extracted within a predetermined time are , VB (t-τ1) and VC (t- τ2) is multiplied only by combinations of τ1 and τ2 when VA (T), VB (T-τ1) and VC (T-τ2) at time T within the predetermined time have the same sign, and obtained Calculate the correlation value using the value obtained by adding each value, based on the standard deviation of the lattice fringe interval shown by the pseudo three-dimensional display of the triple correlation value distribution, or with the time τ1 and τ2 as the axis pulse signal acquiring means for acquiring the pulse signal of the subject by calculating an index value based on the standard deviation in each of the τ1-axis direction and the τ2-axis direction of the distance between the regions for which the correlation value is calculated in the coordinates wherein the calculating means calculates a frequency spectrum by performing a Fourier transform on the pulse interval data generated from the pulse signal, and the spectrum intensity of each frequency within the first frequency band in the frequency spectrum Calculate the ratio of the total value of the spectral intensity of each frequency in a second frequency band higher than the first frequency band and different from the first frequency band to the total value of the above index A data delay is attached to the value, and based on the ratio and the index value, one axis (horizontal axis) is the ratio and the other axis (vertical axis) is the index value. It is characterized in that the stress state is determined by separating the regions in the stress state and the low stress state.

Further, preferably in the present invention, the computing means, when the time-series data extracted for each of the attached sensors A, B, and C are VA(t), VB(t), and VC(t), Each VA(t) extracted within a predetermined time is extracted at different times by arbitrary times τ1 and τ2, which are times equal to an integer multiple of a predetermined value or less of the sampling period at which the time-series data was extracted. VB (t-τ1) and VC (t-τ2) obtained when VA (T), VB (T-τ1) and VC (T-τ2) at time T within the predetermined time have the same sign A correlation value is calculated using a value obtained by multiplying only the combination of τ1 and τ2 and adding the obtained values.

Further, in the present invention, preferably, the calculating means calculates an index value based on a temporal variation of the calculated correlation value, and determines the stress state based on at least one of the correlation value and the index value. do.

Further, in the present invention, preferably, the computing means computes the standard deviation of the distance between the regions for which the correlation values are calculated in the coordinates with the times τ1 and τ2 as the axes in the τ1-axis direction and the τ2-axis direction. An index value is calculated based on the correlation value and the stress state is determined based on at least one of the correlation value and the index value.

In the present invention, preferably, the stress determination device further includes pulse signal acquisition means for acquiring the pulse signal of the subject, and the computing means performs Fourier transform on pulse interval data generated from the pulse signal. A frequency spectrum is calculated by performing, and the total value of the spectral intensity of each frequency in the first frequency band in the frequency spectrum is a frequency band higher than the first frequency band and the first frequency band A ratio to the total value of the spectrum intensity of each frequency in a second frequency band different from the frequency band is calculated, and the stress state is determined based on the ratio and the index value.

Further, in the present invention, preferably, the stress determination device further includes pulse signal acquisition means for acquiring the pulse signal of the subject, and the calculation means calculates the pulse interval from pulse interval data generated based on the pulse signal. A pulse histogram indicating the frequency of appearance of each value is generated, and the total value of the variance of a predetermined amount of data extracted in order from the largest pulse interval in the pulse histogram is extracted in order from the smallest pulse interval. A ratio of a predetermined amount of data to the total value of variance is calculated, and the stress state is determined based on the ratio and the index value.

In the present invention, preferably, the brain potential signal acquisition means further acquires the brain potential signal of the subject using sensors further attached to three different positions on the surface of the temporal region or the occipital region of the subject. , the computing means extracts time-series data in a specific frequency band caused by activity of the amygdala from each of the further acquired brain potential signals, and performs principal component analysis on the extracted time-series data. to calculate the first principal component contribution rate, and determine the stress state based on the first principal component contribution rate and the index value.

Further, in order to achieve the above object, the program as one aspect of the present invention includes: and a pulse signal acquired using a pulse sensor that acquires the brain potential signal of the subject and the pulse signal of the subject, the program for determining stress from each of the acquired brain potential signals to a computer, deep brain a step of extracting time-series data of specific frequency bands caused by the activity of each; and determining the stress state of the subject based on the correlation values, wherein the step of calculating the correlation values is extracted for each of the attached sensors A, B, and C. When the time-series data are VA(t), VB(t), and VC(t), for each VA(t) extracted within a predetermined time, a predetermined sampling period at which the time-series data was extracted VB(t-τ1) and VC(t-τ2) extracted at times different by arbitrary times τ1 and τ2, which are times equal to integral multiples less than or equal to VA(T) at time T within the predetermined time and VB (T-τ1) and VC (T-τ2) are multiplied only by combinations of τ1 and τ2 when they have the same sign, and the obtained values are added to calculate the correlation value, In the step of determining the stress state, the correlation value is calculated based on the standard deviation of the intervals of the lattice fringes indicated by the pseudo-three-dimensional representation of the triple correlation value distribution, or the coordinates having the time τ1 and τ2 as axes. An index value is calculated based on the standard deviation in the τ1-axis direction and the τ2-axis direction, respectively, of the distance between the calculated regions, and Fourier transform is performed on the pulse interval data generated from the pulse signal to obtain the frequency A spectrum is calculated, and a second frequency band higher than the first frequency band and different from the first frequency band of the total value of the spectral intensity of each frequency in the first frequency band in the frequency spectrum Calculate the ratio to the total value of the spectral intensity of each frequency within the frequency band, add the data delay to the index value, and based on the ratio and the index value, one axis (horizontal axis) is the ratio, the other In the two-dimensional coordinates with the axis (vertical axis) as the index value, the areas in the high stress state and low stress state of the subject are Determining the stress state is performed by separating.

Further, to achieve the above object, a method as one aspect of the present invention provides a subject's frontal or temporal surface acquired using sensors attached to three different positions on the subject's frontal or temporal region. and a pulse signal acquired using a pulse sensor that acquires the brain potential signal of the subject and the pulse signal of the subject, wherein the activity of the deep brain is derived from each of the acquired brain potential signals. a step of extracting time-series data of specific frequency bands that are the cause of the above; and determining the stress state of the subject based on the correlation value, wherein the step of calculating the correlation value is time-series data extracted for each of the attached sensors A, B, and C. are VA(t), VB(t), and VC(t), for each VA(t) extracted within a predetermined time, VA(T) and VB( T-τ1) and VC (T-τ2) are multiplied only by the combination of τ1 and τ2 when they have the same sign, and the obtained values are added to calculate the correlation value, and the stress state is based on the standard deviation of the intervals of the lattice fringes indicated by the pseudo-three-dimensional display of the triple correlation value distribution, or the region in which the correlation values in the coordinates with the time τ1 and τ2 as the axes are calculated An index value is calculated based on the standard deviation in each of the τ1-axis direction and the τ2-axis direction of the distance between and a second frequency band higher than the first frequency band and different from the first frequency band of the total value of the spectral intensity of each frequency in the first frequency band in the frequency spectrum Calculate the ratio to the total value of the spectral intensity of each frequency within, add the data delay to the index value, and based on the ratio and the index value, one axis (horizontal axis) is the ratio, the other axis ( The stress condition is determined by separating the regions in the high stress state and the low stress state of the subject in the two-dimensional coordinates with the vertical axis) as the index value. It is characterized by including determining the state.

ADVANTAGE OF THE INVENTION According to this invention, stress can be determined quantitatively, reducing the burden on a test subject.

1 is a schematic configuration diagram of a stress determination device according to an embodiment of the present invention; FIG. FIG. 10 is a diagram showing the potential on the surface of a uniform sphere model with a dipole current source in the center. FIG. 10 is a diagram showing the potential on the surface of a uniform sphere model with a dipole current source in the center. FIG. 10 is a diagram showing the potential on the surface of a uniform sphere model with a dipole current source in the center. FIG. 10 is a diagram showing the potential on the surface of a uniform sphere model with a dipole current source in the center. FIG. 10 is a diagram showing plots on the delay parameter space; FIG. 10 is a diagram showing the time evolution of the potential of each electrode A, B, C on a uniform sphere model surface with a dipole current source in the center. FIG. 4 is a diagram showing attachment positions of electrodes of the brain potential sensor according to the embodiment of the present invention. 1 is a functional block diagram of a stress determination device according to an embodiment of the present invention; FIG. 4 is a flow chart showing information processing for calculating a triple correlation value S by the electronic device according to the embodiment of the present invention. Fig. 3 shows a pseudo-three-dimensional representation of the triple correlation value distribution of subject A on the feature space formed by two delay parameters (τ1, τ2), produced by an electronic device according to an embodiment of the present invention; Fig. 3 shows a pseudo three-dimensional representation of the triple correlation value distribution of subject B on the feature space formed by two delay parameters (τ1, τ2), produced by an electronic device according to an embodiment of the present invention; FIG. 12 is a top view of the three-dimensional representation of FIG. 11 , where regions where the three signals have the same sign are represented in white, and regions where the sign of any one of the three signals is different are represented in black. is. FIG. 13 is a top view of the three-dimensional representation of FIG. 12, with regions where the three signals have the same sign represented in white and regions where the sign of any one of the three signals differs, represented in black. is. Vertical and horizontal distances dxi (i=1, 2, . . . , m) and dyj (j=1, 2, . It is a figure explaining. FIG. 4 is a diagram showing average values and standard deviations of index values SD/triple correlation values S in high stress states and low stress states of 18 subjects obtained using the stress determination device of the first embodiment; FIG. 4 is a diagram showing two-dimensional coordinates obtained by plotting triple correlation values S and index values SD in the high stress state and low stress state of 18 subjects using the stress determination device of the first embodiment. FIG. 4 is a schematic configuration diagram of a stress determination device according to another example of the first embodiment of the present invention; FIG. 10 is a schematic external view of cap-mounted electrodes of the stress determination device according to another example of the first embodiment of the present invention; FIG. 20 is a diagram showing a schematic diagram of a conductive rubber electrode for reference potential measurement of the stress determination device shown in FIG. 19; It is a functional block diagram of the stress determination device of the 2nd Embodiment of this invention. It is a data processing block diagram of the stress determination device of the 2nd Embodiment of this invention. FIG. 10 is a diagram showing pulse interval data generated from a pulse signal in a low stress state, obtained using the stress determination device of the second embodiment; FIG. 10 is a diagram showing pulse interval data generated from a pulse signal in a high stress state, obtained using the stress determination device of the second embodiment; FIG. 24 is a diagram showing a frequency spectrum generated by performing a discrete Fourier transform on the pulse interval data of FIG. 23; FIG. 25 is a diagram showing a frequency spectrum generated by performing a discrete Fourier transform on the pulse interval data of FIG. 24; The first stress index value (SD/S) and the second stress index value (LF/HF) in the high stress state and low stress state of 18 subjects obtained using the stress determination device of the second embodiment ) are plotted two-dimensional coordinates. It is a data processing block diagram of the stress determination device of the 3rd Embodiment of this invention. FIG. 10 is a diagram showing a correlation between a third stress index value (PCAθ) and subjective evaluation; The first stress index value (SD/S) and the third stress index value (PCAθ) in the high stress state and low stress state of 18 subjects obtained using the stress determination device of the third embodiment are It is a figure which shows the plotted two-dimensional coordinate.

A stress determination device according to an embodiment of the present invention will be described below with reference to the drawings. One of the technical features of the stress determination device 100 according to the embodiment of the present invention is that it observes the state of the deep part of the brain to more accurately capture the brain activity associated with stress and determine (evaluate) the stress state of the subject. is to do First, the measurement principle will be described, and then the device configuration and information processing of the stress determination device 100 will be described.

[Measurement principle]
In the measurement in this embodiment, an equivalent dipole power source is assumed in the deep brain. Now consider the case where potential distribution measurements for analyzing this dipole potential activity are limited to electrodes placed at three different locations on the scalp. This phase relationship is evaluated based on the fact that there is a strong phase relationship between the potential waveforms observed at these three electrodes when the power source is deep in the brain. In this way, the temporal behavior of the equivalent dipole power assumed in the deep brain is approximately estimated. If we compare this to seismic waves, seismic waves with epicenters in the surface layer differ greatly from observation point to observation point, whereas seismic waves with epicenters in deep areas have almost the same amplitude and phase for seismometers placed at a short distance. This phenomenon is equivalent to the observation of the P-wave of .

In the measurement in this embodiment, since the potential waveforms appearing on the surface based on the activity in the deep part of the brain are almost in phase on the surface at a short distance, only the data with the same sign of the three potentials are added. Define a method. That is, by subjecting only the data of the same code to the calculation target, it is possible to extract correlated data. However, all data can also be the object of calculation.

As a specific information processing, first, when three potential signals are input, three potential signals having the same sign are selected. In one example, the ear lobe is used as a reference potential in determining the sign of the potential, which does not directly reflect cortical activity. In another example, when a DC component is blocked by a band-pass filter or a digital filter, determination is made based on the positive/negative sign as viewed from the time average for each electrode.

Subsequently, a triple correlation value is calculated. The triple correlation value is the potential signal of one electrode when the potential signals of a specific frequency band (e.g., delta wave band) from the three electrodes are EVA(t), EVB(t), and EVC(t) respectively. , the product of signals with time shifts of τ1 and τ2 is used. Equation 1 shown below is one example of the triple correlation value St. Here, T is the calculation target time of the triple correlation value, Δt is the data sampling period of each potential signal, and N is a constant for normalization, for example, the number of calculations of the product of the three signals. .

Here, the relationship between the triple correlation plot on the delay parameter space obtained by the above calculation and the behavior of the equivalent dipole power source in the deep brain will be explained using a spherical model made of a uniform medium. Hereinafter, for convenience of explanation, each part of the spherical model will be referred to as the North Pole (NP), the South Pole (SP), the equator, etc., likened to the Earth.

Since deep brain activity is equivalently observed on the surface of the brain as if there is a deep microcurrent source, we hypothesize a microcurrent source in the center of the sphere in the direction from the South Pole to the North Pole. The potential distribution produced by this current source on the surface of the sphere is + in the northern hemisphere, - in the southern hemisphere, and zero potential on the equator, as shown in FIG. In addition, this current source rotates clockwise with a cycle of T seconds in a plane including points P1 and P2, which are 180 degrees longitude different from each other on the equator, and NP and SP. The spherical surface potential distribution at each point in time for every 90 degree rotation angle changes sequentially as shown in FIGS. Three electrodes A, B and C are placed on the vertices of a triangle parallel to the planes P1, NP, P2 and SP on the surface of the sphere. Correlation values of potential waveforms measured from each electrode are calculated by Equation 1, and the calculation results are plotted on the delay parameter space of FIG.

The time evolution of the potential of each electrode of A, B, and C is shown in the graph of FIG. 7, and each electrode changes with a sine wave of period T with a phase difference of 1/3T. When the electrode A is used as a reference, the values of τ1 and τ2 where the signs of these electrodes most match are 1/3+k and 2/3+k (k is an integer), respectively. A characteristic with a peak at period T is obtained, as shown. At positions where one of the electrodes is shifted by half a cycle from these peaks, the signs of the electrodes do not match because one electrode is always in the opposite phase to the other two electrodes. Therefore, no values are plotted at positions indicated by plotted white circles.

As described above, the rotation of the equivalent dipole power source in the deep brain can be observed as a plot on a two-dimensional delay parameter space. FIG. 7 and others describe the case where a single equivalent dipole source is smoothly rotated in the spherical deep brain. However, when there are multiple dipoles or when the rotation is not smooth, the plots in FIG. 6 show individual cases that satisfy the same sign condition in a complicated distribution, and appear as fine irregularities in the delay parameter space.

[First Embodiment]
The stress determination device 100 of the first embodiment of the present invention acquires δ waves (for example, 2 to 4 Hz) representing inhibition of the self-reward system by three electrodes placed on the forehead, and generates three time-series data. A value for quantitatively observing the state of the deep part of the brain is calculated from the correlation. The stress determination device 100 determines the calculated value as the stress index value, and determines the stress state of the subject.

<Equipment overview>
FIG. 1 is a schematic configuration diagram of a stress determination device 100 according to a first embodiment of the present invention. Stress determination device 100 includes brain potential sensor 110 and electronic device 120 .

Brain potential sensor 110 includes three electrodes 111 and a communication unit (not shown) that transmits brain potential signals (brain wave data) measured by electrodes 111 to electronic device 120 . Electrodes 111 are attached in contact with the head to measure brain potential signals based on brain activity. Brain potential sensor 110 further includes a reference electrode (not shown). The reference electrode is used as a dead electrode, for example an electrode attached to the earlobe. The communication unit transmits a brain potential signal obtained from each of the three electrodes 111 and a signal obtained from the reference electrode to the electronic device 120, and the electronic device 120 communicates between each of the three electrodes 111 and the reference electrode. Calculate the difference and use it as an input for the three brain potential signals. Alternatively, the communication unit may be configured to transmit three difference signals between each of the three electrodes 111 and the reference electrode to the electronic device 120 as brain potential signals. It should be noted that the electrodes 111 can be any sensor capable of measuring brain potentials of the subject.

When the subject wears the brain potential sensor 110 on the head, the electrodes 111 are arranged in accordance with the international 10-20 method as shown in FIG. (defined as the midpoint of O2) is attached to the electroencephalogram sensor 110 so as to be placed at any position of the electrode arrangement.

In one example, the electroencephalogram sensor 110 uses three electrodes for observing delta waves in the deep part of the frontal lobe. are arranged. In this case, the three electrodes 111 are preferably placed at positions F7, F8 and T3 in the International 10-20 method.

Preferably, the brain potential sensor 110 is a headgear-type brain potential sensor to which the electrodes 111 are attached in advance so that the electrodes 111 come into contact with the forehead when the subject wears it on the head, as shown in FIG. be. The electroencephalogram sensor 110 may be a cap or helmet type in which electrodes 111 are arranged in advance. In one example, as shown in FIG. 1, the brain potential sensor 110 is a headgear to which the electrodes 111 are attached in advance so that the electrodes 111 come into contact with the regions F7, F8, and T3 when the subject wears the headgear. type brain potential sensor. However, the brain potential sensor 110 may have three or more electrodes 110 and the stress determination device 100 may be configured to selectively use brain potential signals obtained from the three electrodes 110 .

The communication unit performs wireless communication and transmits brain potential signals acquired by the electrodes 111 to the electronic device 120 . However, wired communication using an Ethernet (registered trademark) cable, a USB cable, or the like can also be performed. Brain potential sensor 110 may have a plurality of electrodes 111 , and the communication unit may be configured to transmit a brain potential signal measured by one of electrodes 111 to electronic device 120 .

The electronic device 120 includes a processing section 121 , a display section 122 , an input section 123 , a storage section 124 and a communication section 125 . These components are connected by a bus 126, but they may be individually connected as needed.

Electronic device 120 is preferably a smart phone, but can also be a general computer, tablet computer, or the like.

The processing unit 121 includes a processor (for example, CPU) that controls each unit included in the electronic device 120, and performs various processes using the storage unit 124 (for example, main memory) as a work area. The display unit 122 displays a screen to the user under the control of the processing unit 121, and is composed of, for example, a liquid crystal display.

The input unit 123 receives input from the user to the electronic device, and is, for example, a touch panel, a touch pad, a keyboard, or a mouse. Storage unit 124 includes a hard disk, a main memory, and a buffer memory. Programs are stored on the hard disk. However, the hard disk may be any non-volatile storage or non-volatile memory as long as it can store information, and it may be removable. Also, for example, if the electronic device 120 is a smart phone, it includes ROM and RAM. The storage unit 124 stores programs and various types of data that can be referred to as the programs are executed.

The communication unit 125 performs wireless communication, receives brain potential signals from the brain potential sensor 110 , and stores them in the storage unit 124 . However, wired communication using an Ethernet (registered trademark) cable, a USB cable, or the like can also be performed.

FIG. 9 is a functional block diagram of the stress determination device 100 of the first embodiment of the invention. The stress determination device 100 includes brain potential signal acquisition means 201 and calculation means 202 .

The brain potential signal acquisition means 201 has a function of acquiring brain potential signals using electrodes 111 attached to three different positions on the frontal or temporal region of the subject. is one example. The electroencephalogram signal acquisition means 201 preferably has a function of acquiring electroencephalogram signals from three electrodes attached to the head surface of the subject's F7, F8, and T3 regions according to the International 10-20 method.

The computing means 202 extracts brain potential data in a specific frequency band (preferably the delta wave band) caused by deep brain activity from the three brain potential signals acquired at each electrode 111, and extracts the extracted three brain potential data. A correlation value indicating the correlation between the brain potential signals obtained at the respective electrodes 111 is calculated based on the phase relationship of the two time-series data, and the subject's stress state is determined based on the correlation value.

The computing means 202 is implemented by causing the electronic device 120 to execute a program. For example, if the electronic device 120 is a smart phone, the functions of the computing means 202 are implemented when a dedicated application, which is a program, is downloaded and activated. In this embodiment, the computing means 202 determines the stress state by performing the following information processing.

<Information processing>
Assuming that the three electrodes attached to the head are EA, EB, and EC, the computing means 202 (electronic device 120) calculates the potential signal VA as the difference between the brain potential signal obtained from each electrode 111 and the reference electrode. (t), VB(t), and VC(t). Subsequently, the computing means 202 extracts the frequency band (2 to 4 Hz) of the delta wave band using a bandpass filter such as a digital filter. The computing means 202 executes the information processing shown in the flowchart of FIG. 10 on the extracted potential signal. However, the computing means 202 can also extract a specific frequency band other than the delta wave band.

FIG. 10 is a flow chart showing information processing for calculating the triple correlation value S by the calculating means 202 according to this embodiment. FIG. 10 shows a flowchart of processing for calculating the triple correlation value Si (i=1, 2, . . . , T) from the i second to the i+1 second. Note that this flowchart can be modified without departing from the scope of the invention.

When three signals are input in step 1001, in step 1002, the potential of each electrode 111 is divided by the standard deviation (σ _A , σ _B , σ _C ) and normalized (EVA(t)=VA(t )/σ _A , EVB(t)=VB(t)/σ _B , EVC(t)=VC(t)/σ _C ). This normalization process is preferably performed every second, but is not limited to this. The three signals have a time lag of τ1 for electrode E _B and τ2 for electrode E _C with respect to electrode E _A .

Note that the frequency extraction processing described above may be performed after the normalization processing. Also, it is preferable to perform noise processing before the normalization processing. Noise processing, for example, 1) excluding segments of ±100 μV or more, 2) excluding flat potentials (when the potential was constant for 25 msec or more), 3) excluding cases where the potential within ±1 μV lasts for 1 second or more, It consists of the process of

Then in step 1003, the signs of the three signals are all positive (EVA(t)>0, EVB(t-τ1)>0, EVC(t-τ2)>0) or all negative (EVA(t)< 0, EVB(t-τ1) < 0, EVC(t-τ2) < 0) signals are subject to calculation.

In step 1004, a triple correlation value (one element of the triple correlation value) is calculated by adding products of three potential signals with time lag. The calculation of the triple correlation value is performed by shifting Δt seconds until t reaches t=i+1 seconds (S1006, S1007). For example, if the potential data sampling frequency is fs (Hz), when fs=200 Hz, the product of three potential signals is calculated with a shift of Δt=1/fs=0.005 seconds. In this flowchart, the triple correlation value is calculated, and the number of times N when the three signals become positive or negative is obtained (S1005), and finally divided (S1008).

（ｉ＝１、２、…、T、τ１＝Δｔ、２Δｔ、…、１（秒）、τ２＝Δｔ、２Δｔ、…、１（秒）） In steps 1003 to 1007, the triple correlation value Si is calculated by calculating Equation 2 shown below for t when all three signals have the same sign.

(i = 1, 2, ..., T, τ1 = Δt, 2Δt, ..., 1 (second), τ2 = Δt, 2Δt, ..., 1 (second))

In this way, Si is calculated every second up to T seconds of all data (S ₁ , S ₂ , . . . , S _T ). T (seconds) is preferably 10 (seconds). As described above, the triple correlation value is not calculated for all data (T seconds) at once, but is calculated every predetermined time, for example, every second as shown in FIG. The finally calculated triple correlation value S is the average value of the T triple correlation values Si.

Also for the time shifts τ1 and τ2, the triple correlation value S is calculated by shifting by Δt seconds. Possible values of τ1 and τ2 are times equal to integral multiples of Δt and less than 1 second, but the maximum magnitude of these values is not limited to 1 second. Note that the triple correlation value can also be calculated by Equation 2 without performing sign determination of the three signals.

Furthermore, the calculating means 202 is characterized in that the two delay parameters (τ1, τ2) form the triple correlation value S calculated by shifting the delay times τ1 and τ2 by Δt seconds, 2Δt seconds, . It has the function of plotting in space. As a result, a pseudo three-dimensional representation of the triple correlation value distribution plotted on the feature space formed by the two delay parameters (.tau.1, .tau.2) can be obtained.

FIG. 11 is a pseudo three-dimensional display of the triple correlation value distribution based on the brain potential signal acquired from one subject (subject A). Only Si(τ1, τ2) where EVA(t), EVB(t−τ1) and EVC(t−τ2) all had the same sign at a value of t, eg t=i+1, is plotted. By limiting the plotted Si in this way, noise can be removed and a pseudo-three-dimensional representation of the triple correlation value distribution can be shown with better accuracy.

FIG. 12, like FIG. 11, is a pseudo three-dimensional representation of triple correlation value distribution based on brain potential signals acquired from another subject (subject B). While the triple correlation distribution in the feature space of FIG. 11 is smooth, it can be confirmed that the triple correlation distribution in the feature space of FIG. 12 often has complex distributions of fine peaks.

Further, the calculation means 202 has a function of calculating the index value SD using the pseudo three-dimensional representation of the triple correlation value distribution. As shown in FIGS. 11 and 12, within the two delay time parameter spaces, subject A's data has a regular dendritic distribution, whereas subject B's data has an irregular dendritic distribution. sexuality is great. In order to express this difference quantitatively, as shown in FIG. 13, the coordinate axes are rotated so that the rows of trees are parallel to the τ1 and τ2 axes. FIG. 13 is a top view of the three-dimensional display shown in FIG. 11. Regions where the three waveforms have the same sign are shown in white, and regions where one of the three signals has a different sign are shown in black. When such a display is performed, it can be confirmed that the checkered pattern of the subject A is regular, while the checkered pattern of the subject B is disordered as shown in FIG. 14 . An index that quantifies this disturbance is the index value SD.

As shown in FIGS. 13 and 14, each white square area is spaced vertically and horizontally from adjacent white square areas. As shown in FIG. 15, the intervals are dxi (i=1, 2, . . . , m) and dyj (j=1, 2, . . . , n). It is possible to quantify the degree of disorder by determining whether the white squares are aligned vertically and horizontally in the τ1 direction and the τ2 direction of dxi and dyj, respectively, or whether the white squares are disorderly aligned.

Specifically, as shown in Equations 3 and 4, the standard deviation Std_dx of m dxi and the standard deviation Std_dy of n dyj are calculated.

The index value SD is the average value of two standard deviations, as shown in Equation 5.

As described above, the calculation means 202 (stress determination device 100) quantifies and observes the state of the deep brain as a stress index value (first stress index value) using the triple correlation value S and the index value SD. , thereby determining the stress state of the subject. Here, the triple correlation value S indicates the correlation (phase relationship) of the three brain potential signals, while the index value SD indicates the degree of disturbance and variation.

In one example, the computing means 202 determines the value calculated by dividing the index value SD by the triple correlation value S as the stress index value, and determines the stress state of the subject based on the stress index value. In this case, the computing means 202 determines that the greater the stress index value SD/S, the greater the stress of the subject. In one example, when the stress index value SD/S is 9 or more, it is determined that the subject is in a high stress state.

In another example, the computing means 202 determines the triple correlation value S as the stress index value. In this case, it is determined that the smaller the triple correlation value S, the greater the subject's stress. Similarly, in another example, the computing means 202 determines the index value SD as the stress index value. In this case, it is determined that the greater the index value SD, the greater the subject's stress.

<Example>
Based on the following experimental results, it will be explained that the stress condition can be determined using the stress determination device 100 of the first embodiment.

In this experiment, each of the 18 subjects was asked to undergo a resting eye-opening (120 s) state and a task (380 s) state in sequence, and brain potential signals were acquired in each state. At this time, 21 electrodes shown in FIG. 2 were placed on the scalp of the subject, and brain potential signals were obtained from the 21 electrodes. The electrodes used in this experiment are equivalent to the electrodes 111 in the stress determination device 100 .

Here, the task (kanji) is a state in which the subject is forced to read obfuscated kanji out loud in front of the supervisor (high stress state). It is a state (low stress state) where

It should be noted that the acquired EEG signal (particularly EEG signal acquired from the frontal electrodes) contains excessive biological noise other than EEG due to blinking, etc. Therefore, before analyzing the EEG signal, , these noises were removed, and a frequency band of 2 to 4 Hz (delta wave band) was extracted.

Analysis of brain potential signals acquired from electrodes 111 attached to sites F7, F8, and T3, for example, calculation of stress index values, will be described below.

FIG. 16 is a diagram showing the average value and standard deviation of the index value SD/triple correlation value S in the high stress state and low stress state of 18 subjects. As shown in the figure, the greater the calculated stress index value SD/S, the greater the subject's stress. In one example, when the subject's stress index value SD/S is 9 or more, it can be determined that the subject is feeling stress.

FIG. 17 is a diagram plotting the triple correlation value S and the index value SD in the high stress state and low stress state of 18 subjects, one axis (horizontal axis) the triple correlation value S, the other axis ( 2-dimensional coordinates with the vertical axis) as the index value SD. As shown in the figure, the stress state can be determined by separating regions in the subject's high stress state and low stress state in the two-dimensional coordinates.

In this experiment, the analysis using brain potential signals obtained from the electrodes 111 attached to the sites F7, F8, and T3 was explained. This is because in this case the largest difference was obtained between the stressed state and the resting state. However, similar results may be obtained even when brain potential signals obtained from electrodes 111 attached to sites other than these three sites are used.

<Effect>
With such a configuration, in this embodiment, by calculating the triple correlation value and the index value from the brain potential signals acquired from the three electrodes 111 attached to the frontal region or the temporal region, Determine the stress state of the subject. As a result, when the subject wants to determine the stress state, the three electrodes 111 can be attached to the forehead or temporal region, which is easier to attach than the occipital region, so that the burden on the subject can be further reduced. Become. Moreover, it becomes possible to perform quantitative stress determination based on the state of the deep part of the brain.

<Other Examples>
FIG. 18 is a schematic configuration diagram of a stress determination device 100 according to another example of the first embodiment of the present invention. The stress determination device 100 includes a head-mounted part 113 including three electrodes 111 and a reference electrode 112, a 3-channel amplifier/band-pass filter 130 connected to the three electrodes 111 with a signal cable, and a 3-channel amplifier/band-pass filter 130 and a signal and an electronic device 120 connected by a cable.

The reference electrode 112 is an electrode for reference potential measurement, is used as an insensitive electrode, and is preferably an earlobe-connecting clip electrode. The reference electrode 112 is connected to a 3ch amplifier/bandpass filter 130 .

The three electrodes 111 are fixed by fixtures 114 . The head-mounted part 113 includes fixtures 114 that fix the three electrodes 111 . The head mounting portion 113 may be, for example, a boomerang-shaped plastic mounting portion cut out from a helmet, or may be a headgear-shaped mounting portion as shown in FIG. The head-mounted part 113 preferably has electrodes arranged so that when the subject wears the head-mounted part 113, the three electrodes come into contact with the regions Fp1, Fp2, and F8 according to the International 10-20 method. there is The electrode 111 is preferably a porous fiber electrode containing physiological saline, and the upper portion of the electrode 111 is composed of a metal cylinder for wire connection.

FIG. 19 is a schematic external view of cap-mounted electrodes of the stress determination device 100 according to another embodiment. FIG. 20 is a schematic external view of a conductive rubber electrode for reference potential measurement. The head-mounted part 113 is a mesh hat to which three measurement electrodes 111 are attached. The electrode 111 is connected to a shielded cable 116 which is connected to a preamplifier 115, preferably using porous conductive rubber containing saline.

The preamplifier 115 has the amplifier function of the 3ch amplifier/bandpass filter 130 and is connected to the electronic device 120 via the bandpass filter. The reference electrode 112 is a conductive rubber electrode 117 electrically connected to the preamplifier 115, thereby eliminating the need for earlobe-connecting clip electrodes. A metal film 118 is placed between the circumferential conductive rubber electrode 117 and the cap in order to equalize the potential of the conductive rubber and reduce the contact resistance when connecting the cable from the preamplifier 115 . be done.

[Second embodiment]
The stress determination device 100 of the second embodiment of the present invention has the same configuration as the stress determination device 100 of the first embodiment, and further includes a pulse sensor for measuring the subject's pulse.

When the body receives stress, the activity of the amygdala, which governs the expression of emotions, becomes active primarily, but as a result of this, as a secondary reaction, the self-reward system centered on the nucleus accumbens located in the deep frontal region is activated. restraint on In parallel with this, the activity of the amygdala also affects the autonomic nervous system located in the hypothalamus, which is one of the expression mechanisms of emotion, and affects heart rate and blood pressure. In particular, heart rate fluctuates by several percent due to respiration and mental activity in a less stressed living body, which is considered to be proof that the autonomic nervous system is functioning normally. The stress determination device 100 of the second embodiment further uses pulse data to determine the stress state in order to determine the stress state more accurately.

The stress determination device 100 of the second embodiment also uses the triple correlation value S and the index value SD in the same manner as the stress determination device 100 of the first embodiment. In this embodiment, the observation of the state of the deep part of the brain, such as the calculation of the triple correlation value S and the index value SD, is the same as in the first embodiment. explain.

A stress determination device 100 of the second embodiment includes a brain potential sensor 110, an electronic device 120, and a pulse sensor (not shown). A pulse sensor can be any sensor capable of obtaining pulse data of a subject. The pulse sensor includes a communication unit (not shown) that transmits pulse data as a pulse signal to electronic device 120 . The communication unit performs wireless communication and transmits pulse data acquired by the pulse sensor to the electronic device 120 . However, wired communication using an Ethernet (registered trademark) cable, a USB cable, or the like can also be performed.

FIG. 21 is a functional block diagram of the stress determination device 100 of the second embodiment of the invention. The stress determination device 100 includes brain potential signal acquisition means 201 , calculation means 202 , and pulse signal acquisition means 203 . The brain potential signal acquisition means 201 and the calculation means 202 have the same functions as the stress determination device 100 of the first embodiment. Differences from the first embodiment will be mainly described below.

The pulse signal acquisition means 203 has a function of acquiring a pulse signal (pulse data) of a subject, and a pulse sensor is one example. In this embodiment, the computing means 202 determines the stress state by performing the following information processing.

FIG. 22 is a data processing block diagram of the stress determination device 100 of the second embodiment of the present invention. As shown in the figure, the computing means 202 (stress determination device 100) acquires brain potential data (S2201), and calculates a stress index value (first stress index value) such as SD/S (S2202). . The information processing is the same as that of the stress determination device 100 of the first embodiment. Separately from the above process, the computing means 202 acquires pulse data (S2211) and calculates a stress index value (second stress index value) based on the pulse data (S2211).
212). The calculating means 202 determines the subject's stress state using the two index values calculated as described above (S2221).

At this time, since two index values based on brain potential data and pulse data acquired at the same time are used for determination, the calculation means 202 preferably adds a data delay to the first stress index value (S2203). . This is because it takes several minutes, for example, 2 to 3 minutes to acquire pulse data for determining the stress state. This is because it takes more time than calculation of the stress index value of 1. However, the data delay processing may be performed simultaneously with the data calculation in step 2221 .

Next, the process of calculating the second stress index value in step 2212 will be described. The calculating means 202 generates pulse interval data, which is time-series data of RR intervals of the pulse, from the pulse signal.

The calculating means 202 calculates a frequency spectrum by performing a discrete Fourier transform on the pulse interval data. The calculating means 202 calculates the total value of the spectral intensity of each frequency in the first frequency band, which is the low frequency component in the frequency spectrum, in a frequency band higher than the first frequency band and different from the first frequency band. A ratio of the spectral intensity of each frequency in the second frequency band to the total value is calculated. For example, the frequency spectrum is calculated in a frequency band of 0-1 Hz, the first frequency band being 0.05-0.15 Hz and the second frequency band being 0.15-0.40 Hz.

The calculating means 202 determines the calculated ratio as the second stress index value, and determines the stress state based on the first stress index value and the second stress index value. In one example, the subject's high stress state and low stress state are shown in two-dimensional coordinates in which one axis (horizontal axis) is the second stress index value and the other axis (vertical axis) is the first stress index value. The stress state can be determined by isolating the regions in .

In another example of the second embodiment, the computing means 202 generates a pulse histogram showing the frequency of appearance for each pulse interval value from the pulse interval data. The calculating means 202 compares the sum of the variances of the predetermined amount of data extracted in order from the shortest pulse interval in the pulse histogram to the sum of the variances of the predetermined amount of data extracted in order from the shortest pulse interval. Calculate the ratio. In one example, the calculating means 202 calculates the variance of the data corresponding to 7% of the data constituting the pulse histogram, which are a plurality of data extracted in order from the longest pulse interval in the pulse histogram. to the sum of the variances of the data corresponding to the 7% extracted in descending order of the pulse interval.

The calculating means 202 determines the calculated ratio as the second stress index value, and determines the stress state based on the first stress index value and the second stress index value. In one example, the subject's high stress state and low stress state are shown in two-dimensional coordinates in which one axis (horizontal axis) is the second stress index value and the other axis (vertical axis) is the first stress index value. The stress state can be determined by isolating the regions in .

<Example>
Based on the following experimental results, it will be described that the stress state can be determined using the stress determination device 100 of the second embodiment. The experimental conditions were the same as in the first embodiment. A pulse signal was obtained from each of the 18 subjects to calculate the stress index value. FIG. 23 shows pulse interval data generated from the pulse signal in a low stress state, and FIG. 24 shows pulse interval data generated from the pulse signal in a high stress state.

25 shows the frequency spectrum generated by performing the discrete Fourier transform on the pulse interval data of FIG. 23, and FIG. 26 illustrates the frequency spectrum generated by performing the discrete Fourier transform on the pulse interval data of FIG. shows the frequency spectrum. Here, in each subject's low stress state and high stress state, the spectral intensity of the low frequency band (0.05 to 0.15 Hz) relative to the spectral intensity of the high frequency band (0.15 to 0.40 Hz) A ratio (LF/HF) is calculated and used as a second stress index value.

FIG. 27 is a diagram plotting the first stress index value (SD/S) and the second stress index value (LF/HF) in the high stress state and low stress state of 18 subjects, one axis ( LF/HF on the horizontal axis) and SD/S on the other axis (vertical axis). As shown in the figure, the stress state can be determined by separating regions in the subject's high stress state and low stress state in the two-dimensional coordinates. In one example, F1=3(LF/HF)+SD/S-12.5, and if F1>0, it is determined to be in a high stress state, and if F1≦0, it is determined to be in a low stress state. . However, the formula indicated by F1 is an example, and is not limited to this.

<Effect>
With such a configuration, in this embodiment, the first stress index value is calculated from brain potential signals acquired from the three electrodes 111 attached to the frontal region or the temporal region, and the pulse sensor The stress state of the subject is determined by calculating a second stress index value from the pulse signal acquired from the . Accordingly, when the subject wants to determine the stress state, the three electrodes 111 can be attached to the forehead or temporal region, which is easier to attach than the occipital region, and the pulse signal can be measured, thereby reducing the burden on the subject. It is possible to further reduce it. Moreover, it is possible to perform quantitative stress determination based on the state of the deep part of the brain and the state of the pulse.

[Third Embodiment]
The stress determination device 100 of the third embodiment of the present invention has a configuration similar to that of the stress determination device 100 of the first embodiment, and uses three electrodes 111 to specify a specific frequency band (preferably θ wave It has a configuration for acquiring the brain potential signal of the band).

When the body receives stress, the activity of the amygdala located deep inside the temporal lobe becomes active. It is expected that the coherence of the waveform between the three electrodes (T3, C3, T5) straddling the occiput is high. The degree of coherence can be represented here by the first principal component relative contribution (PCA) of the principal component analysis of the three-electrode waveforms. The stress determination device 100 of the third embodiment further uses PCA to determine the stress state in order to determine the stress state more accurately.

The stress determination device 100 of the third embodiment also uses the triple correlation value S and the index value SD in the same manner as the stress determination device 100 of the first embodiment. In this embodiment, the observation of the state of the deep part of the brain, such as the calculation of the triple correlation value S and the index value SD, is the same as in the first embodiment. explain.

A functional block diagram of the stress determination device 100 of the third embodiment of the present invention is the same as that of the stress determination device 100 of the first embodiment. The stress determination device 100 includes brain potential signal acquisition means 201 and calculation means 202 . Brain potential signal acquisition means 201 and calculation means 202
has the same function as the stress determination device 100 of the first embodiment.

In this embodiment, the brain potential signal acquisition means 201 uses three electrodes 111 further attached to the surface of the temporal or occipital region of the subject, which are different from the positions of the electrodes 111 in the first embodiment. brain potential signals are further acquired. The three electrodes were attached to the subject's temporal or occipital surface at three different locations. The electroencephalogram signal acquisition means 201 preferably has a function of acquiring electroencephalogram signals from three electrodes attached to the head surface of the subject at T3, C3, and T5 in the International 10-20 method. In this embodiment, the computing means 202 determines the stress state by performing the following information processing.

FIG. 28 shows a data processing block diagram of the stress determination device 100 of the third embodiment of the present invention. As shown in the figure, the computing means 202 (stress determination device 100) acquires brain potential data (S3101), and from the acquired three brain potential signals, a specific frequency band (preferably delta wave band) are extracted (S3102), and a first stress index value such as SD/S is calculated (S3103). The information processing is the same as that of the stress determination device 100 of the first embodiment. Separately from the above processing, the computing means 202 acquires brain potential data from the subject's head surface different from the position where the brain potential data is acquired in step 3101 (S3111), and from the acquired three brain potential signals, the amygdala Brain potential data in a specific frequency band (preferably θ wave band, for example 5 to 8 Hz) resulting from the activity of each is extracted (S3112), and a stress index value (third stress index value) is calculated (S3113). . The computing means 202 determines the stress state of the subject using the two index values calculated as described above (S3121).

Next, calculation processing of the third stress index value in step 3113 will be described. The computing means 202 performs principal component analysis on the three time-series data in the specific frequency band (preferably the θ wave band) caused by the activity of the amygdala extracted in step 3112 . In the principal component analysis, three brain potential data values are variables. The calculating means 202 calculates the first principal component contribution rate (ratio) by performing principal component analysis.

The calculating means 202 determines the first principal component contribution rate as the third stress index value, and determines the stress state based on the first stress index value and the third stress index value. In one example, the subject's high stress state and low stress state are shown in two-dimensional coordinates in which one axis (horizontal axis) is the third stress index value and the other axis (vertical axis) is the first stress index value. The stress state can be determined by isolating the regions in .

Here, before describing an example of the stress determination device 100 of the third embodiment, an experiment showing the correlation between PCA (PCA θ) calculated based on brain potential in the θ wave band and subjective evaluation will be described.

FIG. 29 shows the results of the questionnaire when five subjects were shown a fear image, a relaxation image, a joy image, and a sadness image. It is a figure which shows the score and the relationship of the correlation coefficient with PCA. From the figure, it can be seen that 4 out of 5 people had a positive correlation between "irritated" and PCA, and all 5 people had a negative correlation with "relaxed" and PCA. It can also be seen that PCA tends to have a high value under high stress conditions and a low value under low stress conditions.

<Example>
Based on the following experimental results, it will be described that the stress state can be determined using the stress determination device 100 of the third embodiment. The experimental conditions were the same as in the first embodiment, but in this experiment, brain potential signals were obtained from the head surface of the regions F7, F8, and T3 in order to calculate the first stress index value. At the same time, brain potential signals were obtained from the head surface of the T3, C3, and T5 regions for the calculation of the third stress index value. Here, in each subject's low stress state and high stress state, the first principal component analysis was performed on three time-series data in the θ wave band (5 to 8 Hz) due to the activity of the amygdala. A component contribution rate (ratio) is calculated and used as a third stress index value.

FIG. 30 is a diagram plotting the first stress index value (SD/S) and the third stress index value (PCA θ) in the high stress state and low stress state of 18 subjects, one axis (horizontal axis ) is PCA θ and the other axis (vertical axis) is SD/S. As shown in the figure, the stress state can be determined by separating regions in the subject's high stress state and low stress state in the two-dimensional coordinates. In one example, F3=12.4*PCA.theta.+SD/S-20, and if F3>0, it is determined to be in a high stress state, and if F3.ltoreq.0, it is determined to be in a low stress state. However, the formula indicated by F3 is an example and is not limited to this.

<Effect>
With such a configuration, in the present embodiment, the first stress index value is calculated from the brain potential signals acquired from the three electrodes 111 attached to the frontal region or the temporal region. The stress state of the subject is determined by calculating the third stress index value from brain potential signals acquired from the three electrodes 111 attached to the head or back of the head. As a result, when the subject wants to determine the stress state, it is sufficient to attach six electrodes 111, so that the burden on the subject can be further reduced. Moreover, it is possible to perform quantitative stress determination based on the state of the deep brain and the state of the amygdala.

In the processes or operations described above, the processes, operations, and combinations can be freely changed as long as there is no contradiction. Moreover, each example or embodiment described above is an illustration for explaining the present invention, and the present invention is not limited to these examples or embodiments. The present invention can be embodied in various forms without departing from the gist thereof. Moreover, the effects described in the present embodiment are merely enumerations of preferred effects produced by the present invention, and the effects of the present invention are not limited to those described in the present embodiment.

100 Stress determination device 110 Brain potential sensor 111 Electrode 112 Reference electrode 113 Head mounted part 114 Fixture 115 Preamplifier 116 Shielded cable 117 Conductive rubber electrode 118 Metal film 120 Electronic device 121 Processing unit 122 Display unit 123 Input unit 124 Storage unit 125 communication unit 126 bus 130 3ch amplifier/band filter 201 brain potential signal acquisition means 202 calculation means 203 pulse signal acquisition means

Claims (3)

brain potential signal acquisition means for acquiring brain potential signals of the subject using sensors attached to three different positions on the frontal or temporal surface of the subject;
Extracting time-series data in a specific frequency band caused by deep brain activity from each of the acquired brain potential signals, and extracting the acquired brain potential based on the phase relationship of the extracted time-series data computing means for calculating a correlation value indicating the correlation of each signal and determining the stress state of the subject based on the correlation value;
The computing means calculates VA extracted within a predetermined time, where VA(t), VB(t), and VC(t) are the time-series data extracted for each of the attached sensors A, B, and C. (t) VB(t−τ1) and VB(t−τ1) extracted at times different from each other by arbitrary times τ1 and τ2, which are times equal to integral multiples of a predetermined value or less of the sampling period at which the time-series data was extracted, respectively VC (t-τ2) is multiplied only by combinations of τ1 and τ2 when VA (T), VB (T-τ1) and VC (T-τ2) at time T within the predetermined time have the same sign , Calculate the correlation value using the value obtained by adding each value obtained,
based on the standard deviation of the grid spacing indicated by the pseudo-three-dimensional representation of the triple correlation value distribution, or
calculating an index value based on the standard deviation in each of the τ1-axis direction and the τ2-axis direction of the distance between the regions for which the correlation value was calculated in the coordinates with the time τ1 and τ2 as the axes;
Further comprising pulse signal acquisition means for acquiring the pulse signal of the subject,
The calculating means generates a pulse histogram indicating the frequency of occurrence of each pulse interval value from the pulse interval data generated from the pulse signal, and a predetermined amount of data extracted in order from the pulse interval in the pulse histogram. calculating the ratio of the total value of the variances to the total value of the variances of the predetermined amount of data extracted in order from the shortest pulse interval, and adding a data delay to the index value;
Based on the ratio and the index value, the area in the high stress state and the low stress state of the subject in two-dimensional coordinates with one axis (horizontal axis) as the ratio and the other axis (vertical axis) as the index value. A stress determination device that determines the stress state by separating the EEG signals of the subject acquired using sensors attached to three different locations on the frontal or temporal surface of the subject and pulse sensors acquiring pulse signals of the subject. A program for determining stress from a pulse signal,
to the computer,
a step of extracting time-series data of a specific frequency band resulting from deep brain activity from each of the acquired brain potential signals;
a step of calculating a correlation value indicating the correlation of each of the acquired brain potential signals based on the phase relationship of the extracted time-series data;
determining the stress state of the subject based on the correlation value;
The step of calculating the correlation value comprises:
When the time-series data extracted for each of the attached sensors A, B, and C are VA(t), VB(t), and VC(t), each VA(t) extracted within a predetermined time is On the other hand, VB (t-τ1) and VC (t-τ2 ) is multiplied only by combinations of τ1 and τ2 when VA (T), VB (T-τ1) and VC (T-τ2) at time T within the predetermined time have the same sign, and each obtained Calculate the correlation value using the value obtained by adding the value of
wherein the step of determining the stress state is based on the standard deviation of grid spacing indicated by a pseudo-three-dimensional representation of a triple correlation value distribution; or
calculating an index value based on the standard deviation in each of the τ1-axis direction and the τ2-axis direction of the distance between the regions for which the correlation value was calculated in the coordinates with the time τ1 and τ2 as the axes;
generating a pulse histogram indicating the appearance frequency of each pulse interval value from the pulse interval data generated from the pulse signal, and summing variances of a predetermined amount of data extracted in descending order of the pulse interval in the pulse histogram; of the predetermined amount of data extracted in order from the shortest pulse interval to the total value of the variances , and adding a data delay to the index value,
Based on the ratio and the index value, the area in the high stress state and the low stress state of the subject in two-dimensional coordinates with one axis (horizontal axis) as the ratio and the other axis (vertical axis) as the index value. A program to determine the stress state by isolating the . EEG signals of the subject acquired using sensors attached to three different locations on the frontal or temporal surface of the subject and pulse sensors acquiring pulse signals of the subject. A method for determining stress from a pulse signal, comprising:
the computer
a step of extracting time-series data of a specific frequency band resulting from deep brain activity from each of the acquired brain potential signals;
a step of calculating a correlation value indicating the correlation of each of the acquired brain potential signals based on the phase relationship of the extracted time-series data;
determining the stress state of the subject based on the correlation value;
The step of calculating the correlation value comprises:
When the time-series data extracted for each of the attached sensors A, B, and C are VA(t), VB(t), and VC(t), each VA(t) extracted within a predetermined time is On the other hand, VB (t-τ1) and VC (t-τ2 ) is multiplied only by combinations of τ1 and τ2 when VA (T), VB (T-τ1) and VC (T-τ2) at time T within the predetermined time have the same sign, and each obtained Calculate the correlation value using the value obtained by adding the value of
wherein the step of determining the stress state is based on the standard deviation of grid spacing indicated by a pseudo-three-dimensional representation of a triple correlation value distribution; or
calculating an index value based on the standard deviation in each of the τ1-axis direction and the τ2-axis direction of the distance between the regions for which the correlation value was calculated in the coordinates with the time τ1 and τ2 as the axes;
generating a pulse histogram indicating the appearance frequency of each pulse interval value from the pulse interval data generated from the pulse signal, and summing variances of a predetermined amount of data extracted in descending order of the pulse interval in the pulse histogram; of the predetermined amount of data extracted in order from the shortest pulse interval to the total value of the variances , and adding a data delay to the index value,
Based on the ratio and the index value, the area in the high stress state and the low stress state of the subject in two-dimensional coordinates with one axis (horizontal axis) as the ratio and the other axis (vertical axis) as the index value. determining a stress condition by isolating the .

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