JP6093422B1 - Brain age presentation device - Google Patents

Abstract

A brain age presentation device capable of simply presenting brain age is provided. A brain age presentation device 301 includes facial skin temperature acquisition means 20 and / or image data acquisition means 120, brain activity estimation means 30, 130, and state visualization means 200. The facial skin temperature acquisition means 20 acquires facial skin temperature data in time series. The image data acquisition unit 120 acquires captured image data of the face in time series. Based on a plurality of components obtained by decomposing facial skin temperature data and / or facial blood flow data by singular value decomposition, principal component analysis, or independent component analysis, the brain activity estimating means 30, 130 Estimate brain activity. The state visualization unit 200 presents the brain age of the subject based on the brain activity of the subject estimated by the brain activity estimation unit 30. [Selection] Figure 23

Description

  The present invention relates to a brain age presentation device.

  In recent years, there has been an increasing need to ascertain brain age. In order to grasp the brain age, an electroencephalogram measurement method (EEG), magnetic resonance imaging (fMRI), or near infrared ray as disclosed in Patent Document 1 (Japanese Patent Laid-Open No. 2013-176406) Spectroscopy (NIRS) may be used.

  However, when estimating the brain age based on the electroencephalogram measurement by each method described above, it is necessary to wear an electrode that requires pretreatment for the subject, and the work in the preparation stage is complicated, There is a problem that it cannot be measured unless it is a measurement room, and it requires a great deal of cost.

  The subject of this invention is providing the brain age presentation apparatus which can present brain age simply.

  The brain age presentation device according to the first aspect of the present invention comprises facial skin temperature acquisition means and / or facial blood flow acquisition means, brain activity estimation means, and brain age presentation means. The facial skin temperature acquisition means acquires facial skin temperature data in time series. The facial skin temperature data includes skin temperature data detected from the subject's face and position data of the detected part. The facial blood flow acquisition means acquires facial blood flow data in time series. The facial blood flow data is obtained based on the RGB data of the captured image data obtained by the RGB processing by performing RGB processing on the captured image data obtained by capturing the face of the subject in time series. The brain activity estimating means determines the brain activity of the subject based on a plurality of components obtained by decomposing facial skin temperature data and / or facial blood flow data by singular value decomposition, principal component analysis, or independent component analysis. presume. The brain age presentation means presents the brain age of the subject based on the brain activity of the subject estimated by the brain activity estimation means.

  In the brain age presentation device according to the first aspect of the present invention, based on the time-series facial skin temperature data and / or facial blood flow data acquired by the facial skin temperature acquisition means and / or facial blood flow volume acquisition means, Brain activity is estimated. For this reason, in this brain age presentation device, it is possible to estimate the brain activity of the subject even without wearing a sensor that requires processing before wearing an electroencephalogram electrode or the like. As a result, it is possible to simply present the brain age.

  The brain age presentation device according to a second aspect of the present invention is the brain age presentation device according to the first aspect, wherein the brain activity estimation means includes a plurality of components whose amplitude of the component waveform is not activated when the brain is activated. A component having a correlation with a change at the time of activation is extracted as a determination component. Then, the brain activity estimating means estimates the brain activity of the subject based on the determination component. In this brain age presentation device, a component having a correlation with brain activation / deactivation among a plurality of components is extracted as a determination component for estimating the brain activity of the subject. For this reason, it is possible to estimate the brain activity from a component that is presumed to be highly related to the brain activity of the subject. As a result, it is possible to improve the estimation accuracy of brain age.

  The brain age presentation apparatus according to a third aspect of the present invention is the brain age presentation apparatus according to the first aspect or the second aspect, wherein the facial skin temperature acquisition means and / or the facial blood flow acquisition means is the sinus of the face of the subject. At least facial skin temperature data and / or facial blood flow data of the peripheral and / or forehead are obtained.

  Here, the brain has a mechanism called a selective brain cooling system that cools the brain independently of the body temperature. As a selective brain cooling mechanism, it is known that heat generated by brain activity is exhausted using the sinuses and the periphery of the forehead. Further, the skin temperature of the face is considered to be proportional to the blood flow volume of the face.

  In the brain age presentation device according to the third aspect of the present invention, based on time series facial skin temperature data and / or facial blood flow data in the paranasal sinuses and / or the forehead portion, which is presumed to reflect brain activity. The brain activity of the subject is estimated. For this reason, in this brain age presentation apparatus, a subject's brain activity can be estimated accurately. As a result, it is possible to improve the estimation accuracy of brain age.

  A brain age presentation device according to a fourth aspect of the present invention is the brain age presentation device according to any one of the first to third aspects, and further includes a brain age storage unit. The brain age storage unit stores an estimation result of brain activity and brain age data indicating the brain age in association with each other. The brain activity estimating means calculates a brain activity estimation result. The brain age presenting means extracts and presents the brain age data of the subject from the brain age storage unit based on the estimation result.

  In the brain age presentation device according to the fourth aspect of the present invention, the brain age storage unit stores the brain activity estimation result and the brain age data indicating the brain age in association with each other, so that the correlation between the brain activity and the brain age is stored. Can be optimized sequentially.

  A brain age presentation device according to a fifth aspect of the present invention is the brain age presentation device according to any one of the first to fourth aspects, comprising facial skin temperature acquisition means and / or facial blood flow acquisition means, and brain age. The presenting means is stored in the smart device.

  In the brain age presentation apparatus according to the fifth aspect of the present invention, information is input / output by the smart device, so that the brain age can be presented easily. In addition, by providing the brain activity estimation means in the server, the calculation load can be reduced.

  A brain age presentation device according to a sixth aspect of the present invention is the brain age presentation device according to any one of the first to fifth aspects, comprising facial skin temperature acquisition means and / or facial blood flow acquisition means, and brain activity. The estimation means and the brain age presentation means are stored in the smart device.

  In the brain age presentation apparatus according to the sixth aspect of the present invention, information input / output and computation are performed by the smart device, so that the brain age can be presented easily.

  A brain age presentation device according to a seventh aspect of the present invention is the brain age presentation device according to any one of the first to sixth aspects, wherein the facial skin temperature acquisition means and / or the facial blood flow acquisition means is a game machine. It is to be mounted on. Further, the facial skin temperature acquisition means and / or the facial blood flow volume acquisition means acquires facial skin temperature data and / or facial blood flow volume data in time series during a predetermined period after the game is started by the game machine.

  In the brain age presentation device according to the seventh aspect of the present invention, the brain age can be presented during the game execution by having the above-described configuration. Moreover, since the game itself by the game machine constitutes the brain function activation task, the brain age can be presented by executing the game.

  In the brain age presentation device according to the first aspect of the present invention, the brain age can be presented simply.

  In the brain age presentation device according to the second and third aspects of the present invention, it is possible to improve the estimation accuracy of the brain age.

  In the brain age presentation device according to the fourth aspect of the present invention, the correlation between brain activity and brain age can be sequentially optimized.

  In the brain age presentation apparatus according to the fifth and sixth aspects of the present invention, the brain age can be simply presented using a smart device.

  In the brain age presentation device according to the seventh aspect of the present invention, the brain age can be presented during game execution.

The figure which shows an example of picked-up image data and the result of having analyzed this. The figure which shows a part of result of having analyzed facial skin temperature data. The figure which shows a part of result of having analyzed facial skin temperature data. The figure which shows the amplitude of the component waveform of the component 2, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the amplitude of the component waveform of the component 3, and the amplitude of (beta) wave among the measured brain waves. The figure which shows a part of result of having analyzed the facial skin temperature data obtained by the control experiment. The figure which shows the component waveform based on the picked-up image data of a face, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on facial skin temperature data, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on the picked-up image data of a face, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on facial skin temperature data, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on the picked-up image data of a face, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on facial skin temperature data, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on the picked-up image data of a face, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on facial skin temperature data, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on the picked-up image data of a face, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on facial skin temperature data, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on the picked-up image data of a face, and the amplitude of (beta) wave among the measured brain waves. The figure which shows the component waveform based on facial skin temperature data, and the amplitude of (beta) wave among the measured brain waves. 1 is a schematic diagram of a brain activity visualization apparatus according to an embodiment of the present invention. The flowchart which shows an example of the flow of a process at the time of identifying the component which shows the change of the skin temperature which reflected the brain function in the brain activity visualization apparatus. 1 is a schematic diagram of a brain activity visualization apparatus according to an embodiment of the present invention. The flowchart which shows an example of the flow of a process at the time of identifying the component which shows the RGB change of the face reflecting a brain function in a brain activity visualization apparatus. The schematic diagram of the brain age presentation device concerning one embodiment of the present invention.

  Before describing the embodiments of the present invention, first, the knowledge obtained by the present inventors, which is an important basis for the inventors of the present invention, will be described.

(1) Key points of knowledge by the present inventors It is known that human brain activities reflect human intellectual activities (cognitive activities, etc.) and emotional activities (activity such as pleasure / discomfort). . Conventionally, attempts have been made to estimate human brain activity. In this case, data detected by any of the electroencephalography, magnetic resonance imaging, and near-infrared spectroscopy may be used. Many.

  Here, for example, when an electroencephalogram measurement method is employed as the detection method, it is necessary to attach an electroencephalogram electrode to the subject. And when attaching an electroencephalogram electrode, since it is necessary to make resistance between skin and an electrode small, the process of grind | polishing the skin or apply | coating a paste to an electrode is needed. In addition, when magnetic resonance imaging is adopted, there are restrictions on measurement conditions such as that measurement outside the MRI room is impossible and metal cannot be brought into the measurement room. Furthermore, when near-infrared spectroscopy is used, it is necessary to wear a probe on the subject. However, if the probe is worn for a long time, the subject feels pain or the subject's hair contacts the probe. Depending on the condition, it may not be detected accurately. In this way, when a conventional detection method is adopted to measure human brain activity, pre-processing when wearing an electroencephalogram electrode or a probe is necessary, measurement conditions are limited, etc. The burden on the subject increases.

  Therefore, there is a need for development of means that can reduce the burden on the subject and can easily estimate human brain activity.

  And the present inventors thought that human brain activity could be estimated based on the blood circulation state of the face that is considered to be proportional to the skin temperature of the human face or the skin temperature of the face. . If it is the skin temperature of the human face, it can be obtained by using a measuring device such as a thermography, and if the blood circulation state of the face, that is, the blood circulation amount of the face, RGB of the captured image of the face obtained using the imaging device It can be estimated from the data. As described above, a facial skin temperature or a photographed image of a face can be acquired without wearing a sensor that requires processing before wearing an electroencephalogram electrode or a probe.

  On the other hand, it is known that the skin temperature of a human face changes under the influence of various factors such as the outside air temperature and / or autonomic nerve activity. Therefore, when trying to estimate brain activity based on facial skin temperature or based on facial blood flow, which is considered to be proportional to facial skin temperature, it is determined whether the acquired data reflects only brain activity. It seems to be very difficult to do.

  As a result of intensive studies, the present inventors have detected the skin temperature of the face, time-series facial skin temperature data including detected temperature data and position data (coordinate data) of the detected part, or time-series face Time-series facial blood flow data calculated based on RGB data obtained from the captured image data was decomposed into multiple components using the singular value decomposition method, principal component analysis method, or independent component analysis method, and decomposed. By analyzing a plurality of components, it was found that components showing changes in facial skin temperature or facial blood flow reflecting brain activity can be identified. The inventors of the present invention have arrived at the present invention that can visualize the subject's physiological state based on the estimated brain activity by estimating and analyzing the subject's brain activity.

(2) Method for acquiring various facial data, and method for analyzing various acquired data (2-1) Method for acquiring facial skin temperature data, and method for analyzing facial skin temperature data Next, the inventors described above A method of acquiring facial skin temperature data and a method of analyzing facial skin temperature data used for obtaining knowledge will be described.

  In this test, facial skin temperature data was obtained from six subjects. Specifically, the subject was seated on a chair installed in an artificial weather room maintained at a room temperature of 25 ° C., and facial skin temperature data was obtained from the entire face of the subject using an infrared thermography device. The infrared thermography device detects the infrared radiation energy emitted from the object with an infrared camera, converts the detected infrared radiation energy into the temperature of the object surface (in this case, the temperature in degrees Celsius), and converts the temperature distribution. It is a device that can display and accumulate as facial skin temperature data (for example, image data representing a temperature distribution). In this test, NEC Avio Infrared Technology Co., Ltd. R300 was used as an infrared thermography apparatus. The infrared camera was installed in front of the subject at a point 1.5 m away from the subject. The facial skin temperature data was acquired for 30 minutes.

  In this test, a brain function activation task was given to the subject while acquiring facial skin temperature data. As a result, facial skin temperature data when the brain was not activated and facial skin temperature data when the brain was activated were acquired. Examples of the brain function activation task include calculation or recognition of numerical values, shapes and colors, or psychological work such as memory of symbols, characters or languages based on the image displayed on the display device or the like by the subject. . In this test, “Mental calculation of multiplication” was adopted as a brain function activation task, and subjects were asked to calculate numbers displayed in a writing format on the display device and to input the answers to the keyboard. In this test, a brain function activation task was given to the subject continuously for 10 minutes after the start of acquisition of facial skin temperature data for 5 minutes.

  For analysis of facial skin temperature data, singular value decomposition was performed on the acquired facial skin temperature data using SVLAB (Singular Value Decomposition) of MATLAB (registered trademark) as an analysis tool. In singular value decomposition, all facial skin temperature data (data for 30 minutes) acquired in time series are targeted, the factor is time data every 30 seconds (60 time points in 30 minutes), and the measure is the period (30 Second) facial skin temperature data (240 × 320 pixels). Then, the facial skin temperature data X was decomposed into a plurality of components by singular value decomposition, and a temporal distribution V of each component, a spatial distribution U, and a singular value S indicating the size of each component were calculated. These relationships are expressed by the following equations. V ′ is a matrix in which the rows and columns of V are exchanged.

  And the time distribution V and the spatial distribution U of each component calculated | required by the singular value decomposition were plotted on the graph, and the component waveform diagram and temperature distribution diagram of each component were created.

  Furthermore, the analysis for identifying the component which shows the change of the skin temperature reflecting brain activity was performed about the created component waveform diagram and temperature distribution diagram of each component.

  About the component waveform figure of each component, it analyzed about the presence or absence of the correlation of the amplitude of the component waveform, and the time of brain non-activation and the time of brain activation. Specifically, it was evaluated whether there was a correlation between the amplitude shown in the component waveform diagram of each component and the brain non-activation period / brain activation period. In this test, among the period of acquiring facial skin temperature data, a period of 5 minutes from the start of data acquisition, which is a period during which no brain function activation task is given to the subject, Data acquisition is a period in which a brain function activation task is given to a subject, with a period of 15 minutes from the time when 15 minutes have elapsed from the start of data acquisition to the end of data acquisition being the non-activation time of the brain The period of 10 minutes from the time when 5 minutes passed from the start to the time when 10 minutes passed was defined as the time of brain activation. Then, the presence or absence of a correlation between the amplitude shown in the component waveform diagram of each component and the time of brain non-activation and the time of brain activation was evaluated. In addition, about the presence or absence of correlation, statistical correlation analysis was conducted and it was judged that there was a correlation when a significance level ((alpha)) is 0.05 or less.

  Regarding the temperature distribution chart of each component, the presence or absence of temperature change at a predetermined part of the face was analyzed. Here, the brain has a mechanism called a selective brain cooling system that cools the brain independently of the body temperature. As a selective brain cooling mechanism, it is known that the heat generated by the brain activity is exhausted using the forehead and the sinuses (including the eyebrows and the nose). Therefore, in this test, it was evaluated whether or not there was a temperature change around the sinuses and the forehead in the temperature distribution chart of each component. In addition, regarding the presence or absence of temperature changes around the sinuses and the forehead in the temperature distribution chart, the presence or absence of temperature changes by visual inspection, or the temperature around the sinuses and the forehead is determined from the average temperature of the entire measurement data Whether there was a difference of 1 standard deviation (SD) or more was used as a reference for the presence or absence of temperature change.

  Since the polarity (plus or minus) of the facial skin temperature data X is determined by the relationship between the spatial distribution U, the singular value S, and the time distribution V, the polarity is inverted in the component waveform diagram and temperature distribution diagram of each component. May appear. For this reason, regarding the evaluation of the component waveform diagram and the temperature distribution diagram, the polarity is not considered as an evaluation target.

  Here, in this infrared thermography apparatus, as described above, infrared radiation energy detected from an object is converted into temperature, and the temperature distribution is used as facial skin temperature data. By the way, when the skin temperature of the face is acquired by using an infrared thermography apparatus for human subjects, temperature changes (so-called noise) that are not related to various brain activities such as facial movement and / or autonomic nerve activity are also faced. It is acquired as skin temperature data. Therefore, in order to detect such a temperature change not related to the brain activity, relative facial skin temperature data in which the total average value of the temperature data included in the facial skin temperature data every 30 seconds is “0” is calculated. The facial skin temperature data created is also subjected to singular value decomposition using the MATLAB® SVD as an analysis tool, and a component waveform diagram and temperature distribution diagram of each component according to the singular value S are created. Then, an analysis was performed to identify a component showing a change in skin temperature reflecting brain activity.

  In the following, for convenience of explanation, the facial skin temperature data acquired by the infrared thermography device is referred to as “facial skin temperature data according to the temperature converted data”, and the temperature converted data every predetermined time (in this test every 30 seconds). The relative facial skin temperature data in which the total average value of the temperature data included in the facial skin temperature data corresponding to “0” is “0” is referred to as “facial skin temperature data according to relative temperature conversion data”.

  For one of the six subjects, in addition to detecting the facial skin temperature using an infrared thermography device, electrodes were connected to the subject's scalp to measure brain waves, and during wakefulness and consciousness became tense. The correlation between the amplitude of the β wave (the brain wave having a frequency of 14 to 30 Hz), which is known as the waveform appearing at the time, and the amplitude of the component waveform diagram was also evaluated. In the electroencephalogram measurement, electrodes were arranged at six parts (F3, F4, C3, C4, Cz, Pz) based on the international method 10-20.

  By the way, it is conceivable that the subject's head moves up and down while the subject is given a brain function activation task. Then, the position of the subject's face with respect to the infrared camera changes. In order to verify whether or not the change in the position of the face affects the change in the skin temperature, a control test was performed on one subject. In the control test to verify the influence of the subject's movement when acquiring the facial skin temperature data, the facial skin temperature data of the subject is acquired using the infrared thermography device as in the above test, but the brain function activation task is The subject was forced to press the keyboard at random times even when it was not given (ie, when the brain was not activated). The facial skin temperature data according to the temperature conversion data obtained by the control experiment and the facial skin temperature data according to the relative temperature conversion data are also subjected to singular value decomposition using the MATLAB® SVD as an analysis tool. Then, a component waveform diagram and a temperature distribution diagram of each component according to the singular value S were created, and an analysis for identifying a component showing a change in skin temperature reflecting brain activity was performed.

(2-2) Method for Acquiring Face Shooting Image Data and Method for Analyzing Face Shooting Image Data FIG. 1A is a diagram showing an example of shooting image data around the paranasal sinuses of a subject's face taken by a photographing device. It is. FIG. 1B is a diagram illustrating an example of a blood flow distribution map (image map).

  Next, a method for acquiring face-captured image data and a method for analyzing face-captured image data used by the inventors to obtain the above knowledge will be described.

  In this test, face image data was obtained from six subjects. Specifically, a photographed image around the sinus of the entire face of the subject using a photographing device that allows the subject to sit in a chair installed in an artificial weather room maintained at room temperature of 25 ° C. and acquire images in time series Data was acquired in time series.

  Further, based on the selective brain cooling mechanism described above, a change in facial blood flow that is considered to be proportional to the facial skin temperature accompanying brain activity is considered to appear around the forehead and / or the sinuses. From this, the present inventors considered that brain activity can be estimated with high accuracy if at least changes in blood flow in the face around the forehead and / or sinuses can be captured. In this test, captured image data around the paranasal sinuses of the subject's face was acquired in time series.

  In this test, a photographing device on the liquid crystal screen side of the iPad Air (registered trademark) manufactured by Apple Inc. was used as a photographing device, and color moving image data was acquired as time-series photographed image data. In addition, the photographing apparatus was installed on the front side of the subject and at a point 1.0 m away from the subject. Then, the moving image data of the face was obtained by continuously capturing the captured image data for 30 minutes along the time axis at a capturing period of 30 frames / second by the capturing device.

  Furthermore, in this test, a brain function activation task was given to the subject while acquiring facial moving image data. Thereby, the moving image data of the face when the brain was not activated and the moving image data of the face when the brain was activated were acquired. In this test, as in the case of the above test, the task of activating the brain function is `` multiplying mental arithmetic '', allowing the subject to calculate the numbers displayed in the writing format on the display device, and inputting the answers to the keyboard. Imposed. In this test, a brain function activation task was given to the subject continuously for 10 minutes after 5 minutes from the start of facial moving image data acquisition.

For analysis of facial moving image data, blood flow data is calculated based on RGB data obtained from the captured moving image data of the face, and MATLAB (registered trademark) SVD is used for the calculated time-series blood flow data. Singular value decomposition was performed as an analytical tool. Here, according to the CIE-L ^* a ^* b ^* color system, an erythema index “a ^* ” correlated with the redness and hemoglobin amount calculated from the RGB data of the image is obtained, and this is used as blood flow data. . In the singular value decomposition, blood flow volume data (here, erythema index) based on RGB data obtained from all moving image data (30-minute data) acquired in time series is targeted, and the factor is set every 30 seconds. Erythema index calculated from time data (60 time points in 30 minutes) and RGB data in that period (every 30 seconds) (taken out frame data for 1 second every 30 seconds, and RGB values obtained from the frame data) Erythema index calculated from the average value of 240 × 320 pixels). Then, by singular value decomposition, time-series blood flow data based on RGB data obtained from facial moving image data is decomposed into a plurality of components, and each component's time distribution V, spatial distribution U, and each component And a singular value S indicating the magnitude of. In addition, these relationships are represented by the same formula as the above formula (Formula 1).

  Then, the time distribution V and the spatial distribution U of each component obtained by singular value decomposition were plotted on a graph, and a component waveform diagram and a blood flow distribution diagram of each component were created.

  Further, analysis was performed on the component waveform diagram and the blood flow distribution diagram of each component that was created to identify a component indicating a change in facial blood flow that reflects brain activity, that is, a component that indicates a change in facial RGB.

  About the component waveform figure of each component, it analyzed about the presence or absence of the correlation of the amplitude of the component waveform, and the time of brain non-activation and the time of brain activation. Specifically, it was evaluated whether there was a correlation between the amplitude shown in the component waveform diagram of each component and the brain non-activation period / brain activation period. In this test, during the period when the captured image data of the face is acquired, the subject is not given a brain function activation task, and the 5-minute period from the start of data acquisition to the time when 5 minutes have elapsed. Data that is a period in which a brain function activation task is given to a subject, with a period of 15 minutes from the start of data acquisition and the period of 15 minutes from the start of data acquisition to the end of data acquisition being the non-activated state of the brain A period of 10 minutes from the time when 5 minutes passed from the start of acquisition to the time when 10 minutes passed was defined as the time of brain activation. Then, the presence or absence of a correlation between the amplitude shown in the component waveform diagram of each component and the time of brain non-activation and the time of brain activation was evaluated. In addition, regarding the presence or absence of the correlation, statistical correlation analysis was performed, and it was determined that there was a correlation when the significance level (α) was 0.01 or less.

  Regarding the blood flow distribution chart of each component, the presence or absence of a change in blood flow at a predetermined part of the face was analyzed. The blood circulation distribution map is created by arranging the spatial distribution U calculated for each pixel at the position of each pixel. In the blood flow distribution chart of each component thus created, it was evaluated whether there was a change in blood flow around the sinuses and in the forehead. In addition, regarding the presence or absence of changes in blood flow around the sinuses and the forehead in the blood flow distribution diagram, the presence or absence of changes in blood flow by visual inspection, or around the sinuses and the forehead shown in FIG. The reference value for the presence or absence of a change in blood flow was that the value of the blood flow was not “0.000”.

  Note that the polarity (plus or minus) of the blood flow data X is determined by the relationship between the spatial distribution U, the singular value S, and the time distribution V. Therefore, the polarity is reversed in the component waveform diagram and the blood flow distribution diagram of each component. May appear. For this reason, regarding the evaluation of the component waveform diagram and the blood flow distribution diagram, the polarity is not considered as an evaluation target.

  Furthermore, in order to verify the correlation between facial skin temperature and facial blood flow, facial skin temperature data was also obtained by infrared thermography equipment while acquiring facial image data in time series from six subjects. Time-series acquisition, and the acquired facial skin temperature data is also subjected to singular value decomposition using the MATLAB (registered trademark) SVD as an analysis tool, and a component waveform diagram of each component according to the singular value S is created. The presence or absence of a correlation between the amplitude of the component waveform and the brain non-activation and brain activation was analyzed. In this test, an apparatus similar to the above test was used as the infrared thermography apparatus. The infrared camera was installed in front of the subject at a point 1.5 m away from the subject.

  By the way, when acquiring captured image data of a face using a photographing device, light may be reflected by the face when the sunlight hits the face during photographing, and this reflected light may enter the lens of the photographing device. is there. Then, the reflected light is recorded in the photographed image data of the photographed face. Here, in the RGB data obtained from the photographed image data, the change in brightness based on the blood flow amount on the face is smaller than the change in brightness based on the reflected light. Therefore, the RGB data obtained from the photographed image data in which the reflected light is recorded. When the blood flow volume calculated based on the analysis was analyzed, it was considered that there was a possibility that a face RGB change (so-called noise) that is not related to the brain activity might be mixed. Therefore, in order to prevent the mixing of RGB changes in the face that are not related to such brain activity, relative blood flow data is obtained from the relative RGB data with the total average value of RGB data every 30 seconds being “0”. The created blood flow data is also subjected to singular value decomposition using the MATLAB (registered trademark) SVD as an analysis tool, and a component waveform diagram and blood flow distribution diagram of each component according to the singular value S are created. Then, an analysis was performed to identify the component showing the RGB change of the face reflecting the brain activity.

  In the following, for convenience of explanation, relative blood flow data based on relative RGB data with a total average value of RGB data every predetermined time (every 30 seconds in this test) being “0” is referred to as “relative conversion. The blood flow data is referred to as “blood flow data”, and the blood flow data based on the RGB data before conversion into relative RGB data is simply referred to as “blood flow data”.

  In addition, while acquiring time-series captured image data of the face with the imaging device for 6 subjects, electrodes are connected to the scalp of each subject to measure brain waves, and brain cells at the time of waking etc. The correlation between the amplitude of the β wave (the brain wave having a frequency of 13 to 30 Hz), which is known as a waveform appearing when active, and the amplitude of the component waveform diagram were also evaluated. In the electroencephalogram measurement, the region of the scalp 19 (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, based on the international method 10-20) , T6, Fz, Cz and Pz).

  Furthermore, it is conceivable that the subject's head moves up and down while the subject is given a brain function activation task. Then, the position of the subject's face with respect to the imaging device changes. In order to verify whether or not the change in the position of the face affects the RGB change in the face, a control test was performed on one subject. In the control test, time-series captured image data of the subject's face is acquired using the imaging device in the same manner as in the above test, but also during the time when the brain function activation task is not given (that is, when the brain is not activated) The subject was forced to press the keyboard at random timing. For chronological blood flow data based on RGB data obtained from time-series photographed image data of the face photographed in this control experiment, singular value decomposition was performed using the SVLAB of MATLAB (registered trademark) as an analysis tool. Then, a component waveform diagram of each component corresponding to the singular value S was created, and the presence or absence of a correlation between the amplitude of the component waveform and the brain non-activation and brain activation was analyzed. In addition, the presence or absence of a correlation between the amplitude of each component waveform and the actual facial movement was analyzed. The actual facial movement is obtained by obtaining the two-dimensional coordinates of the same part of the face from the photographed image data and calculating the movement distance of the face every 30 seconds during photographing with reference to the photographed image data at the start of the control experiment. evaluated. Furthermore, the presence / absence of a correlation between the amplitude of each component waveform and the number of keyboard inputs during shooting was also analyzed. The number of keyboard inputs during shooting was evaluated by calculating a simple moving average every 30 seconds in time-series shot image data.

(3) Analysis results Hereinafter, “components 1, 2,... Each component indicates the ratio of the total dispersion, and the effect of large change is reflected in component 1. Specifically, the effects of the subject's movement and external noise tend to be reflected in the component 1. As a result, the influence of brain activity is often reflected in the component waveforms of component 1 and component 2.

(3-1) Analysis result of facial skin temperature data FIG. 2 is a diagram showing a part of the result of analyzing facial skin temperature data according to temperature conversion data. FIG. 2A shows a component waveform diagram of the component 2 of the subject 1. FIG. 2B shows a temperature distribution diagram of the component 2 of the subject 1. FIG. 3A shows a component waveform diagram of the component 3 of the subject 1. FIG. 3B shows a temperature distribution diagram of the component 3 of the subject 1. 4 and 5 are diagrams showing the relationship between the amplitude of the component waveform and the electroencephalogram. FIG. 4 is a diagram illustrating the amplitude of the component waveform of the component 2 of the subject 1 and the amplitude of the β wave among the measured brain waves. FIG. 5 is a diagram illustrating the amplitude of the component waveform of the component 3 of the subject 1 and the amplitude of the β wave among the measured brain waves. FIG. 6 is a diagram showing a part of the result of analyzing the facial skin temperature data obtained in the control experiment. FIG. 6A shows a component waveform diagram of component 3. FIG. FIG. 6B shows a temperature distribution diagram of the component 3.

  Table 1 shows the analysis results of facial skin temperature data for each subject.

  Of the plurality of components obtained by decomposing time-series facial skin temperature data by singular value decomposition from the results obtained by the above analysis of facial skin temperature data, component 2 and / or component 3 and human It was confirmed that there was a significant correlation with brain activity.

  Also, as shown in FIGS. 4 and 5, a significant correlation was confirmed between the amplitude of each component waveform of component 2 and component 3 and the amplitude of the β wave of the electroencephalogram from the results of the electroencephalogram analysis.

  Furthermore, in the control experiment, there was a significant correlation between component 3 and human brain activity even when the subject was in motion while no brain function activation task was given (see FIG. 6). . From this, it was recognized that among the plurality of components, the movement of the subject at the time of acquiring facial skin temperature data was not affected for component 3.

  From these results, the present inventors obtained the following knowledge.

  The time-series facial skin temperature data obtained from the test subject is decomposed into a plurality of components by singular value decomposition, and as a result of analyzing each decomposed component, component 3 of the plurality of components is a component related to brain activity. Admitted. In other words, time-series facial skin temperature data is decomposed into multiple components by singular value decomposition, components that correlate with brain activation / deactivation are extracted from the decomposed multiple components, and selective brain cooling is performed on the extracted components By analyzing using the mechanism, it was found that a component showing a change in skin temperature reflecting brain activity can be identified from a plurality of components. From this, the present inventors have obtained the knowledge that brain activity can be estimated based on the skin temperature of the human face.

(3-2) Analysis Results of Photographed Image Data of Face FIGS. 7 to 18 are diagrams showing a part of the result of analyzing a component waveform diagram based on photographed image data (blood flow data) or facial skin temperature data of the face. It is. FIG. 7 is a diagram illustrating the amplitude of the component 2 component waveform based on the captured image data of the subject 1 and the measured β wave amplitude of the brain wave of the subject 1. FIG. 8 is a diagram showing the amplitude of the component 2 component waveform based on the facial skin temperature data of the subject 1 and the measured β wave amplitude of the brain wave of the subject 1. FIG. 9 is a diagram showing the amplitude of the component 2 component waveform based on the captured image data of the subject 2 and the measured amplitude of the β wave in the brain wave of the subject 2. FIG. 10 is a diagram illustrating the amplitude of the component 2 component waveform based on the facial skin temperature data of the subject 2 and the measured β wave amplitude of the brain wave of the subject 2. FIG. 11 is a diagram illustrating the amplitude of the component waveform of component 4 based on the captured image data of the subject 3 and the amplitude of the β wave among the measured brain waves of the subject 3. FIG. 12 is a diagram illustrating the amplitude of the component 3 component waveform based on the facial skin temperature data of the subject 3 and the measured β wave amplitude of the brain wave of the subject 3. FIG. 13 is a diagram illustrating the amplitude of the component 3 component waveform based on the captured image data of the subject 4 and the measured β-wave amplitude of the brain wave of the subject 4. FIG. 14 is a diagram showing the amplitude of the component 2 component waveform based on the facial skin temperature data of the subject 4 and the measured β wave amplitude of the brain wave of the subject 4. FIG. 15 is a diagram illustrating the amplitude of the component 2 component waveform based on the captured image data of the subject 5 and the measured β wave amplitude of the brain wave of the subject 5. FIG. 16 is a diagram showing the amplitude of the component 2 component waveform based on the facial skin temperature data of the subject 5 and the measured β wave amplitude of the brain wave of the subject 5. FIG. 17 is a diagram illustrating the amplitude of the component waveform of component 4 based on the captured image data of the subject 6 and the amplitude of the β wave among the measured brain waves of the subject 6. FIG. 18 is a diagram illustrating the amplitude of the component 3 component waveform based on the facial skin temperature data of the subject 6 and the measured β wave amplitude of the brain wave of the subject 6.

  As shown in FIG. 7 to FIG. 18, it was confirmed from the results of the component waveforms and the electroencephalogram analysis that the facial skin temperature and the facial blood flow volume are in a correlation. In the analysis based on any of the skin temperature data of the face and the blood flow data of the face, the amplitude of each component waveform and the amplitude of the β wave of the electroencephalogram measured by the electrode mounted on the top or back of the head A significant correlation was confirmed.

  Table 2 shown below shows the analysis results of the captured image data of the face for each subject.

  As shown in Table 2, a plurality of components obtained by decomposing time-series blood flow data based on the photographed image data of the face by singular value decomposition based on the results obtained by analyzing the photographed image data of the face. Of these, it was confirmed that there was a significant correlation between components 1, 2, 3, 4, 5 and human brain activity. In addition, here, not only the component in which the correlation based on the blood flow data is significant and the correlation based on the relative blood flow data is significant, but also the correlation based on the blood flow data is significant. Components that were not correlated but showed significant correlation in the correlation based on relative blood flow data were also recognized as having significant correlation with human brain activity.

  Table 3 shown below shows the results of the control experiment.

  As shown in Table 3, in the control experiment, when the subject moves while the brain function activation task is not given, the amplitude of the component waveforms of components 1 and 2 and the brain activation time and the brain activation time There was a significant correlation between Furthermore, for Component 2, no significant correlation was observed between the movement distance and the number of keyboard inputs even when the subject moved while the brain function activation task was not given. From this, among components obtained by singular value decomposition of blood flow data based on RGB data acquired from captured image data of a face, components having a significant correlation with brain activity are Even if it is affected by the movement of the subject when acquiring time-series captured image data, it is confirmed that the effect is much smaller than the effect of brain activity (effect of brain activation or non-activation). It was done.

  From these results, the present inventors obtained the following knowledge.

  Blood flow volume data obtained from facial RGB data based on time-series facial captured image data obtained from the subject was decomposed into a plurality of components by singular value decomposition, and each decomposed component was analyzed. Components 1, 2, 3, 4 and 5 were found to be components related to brain activity. That is, blood flow data obtained from facial RGB data based on time-series facial image data is decomposed into a plurality of components by singular value decomposition, and there is a correlation between activation / inactivation of the brain from the decomposed components. It was found that by extracting the components and analyzing the extracted components, it is possible to identify a component showing the RGB change of the face reflecting brain activity from a plurality of components. From this, the present inventors have found that brain activity can be estimated based on time-series captured image data of the human face.

(4) Brain Activity Visualization Device Next, brain activity visualization devices 10 and 110 according to an embodiment of the present invention that have been completed by the present inventors based on the knowledge described above will be described. In addition, the brain activity visualization apparatus according to the present invention is not limited to the following embodiments, and can be appropriately changed without departing from the gist.

  The brain activity visualization devices 10 and 110 according to an embodiment of the present invention include a brain activity estimation unit 30 that estimates brain activity based on facial skin temperature data, and / or a brain that estimates brain activity based on face image data. The activity estimation means 130 is provided. Below, before demonstrating the brain activity visualization apparatuses 10 and 110 which concern on one Embodiment of this invention, each brain activity estimation means 30 and 130 are demonstrated.

(4-1) Brain activity estimating means 30 for estimating brain activity based on facial skin temperature data
FIG. 19 is a schematic diagram of the brain activity visualization apparatus 10 according to an embodiment of the present invention. FIG. 20 is a flowchart showing the flow of processing when the brain activity visualization apparatus 10 identifies a component indicating a change in skin temperature reflecting the brain function.

  The brain activity estimating means 30 provided in the brain activity visualization device 10 estimates the brain activity of the individual from the skin temperature of the face of the individual (subject). As shown in FIG. 19, the brain activity visualization device 10 includes a facial skin temperature acquisition unit 20, a brain activity estimation unit 30, and a state visualization unit 200.

  The facial skin temperature acquisition means 20 detects the skin temperature of at least a part of the individual's face, and acquires facial skin temperature data including the detected temperature data and the position data of the detected part in time series (step S1). Here, the facial skin temperature acquisition means 20 is an infrared thermography apparatus, and includes an infrared camera 21 and a processing unit 22 as shown in FIG. The infrared camera 21 is for detecting infrared radiant energy emitted from an individual's face. Here, it is assumed that the infrared camera 21 detects infrared radiation energy from the entire face of the individual. The processing unit 22 converts the infrared radiant energy detected by the infrared camera 21 into temperature to obtain temperature data, and the temperature distribution of the facial skin temperature over the entire face using the position where the infrared radiant energy is detected as position data (coordinate data). A diagram is created, and the created temperature distribution diagram is processed as facial skin temperature data corresponding to the temperature conversion data. The facial skin temperature data corresponding to the temperature conversion data is accumulated in a storage unit (not shown) included in the processing unit 22.

  Here, the temperature distribution chart of the facial skin temperature in the entire face is created in the processing unit 22, but the present invention is not limited to this, and the temperature distribution chart of the facial skin temperature including at least the periphery of the sinuses and / or the forehead portion. May be created, and this may be the facial skin temperature data corresponding to the temperature conversion data.

  Also, here, while the facial skin temperature data corresponding to the temperature conversion data is acquired by the facial skin temperature acquisition means 20, a brain function activation task is given to the individual for a certain period. That is, the facial skin temperature data corresponding to the temperature conversion data acquired by the facial skin temperature acquisition means 20 includes data for a period during which a brain function activation task is given to an individual. The brain function activation task given to an individual is not particularly limited as long as it is estimated that the brain is activated, for example, depending on the purpose of use of the brain activity visualization device 10 You may be comprised so that the content may be determined suitably.

  The brain activity estimation unit 30 estimates human brain activity based on the facial skin temperature data corresponding to the temperature conversion data acquired by the facial skin temperature acquisition unit 20. Specifically, as shown in FIG. 19, the brain activity estimation means 30 includes a conversion unit 31, an analysis unit 32, and an estimation unit 33.

  The conversion unit 31 converts the temperature data included in the facial skin temperature data corresponding to the temperature conversion data into relative temperature data, and responds to the facial skin temperature data based on the converted relative temperature data, that is, relative temperature conversion data. Facial skin temperature data is created (step S2). Specifically, the conversion unit 31 uses the average value of the temperature data included in the facial skin temperature data corresponding to the temperature conversion data every predetermined time (for example, 30 seconds) as a reference value, and converts the temperature data into a relative value. Convert to temperature data. And the conversion part 31 produces the face skin temperature data according to relative temperature conversion data using the converted relative temperature data and position data.

  The analysis unit 32 converts the facial skin temperature data according to the time-series temperature conversion data and the facial skin temperature data according to the relative temperature conversion data into a plurality of components by singular value decomposition, principal component analysis, or independent component analysis. Decompose (step S3). Here, the analysis unit 32 analyzes the SVLAB of MATLAB (registered trademark) for each of the facial skin temperature data according to the acquired temperature conversion data and the facial skin temperature data according to the converted relative temperature conversion data. To perform singular value decomposition. The singular value decomposition is performed with the factor of time data for a predetermined period (for example, 30 seconds) for the facial skin temperature data according to the temperature conversion data acquired in time series and the facial skin temperature data according to the relative temperature conversion data, The measure is performed as facial skin temperature data corresponding to the temperature conversion data in that period and facial skin temperature data corresponding to the relative temperature conversion data. Then, by singular value decomposition, each of the facial skin temperature data according to the temperature conversion data and the facial skin temperature data according to the relative temperature conversion data is decomposed into a plurality of components, and the time distribution, spatial distribution, and A singular value indicating the magnitude is calculated.

  In addition, the analysis unit 32 determines whether or not each component satisfies the first condition and the second condition in order to identify a component indicating a change in skin temperature reflecting brain activity from a plurality of components decomposed by singular value decomposition. (Step S4a, Step S4b, Step S5a, and Step S5b). Here, the analysis unit 32 first determines whether or not the first condition is satisfied for each component based on the facial skin temperature data corresponding to the temperature conversion data (step S4a), and the first in step S4a. It is determined whether or not the second condition is satisfied for the component based on the facial skin temperature data corresponding to the temperature conversion data determined to satisfy the condition (step S4b). Of the components based on the facial skin temperature data corresponding to the relative temperature conversion data, the first condition is satisfied only for components that match the components determined to satisfy the first condition and the second condition in step S4a and step S4b. (Step S5a), and then the second condition is satisfied for the component based on the facial skin temperature data corresponding to the relative temperature conversion data determined to satisfy the first condition in Step S5a. It is determined whether or not it has been performed (step S5b). However, the order of the determination in the analysis unit 32 is not limited to this. For example, each component based on the facial skin temperature data corresponding to the temperature conversion data and the facial skin temperature data corresponding to the relative temperature conversion data are used. It may be determined whether or not each component based on the first condition and the second condition is satisfied, and a component having a matching determination result may be finally extracted.

  The first condition is a condition that the amplitude of the component waveform of the component decomposed by the singular value decomposition is correlated with a change at the time of non-activation of the brain and activation of the brain. The analysis unit 32 extracts a component that satisfies the first condition from the plurality of components as a determination component. Here, there is a certain period during which the brain function activation task is given to an individual while acquiring facial skin temperature data corresponding to the temperature conversion data. The analysis unit 32 uses the period when the brain function activation task is not given to the individual as the non-activation time of the brain, and sets the period when the brain function activation task is given to the individual as the brain activation time. The period in which the activation task is given and the period in which it is not given and the component waveform of each component are compared and analyzed. The analysis unit 32 uses the comparison analysis result based on the component waveform data to evaluate whether the component waveform of each component and the brain non-activation time and the brain activation time are correlated, and a plurality of components Among these, the component evaluated as having a correlation is extracted as a determination component that satisfies the first condition. On the other hand, the analysis unit 32 determines that a component evaluated as having no correlation among the plurality of components is not a component that does not satisfy the first condition and shows a temperature change reflecting human brain activity (step S6).

  Here, a brain function activation task is given to an individual for a certain period when acquiring facial skin temperature data corresponding to temperature conversion data, and based on this, the analysis unit 32 extracts a determination component. The contents of one condition, that is, the determination component extraction means in the analysis unit 32 is not limited to this. For example, when a component showing a component waveform correlated with the non-activation time and the activation time of the brain among a plurality of components by performing an experiment or the like in advance, the analysis unit 32 The component specified from the plurality of components is extracted as a determination component. Further, in the case where the human activity known to be related to activation / deactivation of the brain such as eye movement or tapping is detected in the present brain activity visualization device, the analysis unit 32 analyzes the detection result and each A determination component may be extracted from a plurality of components by comparative analysis and evaluation of the component waveform of the component. Note that the criterion for determining whether or not the first condition is satisfied by the analysis unit 32 is appropriately determined by simulation, experiment, desktop calculation, or the like according to the purpose of use of the brain activity visualization device 10 or the like.

  The second condition is a condition that there is a temperature change in a predetermined part of the human face in the extracted determination component. The analysis unit 32 determines a component that satisfies the second condition among the determination components as a component that is highly likely to be related to human brain activity, and extracts it as a candidate component. That is, the analysis unit 32 determines whether or not the determination component is related to human brain activity based on the presence or absence of a temperature change at a predetermined part of the human face. Specifically, the analysis unit 32 determines whether a temperature change has occurred around the sinuses and / or the forehead based on the extracted temperature distribution data of the determination component, and the temperature change has occurred. In this case, it is determined that the determination component is a component that is highly likely to be related to human brain activity that satisfies the second condition, and is extracted as a candidate component. On the other hand, when no temperature change occurs around the sinuses and / or the forehead part, the analysis unit 32 does not satisfy the second condition and shows a change in skin temperature reflecting brain activity. It is determined that it is not a component (step S6). Note that the criterion for determining whether or not the second condition is satisfied by the analysis unit 32 is appropriately determined by simulation, experiment, desktop calculation, or the like according to the purpose of use of the brain activity visualization device 10 or the like.

  And the analysis part 32 identifies the component determined to satisfy | fill 2nd conditions in step S5b as a component which shows the change of the skin temperature reflecting brain activity (step S7). That is, the component identified as the component indicating the change in skin temperature reflecting the brain activity in step S7 is the candidate component extracted by decomposing and analyzing the facial skin temperature data corresponding to the temperature conversion data by singular value decomposition And the candidate skin component extracted by analyzing the facial skin temperature data corresponding to the relative temperature conversion data by decomposing and analyzing by singular value decomposition. Note that candidate components that do not match in both analyzes are determined not to be components indicating changes in skin temperature reflecting brain activity in step S6.

  The estimation unit 33 estimates the human brain activity based on the component identified by the analysis unit 32 as the component indicating the change in skin temperature reflecting the human brain activity. Specifically, the estimation unit 33 estimates the amount of brain activity when acquiring facial skin temperature data based on the component waveform data of the component identified by the analysis unit 32.

(4-1-1) Modification 1A
The brain activity estimating means 30 has a conversion unit 31, and facial skin temperature data corresponding to the relative temperature conversion data is created by the conversion unit 31. And the analysis part 32 respond | corresponded not only the face skin temperature data according to the temperature conversion data acquired by the face skin temperature acquisition means 20, but the relative temperature data based on the temperature data converted into relative temperature data. The facial skin temperature data is also decomposed into a plurality of components by singular value decomposition, and each component is analyzed.

  Instead of this, the brain activity estimating means 30 may not have the conversion unit 31. In this case, it is possible to omit processing for creating facial skin temperature data corresponding to the relative temperature conversion data or analyzing data based on the facial skin temperature data corresponding to the relative temperature conversion data.

  However, in order to accurately identify a component related to human brain activity, the brain activity estimation means 30 has a conversion unit 31 as in the above embodiment, and the analysis unit 32 performs facial skin temperature acquisition means. A plurality of facial skin temperature data corresponding to the relative temperature data based on the temperature data converted to the relative temperature data as well as the facial skin temperature data corresponding to the temperature converted data acquired by 20 are obtained by singular value decomposition. It is desirable that each component is analyzed after being decomposed into these components.

(4-1-2) Modification 1B
The facial skin temperature acquisition means 20 is an infrared thermography device that can acquire temperature data in a non-contact state with an object.

  However, if the skin temperature of at least a part of an individual's face is detected and the facial skin temperature data including the detected temperature data and the position data of the detected part can be acquired in time series, the facial skin temperature acquisition means is infrared. It is not limited to a thermography device.

  For example, the facial skin temperature acquisition means may be a device including a temperature sensor. Specifically, a temperature sensor is attached to a predetermined part of an individual's face, and time-series facial skin temperature data is based on temperature data detected by the temperature sensor and position data of the part where the temperature sensor is attached. May be acquired. As described above, even when facial skin temperature data is acquired in a state in which the temperature sensor is in contact with the target individual, the temperature sensor does not require pre-wearing processing like an electroencephalogram electrode. Compared with conventional detection methods such as measurement, magnetic resonance imaging, and near infrared spectroscopy, data can be easily acquired. Thereby, human brain activity can be estimated simply.

(4-2) Brain activity estimating means 130 for estimating brain activity based on face image data.
FIG. 21 is a schematic diagram of the brain activity visualization apparatus 110 according to the embodiment of the present invention. FIG. 22 is a flowchart illustrating an example of a processing flow when the component indicating the RGB change of the face reflecting the brain function is identified in the brain activity visualization apparatus 110.

  The brain activity estimating means 130 included in the brain activity visualization device 110 is a device for estimating the brain activity of the individual from the captured image data of the face of the individual (subject). As shown in FIG. 21, the brain activity visualization apparatus 110 includes image data acquisition means 120, brain activity estimation means 130, and state visualization means 200.

  The image data acquisition unit 120 acquires captured image data of at least a part of an individual's face in time series (step S101). The image data acquisition unit 120 is not particularly limited as long as it has at least a photographing device, and examples thereof include a portable terminal with a photographing device such as a smartphone or a tablet (for example, iPad: registered trademark). It is done. Here, the image data acquisition unit 120 includes a camera 121 as a photographing apparatus and a storage unit 122, as shown in FIG. The camera 121 is for acquiring captured image data of an individual's face in time series. Here, the camera 121 captures a moving image of the entire face of the individual and acquires the captured moving image data. The storage unit 122 accumulates time-series photographed image data photographed by the photographing apparatus. Here, the storage unit 122 accumulates moving image data acquired by the camera 121.

  Here, the moving image of the entire face is captured by the camera 121, but the present invention is not limited to this, and it is only necessary to capture a moving image including an image of at least the forehead portion and / or the sinuses around the face.

  Also, here, while the time-series captured image data of the face is acquired by the image data acquisition means 120, a brain function activation task is given to the individual for a certain period. That is, the captured image data acquired by the image data acquisition unit 120 includes data for a period in which a brain function activation task is given to an individual. The brain function activation task given to an individual is not particularly limited as long as it is estimated that the brain is activated, for example, depending on the purpose of use of the brain activity visualization device 110 You may be comprised so that the content may be determined suitably.

  The brain activity estimation unit 130 estimates human brain activity based on time-series captured image data of the face acquired by the image data acquisition unit 120. Specifically, as shown in FIG. 21, the brain activity estimation unit 130 includes an RGB processing unit 131, a conversion unit 132, a blood flow rate calculation unit 133, an analysis unit 134, and an estimation unit 135. FIG. 21 shows an aspect in which the brain activity estimation means 130 exists as one device having the RGB processing unit 131, the conversion unit 132, the blood flow calculation unit 133, the analysis unit 134, and the estimation unit 135. However, the present invention is not limited to this, and part or each of the RGB processing unit 131, the conversion unit 132, the blood flow volume calculation unit 133, the analysis unit 134, and the estimation unit 135 exists as an independent device. May be. In addition, here, the facial blood flow volume acquisition unit is configured by the image data acquisition unit 120, the RGB processing unit 131, the conversion unit 132, and the blood flow volume calculation unit 133.

  The RGB processing unit 131 performs RGB processing that decomposes the captured image data acquired by the image data acquisition unit 120 into three color components, an R component, a G component, and a B component (step S102). Here, the RGB processing may be performed on the captured image data of the entire face, but here, in order to reduce the calculation processing amount and noise, the data on the forehead part and / or the sinuses around the captured image data is reduced. It is assumed that RGB processing is performed only on the extracted data.

  The conversion unit 132 converts the RGB data of the captured image data obtained by the RGB processing into relative RGB data (step S103). Specifically, the conversion unit 132 converts the RGB data into relative RGB data using the average value of the RGB data obtained from the acquired captured image data every predetermined time (for example, 30 seconds) as a reference value. To do.

  The blood flow volume calculation unit 133 calculates time-series blood flow volume data of the face based on the RGB data of the captured image data obtained by the RGB processing (step S104).

  The analysis unit 134 decomposes the time-series relative converted blood flow data into a plurality of components by singular value decomposition, principal component analysis, or independent component analysis (step S105). Here, the analysis unit 134 performs singular value decomposition on the relative converted blood flow data using the SVLAB of MATLAB (registered trademark) as an analysis tool. Specifically, the singular value decomposition is performed on time-series relative converted blood flow data, with the factor being time data for each predetermined period (for example, 30 seconds), and the measure is calculated from relative RGB data for each period. This is performed as the calculated relative blood flow volume data for each pixel. Then, by singular value decomposition, the time-series relative converted blood flow data is decomposed into a plurality of components, and a time distribution, a spatial distribution, and a singular value indicating the size of each component are calculated.

  Further, the analysis unit 134 determines whether or not each component satisfies a predetermined condition in order to identify a component indicating the RGB change of the face reflecting the brain activity from the plurality of components decomposed by the singular value decomposition (step) S106). Here, as the predetermined condition, for example, the condition that the amplitude of the component waveform of the component decomposed by the singular value decomposition is correlated with the change at the time of non-activation of the brain and the activation of the brain (hereinafter referred to as the first condition). ), A condition that there is a change in blood flow in a predetermined part of the human face in a component decomposed by singular value decomposition (hereinafter referred to as a second condition), and the like. As the predetermined condition determined by the analysis unit 134, one or a plurality of conditions may be set. Here, it is assumed that the first condition is set as the predetermined condition.

  And the analysis part 134 extracts the component which satisfy | fills predetermined conditions among several components as a component for determination. Further, the analysis unit 134 identifies a component satisfying all the conditions included in the predetermined condition among the extracted determination components as a component indicating the RGB change of the face reflecting the brain activity (step S107). On the other hand, the analysis unit 134 determines that the component determined not to satisfy at least one condition included in the predetermined condition among the plurality of components is not a component indicating the RGB change of the face reflecting the brain activity (step S108). ).

  Here, only one condition (first condition) is set as the predetermined condition as described above, and the brain function activation task is given to the individual while acquiring time-series captured image data of the face. There is a given period of time. For this reason, the analysis unit 134 sets the period when the brain function activation task is not given to the individual as the non-activation time of the brain, and sets the period when the brain function activation task is given to the individual as the brain activation time The period in which the brain function activation task is given and the period in which the brain function activation task is not given, and the component waveform of each component are compared and analyzed. And the analysis part 134 evaluates whether the component waveform of each component and the time of non-activation of a brain and the time of activation of a brain have a correlation using the comparative analysis result based on component waveform data, Among these components, a component evaluated as having a correlation is extracted as a determination component that satisfies a predetermined condition, and is identified as a component indicating a change in RGB of the face reflecting brain activity. On the other hand, the analysis unit 134 determines that a component evaluated as having no correlation among the plurality of components is not a component that does not satisfy the predetermined condition and indicates a change in RGB of the face that reflects human brain activity.

  Here, a brain function activation task is given to an individual for a certain period when time-series captured image data of the face is acquired, and the analysis unit 134 extracts a determination component based on this, The content of the first condition, that is, the determination component extraction means in the analysis unit 134 is not limited to this. For example, when a component indicating a component waveform that is correlated with the non-activation time and the activation time of the brain among a plurality of components is identified by performing an experiment or the like in advance, the analysis unit 134 The component specified from the plurality of components is extracted as a determination component. In addition, when the human activity known to be related to activation / deactivation of the brain, such as eye movement or tapping, is also detected in the brain activity visualization device 110, the analysis unit 134 A determination component may be extracted from a plurality of components by comparative analysis and evaluation of the component waveform of each component. Note that the criterion for determining whether or not the first condition is satisfied by the analysis unit 134 is appropriately determined by simulation, experiment, desktop calculation, or the like according to the purpose of use of the brain activity visualization device 110 or the like.

  When the second condition is set as the predetermined condition, the analysis unit 134 extracts a determination component based on whether or not there is a change in the blood flow rate of the face at a predetermined part of the human face. Specifically, the analysis unit 134 determines whether or not there is a change in blood flow around the sinuses and / or the forehead based on the blood flow distribution map corresponding to a plurality of components decomposed by the singular value decomposition. If a change in blood flow occurs, it is determined that the component satisfies the second condition. On the other hand, when there is no change in the blood flow volume around the sinuses and / or the forehead, the analysis unit 134 determines that the component does not satisfy the second condition. Note that the criterion for determining whether or not the second condition is satisfied by the analysis unit 134 is appropriately determined by simulation, experiment, desktop calculation, or the like according to the purpose of use of the brain activity visualization device 110 or the like.

  Further, when time-series blood flow data based on RGB data before being converted into relative RGB data is calculated by the blood flow calculation unit 133, the blood flow data is subjected to singular value decomposition by the analysis unit 134. With respect to a plurality of components obtained by performing the same, it may be determined whether or not the first condition and / or the second condition is satisfied, and a determination component may be extracted.

  The estimation unit 135 estimates the human brain activity based on the component identified by the analysis unit 134 as the component indicating the RGB change of the face reflecting the human brain activity. Specifically, the estimation unit 135 estimates the amount of brain activity when acquiring facial image data based on the component waveform data of the component identified by the analysis unit 134.

(4-2-1) Modification 2A
The blood flow rate calculation unit 133 may calculate the blood flow rate data of the face mainly using the R component of each pixel included in the RGB data. In addition, blood flow volume data is not necessarily limited to the erythema index as long as blood flow volume data can be calculated based on RGB data.

(4-2-2) Modification 2B
The blood flow volume calculation unit 133 calculates relative converted blood flow volume data based on the relative RGB data converted by the conversion unit 132, but instead of or in addition to this, the blood flow volume calculation unit 133 converts the blood flow volume data into relative RGB data. The blood flow volume data may be calculated based on the RGB data before the check. Here, since the blood flow volume data calculated based on the RGB data before being converted into the relative RGB data easily includes a component correlated with the brain activity (the test power is high), for example, the relative RGB data Blood flow volume data calculated based on RGB data before conversion into data may be analyzed prior to relative converted blood flow volume data calculated based on relative RGB data. In addition, for example, first, the blood flow data is analyzed to extract a component having a significant correlation, and the relative conversion blood flow data is calculated by analyzing only the component corresponding to the extracted component. Can be reduced.

(4-3) State visualization means 200
The state visualizing means 200 is visualized by displaying the physiological state of the subject based on the brain activity of the subject estimated by the brain activity estimating means 30 and / or the brain activity estimating means 130. For example, the state visualization unit 200 may include an analysis unit 201 that analyzes the physiological state of the subject by analyzing changes in the amount of brain activity of the subject. Specifically, the analysis unit 201 analyzes the change in the amount of brain activity with respect to the stimulus (visual stimulus, auditory stimulus, tactile stimulus, olfactory stimulus, taste stimulus, etc.) given to the subject, so that the subject Physiological state is determined. In addition, about the kind and level of a physiological state, based on the raise degree and / or duration of brain activity amount, according to the use of the brain activity visualization apparatuses 10 and 110, you may be able to install suitably. Then, the physiological state of the subject analyzed by the analysis unit 201 is output from the display unit 202 of the state visualization means 200 to the administrator, so that the administrator can know the physiological state of the subject. As the display unit 202, any display device that displays an image or a message can be adopted as long as it can visualize information on the analyzed physiological state of the subject person to the administrator. .

  In addition, when various components of time series are acquired by the facial skin temperature acquisition unit 20 and / or the image data acquisition unit 120 after the components reflecting the brain activity are identified in the analysis units 32 and 134, In the activity visualization apparatuses 10 and 110, the acquired various data are further decomposed into a plurality of components by singular value decomposition, and only the identified components are analyzed, so that the physiological state of the subject can be known in real time. .

  Furthermore, there is a conventional technique for acquiring the subject's facial skin temperature or captured image from heartbeat information, biological information, or the like. However, various techniques obtained from the facial skin temperature acquisition means 20 and / or the image data acquisition means 120 are available. By adopting a conventional technique for a component obtained by performing singular value decomposition or the like on the data, heart rate information and biological information can be obtained with high accuracy. Therefore, the analysis unit 32 and / or the analysis unit 134 have a function of analyzing a plurality of components subjected to singular value decomposition to acquire heart rate information and biological information, and based on the acquired heart rate information and biological information, the exchange nerve / parasympathy You may give the estimation part 33,135 of the said embodiment the function which estimates the function of a nerve.

(5) Features (5-1)
In the present embodiment, human brain activity is estimated based on time-series facial skin temperature data and / or facial blood flow data acquired by the facial skin temperature acquisition means 20 and / or the image data acquisition means 120. For this reason, human brain activity can be estimated without mounting a sensor that requires processing before mounting an electroencephalogram electrode or the like. Therefore, it is possible to easily estimate human brain activity and visualize the physiological state of the subject based on the estimated brain activity.

(5-2)
Here, when time-series facial skin temperature data and / or image data is acquired, a human brain function is activated by whether or not a human brain function activation task is actually given. If the situation is activated or not activated, the component having a correlation between the component waveform of each component and the activation and non-activation of the brain is the skin temperature reflecting the brain activity and / or It can be said that it is a component with high possibility of being a component which shows the change of blood flow volume.

  In this embodiment, while the facial skin temperature acquisition means 20 and / or the image data acquisition means 120 are acquiring time-series facial skin temperature data and / or image data, the brain function activation task is performed on the individual. Given for a certain period. That is, in this embodiment, a situation is created in which the human brain is activated or not activated by actually giving or not giving a brain function activation task to an individual. The time series data acquired in this way is decomposed into a plurality of components by singular value decomposition, and the correlation between the component waveform and activation and non-activation of the brain is evaluated for each component. Are extracted from a plurality of components as determination components. For this reason, for example, compared with a case where a predetermined component specified in advance by experiments or the like is extracted from a plurality of components as an extraction component, a plurality of components that are less relevant to human brain activity are extracted as a plurality of extraction components. The possibility of being extracted from the components can be reduced.

(5-3)
Here, the brain has a mechanism that cools the brain independently of the body temperature, which is a selective brain cooling mechanism. As a selective brain cooling mechanism, it is known that heat generated by brain activity is exhausted using the forehead portion and the periphery of the sinuses. If it does so, the change of the blood flow volume of the face correlating with the facial skin temperature and facial skin temperature accompanying brain activity will appear in a forehead part and / or a sinus periphery.

  In the present embodiment, various data around the forehead and / or the sinuses are analyzed, and the determination component is extracted. For this reason, it is possible to accurately extract components related to human brain activity.

(6) Application example of brain activity visualization apparatus Next, an application example of the brain activity visualization apparatus according to the present invention will be described.

(6-1) When Used for Estimating Brain Age An example of using the brain activity visualization devices 10 and 110 of the embodiment or the modified example for estimating the brain age of the subject will be described. For example, by incorporating the brain activity visualization devices 10 and 110 described above into the brain age presentation device 301, it is possible to determine the emotion to be examined.

  Specifically, as shown in FIG. 23, a brain age storage unit 204 is provided in the state visualization means 200. The brain age storage unit 204 is a memory that stores an estimation result of brain activity and “brain age data” indicating the brain age in association with each other. Here, for example, data on the amount of brain activity when the same brain function activation task is given to a plurality of subjects for a certain period is collected. Then, the brain activity estimation results are classified according to the age of the subject, and the classified brain activity estimation results and brain age data are associated with each other and stored in the emotion storage unit 204. The correlation between the estimation result of the brain activity and the brain age data indicating the brain age is sequentially optimized.

  Then, the state visualization unit 200 extracts brain age data from the brain age storage unit 204 from the estimation result of the brain activity calculated by the brain activity estimation units 30 and 130. Thereby, the brain age presentation device 301 can present the brain age of the subject on the display unit 202.

  In short, the brain age presentation device 301 includes facial skin temperature acquisition means 20 and / or image data acquisition means 120, brain activity estimation means 30 and 130, and state visualization means (brain age presentation means) 200. The facial skin temperature acquisition means 20 acquires facial skin temperature data in time series. The image data acquisition unit 120 acquires captured image data of the face in time series. Based on a plurality of components obtained by decomposing facial skin temperature data and / or facial blood flow data by singular value decomposition, principal component analysis, or independent component analysis, the brain activity estimating means 30, 130 Estimate brain activity. The state visualization unit 200 presents the brain age of the subject based on the brain activity of the subject estimated by the brain activity estimation unit 30. Therefore, the brain age presentation device 301 can easily present the brain age without wearing a sensor that needs to be processed before wearing an electroencephalogram electrode or the like.

(6-2) When using a game machine The brain age presentation device 301 may be stored in a game machine. Specifically, the facial skin temperature acquisition means 20 and / or the image data acquisition means 120 are mounted on a game machine. Here, the facial skin temperature acquisition means 20 and / or the image data acquisition means 120 acquire facial skin temperature data and / or captured image data of the face in time series for a predetermined period after the game is started by the game machine. With such a configuration, the brain age presentation device 301 can present the brain age during the execution of the game.

  Further, the game itself by the game machine may constitute a brain function activation task. In this case, an estimation formula for age and brain activity progression is created in advance based on data collected from a plurality of subjects according to the contents of the game. Each time the game is executed, the brain age is estimated based on the estimation formula created for each game. Thus, every time the game is executed, “brain age x year old” or the like is presented.

  The brain age presentation device 301 may provide anti-aging diagnostic information and the like together with the presentation of the brain age.

(7) Use of Smart Device The brain activity visualization apparatuses 10 and 110 according to the present embodiment may be stored in a smart device. Specifically, facial skin temperature acquisition means 20 and / or image data acquisition means 120, brain activity estimation means 30, 130, and state visualization means 200 are stored in a smart device such as a multi-function mobile phone. Also good. Furthermore, when the brain activity visualization devices 10 and 110 used as the brain age presentation device are stored in the smart device, the user can input and output information and perform calculations with the smart device that is carried, so that the brain age can be easily set. You can get it.

  Instead of the above-described embodiment, the facial skin temperature acquisition means 20 and / or the image data acquisition means 120 and the state visualization means 200 are stored in a smart device such as a multi-function mobile phone, and the brain activity estimation means 30, 130 are stored. May be employed in the server device. In this case, the calculation load on the smart device body can be reduced. Further, by managing data on the server device, it is possible to improve the accuracy of the output result by collecting and optimizing a large number of data. For example, by accurately optimizing the relationship between the estimation result of brain activity and the brain age data to be examined, the accuracy of the brain age determination can be improved.

<Appendix>
In addition, this invention is not limited to the said embodiment as it is. The present invention can be embodied by modifying the components without departing from the scope of the invention in the implementation stage. In addition, the present invention can form various inventions by appropriately combining a plurality of constituent elements disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements may be appropriately combined in different embodiments.

  According to the present invention, since brain activity can be easily estimated, application to a brain activity visualization device that visualizes the physiological state of a subject based on brain activity is effective.

DESCRIPTION OF SYMBOLS 20 Facial skin temperature acquisition means 200 State visualization means 201 Analysis part 204 Brain age memory | storage part 301 Brain age presentation apparatus 10,110 Evaluation apparatus 30,130 Brain activity estimation means

JP 2013-176406 A

Claims (7)

Facial skin temperature acquisition means (20) for acquiring the skin temperature data detected from the face of the subject and the face skin temperature data including the position data of the detected part in time series and / or the face of the subject was photographed in time series. Facial blood flow volume acquisition means for acquiring facial blood flow volume data in time series based on RGB data of the captured image data obtained by RGB processing performed on the captured image data;
Decomposing the facial skin temperature data acquired by the facial skin temperature acquisition means and / or the facial blood flow volume data acquired by the facial blood flow volume acquisition means by singular value decomposition, principal component analysis, or independent component analysis. Brain activity estimating means (30, 130) for estimating the brain activity of the subject based on a plurality of components obtained in
A brain age presenting means (200) for presenting the brain age of the subject based on the brain activity of the subject estimated by the brain activity estimating means;
A brain age presentation device (301) comprising: The brain activity estimating means includes
Among the plurality of components, the component waveform amplitude is correlated with a change at rest and activation of the brain as a component for determination,
Based on the determination component, the brain activity of the subject is estimated.
The brain age presentation apparatus according to claim 1. The facial skin temperature acquisition means and / or the facial blood flow acquisition means acquire at least the facial skin temperature data and / or the facial blood flow data in the vicinity of the sinuses and / or the forehead of the subject's face. ,
The brain age presentation device according to claim 1 or 2. A brain age storage unit (204) for storing the brain activity estimation result and the brain age data indicating the brain age in association with each other;
The brain activity estimating means calculates a brain activity estimation result,
The brain age presentation means extracts and presents the brain age data of the subject from the brain age storage unit based on the estimation result,
The brain age presentation apparatus according to any one of claims 1 to 3. The facial skin temperature acquisition means and / or the facial blood flow acquisition means and the brain age presentation means are stored in a smart device.
The brain age presentation apparatus according to any one of claims 1 to 4. The facial skin temperature acquisition means and / or the facial blood flow acquisition means, the brain activity estimation means, and the brain age presentation means are stored in a smart device.
The brain age presentation apparatus according to any one of claims 1 to 5. The facial skin temperature acquisition means and / or the facial blood flow amount acquisition means are mounted on a game machine, and the facial skin temperature data and / or the facial blood circulation during a predetermined period after the game is started by the game machine. Get quantity data in time series,
The brain age presentation apparatus according to any one of claims 1 to 6.