TWI710353B - Information processing device, information processing method, information processing program - Google Patents

Abstract

An information processing device is adopted that includes a receiving unit, a memory unit, and a calculation unit. The receiving unit receives detections detected through a head-mounted device that is worn on the head of a user and detects the blood flow of the head Value, the identifier of the predetermined action performed by the user when the detection value is detected, and the identifier of the user, the memory unit combines the received detection value, the identifier of the predetermined action, and the The user’s identifier is assigned and stored in correspondence. The calculation unit calculates a brain activity value based on the detection value stored in the memory unit. The brain activity value shows the user’s performance when the user performs the predetermined action The extent of the state of brain activity.

Description

Information processing device, information processing method, information processing program

The invention relates to an information processing device, an information processing method, and an information processing program.

Historically, a measurement system has been provided in which a near-infrared irradiating unit and a near-infrared detection unit are installed in a headset called a headphone group, and the change in blood flow on the brain surface is detected, and the detected data is processed by a data processing device. Data in order to obtain information showing the activity state of the brain.

[Prior Technical Literature]

[Patent Literature]

[Patent Document 1] Japanese Patent Laid-Open No. 2006-320735

It has been implemented so far: For a plurality of users (subjects), the brain activity state when performing various actions is measured. Take an action The state of brain activity at the time should be affected by the various nature of the action and the attributes of the user. However, it has hardly been implemented so far: the brain activity state is analyzed in consideration of the various nature of the action and the attributes of the user. Expectation: For the brain activity state, analyze the nature of the action, the attributes of the user, etc., and use the brain activity state.

The purpose of the present invention is to provide an information processing device that analyzes the brain activity state measured by a user.

In order to solve the above-mentioned problems, the following means are adopted.

That is, the first aspect is an information processing device that includes: The detection value detected for the blood flow of the user's head, the identifier of the predetermined action performed by the user when the detection value is detected, and the identifier of the user are assigned to a memory unit that is stored in correspondence. ;with Based on the detection value stored in the memory unit, a calculation unit that calculates a brain activity value showing the degree of the state of the user's brain activity when the user performs the predetermined action.

The disclosed aspect can be realized by the program being executed by the information processing device. That is, the disclosed structure can be defined as a program for the information processing device to execute the processing performed by each means in the above aspect, or a computer readable recording medium that records the program. In addition, the disclosed structure can also be defined as the method by which the information processing device executes the processing performed by the aforementioned means. The disclosed structure can also be defined as a system including an information processing device that performs the processing performed by the aforementioned means.

Describe the steps of the program, of course, include processing performed in a time series according to the described order, and processing performed in series when necessary, but also includes processing performed in parallel or individually. You may omit a part of the steps of the description program.

According to the disclosed technology, an information processing device that analyzes the brain activity state measured by the user can be provided.

10‧‧‧Headset

11‧‧‧Control Department

13‧‧‧Wireless Communication Department

115‧‧‧Sensor

125‧‧‧Sensor

20‧‧‧User terminal

21‧‧‧CPU

22‧‧‧Memory

23‧‧‧Wireless Communication Department

24‧‧‧Public Exchange and Communications Department

25‧‧‧Display

26‧‧‧Operation Department

27‧‧‧Output

28‧‧‧Camera Department

29‧‧‧Positioning Department

2A‧‧‧Physical Sensor Department

30‧‧‧Server

31‧‧‧CPU

32‧‧‧Memory

34‧‧‧Public Exchange and Communications Department

35‧‧‧Display

36‧‧‧Operation Department

37‧‧‧Output

[Fig. 1] Fig. 1 is a diagram illustrating an example of a configuration related to information processing of a measurement system according to an embodiment of the present invention.

[Fig. 2] Fig. 2 is a diagram illustrating a configuration example of a head-mounted device.

[Fig. 3] Fig. 3 is a diagram illustrating a configuration example of a user terminal.

[Fig. 4] Fig. 4 is a diagram showing an example of the configuration of a server.

[Fig. 5] Fig. 5 is a drawing for an example of a personal information table.

[Fig. 6] Fig. 6 is a diagram illustrating an example of an operation flow of the measurement system of the present embodiment.

[Fig. 7] Fig. 7 is a drawing for an example of a weight table.

Hereinafter, the embodiments will be described with reference to the drawings. The configuration of the embodiment is illustrative, and the configuration of the invention is not limited to the specific configuration of the disclosed embodiment. It is also possible to adopt the specific structure according to the embodiment as appropriate when implementing the invention.

(Configuration example)

FIG. 1 is a diagram illustrating an example of a configuration related to information processing of a measurement system according to an embodiment of the present invention. This measurement system detects measurement data (also referred to as detection values) indicating changes in blood flow from the user's head, and obtains brain activity information (brain activity waveforms) indicating the activity state of the user's brain. In addition, this measurement system calculates the basic waveform of the attribute of brain activity from the acquired brain activity information.

As shown in Fig. 1, this measurement system has head-mounted devices 10-1, 10-2, 10-3, user terminals 20-1, 20-2, 20-3, and a server 30. Herein, when collectively referred to as head-mounted devices 10-1, 10-2, and 10-3, they are referred to as head-mounted devices 10. In addition, when collectively referring to user terminals 20-1, 20-2, and 20-3, they are referred to as user terminals 20. The number of headsets 10 and user terminals 20 is not limited to three, respectively.

FIG. 2 is a diagram showing an example of the structure of the head-mounted device. The head-mounted device 10 has a control unit 11, a wireless communication unit 13, and a pair of sensors 115 and 125 in terms of information processing. The control unit 11 controls the measurement and communication of the headset 10. The control unit 11 has a processor and memory such as a CPU (Central Processing Unit) or a DSP (Digital Signal Processor), etc., and can be deployed in the memory through executable The computer program, firmware, etc. on the memory are executed. However, the control unit 11 may be a dedicated hardware circuit, FPGA (Field Programmable Gate Array), etc., which activates the wireless communication unit 13 and the sensors 115 and 125 to perform joint processing with each component. In addition, the control unit 11 may be doped with CPU, DSP, dedicated hardware circuits, etc.

The headgear 10 is wound in a headgear shape to be worn on the head of the user, and has a structure that is fixed to the head of the user by tightening a fixing member.

The wireless communication unit 13 is connected to the control unit 11 and the sensors 115 and 125 through a predetermined interface. Among them, the wireless communication unit 13 may also be configured to obtain data from the sensors 115 and 125 via the control unit 11. The wireless communication unit 13 communicates with the user terminal 20 via the network N1. The network N1 is a network that conforms to specifications such as Bluetooth (registered trademark), wireless LAN (Local Area Network), ZigBee, etc. The wireless communication unit 13 is an example of a transfer means. Among them, in this measurement system, the specifications of the wireless interface of the wireless communication unit 13 are not limited.

When communicating under the network N1, the header part of the communication header or the user data part (payload part) in the communication data is embedded with an identifier identifying the headset 10 so that the user terminal 20 can Identify the user (subject).

In addition, in this measurement system, the wireless communication unit 13 may be replaced, or a communication unit for wired communication can be installed at the same time as the wireless communication unit 13. That is, the headset 10 and the user terminal 20 are in wired communication The interface can be connected. In this case, the wired communication interface is not limited, and various interfaces such as USB (Universal Serial Bus) and PCI Express can be used according to the purpose of the measurement system.

The sensors 115 and 125 both irradiate near-infrared rays to the head, receive part of the near-infrared rays that are absorbed and scattered near the cerebral cortex of the brain, and convert them into electrical signals. The cerebral cortex of the brain, for example, the blood flow varies according to the activity state of the brain. As a result, in each part of the cerebral cortex, the amount of heme bound to oxygen in the blood and the amount of heme not bound to oxygen change. Due to changes in the amount of hemoglobin, changes in the amount of oxygen, etc., the absorption characteristics or scattering characteristics of near-infrared rays near the cerebral cortex may change. The sensors 115 and 125 convert the near-infrared rays whose light intensity changes due to the near-infrared absorption rate or transmittance due to such changes in the state of blood flow near the cerebral cortex into electrical signals and output them. The sensors 115 and 125 are examples of detection means.

The sensors 115 and 125 include, for example, a near-infrared light source that irradiates near-infrared rays, and a light receiving unit that receives near-infrared rays. Near-infrared light sources, such as LED (Light Emitting Diodes), infrared lamps, etc. In addition, the light receiving unit includes photoelectric elements such as photodiodes and photoelectric crystals, amplifiers, and AD (Analog Digital) converters. In addition, the near-infrared light source and the light receiving unit may not be provided as a pair. For example, a plurality of light receiving units may be provided for one near-infrared light source.

Fig. 3 is a diagram showing an example of the configuration of a user terminal. The user terminal 20 obtains the change data of the near-infrared absorption rate or transmittance in the vicinity of the cerebral cortex of the user from the headset 10, Provides services that include various information processing related to the activity state of the user's brain. The user terminal 20 is an example of an information processing device (computer). The user terminal 20 can be realized by using a dedicated or general-purpose computer such as a PC (Personal Computer), a smart phone, a cell phone, a tablet terminal, a car navigation device, a PDA (Personal Digital Assistant), or an electronic device equipped with a computer . The user terminal 20 can be installed in, for example, a fitness club, a cram school, etc.

The user terminal 20 has a CPU 21, a memory 22, a wireless communication unit 23, a public exchange communication unit 24, a display unit 25, an operation unit 26, an output unit 27, an imaging unit 28, a positioning unit 29, and a physical sensor unit 2A. The CPU 21 executes processing as the user terminal 20 through a computer program executable in the memory 22. The processing of the user terminal 20 includes, for example, a service including various information processing related to the activity state of the user's brain. The CPU 21 that executes such a computer program is an example of calculation means.

The memory 22 stores computer programs executed by the CPU 21 or data processed by the CPU 21. The memory 22 may include volatile memory and non-volatile memory.

The wireless communication unit 23 is similar to the wireless communication unit 13 of the headset 10. The wireless communication unit 23 is an example of receiving means. In addition, the user terminal 20 may replace the wireless communication unit 13 or have a communication unit that communicates by wire at the same time as the wireless communication unit 13.

The public exchange communication unit 24 communicates with servers on the network N2, such as the server 30, etc., via the network N2. Network N2, public The switching network is, for example, a mobile phone network. When the network N2 is a mobile phone network, the public exchange communication unit 24 is connected to the network N2 via a base station of the mobile phone network. Among them, the network N2 can also be a network including the access network and the Internet of the communication device to the Internet provider. The access network to the communication device of the Internet provider is, for example, the optical network provided by the communication operator, ADSL (Asymmetric Digital Subscriber Line), etc. The network N2 is an example of a public wireless network. In addition, the public exchange communication unit 24 is an example of a public wireless communication means. Among them, in this measurement system, the network N2 is not limited to the public switched network, but can also be dedicated lines for local networks such as LAN (Local Area Network), companies, operators, government agencies, schools, research institutions, etc. , VPN (Virtual Private Network) and other wide area networks. Hereinafter, companies, business operators, government agencies, schools, research institutions, etc. are all referred to as enterprises.

The display unit 25 is, for example, a liquid crystal display, an EL (Electro-Luminescence) panel, etc., and displays output information from the CPU 21. The operation unit 26 is, for example, a button, a touch panel, etc., and accepts user operations. The output unit 27 is, for example, a vibrator that outputs vibration, a speaker that outputs sound or sound, or the like. The imaging unit 28 is, for example, a camera including a solid-state imaging element. For solid-state imaging devices, CCD (Charge-coupled device) image sensors, CMOS (Complementary Metal Oxide Semiconductor) image sensors, etc. can be used.

The positioning unit 29 is, for example, a GPS (Global Positioning System) receiver, which receives radio waves from GPS satellites, and calculates the current position (latitude, longitude, etc.), time, etc. Among them, in the position measuring section 29, The system is not limited to those with GPS receivers. For example, when the public exchange communication unit 24 is a mobile phone network, the positioning unit 29 can also perform positioning based on the distance from the mobile phone base station.

The physical sensor unit 2A is, for example, an acceleration sensor or an angular acceleration sensor. Among them, the physical sensor part 2A can also be a temperature sensor, a humidity sensor, an air pressure sensor, or a water pressure sensor.

FIG. 4 is a diagram showing an example of the structure of the server. The server 30 is connected to the network N2. The server 30 is a general information processing device. An information processing device is equipped with a CPU for calculation and control, a memory and memory section for storing data used in calculations, etc., an input section for receiving input of information from users, etc., and outputting information through video, sound, etc. The output unit, and the communication unit for sending and receiving information with other devices, etc. The server 30 can be implemented using a dedicated or general-purpose computer such as a PC, a workstation (WS, Work Station), or an electronic device equipped with a computer.

The server 30 has a CPU 31, a memory 32, a public exchange communication unit 34, a display unit 35, an operation unit 36, and an output unit 37. The CPU 31 executes processing as the server 30 through a computer program executable in the memory 32. The processing of the server 30 is, for example, a service including various information processing related to the above-mentioned brain activity state. The CPU 31 that executes such a computer program is an example of calculation means.

The memory 32 is for storing computer programs executed by the CPU 31 or data processed by the CPU 31. The memory 32 may include volatile memory and non-volatile memory. In addition, in memory 32, it stores Personal information table T20 containing the personal information of the user.

Fig. 5 is a diagram showing an example of a personal information table. In the personal information table T20 in Fig. 5, personal information such as the user’s identifier (ID), name, address, date of birth, height, weight, gender, disease (cerebral infarction, diabetes, etc.), medical history, etc. are pressed The user gives the correspondence and stores it.

The public exchange communication unit 34 communicates with devices on the network N2, such as the user terminal 20, etc., via the network N2. The network N2 is a public switching network, such as a mobile phone network. In addition, the public exchange communication unit 34 is an example of a public wireless communication means.

The display unit 35 is, for example, a liquid crystal display, an EL (Electro-Luminescence) panel, etc., and displays output information from the CPU 31. The operation unit 36 is, for example, a button, a touch panel, etc., and accepts user operations. The output unit 37 is, for example, a vibrator that outputs vibration, a speaker that outputs sound or sound, or the like.

<Action example>

An operation example of the measurement system of this embodiment will be described.

FIG. 6 is a diagram illustrating an example of the operation flow of the measurement system of this embodiment. The head-mounted device 10 of the measurement system of this embodiment is attached to the head of a user (subject), and is in a state capable of measuring brain activity (cerebral blood flow). The headset 10 is connected to the user terminal 20, respectively. The calibration of the headset 10 is deemed to be completed. The user terminal 20 is connected to the server 30.

In S101, the user terminal 20 causes the user wearing the headset 10 to perform a predetermined action. Set actions, such as watching animation, running games, music appreciation, exercise, eating, experimenting, meditation, sleep, yoga, running apps, etc. A given action can also be a specific action in a specific application. In terms of applications, the department includes, for example, brain training applications. For each predetermined action, a unique identifier is assigned in advance. In addition, each user is assigned a unique identifier in advance. For example, the user terminal 20 displays the animation on the display unit 25 when viewing the animation, outputs the sound accompanying the animation to the output unit 27, and allows the user to watch the animation. In addition, the user terminal 20 outputs video, sound, etc. for the game to the display unit 25 and the output unit 27 when the game is running, and allows the user to operate the game through the operation unit 26 to perform the running of the game. In addition, the user terminal 20 outputs a signal of the start and end of a predetermined exercise, which is a predetermined activity, through the display unit 25, the output unit 27, and the like through video, audio, and the like. The user terminal 20 may accept input of the operation of the signal of the start and end of a predetermined exercise as a predetermined action through the input unit 26.

When the user is made to perform a predetermined action, the user terminal 20 causes the head-mounted device 10 to measure the user's brain activity state. The brain activity state is measured at a predetermined sampling frequency. Each head-mounted device 10 measures the brain activity state of the user through the sensors 115 and 125, and transmits a detection value showing the brain activity state to the user terminal 20 via the wireless communication unit 13. The user terminal 20 is worn from the head via the wireless communication unit 23 When the device 10 receives the detection value (brain activity waveform) showing the brain activity state, it is stored in a memory device such as the memory 22. Here, the detected value may be the measured value itself, may be processed into information that is easy to send to the user terminal 20, or may be information that collects the measured values for a fixed period of time, etc. . The detection value may be a value based on the value of the head-mounted blood flow change measured by the head-mounted device 10. The detection value showing the state of brain activity is stored as time-series data, along with time information, the identifier of a predetermined action, and the identifier of the user. The user terminal 20 may always acquire a detection value showing the brain activity state as a measurement result, and extract the detection value showing the brain activity state during a predetermined action period. The detection value (brain activity waveform) is, for example, the time change of cerebral blood flow and the time differential of cerebral blood flow.

In S102, the user terminal 20 transfers the detection value (brain activity waveform) showing the state of brain activity stored in the memory means together with the identifier of the predetermined action, the identifier of the user, and the time information through the public exchange communication unit 24 and sent to the server 30. The server 30 receives the detection value indicating the state of brain activity, the identifier of the predetermined action, the identifier of the user, and the time information from each user terminal 20 via the public exchange communication unit 34. The server 30 associates the received brain activity waveforms with the identifier (ID) of the predetermined action, the identifier (ID) of the user, and time information, and stores them in the memory 32. The memory 32 in the server 30 accumulates brain activity waveforms when various users perform various predetermined actions (actions). The server 30 can also send the brain activity waveform timing data, the identifier of the predetermined action, the start time of the predetermined action, and the end of the predetermined action. The beam time is associated with the identifier of the user who performed the predetermined action, and is stored in the memory 32. The memory 32 in the server 30 can extract brain activity waveforms when a specific user performs a specific predetermined action, and only store brain activity waveforms and the like.

Here, brain activity adopts attributes (marked attributes) including visual function, auditory function, motor function, short-term memory, and long-term memory. The attributes of brain activity are the inherent properties, characteristics, and characteristics of brain activity. The attributes of brain activity (brain activity attributes) are mainly attributes corresponding to the functions of the body. Visual function is the function of obtaining visual information from the eyes. Hearing function is the function of obtaining audio information from the ear. Motor function is the function of making hands, feet and other movements. Short-term memory is the memory that is maintained for a few seconds to tens of seconds after the reminder of the memorized object. Long-term memory refers to the memory maintained for a period longer than the period of maintaining short-term memory after the reminder of the memory object. The types of attributes of brain activity are not limited to these. In terms of brain activity, the system can include more types of attributes.

In addition, the various attributes of brain activity are based on inherent brain activity waveforms (basic waveforms). That is, for example, when visual information is obtained from the user's eyes (when the visual function is used), the brain activity waveform includes the brain activity waveform inherent to the visual function. Among them, the basic waveform of each attribute of brain activity does not require mathematically strict independence. The brain activity waveform measured through the head-mounted device 10 is the sum of the basic waveforms of each attribute of the brain activity. The size of the basic brain activity waveform included in the brain activity waveform depends on the size corresponding to the degree to which each attribute of the brain activity is used.

The server 30 is for each predetermined action, preset interest The estimated brain activity waveform when the user performs a predetermined action. The set brain activity waveform is expressed as the sum of the inherent brain activity waveforms of each attribute of the brain activity. The brain activity waveform Sapp1 (the brain activity waveform when a certain predetermined action is performed) set for a certain predetermined action (app1) is expressed as follows, for example.

[Numerical formula 1] S _{app 1} =( M _{a 1} M _{b 1} M _{c 1} M _{d 1} M _{e 1} )( X _a X _b X _c X _d X _e ) ^T = M _{a 1} X _a + M _{b 1} X _b + M _{c 1} X _c + M _{d 1} X _d + M _{e 1} X _e (1)

Here, Xa is the unique brain activity waveform (basic waveform) of the visual function of one of the attributes of brain activity, Xb is the basic waveform of auditory function, Xc is the basic waveform of motor function, and Xd is the basic waveform of short-term memory. , Xe is the basic waveform of long-term memory. In addition, Ma1 is the weight (attribute value) of the visual function of the predetermined action (app1), Mb1 is the weight of the auditory function of the predetermined action, Mc1 is the weight of the motor function of the predetermined action, and Md1 is the weight of the predetermined action. The weight of the short-term memory of the action, Me1 is the weight of the long-term memory of the given action. The weight of an attribute of brain activity related to a predetermined action is a quantity that shows the magnitude of brain activity expected for the attribute. The weight of the attribute of the brain activity related to the predetermined action is the tendency to show the brain activity state of the predetermined action. The greater the weight (attribute value) of the attribute of a certain brain activity in a certain predetermined action, the more actively the function of the attribute of the brain activity functions when the predetermined action is performed. The weight of each attribute is based on the design of tools or applications that support the predetermined actions of the creator of the animation (the predetermined action is the case of watching the animation), the creator of the game (the predetermined action is the case of the game running), etc. Which is preset. These tools, applications, etc. are based on the user terminal 20 Provided for other control equipment. The person who sets the weight of each attribute is to set the weight of each attribute according to the characteristics of a given action. The weight of the attribute of the brain activity related to the predetermined action is stored in the memory 32 of the server 30 as the weight table T10.

Fig. 7 is a drawing for an example of a weight table. In the weight table T10 of FIG. 7, the name of the predetermined action, the ID of the predetermined action, and the weight (attribute value) of each attribute of the brain activity are associated and stored. In the weight table T10, it is the weight of attributes for a plurality of predetermined actions, including ID and brain activity. For example, in the name of a given action "action A", the system ID is stored as "001", the weight of visual function (attribute value) is stored as "3", the weight of auditory function is stored as "0", The weight is stored as "1", the short-term memory weight is stored as "2", and the long-term memory weight is stored as "0".

In S103, the server 30 calculates the basic waveforms for each individual brain activity based on the brain activity waveforms of various users accumulated in the memory 32 when performing various predetermined actions. The weight of each attribute of brain activity is assigned by each person according to the predetermined action through the weight table T10. Therefore, the server 30 uses the brain activity waveform accumulated in the memory 32 and the weight of the weight table T10 to calculate the basic waveform (Xa,..., Xe). When there are 5 types of brain activity attributes, in principle, the basic waveform can be calculated as long as the brain activity waveforms of more than 5 predetermined actions are included. That is, by diagonalizing the matrix of the weights of the brain activity attributes of a plurality of predetermined actions, the basic waveforms (Xa, ..., Xe) according to the brain activity attributes can be calculated. More In general, for example, when there is a brain activity waveform (Sapp1~Sapp5) related to a predetermined action (app1~app5), use the weight (Ma1~Me1,.) based on the attribute of the brain activity related to the predetermined action (app1~app5). .., Ma5~Me5), the basic waveforms (Xa,..., Xe) of each attribute of brain activity are represented by the following formula, for example.

Solving this formula (2) makes it possible to find the basic waveforms (Xa,..., Xe) for each attribute of brain activity. By using more brain activity waveforms, more accurate basic waveforms can be calculated. The server 30 can also calculate the basic waveform after averaging the brain activity waveforms of each person according to a predetermined action. The server 30 stores the calculated basic waveforms (Xa,..., Xe) according to the brain activity in the memory 32. In addition, when the measured length (length of time) differs depending on the brain activity waveform, the instantaneous response function can be estimated from the deconvolution of the rectangular wave to eliminate the influence of the difference in measured length.

In S104, the server 30 calculates the brain activity values of the brain activity waveforms of various users accumulated in the memory 32 when performing various predetermined actions. The brain activity value is calculated based on the amplitude width of the brain activity waveform (the difference between the maximum value and the minimum value), the cumulative value of the change amount of the brain activity waveform, the change speed of the brain activity waveform, the repetitive reproducibility of the brain activity waveform, and the like. The brain activity value is a value showing the degree of brain activity, the greater the brain activity value, Indicates that the brain is more active. The server 30 can calculate the brain activity value for each user, each scheduled action, and each date and time. The server 30 stores the calculated brain activity value in the memory 32. Furthermore, the server 30 displays (outputs) the brain activity value stored in the memory 32 through the display unit 35 and/or the display means of the output unit 37 or the like. The user can confirm the brain activity value displayed on the display unit 35 and/or the display means of the output unit 37 and recognize the result of a predetermined action. Each user can, for example, use past brain activity as a reference and perform various actions in order to activate brain activity.

In addition, the server 30 may also calculate the brain activity value of each user according to the attributes of the user. For example, when the server 30 calculates the brain activity value when a male over the age of 20 performs a predetermined action "action A", it extracts the brain activity waveform of the predetermined action "action A", and assigns the corresponding brain activity waveform from the brain activity waveform Among the users, using the personal information table T20, etc., men over the age of 20 are extracted, and the brain activity waveforms of the extracted users are used to calculate the brain activity value. In this way, the distribution of brain activity values, brain activity values, etc., under specific conditions can be calculated. The server 30 stores the calculated brain activity value, brain activity value, etc. distribution in the memory 32. The server 30 displays the brain activity value and the distribution of the brain activity value stored in the memory 32 through the display unit 35 and/or the display means of the output unit 37. The user who confirms the displayed brain activity value, for example, compares the brain activity value of a user with a specific disease when performing a specific predetermined action with the brain activity value of a user who does not have the disease. When there is a statistically significant difference, the brain activity waveform during the predetermined action can be used (Brain activity value), and used to detect people with the disease.

In addition, the server 30 may calculate the weight of each attribute from the basic waveform and the brain activity waveform of each attribute of brain activity for a specific predetermined action of a specific user based on equation (1). At this time, for example, the weight of each attribute is adjusted so that the sum of the basic waveform of each attribute and the weight multiplier becomes a brain activity waveform, and the weight of each attribute is calculated. The server 30 stores the calculated weight of each attribute in the memory 32. The server 30 displays the weight of each attribute stored in the memory 32 through the display unit 35 and/or the display means of the output unit 37 and the like. In this way, the weight of each attribute of the brain activity set in the predetermined action is compared with the weight calculated here, so that the function of confirming the attribute of the brain activity is more active. The weight of each attribute calculated here can also be the brain activity value of each attribute.

In addition, the brain activity value of the specific brain activity attribute of the user (group A) with a specific disease, and the specific brain activity attribute of the user (group B) who does not have the specific disease When there is a statistically significant difference in the brain activity value, the brain activity value of the specific brain activity attribute of the unknown user for the specific disease is used to determine the brain activity value close to group A or close to group B The value of brain activity, making it possible to determine whether the disease is possible. In addition, exploring the attributes of brain activity that produce significant differences makes it possible to derive the attributes of brain activity for users who have discovered a specific disease. Furthermore, it is designed to perform a predetermined action with a significant weight of the attribute of the derived brain activity, so that it is easy to find a user with a specific disease through the predetermined action.

In addition, the server 30 may display the comparison of the brain activity value of others and the brain activity value of oneself when a plurality of users perform the same predetermined action on the display unit 35 and/or the display means of the output unit 37. With this, the user can grasp, for example, the overall rank of the user. Furthermore, the same user’s past brain activity value when the same user performs the same predetermined action is compared with his current brain activity value, so that the user can confirm the current brain activity value compared with the past self The state of brain activity.

When the server 30 has a large difference between the weight of the attribute of the brain activity set for a specific predetermined action (refer to FIG. 7) and the weight of the attribute of the brain activity calculated from the waveform of the brain activity when the predetermined action is performed , You can also change the set weight to the calculated weight. In addition, the content of the predetermined action (the content of the animation, the content of the game, etc.) may be corrected so that the weight of the calculated attribute of the brain activity becomes the weight of the set attribute of the brain activity. By this, it is possible to design a predetermined action (animation, game creation, etc.) so that the brain activity state of the attribute of the specific brain activity becomes active.

The server 30 can also recalculate the basic waveforms of each attribute of brain activity for each of the predetermined periods. In this case, the server 30 uses statistical methods such as outlier removal to calculate the basic waveforms of each attribute of brain activity. The more samples of the brain activity waveform change, the closer the basic waveform of each attribute of brain activity should be to the actual value.

Here, the attributes of brain activity are set to 5 types, but the types of attributes of brain activity are not limited to 5 types. It can be further added. When adding a new attribute of brain activity, the designer of the existing predetermined action sets the weight of the attribute of the new brain activity in a manner that reflects the content of the predetermined action. In addition, when the weight of the attribute of the new brain activity is expressed as a function of the weight of each attribute of the existing brain activity, the function is used to determine the weight of the attribute of the new brain activity.

The structure of the above-mentioned embodiment can be implemented by combining these as much as possible.

(Function and effect of implementation form)

In the measurement system of this embodiment, the user terminal 20 measures the brain activity state when the user performs a predetermined action through the headset 10. The server 30 acquires the brain activity state transmitted from a plurality of user terminals 20. The server 30 calculates the basic waveform of the brain activity of each individual according to the attribute of the brain activity based on the plurality of measured brain activity states. In addition, the server 30 calculates and outputs the brain activity value of each person according to a predetermined action, each person according to the user, each person according to the attribute of the user, and each person according to the attribute of the brain activity.

According to the measurement system of the present embodiment, it is possible to calculate the distribution of brain activity values, brain activity values, etc. under specific conditions (predetermined actions, users, attributes of users, attributes of brain activity, or combinations of these). The user who performs the measurement of the brain activity state compares the brain activity value of the other person with his own brain activity value, so that he can grasp the overall rank of himself and the rank of himself under predetermined conditions.

10-1、10-2、10-3‧‧‧Headset

20-1, 20-2, 20-3‧‧‧User terminal

30‧‧‧Server

N1,N2‧‧‧Network

Claims (9)

An information processing device comprising: a detection value detected for the blood flow of the head of a user, an identifier of the predetermined action performed by the user when the detection value is detected, and the identifier of the user A memory unit that is stored in correspondence is assigned; and a calculation unit that calculates a brain activity value that shows the state of the user's brain activity when the user performs the predetermined action based on the detection value stored in the memory unit; The brain activity has a plurality of brain activity attributes, and the memory unit stores the weights of the individual brain activity attributes that show the tendency of the brain activity state and the identifier of the predetermined action according to the predetermined action. The calculation unit calculates the basic waveforms for each of the brain activity attributes based on the detection values stored in the memory unit and the weights assigned to the predetermined actions corresponding to the detection values according to the brain activity attributes. The memory unit stores the aforementioned basic waveforms for each of the aforementioned brain activity attributes. For example, the information processing device of the first item in the scope of the patent application, wherein the calculation unit calculates the value of each brain activity attribute based on the detection value stored in the memory unit and the basic waveform of each of the brain activity attributes. Brain activity value. For example, the information processing device of item 1 or 2 of the scope of patent application has an output unit that outputs the brain activity value stored in the memory unit. A method of information processing, Computer execution: The detection value detected for the blood flow of the user's head, the identifier of the predetermined action performed by the user when the detection value is detected, and the identifier of the user are associated and stored In the memory means, a brain activity value showing the state of the user’s brain activity when the user performs the predetermined action is calculated based on the detection value stored in the memory means; the brain activity has a plurality of brain activity attributes , The aforementioned computer execution: According to the aforementioned predetermined actions, the weights of the aforementioned brain activity attributes showing the tendency of the brain activity state and the identifiers of the aforementioned predetermined actions are assigned and stored in the aforementioned memory means according to the aforementioned predetermined actions. The aforementioned detection value of the memory means and the aforementioned weights assigned to the aforementioned predetermined action corresponding to the aforementioned detection value according to the aforementioned brain activity attributes are calculated, and the basic waveforms of the aforementioned brain activity attributes are calculated, and stored in the aforementioned memory means according to the aforementioned brain activity. The aforementioned basic waveform of each activity attribute. For example, the information processing method of item 4 of the scope of patent application, wherein the aforementioned computer executes: calculating according to the aforementioned brain activity attributes based on the aforementioned detection values stored in the aforementioned memory means and the aforementioned basic waveforms of the aforementioned brain activity attributes Brain activity value. For example, the information processing method of item 4 or 5 of the scope of patent application, in which the computer executes: Output the aforementioned brain activity value stored in the aforementioned memory means. An information processing program for computer execution: the detection value detected for the blood flow of the user's head, the identifier of the predetermined action performed by the user when the detection value is detected, and the user's The identifier is assigned and stored in the memory means, and based on the detection value stored in the memory means, a brain activity value showing the state of the user’s brain activity when the user performs the predetermined action is calculated; the brain activity Having a plurality of brain activity attributes for the aforementioned computer to execute: according to the aforementioned predetermined actions, the weights of the aforementioned brain activity attributes and the aforementioned predetermined action identifiers are assigned and stored in the aforementioned predetermined actions. The memory means calculates the basic waveform of each of the brain activity attributes based on the detection value stored in the memory means and the weight assigned to the predetermined action corresponding to the detection value according to the brain activity attribute. The aforementioned memory means stores the aforementioned basic waveforms according to the aforementioned brain activity attributes. For example, the information processing program of item 7 of the scope of patent application, which is executed by the aforementioned computer: according to the aforementioned detection value stored in the aforementioned memory means, and the aforementioned basic waveform of each of the aforementioned brain activity attributes, it is calculated according to the aforementioned brain activity attributes. Brain activity value. For example, the information processing program of item 7 or 8 of the scope of patent application is for computer execution: Output the aforementioned brain activity value stored in the aforementioned memory means.

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