Brain activity training device, training method, and program

Abstract

One embodiment of the present invention generates and presents, to a user, presentation information representing a movement image associated with a body part of the user, and during the presentation period of this presentation information, acquires brain activity data representing the user's brain activity, as well as acquiring body movement data representing the movement of the body part of the user. The embodiment then obtains information representing the degree of progress of the user's training, and on the basis of this information representing the degree of progress, adjusts and changes the content of the presentation information from first content in which the user is prompted to recall the movement image in the user's brain while moving the body part, to second content in which the user is prompted to recall the movement image in the user's brain without moving the body part.

Description

Brain activity training device, training method, and program

[0001] One aspect of the present invention relates to a brain activity training device, a training method, and a program for training brain activity for recalling motor imagery.

[0002] There is a known technology that estimates recalled operation commands based on differences in brain waves. This technology is called a brain-computer interface (BCI). This technology makes it possible to control an object using brain waves, and is expected to enable people with physical disabilities to easily operate devices such as wheelchairs.

[0003] To use BCI, for example, a deep learning model is trained to learn the types of commands that are recalled in a person's brain and the corresponding EEG patterns, and the type of recalled command is estimated based on the measured EEG patterns. A typical command input method is motor imagery. For example, different EEG patterns are observed when imagining moving the right hand and when imagining moving the left hand. Therefore, if different commands are associated with each motor image, it becomes possible to input commands to the BCI. This technology is called MI-BCI (see, for example, Non-Patent Document 1).

[0004] Wei, Chun-Shu, Toshiaki Koike-Akino, and Ye Wang. "Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification." 2019 9th International IEEE / EMBS Conference on Neural Engineering (NER). IEEE, 2019.

[0005] The technology described in Non-Patent Document 1 utilizes the fact that the electroencephalograms generated in the brain when imagining a movement are generated in different brain activation areas for each movement image, and estimates which movement a person is imagining by capturing the differences in brain activation areas between movement images from the electroencephalograms.

[0006] However, for humans, it is difficult to conjure up motor imagery in the brain without moving the body, and it is also difficult to consistently generate similar brain wave patterns even when performing the same task.

[0007] This invention was made with the above-mentioned circumstances in mind, and aims to provide a technology that makes it easy to recall motor images in the brain and enables highly reproducible generation of brain activity patterns corresponding to motor images.

[0008] In order to solve the above problems, one aspect of a brain activity training device or training method according to the present invention generates presentation information representing a motor image associated with a user's body part and presents it to the user, and during the presentation period of this presentation information, acquires brain activity data representing the user's brain activity and physical movement data representing the movement of the user's body part, obtains information representing the user's training progress, and based on this information representing the progress, adjusts the content of the presentation information so as to change from first content that causes the user to imagine the motor image in the user's brain while moving the body part to second content that causes the user to imagine the motor image in the user's brain without moving the body part.

[0009] According to one aspect of the present invention, for example, immediately after the start of training, brain activity training is repeatedly performed using presented information adjusted to evoke exercise images in the user's mind while moving body parts, thereby encouraging the user to exercise with physical movements.

[0010] On the other hand, depending on the progress of the training, the content of the presented information is adjusted to change to a second content that evokes an exercise image in the brain without moving any body part, so that the user can gradually evoke an exercise image in the brain without relying on physical movement, thereby effectively improving the reproducibility when evoked in the brain an exercise image.

[0011] In other words, according to one aspect of the present invention, a technology can be provided that makes it easy to recall motor images in the brain and that can generate brain activity patterns corresponding to motor images with high reproducibility.

[0012] Fig. 1 is a diagram showing an example of the configuration of a system including a brain activity training device according to an embodiment of the present invention. Fig. 2 is a block diagram showing an example of the hardware configuration of a brain activity training device according to an embodiment of the present invention. Fig. 3 is a block diagram showing an example of the software configuration of a brain activity training device according to an embodiment of the present invention. Fig. 4 is a flowchart showing an example of the processing procedure and processing content of brain activity training processing executed by a control unit of the brain activity training device shown in Fig. 3. Fig. 5 is a flowchart showing the processing procedure and processing content of presentation information adjustment processing in the brain activity training processing shown in Fig. 4.

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0014] [One Embodiment] (Configuration Example) (1) System FIG. 1 is a diagram showing an example of the configuration of a system including a brain activity training device CS according to one embodiment of the present invention.

[0015] The system according to one embodiment includes an electroencephalogram (EEG) sensor BS attached to the head of a user US to be trained, and an electromyogram (EMG) sensor MS attached to a body part of the user US. The electroencephalogram sensor BS and the electromyogram sensor MS are connected to a brain activity training device CS via, for example, a signal cable.

[0016] The system also has an input device IN and a display device DP located at positions where they can be operated or viewed by the user US. These input device IN and display device DP are also connected to the brain activity training apparatus CS via, for example, a signal cable.

[0017] The brain wave sensor BS uses, for example, an EEG (Electro Encephalogram) sensor, and measures brain waves generated from different parts of the brain of the user US via electrodes, and outputs each measured brain wave data to the brain activity training device CS.

[0018] The electromyographic sensor MS measures, on a time axis, the action potential generated when muscle fibers contract, and outputs the measured electromyographic data to the brain activity training device CS.

[0019] The display device DP is a display device that displays images, etc., generated by the brain activity training device CS, that instruct the user US on body movements to evoke motor imagery. The display device DP also displays feedback information, generated by the brain activity training device CS, that reflects the estimated results of the motor imagery of the user US. Note that the display device DP may be a display integrated into the brain activity training device CS.

[0020] The input device IN is composed of a mouse, keyboard, voice input device, etc., and is used to input instructions to start and end training to the brain activity training apparatus CS.

[0021] (2) Brain Activity Training Device CS FIGS. 2 and 3 are block diagrams showing the hardware and software configurations, respectively, of the brain activity training device CS according to an embodiment of the present invention.

[0022] 2 and 3, the brain activity training device CS includes a control unit 1 that uses a hardware processor such as a central processing unit (CPU). A storage unit having a program storage unit 2 and a data storage unit 3, a sensor interface (hereinafter, interface will be abbreviated as I / F) unit 4, and an input / output I / F unit 5 are connected to the control unit 1 via a bus 6.

[0023] The sensor I / F unit 4 receives the electroencephalogram data output from the electroencephalogram sensor BS and the electromyogram data output from the electromyogram sensor MS, converts them into data that can be processed by the control unit 1, and outputs them.

[0024] The input / output I / F section 5 outputs the display data generated by the control section 1 to the display device DP, and also takes in input data input by the system administrator or user US via the input device IN.

[0025] Note that, instead of signal cables, a low-power wireless interface such as Bluetooth (registered trademark) may be used as a connection means between the sensor I / F unit 4 and the electroencephalogram sensor BS and the electromyography sensor MS, and between the input / output I / F unit 5 and the display device DP and the input device IN. Using a wireless interface can reduce the burden on the user when undergoing training.

[0026] The program storage unit 2 is, for example, a combination of a non-volatile memory such as a hard disk drive (HDD) or a solid state drive (SSD) as a storage medium that can be written to and read from at any time, and a non-volatile memory such as a read only memory (ROM), and stores application programs necessary for executing various processes related to one embodiment of the present invention, in addition to middleware such as an operating system (OS).

[0027] The data storage unit 3 is, for example, a combination of a non-volatile memory such as an HDD or SSD as a storage medium that can be written to and read from at any time, and a volatile memory such as a RAM (Random Access Memory), and its storage area is provided with a brain activity data storage unit 31, a muscle activity data storage unit 32, and a movement image storage unit 33.

[0028] The brain activity data storage unit 31 functions as a buffer memory that temporarily stores the brain wave data output from the brain wave sensor BS.

[0029] The muscle activity data storage unit 32 functions as a buffer memory that temporarily stores the electromyogram data output from the electromyogram sensor MS.

[0030] The movement image storage unit 33 stores a plurality of pieces of movement image information for presenting types of movement images to the user US. Examples of movement images include right hand movement, left hand movement, both feet movement, and tongue movement.

[0031] The control unit 1 includes, as processing function units according to one embodiment of the present invention, a brain activity data acquisition processing unit 11, a brain activity motor image estimation processing unit 12, a parameter update processing unit 13, a muscle activity data acquisition processing unit 14, a muscle activity motor image estimation processing unit 15, a presentation information adjustment processing unit 16, and a presentation information output processing unit 17.

[0032] Each of the processing units 11 to 17 is realized by causing a hardware processor of the control unit 1 to execute an application program stored in the program storage unit 2. Note that some or all of the processing units 11 to 17 may be realized using hardware such as an LSI (Large Scale Integration) or an ASIC (Application Specific Integrated Circuit).

[0033] The brain activity data acquisition processing unit 11 receives the brain wave data output from the brain wave sensor BS via the sensor I / F unit 4, and temporarily stores the received brain wave data in the brain activity data storage unit 31 as data representing brain activity.

[0034] The brain activity motor imagery estimation processing unit 12 includes a machine learning model such as a CNN (Convolutional Neural Network). The brain activity motor imagery estimation processing unit 12 reads the electroencephalogram data for each unit time length from the brain activity data storage unit 31, extracts its feature amounts, and inputs the extracted feature amounts into the machine learning model to estimate motor imagery corresponding to the feature amounts of the electroencephalogram data. Note that methods other than those using a machine learning model can also be used to estimate motor imagery of brain activity.

[0035] The parameter update processing unit 13 calculates the error between the motor image estimated by the brain activity motor image estimation processing unit 12 and the correct label of the motor image stored in the motor image storage unit 33, and updates the motor image estimation parameters based on the calculated error so as to reduce the error.

[0036] The muscle activity data acquisition processing unit 14 acquires the electromyogram data output from the electromyogram sensor MS via the sensor I / F unit 4, and stores the acquired electromyogram data in the muscle activity data memory unit 32 as data representing the muscle activity of the user US.

[0037] The muscle activity motor image estimation processing unit 15 reads the electromyogram data for each unit time length from the muscle activity data storage unit 32 and calculates the probability distribution of motor images due to muscle activity by comparing the signal level with, for example, a threshold value.

[0038] The presentation information adjustment processing unit 16 generates presentation information of motor imagery according to the progress of the training of the user US, based on the motor imagery estimated by the brain activity motor imagery estimation processing unit 12 and the motor imagery estimated by the muscle activity motor imagery estimation processing unit 15. The progress of the training is determined based on an error in the brain activity motor imagery calculated by the parameter update processing unit 13. An example of the adjustment processing of the presentation information will be described in the operation example.

[0039] The presented information output processing unit outputs the presented information of the exercise image generated by the muscle activity exercise image estimation processing unit 15 to the display device DP via the input / output I / F unit 5 to display it.

[0040] (Example of Operation) Next, an example of operation of the brain activity training apparatus CS configured as above will be described.

[0041] FIG. 4 is a flowchart showing an example of the processing procedure and processing content of the brain activity training processing executed by the control unit 1 of the brain activity training device CS.

[0042] (1) Acquisition of brain activity data and muscle activity data During training, the user US wears an electroencephalogram sensor BS on his / her head and an electromyography sensor MS on each body part corresponding to the type of exercise image, for example, the right forearm, the left forearm, and the gastrocnemius muscles of both legs.

[0043] In this state, when the system administrator or user operates the input device IN to input a training start instruction, and this training start instruction is detected in step S10, the control unit 1 of the brain activity training device CS executes the brain activity training process as follows.

[0044] That is, in step S11, the control unit 1 of the brain activity training device CS first reads out exercise image information from the exercise image memory unit 33, and outputs the read exercise image information from the input / output I / F unit 5 to the display device DP for display.

[0045] For example, if the user US wants to visualize the movement of his or her right hand, the control unit 1 displays an image representing the movement of the right hand as the movement image information on the display device DP. In response, the user US looks at the displayed movement image information and actually moves his or her right hand, while simultaneously visualizing the image of moving the right hand in his or her mind.

[0046] In this state, in step S12, the control unit 1 of the brain activity training device CS receives the brain wave data output from the brain wave sensor BS via the sensor I / F unit 4 under the control of the brain activity data acquisition processing unit 11, and stores the received brain wave data in the brain activity data storage unit 31 as data representing brain activity.

[0047] In parallel with this, in step S13, the control unit 1 of the brain activity training device CS, under the control of the muscle activity data acquisition processing unit 14, receives, via the sensor I / F unit 4, electromyogram data output from the electromyogram sensors MS attached to the "right forearm," "left forearm," and "gastrocnemius muscles of both legs," and stores each received electromyogram data in the muscle activity data storage unit 32 as data representing the muscle activity of each body part.

[0048] (2) Estimation of motor imagery based on brain activity and update of motor imagery estimation parameters Next, in step S14, the control unit 1 of the brain activity training device CS reads out the electroencephalogram data from the brain activity data storage unit 31 for each unit time length under the control of the brain activity motor imagery estimation processing unit 12, and inputs the read-out electroencephalogram data into a machine learning model for estimation. Then, the machine learning model estimates motor imagery corresponding to the electroencephalogram data, and outputs the estimation result.

[0049] For example, the brain activity motor imagery estimation processing unit 12 extracts features from the read electroencephalogram data and inputs these features into a machine learning model. The machine learning model then obtains an estimated probability distribution of motor imagery. Specifically, the probability distribution of motor imagery obtained is as follows: "right hand movement": (0.7), "left hand movement": (0.2), "both feet movement": (0.1).

[0050] Next, in step S15, the control unit 1 of the brain activity training device CS, under the control of the parameter update processing unit 13, calculates the error between the probability distribution of the motor image estimated by the brain activity motor image estimation processing unit 12 and the motor image presented to the user US as teaching data, i.e., the correct label of the presented motor image.

[0051] For example, when a right hand movement is presented to the user US, the correct labels of the probability distribution of motor imagery are "right hand movement": (1.0), "left hand movement": (0.0), and "both feet movement": (0.0). The parameter update processing unit 13 calculates the word difference between the motor imagery probability distribution estimated by the brain activity motor imagery estimation processing unit 12 and the motor imagery probability distribution as follows:

[0052] Error = abs(1.0-0.7) +abs(0.0-0.2) +abs(0.0-0.1).

[0053] In the above formula, abs(*) represents the absolute value function of *. In the above example, the error is calculated as the sum of the absolute difference values ​​of the movement images, but other calculation methods may also be used.

[0054] The parameter update processing unit 13 updates the motor imagery estimation parameter based on the calculated error, for example, by subtracting a value obtained by differentiating the error value with the motor imagery estimation parameter from the motor imagery estimation parameter.

[0055] (3) Estimation of Motor Imagery Based on Muscle Activity In step S16, the control unit 1 of the brain activity training device CS, under the control of the muscle activity motor imagery estimation processing unit 15, reads out each piece of electromyogram data corresponding to the "right hand," "left hand," and "both feet" from the muscle activity data storage unit 32 for each unit time length, and compares the signal level of each piece of electromyogram data read out with a preset threshold value associated with each of the "right hand," "left hand," and "both feet." The muscle activity motor imagery estimation processing unit 15 then regards the comparison result as a probability distribution of motor imagery based on the movement of each body part.

[0056] In this example, the user US moves only his / her right hand in accordance with the presented information of the displayed motor image, and as a result, electromyogram data is output only from the electromyogram sensor MS attached to the right forearm. Therefore, the muscle activity motor image estimation processing unit 15 obtains a motor image probability distribution, for example, of "right hand movement": (1.0), "left hand movement": (0.0), and "both feet movement": (0.0).

[0057] (4) Adjustment and output of presented information Next, in step S17, under the control of the presented information adjustment processing unit 16, the control unit 1 of the brain activity training device CS adjusts the presented information to be fed back to the user US as feedback, depending on the progress of the user US's training to recall motor images in his or her brain, as follows:

[0058] FIG. 5 is a flowchart showing an example of the procedure and content of the adjustment process executed by the presentation information adjustment processing unit 16 .

[0059] That is, at the start of the exercise imagery recall training, the presentation information adjustment processing unit 16 first sets a phase in which a physical movement is recommended in step S20. The physical movement recommendation phase is a phase in which the user US moves a body part corresponding to the exercise imagery and recalls the exercise imagery in the user's brain.

[0060] For example, in step S21, the presentation information adjustment processing unit 16 sets the estimation step α to "0," and determines whether the estimation accuracy of the motor imagery based on brain activity has converged in step S22. More specifically, it determines whether the error value of the motor imagery estimation parameter calculated by the parameter update processing unit 13 has decreased to or below a threshold value over a predetermined estimation step period.

[0061] If the result of the above determination is that the estimation accuracy has not converged, the presentation information adjustment processing unit 16 adjusts the presentation information to be presented to the user US based on the MI indicating the estimation result of the motor image based on the brain activity in step S23. BS The MI shows the estimated results of motor imagery based on physical movements. MS However, for each estimation step α, the motor imagery MI MS The presented information is, for example, BS +abs{(1-α)*MI MS} is generated as follows.

[0062] The generated presentation information is output to the display device DP via the input / output I / F unit 5 under the control of the presentation information output processing unit 17, and is displayed.

[0063] The presentation information adjustment processing unit 16 then sets the estimation step to α=α+β in step S24, and then returns to step S22 to determine whether the estimation accuracy has converged. Thereafter, the presentation information adjustment processing unit 16 similarly repeats the processes in steps S22 to S24 until the estimation accuracy of the motor imagery due to brain activity converges.

[0064] That is, in the physical movement recommendation phase, while the user US actually performs a physical movement, the presentation information adjusted to evoke a motor image corresponding to the physical movement in the brain is presented to the user as feedback. BS Compared to the estimated component MI of motor imagery due to physical movement, MS Therefore, exercise accompanied by physical movement of the user US is encouraged, and it is possible to improve the reproducibility of brain activity patterns in exercise imagery with physical movement.

[0065] On the other hand, suppose that it is determined in step S22 that the estimation accuracy of motor imagery based on brain activity has converged. In this case, the presentation information adjustment processor 16 proceeds to step S25 and sets a non-exercise recommendation phase in which physical movement is not recommended. The presentation information adjustment processor 16 then sets the estimation step α to "0" in step S26, and then determines in step S27 whether the estimation accuracy of motor imagery based on brain activity has converged. Specifically, it determines whether the error value of the motor imagery estimation parameter calculated by the parameter update processor 13 has decreased below a threshold value over a predetermined estimation step.

[0066] If the result of the above determination is that the estimation accuracy has not converged, the presentation information adjustment processing unit 16 adjusts the presentation information to be presented to the user US based on the motor imagery estimation result MI based on brain activity in step S28. BS The results of the exercise image assessment based on muscle activity (MI) MS More specifically, in order to gradually reduce the movements of the body parts, the component of the motor imagery estimation result due to muscle activity is reduced with each estimation step α, by subtracting abs(MI BS -α*MI MS ) Presentation information is generated.

[0067] The generated presentation information is output to the display device DP via the input / output I / F unit 5 under the control of the presentation information output processing unit 17, and is displayed.

[0068] The presentation information adjustment processing unit 16 then sets the estimation step to α = α + β in step S29, and then returns to step S27 to determine whether the estimation accuracy of the motor imagery based on brain activity has converged. Thereafter, the presentation information adjustment processing unit 16 similarly repeats the processes in steps S27 to S29 until the estimation accuracy of the motor imagery based on brain activity has converged.

[0069] As described above, in the non-exercise phase, if muscle activity is still occurring, the presented information is adjusted in a punitive manner to gradually stop the activity of the body part. As a result, the user US can reproducibly recall in his or her mind the exercise image corresponding to that body part without moving the body part.

[0070] In step S26, the control unit 1 of the brain activity training device CS assumes that the error value indicating the estimation accuracy of motor imagery based on brain activity has continuously decreased to a certain value or less for a predetermined estimation period. If this occurs, the control unit 1 considers that the estimation accuracy of motor imagery based on brain activity has converged, and determines in step S18 whether an instruction to end training has been input. If training has not ended, the control unit 1 returns to step S11 and continues to execute the series of training processes in steps S11 to S18, and ends the training process when training has ended.

[0071] As described above, in one embodiment, in the period immediately after the start of training, the exercise recommendation phase recommends physical movements, and the training process is repeatedly executed so that the user US performs physical movements while evoking corresponding exercise images in the user's brain. This can encourage the user US to perform exercises involving physical movements.

[0072] In addition, in the exercise recommendation phase, the presented information is adjusted to gradually reduce physical movements, so that the user US can gradually imagine exercise images in their mind without relying on physical movements.

[0073] Furthermore, when the estimation accuracy of the motor imagery based on brain activity converges in the recommended exercise phase, a non-recommended exercise phase is set. If muscle activity occurs in this non-recommended exercise phase, the motor imagery estimation result MI based on brain activity is calculated.BS The calculation result of motor imagery based on muscle activity is MI. MS The presentation information generated by subtracting the value of ...

[0074] Other Embodiments (1) In one embodiment, an electromyographic sensor is used to measure physical activity. However, the present invention is not limited to this. For example, an acceleration sensor may be attached to each joint of the body, and a motion sensor may acquire three-dimensional position information and velocity information of each joint, and the acquired information may be used as data representing physical activity.

[0075] (2) The process of estimating motor imagery based on brain activity does not necessarily have to use a machine learning model, but may be realized by a determination circuit using a threshold value, etc. In addition, when presenting information on motor imagery to the user US in one embodiment, the information is visually displayed on the display device DP, but the information on motor imagery may be converted into an audio message and the audio message may be output as a loudspeaker.

[0076] (3) In the embodiment, the brain activity training device CS is configured as a personal computer. However, it may be configured as a server computer located on the Web or the cloud. In this case, it is preferable that the trained BCI command estimation model is downloaded from the server computer to the personal computer used by each user, so that commands can be input using the BCI thereafter.

[0077] (4) In addition, the functional configuration of the brain activity training device, the processing procedures and processing contents of the training control, the method of determining the progress of the training, the contents of the presented information, etc. can be modified in various ways within the scope of the gist of this invention.

[0078] Although the embodiments of the present invention have been described in detail above, the above description is merely an example of the present invention in every respect. It goes without saying that various improvements and modifications can be made without departing from the scope of the present invention. In other words, when implementing the present invention, specific configurations according to the embodiments may be appropriately adopted.

[0079] In short, this invention is not limited to the above-described embodiments, and in the implementation stage, the components can be modified and embodied without departing from the spirit of the invention. Furthermore, various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiments. For example, some components may be omitted from all the components shown in the embodiments. Furthermore, components from different embodiments may be appropriately combined.

[0080] CS...brain activity training device BS...brain wave sensor DP...display device IN...input device 1...control unit 2...program storage unit 3...data storage unit 4...sensor I / F unit 5...input / output I / F unit 6...bus 11...brain activity data acquisition processing unit 12...brain activity motor imagery estimation processing unit 13...parameter update processing unit 14...muscle activity data acquisition processing unit 15...muscle activity motor imagery estimation processing unit 16...presentation information adjustment processing unit 17...presentation information output processing unit 31...brain activity data storage unit 32...muscle activity data storage unit 33...motor imagery storage unit

Claims

1. A brain activity training device comprising: a first processing unit that generates presentation information representing an exercise image associated with a user's body part and presents the generated presentation information to the user; a second processing unit that acquires brain activity data representing the user's brain activity during the presentation period of the presentation information; a third processing unit that acquires physical movement data representing the movement of the user's body part during the presentation period of the presentation information; a fourth processing unit that obtains information representing the user's training progress; and a fifth processing unit that adjusts the content of the presentation information based on the information representing the progress from first content that causes the user to elicit the exercise image in the user's brain while moving the body part to second content that causes the user to elicit the exercise image in the user's brain without moving the body part.

2. The brain activity training device described in claim 1, wherein the fourth processing unit calculates the estimated accuracy of the motor image based on the error between the motor image presented by the presentation information and the user's estimated motor image estimated based on the brain activity data, and uses this estimated accuracy as information representing the degree of progress.

3. The brain activity training device described in claim 2, wherein the fifth processing unit adjusts the content of the presented information to gradually change from the first content to the second content based on the estimated accuracy of the motor image.

4. The brain activity training device of claim 2, wherein the fifth processing unit has: a processing unit that sets a first phase recommending that the user move the body part during a first period from the start of training, and sets the content of the presented information to the first content; and a processing unit that sets a second phase recommending that the user not move the body part during a second period after the estimation accuracy of the motor image has converged to less than a predetermined threshold during the first phase, and sets the content of the presented information to the second content.

5. The brain activity training device described in claim 4, wherein the fifth processing unit generates the first content in the first phase by adding a value representing an element that evokes the movement image in the user's brain to a value obtained by decreasing an element that causes the user to move the body part by a fixed value at regular intervals.

6. The brain activity training device described in claim 4, wherein the fifth processing unit generates the second content in the second phase by subtracting a value obtained by increasing an element that causes the user to move the body part by a fixed value at regular intervals from a value representing an element that evokes the movement image in the user's brain.

7. A brain activity training method executed by an information processing device, comprising the steps of: generating presentation information representing a movement image associated with a body part of a user, and presenting the generated presentation information to the user; acquiring brain activity data representing the brain activity of the user during the presentation period of the presentation information; acquiring physical movement data representing the movement of the body part of the user during the presentation period of the presentation information; obtaining information representing the progress of the user's training; and adjusting, based on the information representing the progress, the content of the presentation information to change from first content that causes the user to imagine the movement image in the user's brain while moving the body part, to second content that causes the user to imagine the movement image in the user's brain without moving the body part.

8. A program that causes a processor provided in a brain activity training device to execute at least one of the processes executed by the first to fifth processing units provided in the brain activity training device according to any one of claims 1 to 6.

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