KR102602146B1 - Method for adjusting dynamic environment in game based on brain wave data - Google Patents

Abstract

Embodiments provide a method for dynamically adjusting the environment in a game based on brain wave data. A method for adjusting a dynamic environment in a game based on brain wave data according to an embodiment of the present invention includes the steps of outputting game content including at least one user interface through an output unit of a content providing device; Generating brain wave data by detecting the user's brain waves using a wearable module connected to the content providing device; Generating alpha wave data by applying a band-pass filter to the brain wave data, by a control unit; It may include adjusting the difficulty of the game content by the control unit based on the alpha wave data.

Description

Method for adjusting dynamic environment in game based on brain wave data {METHOD FOR ADJUSTING DYNAMIC ENVIRONMENT IN GAME BASED ON BRAIN WAVE DATA}

Embodiments of the present invention relate to a method for adjusting the dynamic environment in a game based on brain wave data. More specifically, the game difficulty is adjusted based on brain wave data obtained by sensing the user's brain waves in real time, and a virtual game suitable for the user is provided. It's about how to implement the environment.

Electroencephalogram refers to the electrical flow that occurs when signals are transmitted between cranial nerves. By measuring these brain waves, the current brain activity state of a person can be measured, and the user's cognitive ability can be quantitatively calculated based on the brain wave measurement results.

Existing games and digital content that rely on physical input have a passive approach that only responds to the user's direct input. In this existing game environment, users must set the difficulty level themselves, and it is impossible to adjust the difficulty level optimally for each user playing the game. Additionally, qualitative difficulty descriptions (easy, normal, difficult, etc.) are abstract concepts, so it is difficult for users playing the game to predict how easy the game will feel to them.

Embodiments provide a method of adjusting difficulty in a game based on brain waves by sensing the user's brain waves in real time to solve the above-mentioned problems.

The technical challenges to be achieved in the embodiments are not limited to the matters mentioned above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. You can.

A method for adjusting a dynamic environment in a game based on brain wave data according to an embodiment of the present invention includes the steps of outputting game content including at least one user interface through an output unit of a content providing device; Generating brain wave data by detecting the user's brain waves using a wearable module connected to the content providing device; Generating alpha wave data by applying a band-pass filter to the brain wave data, by a control unit; It may include adjusting the difficulty of the game content by the control unit based on the alpha wave data.

calculating, by the control unit, the user's interest level based on the alpha wave data; The method may further include outputting, through an output unit of the content providing device, an interest level gauge that visually indicates the level of interest.

The alpha wave data is generated based on the intensity value of the alpha wave extracted by applying a bandpass filter to the EEG data and the rate of change of the intensity value of the alpha wave, and is generated by the control unit based on the intensity value of the alpha wave. determining the user's sense of security; The method may further include determining, by the control unit, at least one of a stamina recovery speed of the character of the game content, the brightness of the character's torch, and the puzzle solving speed of the character based on the sense of stability.

The alpha wave data is generated based on the intensity value of the alpha wave extracted by applying a bandpass filter to the EEG data and the rate of change of the intensity value of the alpha wave, and is generated by the control unit based on the intensity value of the alpha wave. determining the difficulty level of the game content; It may further include determining, by the control unit, at least one of a monster generation frequency and a help item generation frequency of the game content based on the difficulty level.

The method for adjusting the dynamic environment in a game based on brain wave data according to embodiments of the present invention can overcome the limitations of existing games and digital content that rely on physical input and provide active game content optimized for users.

The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.
Figure 1 is a diagram schematically showing a user-customized cognitive ability training system based on brain wave data according to an embodiment of the present invention.
Figure 2 is a block diagram showing the configuration of a content providing device according to an embodiment of the present invention.
FIG. 3 is a block diagram for explaining the control unit of the content providing device of FIG. 2.
Figure 4 is a diagram for explaining a wearable module according to an embodiment of the present invention.
Figure 5 is a block diagram for explaining the structure of a server according to an embodiment of the present invention.
Figure 6 is a flowchart illustrating a method for adjusting the dynamic environment in a game based on brain wave data according to an embodiment of the present invention.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

Figure 1 is a diagram schematically showing a user-customized cognitive ability training system based on brain wave data according to an embodiment of the present invention. Figure 2 is a block diagram showing the configuration of a content providing device 100 according to an embodiment of the present invention. FIG. 3 is a block diagram for explaining the control unit 110 of the content providing device 100.

Referring to FIG. 1, in a network environment, the content providing device 100 can communicate with the server 300 through the network. The server 300 can manage the content providing device 100 and user terminals (not shown) connected to the server 300. The server 300 receives brain wave data obtained by measuring the user's brain waves, biometric information generated by analyzing the brain wave data, and selection information and feedback data according to the results of comparing a plurality of biometric information from the content providing device 100. You can save this.

Referring to FIG. 2, the content providing device 100 according to an embodiment of the present invention may include a control unit 110, an output unit 120, a mounting unit 130, and a support unit 140.

The output unit 120 can output multimedia content. The control unit 110 may select one of a plurality of multimedia contents that can be output from the processor 111 based on the user's input. For example, the control unit 110 may select the multimedia content corresponding to the user's touch input from among icons for each of a plurality of multimedia contents output on the screen of the output unit 120.

The output unit 120 may include a display module. The display module may include, for example, a display, a holographic device, or a projector and control circuitry for controlling the device. Alternatively, the display module may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch. In this case, the display module may function as an input device that generates an input signal through user manipulation.

The control unit 110 may control the overall operation of the content providing device 100. Referring to FIG. 4 , the control unit 110 of the content providing device 100 may include a processor 111, a memory 112, a communication module 113, and an antenna module 114. Alternatively, at least one of these components may be omitted or one or more other components may be added to the control unit 110. In other embodiments, some of these components may be integrated into one component.

The processor 111 of the control unit 110 may determine at least one of the user's brain activity, concentration, and emotional state of the user based on brain wave data received from the wearable module 200, which will be described later. The processor 111 may generate biometric information based on at least one of the user's brain activity, concentration, and emotional state of the user.

Here, the user's emotional state may be classified into, for example, a positive emotional state and a negative emotional state. In another example, the user's emotional state may be classified into multiple states. The plurality of states may include at least one of a first state feeling happy, a second state feeling annoyed, a third state feeling bored, and a fourth state feeling angry.

The processor 111 may analyze biometric information, select multimedia content from the memory 112 based on the biometric information, and output it to the output unit 120.

The processor 111 may compare biometric information generated at a plurality of viewpoints, generate feedback data based on the comparison result, and store it in the memory 112. Each of the plurality of viewpoints may mean a viewpoint at which different multimedia content is being output (played) from the output unit 120. For example, the point in time when first content is being output may be defined as the first point in time, and the point in time when second content different from the first content is being output may be defined as the second point in time.

Here, the feedback data means that the processor 111 compares the newly acquired user's biometric information with the existing biometric information at the time the multimedia content selected based on the user's biometric information is displayed through the output unit 120. This may be data that reflects an evaluation of how much activity and concentration have increased. For example, if the processor 111 displays new content different from the multimedia content currently displayed on the output unit 120 based on the user's first biometric information, at this time, the user's newly acquired second biometric information As a result of analyzing, if the user's brain activity or concentration increases compared to the first biometric information, the processor 111 sends feedback data including a score proportional to the increase (for example, a score with a positive value). can be created. Conversely, if the user's brain activity or concentration has actually decreased, the processor 111 may generate feedback data including a score with a negative value.

The processor 111 stores commands or data received from other components (e.g., wearable module 200, server 300) in volatile memory, processes the commands or data stored in the volatile memory, and produces result data. can be stored in non-volatile memory. In one embodiment, the processor 111 is a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit (NPU) It may include a processing unit) and a sensor hub processor. For example, when the content providing device 100 includes a main processor and a auxiliary processor, the auxiliary processor may be set to use less power than the main processor or be specialized for a designated function. The auxiliary processor may be implemented separately from the main processor or as part of it.

The auxiliary processor is, for example, at least one of the components of the content providing device 100, on behalf of the main processor while the main processor is in an inactive state, or together with the main processor while the main processor is in an active state. At least some of the functions or states related to the components can be controlled. In one embodiment, a coprocessor may be implemented as part of another functionally related component.

In one embodiment, when the co-processor is a neural network processing device, the co-processor may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the content providing device 100 itself, where the artificial intelligence model is performed, or may be performed through a separate server.

Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The processor 111 performs supervised learning on the multi-layer neural network using a multi-layer neural network including an input layer, one or more hidden layers, and an output layer, and biometric information and training data with the feedback data as input and the evaluation score as output. It may include a learning engine that calculates optimized hyperparameters based on learning data.

Here, the input layer can receive biometric information and feedback data. One or more hidden layers can output the output values by multiplying each output value of the input layer by a weight and adding a bias. The output layer can receive the output value of the hidden layer and output an output vector using an activation function.

Additionally, a multi-layer neural network may include a loss function layer connected to the output layer. The output vector can be input to the loss function layer. The loss function layer can output loss values using a loss function that compares the output vector and the correct answer vector for each feedback data. Parameters of a multi-layer neural network can be learned in a direction that reduces the loss value.

The multi-layer neural network structure and the operation of the learning engine of the processor 111 may be the same or similar to the structure of the server processor and the learning operation of the server processor, which will be described later with reference to FIGS. 5 and 6.

The memory 112 of the control unit 110 can store a plurality of multimedia contents. The content providing device 100 may select one of a plurality of multimedia contents stored in the memory 112 based on a user's input and output the selected content through the output unit 120 .

Additionally, the memory 112 may store a first threshold regarding the user's brain activity and a second threshold regarding the user's brain concentration. Here, the first threshold may mean any one value within the distribution range of the user's brain activity. For example, when the user's brain activity is expressed as a value in the range of 0 to 100, the first threshold may be any one of the values in the range of 0 to 100, for example, 50. However, this is an example, and embodiments of the present invention are not limited thereto. Likewise, the second threshold may mean any one value within the concentration distribution range of the user's brain, where the minimum and maximum values of the 'range' may vary.

The processor 111 may change the first and/or second threshold values based on user information, biometric information, feedback data, etc. and store them in the memory 112. For example, the processor 111 analyzes the biometric information, and if the user's brain activity among the biometric information is below the first threshold, the processor 111 stores content different from the multimedia content currently being output from the output unit 120 in the memory 112. ) can be selected and output to the output unit 120. Alternatively, when the concentration of the user's brain among the biometric information falls below the second threshold, the processor 111 selects content different from the multimedia content currently being output from the output unit 120 from the memory 112 and outputs the content to the output unit 120. It can be output at (120).

Additionally, the memory 112 may store trigger information regarding the user's emotional state. Here, the trigger information regarding the user's emotional state may be information regarding the threshold value of biometric information required to change the user's emotional state from one state to another.

In addition, the memory 112 may store various data used by at least one component of the content providing device 100. Data may include, for example, input data or output data for software and instructions related thereto. Memory 112 may include volatile memory or non-volatile memory.

The communication module 113 may support establishing a wired or wireless communication channel between the content providing device 100 and an external electronic device, and performing communication through the established communication channel. The communication module 113 operates independently of the processor 111 and may include one or more communication processors that support wired communication or wireless communication. In one embodiment, the communication module 113 may include a wireless communication module or a wired communication module. Among these communication modules, the corresponding communication module can communicate with the content providing device 100 through the network 400. These various types of communication modules may be integrated into one component or may be implemented as a plurality of separate components.

The wireless communication module may support 5G networks after the 4G network and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module may support high frequency bands, for example, to achieve high data rates. Wireless communication modules use various technologies to secure performance in high frequency bands, such as beamforming, massive MIMO (multiple-input and multiple-output), and full-dimensional multiple input/output (FD). -It can support technologies such as full dimensional MIMO (MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module can support various requirements specified in the content providing device 100, external electronic devices, or network systems. In one embodiment, the wireless communication module may support peak data rate for realizing eMBB, loss coverage for realizing mmTC, or U-plane latency for realizing URLLC.

The antenna module 114 may transmit signals or power to or receive signals or power from an external electronic device. In one embodiment, the antenna module 114 may include an antenna that includes a radiator made of a conductor or a conductive pattern formed on a substrate. In one embodiment, antenna module 114 may include multiple antennas. In this case, at least one antenna suitable for a communication method used in a communication network such as the network 400 may be selected from the plurality of antennas by, for example, the communication module 113. Signals or power may be transmitted or received between the communication module 113 and an external electronic device through the selected at least one antenna.

In one embodiment, commands or data may be transmitted or received between content providing devices 100 connected to network 400. The content providing device 100 may be the same or a different type of device from the content providing device 100. In one embodiment, all or part of the operations performed on the content providing device 100 may be performed on the content providing device 100.

For example, when the content providing device 100 needs to perform a certain function or service automatically or in response to a request from a user or another device, the content providing device 100 executes the function or service on its own. Instead of or additionally, one or more external electronic devices may be requested to perform at least a portion of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the content providing device 100. The content providing device 100 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The content providing device 100 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing.

The mounting portion 130 of the content providing device 100 may be formed outside the content providing device 100 to accommodate the wearing module 200 when the wearing module 200 is not in use. The shape of the mounting portion 130 may vary. In one embodiment, the mounting unit 130 may have a cylindrical shape attached to the bezel portion of the output unit 120 of the content providing device 100, as shown in FIG. 2 .

The support unit 140 may support the content providing device 100 so that the content providing device 100 can be stably fixed to the ground while the user is enjoying multimedia content or a game using the content providing device 100.

Figure 4 is a diagram for explaining the wearable module 200 according to an embodiment of the present invention.

Referring to FIG. 4, the wearable module 200 may detect the user's brain waves by using only one or part of a plurality of sensors, or by using all of the plurality of sensors. In this case, each of the plurality of sensors may be attached close to each part of the user's brain (eg, frontal lobe, parietal lobe, temporal lobe, occipital lobe, etc.). Accordingly, each of the plurality of sensors can individually detect brain waves occurring in a specific part of the brain.

The wearing module 200 may be formed in the shape of a headband so that at least a portion of it contacts the user's head.

The wearable module 200 may include a plurality of sensors, a communication unit 220, and a cable 230. The plurality of sensors may include a first sensor 210_1 and a second sensor 210_2. The number of sensors included in the plurality of sensors may vary. Although only two sensors are shown in FIG. 4, embodiments of the present invention are not limited thereto. Each sensor 210_1 and 210_2 may include a metal measurement electrode for detecting electroencephalograms. Each sensor 210_1 and 210_2 can individually detect brain waves flowing from the user's brain. When the user wears the wearable module 200 on the head, the measuring electrode may contact the user's scalp or facial skin. Each sensor (210_1, 210_2) can individually detect brain waves flowing from different parts of the brain. For example, the first sensor 210_1 may detect the first brain wave flowing from the frontal lobe, and the second sensor 210_2 may detect the second brain wave flowing from the occipital lobe. Alternatively, the first sensor 210_1 may detect the first brain wave flowing from the left brain, and the second sensor 210_2 may detect the second brain wave flowing from the right brain.

The communication unit 220 transmits brain wave data acquired by a plurality of sensors of the wearable module 200 to an external electronic device (e.g., the content providing device 100) or receives signals or data from an external electronic device. You can. The communication unit 220 may establish a wired or wireless communication channel between an external electronic device, such as the content providing device 100, and the wearable module 200, and support communication through the established communication channel.

The communication unit 220 may include a wireless communication module or a wired communication module. Among these communication modules, the corresponding communication module can communicate with the content providing device 100 through a network. These various types of communication modules may be integrated into one component or may be implemented as a plurality of separate components.

The cable 230 may be connected to the content providing device 100. The wearable module 200 can receive electrical energy from the content providing device 100 through the cable 230. Additionally, the cable 230 may include a communication cable capable of transmitting data when the wearable module 200 exchanges data with the content providing device 100 using a wired communication method. That is, the wearable module 200 can transmit a signal or power to an external electronic device or receive it from the outside through the cable 230. The content providing device 100 according to an embodiment of the present invention includes a cable 230 and is provided integrally with the wearable module 200, thereby increasing device management convenience and transmitting data using a wired communication method to provide stable data. Processing can be provided.

Figure 5 is a block diagram for explaining the structure of a server according to an embodiment of the present invention.

Referring to FIG. 5 , the server 300 may include a server processor 310, a server memory 320, and a user management unit 330. The server processor 310 may receive brainwave data, biometric information, and feedback data stored in the memory 112 of the content providing device 100. The server memory 320 may store received brainwave data, biometric information, and feedback data.

The server processor 310 may generate a multimedia content selection signal output from the content providing device 100 using a learning model.

To this end, the server processor 310 may include a multi-layer neural network 311 and a learning engine 312.

The learning engine 312 may perform supervised learning in advance on the multi-layer neural network 311 using a plurality of learning data. A multilayer neural network is a prediction model implemented in software or hardware that imitates the computational ability of a biological system using a large number of artificial neurons (or nodes).

The learning engine 312 uses biometric information, feedback data, and selection signals as input values and the evaluation score as output values so that the multi-layer neural network 311 can accurately generate a content selection signal based on the learning data. The multi-layer neural network 311 can be supervised using data. Here, the evaluation score may be a score generated based on the correlation between biometric information and feedback data. The evaluation score is the correlation between the concentration and activity values of the user's brain included in the first biometric information and the feedback data obtained by comparing the second biometric information according to the multimedia content selected by the processor 111 based on the first biometric information. It may be a score generated based on a relationship. A higher evaluation score may mean that the selection result of the processor 111 is more positive.

At this time, supervised learning refers to learning to find an output value according to a given input value by using data with input values and corresponding output values as learning data, and refers to learning that takes place when the correct answer is known. The set of input and output values given in supervised learning is called training data.

The multi-layer neural network 311 may include an input layer, one or more hidden layers, and an output layer.

In one embodiment, the multilayer neural network 311 receives an input value, multiplies the weight for each of the input layer and the output value of the input layer having nodes corresponding to the number of components of the first feature vector, and adds a bias. Multiply the weight for each of the first hidden layer to be output and the output value of the first hidden layer, add the bias, multiply the weight for each of the output values of the second hidden layer and the second hidden layer to be output, and use the result as an activation function It may include an output layer that outputs using .

For example, the activation function may be a Softmax function, but embodiments of the present invention are not limited thereto, and the activation function may be various other functions such as the LeRU function. Weights and biases can be continuously updated through supervised learning.

Specifically, the output vector can be input to a loss function layer connected to the output layer. The loss function layer can output loss values using a loss function that compares the output vector with the correct answer vector for each training data. Parameters of the multi-layer neural network 311 can be supervised learning in a direction that reduces the loss value.

For example, the loss function layer can calculate the loss value according to [Equation 1]. In [Equation 1], N is the number of a plurality of learning data, n is a natural number identifying the learning data, k is a natural number identifying the value of the nth learning data, and nk is the kth value of the nth learning data. , t may mean the correct answer data, y may mean the output vector, and E may mean the loss value.

Alternatively, the loss function layer can calculate the loss value according to [Equation 2]. In [Equation 2], n is the number of training data for each class, y and j are identifiers representing the classes, C is a constant value, M is the number of classes, x_y is the probability that the training data belongs to class y, and x_j is the learning data. The probability value that the data belongs to class j, L may mean the loss value.

The server processor 310 may be manufactured in the form of at least one hardware chip and mounted on a device. For example, the server processor 310 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or an existing general-purpose processor (e.g., CPU or application processor) or a graphics-specific processor (e.g., It may be manufactured as part of a GPU and mounted on the various devices mentioned above.

In this case, the server processor 310 may be mounted on one device or may be mounted on a separate device. Meanwhile, the learning model of the server processor 310 may be implemented as a software module. If the learning model is implemented as a software module (or a program module containing instructions), the software module may be stored on non-transitory computer readable media. there is. Additionally, in this case, at least one software module may be provided by an operating system (OS) or a predetermined application. Alternatively, part of at least one software module may be provided by an operating system (OS), and the remaining part may be provided by a predetermined application.

The user management unit 330 may receive and store user information from a user terminal on which the game application program provided by the server 300 is installed, and manage the user terminal. The user management unit 330 may perform a membership registration process for a game application, store and manage various information of users who have registered as members, and provide functions related to the service. The user management unit 330 may be linked with an authentication system and payment system for user authentication of the game application service or purchase payment related to the service.

In one embodiment, the user management unit 330 may extract and store unique identification information for the user terminal based on user information provided from the user terminal. The unique identification information may be, for example, an ID (Identification) of each user terminal, or a unique IP (Internet Protocol) address.

The server 300 may have the same hardware configuration as a typical web server or WAP server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Visual Basic, and Visual C, and may include program modules that perform various functions. In addition, the server 300 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 300 includes a series of application programs running on the server 300 and, in some cases, various databases (DBs) built internally or externally, hereinafter referred to as "databases". It should be understood as a broad concept that includes “DB”).

FIG. 6 is a flowchart illustrating a method of providing user-customized cognitive ability training according to an embodiment of the present invention. FIG. 6 is a flowchart illustrating a method of adjusting a dynamic environment in a game based on brain wave data according to an embodiment of the present invention. am.

Referring to FIG. 6, first, in step S610, the output unit 120 of the content providing device 100 outputs game content including at least one user interface.

Next, in step S620, the wearable module 200 detects the user's brain waves in response to the output game content and generates brain wave data.

When the user wears the wearable module 200, power is supplied to the wearable module 200, and the content providing device 100 and the wearable module 200 are connected. The wearable module 200 may detect the user's brain waves using only one or part of the plurality of sensors, or all of the plurality of sensors. When the wearable module 200 detects brain waves using a plurality of sensors, each sensor can individually detect brain waves flowing from the user's brain.

Here, the brain wave response data can be expressed as a waveform whose amplitude changes over time. Additionally, the brain wave response data may include at least one characteristic wave data. Characteristic wave data may refer to data for each individual brain wave (delta wave, theta wave, alpha wave, beta wave, gamma wave, etc.) that can be obtained by measuring the user's brain wave.

Brain waves can be classified into multiple individual brain waves according to frequency and amplitude. Brain waves are electrical waves that combine the activities of tens of thousands of brain cells, and are divided into brain waves generated by external stimulation and brain waves generated by brain activity without external stimulation. In this case, brain waves generated by brain activity without external stimulation can be classified into delta waves, theta waves, alpha waves, beta waves, and gamma waves according to their natural frequencies.

Delta waves vibrate 1 to 4 times per second at a frequency of 1 to 4 Hz, making them the least likely to vibrate among brain waves. Delta waves appear during very deep sleep, deep meditation, or unconsciousness.

Theta waves oscillate 4 to 7 times per second with a frequency of 4 to 7 Hz. It usually appears at moments of creative thinking, emotional stability, or falling asleep. It appears more often in children than adults and also occurs during meditation.

Alpha waves have a frequency of 8 to 12 Hz and are brain waves that appear when the mind is in a comfortable and stable state. In particular, stable alpha waves appear when you are in a relaxed state with your eyes closed. Alpha waves are assumed to be connected to the visual area of the brain because they become weaker when the eyes are open and stronger when they are closed.

Beta waves have a frequency of 18 to 25 Hz and are brain waves that generally appear while thinking and doing activities with the eyes open. In particular, it often appears in anxious or tense states or when processing complex calculations. It also appears when you have a lot of thoughts or are worried.

Gamma waves have a frequency of 30 to 100 Hz and appear well when one is anxious or has deep concentration.

Hereinafter, in this specification, the characteristic wave may mean any one of the above-mentioned delta wave, theta wave, alpha wave, beta wave, and gamma wave.

The wearable module 200 may detect a changed brain wave when the user views the image displayed on the output unit 120 and generate brain wave response data based on this. Characteristic wave data included in the brain wave response data may be generated based on the intensity value and rate of change of the intensity value of each individual brain wave (delta wave, theta wave, alpha wave, beta wave, and gamma wave).

In step S630, the control unit 110 of the content providing device 100 applies a band-pass filter to the brain wave data to generate alpha wave data.

A bandpass filter is a filter that only passes signals between specific frequencies. A bandpass filter can be implemented using an RLC circuit.

The processor 111 of the control unit 110 may generate data about alpha waves based on brain wave data. The processor 111 may generate alpha wave data based on the intensity value of the alpha wave and the rate of change of the intensity value. That is, alpha wave data can be generated based on the intensity value of the alpha wave and the rate of change of the intensity value.

In step S640, the control unit 110 of the content providing device 100 adjusts the difficulty level of the game content based on alpha wave data.

In step S650, the control unit 110 of the content providing device 100 calculates the user's interest in the game content based on alpha wave data.

In one embodiment, the processor 111 of the control unit 110 may calculate the user's interest in game content using [Equation 3]. In [Equation 3], IA may mean the user's interest, D may mean the difficulty of the game content, and Palpha may mean the alpha wave intensity value.

In step S660, an interest gauge that visually indicates the calculated interest is output by the output unit 120 of the content providing device 100.

In step S670, the control unit 110 of the content providing device 100 determines the user's sense of stability based on the intensity value of the alpha wave.

In step S680, at least one of the physical strength recovery speed of the character of the game content, the brightness of the character's torch, and the puzzle solving speed of the character based on the user's sense of stability determined by the control unit 110 of the content providing device 100. Decide.

As described above, the method of providing user-tailored cognitive training content of the present invention measures the user's brain waves in real time, compares and analyzes the brain wave data before and after the user performs the cognitive training content, and provides cognitive training content suitable for the user. can be provided.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (4)

Outputting game content including at least one user interface through an output unit of the content providing device;
Generating brain wave data by detecting the user's brain waves using a wearable module connected to the content providing device;
Generating alpha wave data by applying a band-pass filter to the brain wave data, by a control unit;
A step of adjusting the difficulty level of the game content based on the alpha wave data, by the control unit,
calculating, by the control unit, the user's interest level based on the alpha wave data;
It further includes outputting, by an output unit of the content providing device, an interest level gauge that visually indicates the level of interest,
[Equation]

Further comprising calculating the level of interest by the control unit using the [mathematical formula], where IA is the user's interest level, D is the difficulty level of the game content, and Palpha is the alpha wave. meaning the intensity value,
A method for dynamic environment adjustment in games based on brain wave data. delete According to paragraph 1,
The alpha wave data is generated based on the intensity value of the alpha wave extracted by applying a bandpass filter to the EEG data and the rate of change of the intensity value of the alpha wave,
determining, by the control unit, the user's sense of stability based on the intensity value of the alpha wave;
Determining, by the control unit, at least one of a physical strength recovery speed of a character of the game content, a torch brightness of the character, and a puzzle solving speed of the character based on the sense of stability,
A method for dynamic environment adjustment in games based on brain wave data. According to paragraph 1,
The alpha wave data is generated based on the intensity value of the alpha wave extracted by applying a bandpass filter to the EEG data and the rate of change of the intensity value of the alpha wave,
determining, by the control unit, a difficulty level of the game content based on the intensity value of the alpha wave;
Determining, by the control unit, at least one of a monster generation frequency and a help item generation frequency of the game content based on the difficulty level,
A method for dynamic environment adjustment in games based on brain wave data.

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