Therapy provision method, device and computer program through electroencephalogram data analysis

Abstract

A method, device, and computer program for providing therapy through electroencephalogram (EEG) data analysis according to various embodiments of the present invention is provided. The method for providing therapy through electroencephalogram (EEG) data analysis according to various embodiments of the present invention is a method performed by a computing device, and includes the steps of acquiring electroencephalogram data of a user, determining a therapy protocol based on the acquired electroencephalogram data, and providing therapy to the user using the determined therapy protocol.

Description

[Technical Field]

[0001] Various embodiments of the present invention provide a method, apparatus, and computer program for providing therapy through electroencephalogram data analysis.

[0002] The present invention relates to a method, device and computer program for providing therapy through analysis of electroencephalogram data, and more particularly to a method, device and computer program for providing optimized therapy through analysis of electroencephalogram data of a user. [Background technology]

[0003] Although pharmacological treatments are available for various brain disorders, many believe that pharmacological treatment alone is insufficient. In addition to pharmacological treatments, various neurostimulation methods are being researched.

[0004] Traditional neurostimulation methods include transcranial magnetic stimulation (tMS), transcranial alternating current stimulation (tACS), transcranial direct current stimulation (tDCS), transcranial random noise stimulation (tRNS), and photobiomodulation.

[0005] Here, the photobiomodulation method is based on the principle of using light of mainly infrared wavelength to induce photochemical changes within the mitochondrial cellular structure.

[0006] More specifically, the biochemical mechanisms of photobiomodulation interactions can be categorized into direct and indirect effects. Direct effects include increased activity of ion channels such as Na+ / K+ATPase, while indirect effects include modulation of important second messengers such as calcium, cyclic adenosine monophosphate (cAMP), and reactive oxygen species (ROS), all of which lead to diverse biological cascades. These biological cascades not only result in effects such as homeostatic and protective, antioxidant, and proliferative gene factor activation, but also in hierarchical responses such as insufficient cerebral blood flow, which can lead to neurocognitive disorders.

[0007] However, there has been no prior art technology that can appropriately present such an adjustment method according to the user's situation. Summary of the Invention [Problem to be solved by the invention]

[0008] The problem to be solved by the present invention is to provide a method, device, and computer program for providing therapy through EEG data analysis, which can provide optimal therapy, i.e., therapy that induces EEG suitable for the user, by analyzing the user's EEG data to determine a therapy protocol and providing therapy to the user based on the protocol, taking into account the user's current condition and any brain disorders the user has.

[0009] The problems to be solved by the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0010] A method for providing therapy through electroencephalogram data analysis according to one embodiment of the present invention for solving the above-mentioned problems is a method performed by a computing device, and may include steps of acquiring electroencephalogram data of a user, determining a therapy protocol based on the acquired electroencephalogram data, and providing therapy to the user using the determined therapy protocol.

[0011] In various embodiments, the step of determining the therapy protocol may include a step of analyzing the acquired electroencephalogram data to determine the need for providing therapy to the user, and if it is determined that the user needs therapy, determining a therapy protocol for the user, wherein the determined therapy protocol includes at least one of information regarding the session, treatment area, delivery time, and light wavelength of the therapy to be provided to the user.

[0012] In various embodiments, the step of determining the therapy protocol may include a step of identifying an abnormal area based on the acquired EEG data, and a step of determining a frequency and duration of therapy as a therapy protocol for the user to induce the EEG corresponding to the identified abnormal area to a preset reference value.

[0013] In various embodiments, the step of identifying the abnormal area may include a step of setting a target and a step of identifying the abnormal area by comparing a position-specific EEG value included in the acquired EEG data with a position-specific EEG reference value according to the set target.

[0014] In various embodiments, determining the therapy protocol may include analyzing the acquired electroencephalogram data and determining a therapeutic area of ​​therapy as a therapy protocol for the user.

[0015] In various embodiments, the step of determining the treatment area may include, when the acquired electroencephalogram data is analyzed and a specific brain disease is diagnosed for the user, determining the area where the specific brain disease is diagnosed as a first treatment area and determining the area related to the treatment of the diagnosed specific brain disease as a second treatment area.

[0016] In various embodiments, the step of determining the therapy protocol may include a step of determining a therapy protocol such that, if an abnormality area for the user is identified by analyzing the acquired electroencephalogram data, therapy is provided only to the identified abnormality area.

[0017] In various embodiments, the step of determining the therapy protocol may include a step of individually determining a therapy protocol corresponding to each of the identified two or more abnormal areas when two or more abnormal areas are identified for the user by analyzing the acquired electroencephalogram data.

[0018] In various embodiments, the step of determining the therapy protocol may include the steps of: analyzing the acquired electroencephalogram data to identify an abnormal area for the user; calculating an index corresponding to the degree of abnormality of the identified abnormal area based on the electroencephalogram corresponding to the identified abnormal area; and determining a therapy frequency as a therapy protocol for the user based on the calculated index.

[0019] In various embodiments, the step of acquiring the EEG data includes a step of acquiring the EEG data of the user measured through a plurality of channels included in an EEG measuring device according to a pre-set measurement protocol, and the distance between each of the plurality of channels can be adjusted according to the size of the user's head.

[0020] A computing device for performing a method for providing therapy through electroencephalogram data analysis according to another embodiment of the present invention for solving the above-mentioned problems includes a processor, a network interface, a memory, and a computer program loaded into the memory and executed by the processor, the computer program including instructions for acquiring electroencephalogram data of a user, instructions for determining a therapy protocol based on the acquired electroencephalogram data, and instructions for providing therapy to the user using the determined therapy protocol.

[0021] A computer program according to yet another embodiment of the present invention for solving the above-mentioned problems may be stored on a recording medium readable by a computing device to execute a method for providing therapy through electroencephalogram data analysis, the method including the steps of acquiring electroencephalogram data of a user, determining a therapy protocol based on the acquired electroencephalogram data, and providing therapy to the user using the determined therapy protocol, in combination with a computing device.

[0022] Other specific details of the invention are included in the detailed description and drawings. [Effects of the Invention]

[0023] According to various embodiments of the present invention, by analyzing the user's brain wave data to determine a therapy protocol and providing therapy to the user based on the protocol, it is possible to provide optimal therapy, i.e., therapy that induces brain waves that are suitable for the user, taking into account the user's current condition and any brain disorders the user may have.

[0024] The effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a diagram illustrating a system for providing therapy through electroencephalogram data analysis according to an embodiment of the present invention.

[0026] [Figure 2] 10 is a hardware configuration diagram of a computing device that performs a method for providing therapy through electroencephalogram data analysis according to another embodiment of the present invention.

[0027] [Figure 3] 10 is a flowchart of a method for providing therapy through electroencephalogram data analysis according to yet another embodiment of the present invention.

[0028] [Figure 4] 1 is a diagram illustrating a mechanical configuration of a therapy providing device that provides therapy in various embodiments. [Figure 5] 1 is a diagram illustrating a mechanical configuration of a therapy providing device that provides therapy in various embodiments.

[0029] [Figure 6] 1 is a diagram illustrating an electroencephalogram (EEG) measurement and therapy delivery module included in a therapy delivery device according to various embodiments.

[0030] [Figure 7] 10 is a diagram comparing electroencephalograms (EEG) before and after providing therapy according to a method of providing therapy through electroencephalogram (EEG) data analysis in various embodiments; [Figure 8] 10 is a diagram comparing electroencephalograms (EEG) before and after providing therapy according to a method of providing therapy through electroencephalogram (EEG) data analysis in various embodiments;

[0031] [Figure 9] 1 is a diagram illustrating an example of a drive screen of an application provided by a computing device in various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0032] The advantages and features of the present invention, as well as methods for achieving them, will become more apparent from the following detailed description of the embodiments in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, and may be embodied in various different forms. However, the present invention is defined only by the scope of the claims, and the present invention is not limited to the embodiments disclosed below.

[0033] The terms used in this specification are for the purpose of describing the embodiments and are not intended to limit the present invention. In this specification, the singular includes the plural unless otherwise specified in the context. The terms "comprises" and / or "comprising" used in this specification do not exclude the presence or addition of one or more other elements in addition to the elements referenced. The same reference numerals refer to the same elements throughout this specification, and "and / or" includes each and every combination of one or more of the referenced elements. Although terms such as "first," "second," etc. are used to describe various elements, it is understood that these elements are not limited by these terms. These terms are used merely to distinguish one element from another. Therefore, it is understood that a first element referred to below may also be a second element within the technical spirit of the present invention.

[0034] Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used in the sense that they can be commonly understood by a person of ordinary skill in the art to which the present invention belongs. Furthermore, terms defined in commonly used dictionaries should not be interpreted ideally or excessively unless they are clearly and specifically defined.

[0035] The terms "module" and "module" used herein refer to software or hardware components, such as FPGAs or ASICs, that perform a certain function. However, "module" or "module" is not limited to software or hardware. A "module" or "module" may be configured to reside on an addressable storage medium or to execute on one or more processors. Thus, by way of example, a "module" or "module" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within a component or "module" or "module" may be combined into fewer components and "modules" or "modules" or further separated into additional components and "modules" or "modules."

[0036] Spatially relative terms such as "below," "beneath," "lower," "above," and "upper" may be used to easily describe the relationship of one component to another, as illustrated in the drawings. Spatially relative terms should be understood to include different orientations of components in use or operation in addition to the orientation depicted in the drawings. For example, if a component depicted in the drawings were turned over, a component described as "below" or "beneath" another component would be positioned "above" the other component. Thus, the exemplary term "below" can encompass both an orientation of below and above. Components may be oriented in other directions, and the spatially relative terms may be interpreted accordingly.

[0037] In this specification, the term "computer" refers to any type of hardware device including at least one processor, and may also encompass software configurations operating on the hardware device, depending on the embodiment. For example, the term "computer" may refer to, but is not limited to, smartphones, tablet PCs, desktops, laptops, and user clients and applications running on each device.

[0038] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0039] Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited to this, and depending on the embodiment, at least some of each step may be performed by different devices.

[0040] FIG. 1 is a diagram illustrating a system for providing therapy through electroencephalogram data analysis according to an embodiment of the present invention.

[0041] Referring to FIG. 1, a system for providing therapy through electroencephalogram data analysis according to one embodiment of the present invention may include a computing device 100, a user terminal 200, a therapy providing device 300, an external server 400, and a network 500.

[0042] Here, the system for providing therapy through EEG data analysis shown in FIG. 1 is according to one embodiment, and its components are not limited to the embodiment shown in FIG. 1, and may be added, modified, or deleted as necessary.

[0043] In one embodiment, the computing device 100 can provide therapy to a user by performing a therapy providing method through electroencephalogram data analysis. For example, the computing device 100 can be connected to the therapy providing device 300 through the network 500, collect electroencephalogram data from a user wearing the therapy providing device 300, analyze the collected electroencephalogram data to determine a therapy protocol, and transmit a control command to the therapy providing device 300 to control operation according to the determined therapy protocol, thereby causing the therapy providing device 300 to provide therapy according to the therapy protocol.

[0044] Here, the computing device 100 is described as being provided separately outside the therapy providing device 300 and controlling the operation of the therapy providing device 300 outside the therapy providing device 300, but this is merely an example and is not limited thereto, and the computing device 100 may be embodied in a form in which it is built into the therapy providing device 300 and controls the operation of the therapy providing device 300 within the therapy providing device 300.

[0045] In various embodiments, the computing device 100 may be connected to the user terminal 200 via a network 500 and may provide various information related to a method of providing therapy through electroencephalogram data analysis to the user terminal 200. For example, the computing device 100 may provide the user terminal 200 with a UI (e.g., FIG. 9) that outputs electroencephalogram data collected from the user, information about the analysis results of the electroencephalogram data, and information about the therapy derived through the electroencephalogram data analysis.

[0046] Here, the user terminal 200 may refer to any type of entity(ies) in a system having a mechanism for communicating with the computing device 100. For example, the user terminal 200 may include a personal computer (PC), a notebook computer, a mobile terminal, a smartphone, a tablet PC, a wearable device, etc., and may include any type of terminal that can connect to a wired / wireless network. The user terminal 200 may also include any computing device implemented by at least one of an agent, an application programming interface (API), and a plug-in. The user terminal 200 may also include an application source and / or a client application.

[0047] In various embodiments, the user terminal 200 can be a device that controls the operation of the therapy providing device 300 and checks the results of data processing performed through the computing device 100. For example, the user can receive and check an EEG measurement protocol from the computing device 100 through the user terminal 200, and can check whether the EEG measurement has been performed properly without errors based on the EEG measurement protocol, and can control whether or not to perform therapy based on the EEG analysis results.

[0048] Here, the network 500 may refer to a connection structure that allows information exchange between nodes such as a plurality of terminals and servers, etc. For example, the network 500 may include a local area network (LAN), a wide area network (WAN), the Internet (WWW), a wired / wireless data communication network, a telephone network, a wired / wireless television communication network, etc.

[0049] Wireless data communication networks may include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, NFC (Near-Field Communication) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc.

[0050] In one embodiment, the external server 400 may be connected to the computing device 100 through a network, and may store and manage various information and data required for the computing device 100 to perform the method for providing therapy through analysis of electroencephalogram data, or may receive, store, and manage various information and data derived from the computing device 100 performing the method for providing therapy through analysis of electroencephalogram data. For example, the external server 400 may be, but is not limited to, a storage server separately provided outside the computing device 100. Hereinafter, a hardware configuration of the computing device 100 that performs the method for providing therapy through analysis of electroencephalogram data will be described with reference to FIG. 2.

[0051] FIG. 2 is a hardware configuration diagram of a computing device that performs a method for providing therapy through electroencephalogram data analysis according to another embodiment of the present invention.

[0052] 2, in various embodiments, a computing device 100 may include one or more processors 110, a memory 120 into which a computer program 151 executed by the processor 110 is loaded, a bus 130, a communication interface 140, and storage 150 for storing the computer program 151. Only components relevant to the embodiments of the present invention are illustrated in FIG. 2. Therefore, those skilled in the art will recognize that other general-purpose components may be included in addition to the components illustrated in FIG. 2.

[0053] The processor 110 controls the overall operation of each component of the computing device 100. The processor 110 may be configured to include a central processing unit (CPU), a microprocessor unit (MPU), a microcontroller unit (MCU), a graphic processing unit (GPU), or any other type of processor commonly known in the technical field of the present invention.

[0054] Furthermore, the processor 110 may perform operations for at least one application or program for performing a method according to an embodiment of the present invention, and the computing device 100 may include one or more processors.

[0055] In various embodiments, the processor 110 may further include a random access memory (RAM, not shown) and a read-only memory (ROM, not shown) that temporarily and / or permanently store signals (or data) processed within the processor 110. The processor 110 may also be implemented in the form of a system on a chip (SoC) that includes at least one of a graphics processing unit, RAM, and ROM.

[0056] The memory 120 stores various data, instructions, and / or information. The memory 120 can load a computer program 151 from the storage 150 to perform the methods / operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 can perform the methods / operations by executing one or more instructions constituting the computer program 151. The memory 120 may be embodied as a volatile memory such as a RAM, although the scope of the present disclosure is not limited in this respect.

[0057] The bus 130 provides a communication function between the components of the computing device 100. The bus 130 may be implemented as various types of buses such as an address bus, a data bus, and a control bus.

[0058] The communication interface 140 supports wired / wireless Internet communication for the computing device 100. The communication interface 140 may also support various communication methods other than Internet communication. To this end, the communication interface 140 may be configured to include a communication module that is well known in the art of the present invention. In some embodiments, the communication interface 140 may be omitted.

[0059] The storage 150 can non-temporarily store a computer program 151. When performing a therapy providing process through EEG data analysis through the computing device 100, the storage 150 can store various information required to provide the therapy providing process through EEG data analysis.

[0060] Storage 150 may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, etc., a hard disk, a removable disk, or any form of computer-readable recording medium widely known in the technical field to which the present invention belongs.

[0061] The computer program 151 may include one or more instructions that, when loaded into the memory 120, cause the processor 110 to perform the methods / operations according to various embodiments of the present invention. That is, the processor 110 can perform the methods / operations according to various embodiments of the present invention by executing the one or more instructions.

[0062] In one embodiment, the computer program 151 may include one or more instructions for performing a method for providing therapy through analysis of brainwave data, including the steps of acquiring brainwave data of a user, determining a therapy protocol based on the acquired brainwave data, and providing therapy to the user using the determined therapy protocol.

[0063] The steps of a method or algorithm described in connection with the embodiments of the present invention may be embodied directly in hardware, in a software module executed by hardware, or in a combination thereof. The software module may reside in Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable storage medium commonly known in the art to which the present invention pertains.

[0064] The components of the present invention may be embodied as a program (or application) stored on a medium for execution in conjunction with a computer, which is hardware. The components of the present invention may be implemented as software programming or software elements. Similarly, embodiments include various algorithms embodied as a combination of data structures, processes, routines, or other programming constructs, and may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc. Functional aspects may be embodied as algorithms executed by one or more processors. A method for providing therapy through EEG data analysis provided by a computing device 100 will now be described with reference to FIGS. 3 to 6.

[0065] FIG. 3 is a flowchart of a method for providing therapy through electroencephalogram data analysis according to yet another embodiment of the present invention.

[0066] Referring to FIG. 3, in step S110, the computing device 100 may acquire brain wave data of a user.

[0067] In various embodiments, the computing device 100 may be connected to the therapy providing device 300 via a network 500 and may acquire the user's brain wave data measured using an electroencephalogram measuring device included in the therapy providing device 300.

[0068] Here, the brain wave data may refer to a plurality of unit brain wave data (e.g., independent brain wave signals measured through each channel) included in an brain wave measuring device and measured through a plurality of channels (e.g., a total of 19 channels (e.g., Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz)) attached to different positions on the user's head (scalp). For example, the therapy providing device 300 may include an brain wave measuring device including a plurality of channels provided at positions compatible with the 10-20 System (10-20 electrode placement method), and may measure brain waves through a plurality of channels included in the brain wave measuring device to measure brain wave data including a plurality of unit brain wave data, and the computing device 100 may acquire the brain wave data measured by the above method.

[0069] In various embodiments, the distance between the multiple channels (multiple EEG measurement channels) included in the EEG measuring device can be adjusted according to the size of the user's head. That is, the distance between the multiple channels can be mechanically adjusted according to the size of the user's head to maintain an inter-electrode ratio of 10-20 system.

[0070] More specifically, as shown in Figures 5 and 6, brain waves measured by an EEG measuring device can be measured through channels that maintain a constant distance ratio. For example, the EEG measuring device can be configured with a structure that allows the distance between channels that measure brain waves to be maintained constant, thereby maintaining a constant ratio between electrodes according to the size of the user's head and measuring brain waves at an accurate position that conforms to the 10-20 system. In addition, the EEG measuring device can match the position where brain waves are measured with the position where therapy is provided, allowing for accurate therapy to be provided to the area that requires treatment.

[0071] In various embodiments, the computing device 100 can collect EEG data measured from a user not only when the user is in a normal state and not performing any other actions, but also when the user is performing various tests (e.g., verbal fluency test, Boston naming test, brief mental status test, word list memory test, constructed behavior test, word list recall test, word list recognition test, constructed recall test, line drawing test A / B, etc.).

[0072] In various embodiments, the computing device 100 may process multiple sets of electroencephalogram data collected for multiple users to generate multiple sets of quantified electroencephalogram data (QEEG).

[0073] In various embodiments, the computing device 100 can present a measurement protocol to the user terminal 200 and can acquire electroencephalogram data measured according to the measurement protocol. More specifically, the computing device 100 can present information on how to measure electroencephalograms, i.e., a measurement protocol that is a guide on how to measure electroencephalograms.

[0074] As an example, the computing device 100 may present a measurement protocol in which electroencephalograms are taken in an eyes-open state for 30 seconds, followed by an eyes-closed state for 30 seconds.

[0075] As another example, the computing device 100 may determine a measurement protocol according to requirements of a pre-generated electroencephalogram (EEG) analysis model and provide the protocol to the user. At this time, the pre-generated electroencephalogram (EEG) analysis model may be changed depending on the target setting. More specifically, if the pre-generated electroencephalogram (EEG) analysis model is a model trained using two-minute eyes-closed state EEG data of an AD (Alzheimer's Disease) patient as training data, and the target set for the user is AD, the computing device 100 may determine a measurement protocol for measuring EEGs in an eyes-closed state for two minutes through the pre-generated EEG analysis model and provide the corresponding protocol to the user.

[0076] Therefore, the computing device 100 can determine a measurement protocol that reflects the requirements for EEG data analysis, and by providing this to the user, can guide the user to measure EEG in a way that suits the purpose of therapy.

[0077] Here, the pre-generated EEG analysis model may be configured as an EEG analysis model, and may be a model that predicts the state of the third EEG data using the EEG data and the labeled EEG data as learning data. For example, the pre-generated EEG analysis model may predict that the third EEG data corresponds to Alzheimer's disease.

[0078] Here, an EEG analysis model (e.g., a neural network) is composed of one or more network functions, and the one or more network functions may be composed of a set of interconnected computational units that may generally be referred to as "nodes." Such "nodes" may also be referred to as "neurons." The one or more network functions are composed of at least one or more nodes. The nodes (or neurons) that make up the one or more network functions may be connected to each other by one or more "links."

[0079] In the EEG analysis model, one or more nodes connected through links can form a relative input node-output node relationship. The concepts of input node and output node are relative, and any node that has an output node relationship with one node can also have an input node relationship with another node, and vice versa. As described above, the input node-output node relationship can be generated around links. One or more output nodes can be connected to one input node through links, and vice versa.

[0080] In a relationship between an input node and an output node connected through a link, the value of the output node may be determined based on data input to the input node. Here, the node connecting the input node and the output node may have a weight. The weight may be variable and may be changed by a user or an algorithm to perform a desired function of the EEG analysis model. For example, when one or more input nodes are connected to one output node through respective links, the output node may determine its output node value based on the value input to the input node connected to the output node and the weight set for the link corresponding to each input node.

[0081] As described above, an EEG analysis model has one or more nodes connected to each other through one or more links, forming an input node and output node relationship within the EEG analysis model. The characteristics of the EEG analysis model can be determined by the number of nodes and links, the correlation between the nodes and links, and the weights assigned to each link within the EEG analysis model. For example, if two EEG analysis models have the same number of nodes and links but different weights between the links, the two EEG analysis models can be recognized as different from each other.

[0082] Some of the nodes constituting the EEG analysis model may constitute a layer based on their distance from the first input node. For example, a set of nodes whose distance from the first input node is n may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be traversed to reach the corresponding node from the first input node. However, this definition of a layer is arbitrary for the purpose of explanation, and the order of layers in the EEG analysis model may be defined in a manner different from that described above. For example, the layer of a node may be defined by its distance from the final output node.

[0083] The first input node may refer to one or more nodes to which data is directly input without passing through a link in relation to other nodes within the electroencephalogram analysis model. Alternatively, it may refer to a node that does not have any other input nodes connected to it in relation to the links within the electroencephalogram analysis model network. Similarly, the final output node may refer to one or more nodes that do not have any output nodes in relation to other nodes within the electroencephalogram analysis model. Furthermore, the hidden node may refer to a node that constitutes the electroencephalogram analysis model other than the first input node or the final output node. The electroencephalogram analysis model according to an embodiment of the present invention may have more nodes in the input layer than in the hidden layer close to the output layer, and may be an electroencephalogram analysis model in which the number of nodes decreases as one progresses from the input layer to the hidden layer.

[0084] The EEG analysis model may include one or more hidden layers. Hidden nodes in a hidden layer may receive the output of a previous layer and the output of surrounding hidden nodes as input. The number of hidden nodes in each hidden layer may be the same or different. The number of nodes in an input layer may be determined based on the number of data fields in the input data and may be the same or different from the number of hidden nodes. Input data input to the input layer may be operated by hidden nodes in the hidden layer and output by a fully connected layer (FCL), which is an output layer.

[0085] In various embodiments, the electroencephalogram analysis model may be a deep learning model.

[0086] A deep learning model (e.g., a deep neural network (DNN)) can refer to an EEG analysis model that includes multiple hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to understand the latent structures of data, i.e., the latent structures of photos, text, videos, audio, and music (e.g., what objects are in the photo, what is the content and emotion of the text, what is the content and emotion of the audio, etc.).

[0087] Deep neural networks can include, but are not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q-networks, U-networks, Siamese networks, etc.

[0088] In various embodiments, the network function may include an autoencoder, which may be a type of artificial neural network for outputting output data that is similar to input data.

[0089] An autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged between the input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetric with the input layer). The nodes in the dimensionality reduction layer and the dimensionality restoration layer may be symmetric or asymmetric. An autoencoder may also perform nonlinear dimensionality reduction. The number of input and output layers may correspond to the number of sensors remaining after preprocessing of the input data. In an autoencoder structure, the number of nodes in the hidden layer included in the encoder may decrease as it moves away from the input layer. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, sufficient information may not be transmitted; therefore, it may be maintained at a certain number or more (e.g., more than half of the number of nodes in the input layer).

[0090] In step S120, the computing device 100 analyzes the electroencephalogram data acquired in step S110 and determines a therapy protocol for the user.

[0091] In various embodiments, the computing device 100 can analyze the user's brainwave data and determine a therapy protocol for the user if it determines that therapy is needed for the user.

[0092] Here, the therapy protocol is used to define the therapy to be provided to the user, and may include, for example, the therapy session (e.g., cycle and number of sessions), therapy frequency, therapy treatment area, therapy duration, and therapy light wavelength. For example, the therapy protocol may be determined in the form of, but is not limited to, "(12 sessions for 4 weeks), (13-14 Hz), (Frontal, Global, Temporal areas), (10 minutes), (Near-infrared wavelength)."

[0093] More specifically, the computing device 100 can first determine the need for providing therapy to the user by analyzing the user's brain wave data.

[0094] For example, the computing device 100 can analyze the user's electroencephalogram data to determine whether the user has a brain disease, and if it is determined that the user has a brain disease, it can determine that the user needs to receive therapy.

[0095] As another example, the computing device 100 can compare the user's EEG data with that of a normal person having the same attributes as the user (e.g., age and gender), and if there is an error between the user's EEG data and the normal person's EEG data that exceeds a predetermined range, it can determine that therapy needs to be provided to the user.

[0096] As another example, the computing device 100 may analyze the user's electroencephalogram data and determine that the user needs therapy if an abnormality is identified. Here, the abnormality may refer to, but is not limited to, an abnormal brain region or a brain region associated with abnormal electroencephalograms based on the electroencephalogram data.

[0097] In various embodiments, the computing device 100 can identify abnormalities by analyzing the user's electroencephalogram data based on the user profile.

[0098] In addition, the user profile may include information about the user, such as the user's age, sex, primary hand, cognitive score (e.g., MMSE score), existing EEG measurement results, and statistics on the existing EEG measurement results. For example, the computing device 100 may statistically generate (e.g., average) EEG data from normal people having the same profile as the user to set position-specific EEG reference values ​​for normal people, compare the position-specific EEG reference values ​​for normal people with the position-specific EEG values ​​included in the EEG data, and identify as abnormal areas any areas corresponding to positions where the difference between the EEG value and the EEG reference value is equal to or greater than a predetermined critical value.

[0099] In various embodiments, the computing device 100 may identify an abnormal area for a user based on a preset target. For example, the computing device 100 may set a target in advance, compare a position-specific EEG value included in the EEG data with a position-specific EEG reference value for the target, and identify a region corresponding to a position where the difference between the EEG value and the EEG reference value is equal to or greater than a critical value as an abnormal region.

[0100] Here, the target refers to the purpose of providing therapy, the disease to be treated through therapy, and / or the effect to be obtained through therapy. For example, the target may be, but is not limited to, a specific brain disease (e.g., AD, Lewy Body Dementia, Parkinson's Disease, stroke, ADHD, depression, memory impairment, traumatic brain injury, etc.) and / or the treatment of a specific brain disease, health care, or health promotion such as normal EEG. For example, if the pre-set target is "normal EEG," the computing device 100 may set a position-specific EEG reference value using a position-specific EEG value included in the normal EEG data, and may identify an abnormal position by comparing the position-specific EEG reference value set using the normal EEG data with the position-specific EEG value based on the user's EEG data.

[0101] In various embodiments, the computing device 100 may identify an abnormality based on the user's measured electroencephalogram history. For example, if there are first to nth electroencephalograms corresponding to the user's resting state based on the user's electroencephalogram history, the computing device 100 may identify an abnormality based on the user's first to nth electroencephalogram data. Furthermore, the computing device 100 may identify an abnormality based on the user's measured electroencephalogram history only if it is measured within a specific period. For example, if the computing device 100 measures the user five times when the user was 59 years old and five times when the user was 60 years old, and then uses the user's electroencephalogram history to identify an abnormality at age 60, the computing device 100 may identify an abnormality based only on the five electroencephalogram data measured at age 60. However, the computing device 100 may identify an abnormality based on electroencephalogram data measured within a certain period, regardless of age.

[0102] Thereafter, the computing device 100 can determine a therapy protocol if it determines that therapy is required for the user.

[0103] In various embodiments, when an abnormal region is identified based on the EEG data, the computing device 100 may determine, as a therapy protocol for the user, a therapy frequency and delivery time for inducing the EEG corresponding to the identified abnormal region to a preset reference value. For example, if the target set for the user is AD and the region identified as the abnormal region is region O1 or O2, the computing device 100 may determine a therapy frequency and delivery time (e.g., 10 Hz, 3 minutes) for the purpose of inducing the EEG in region O1 or O2 for the AD patient to a normal level.

[0104] In various embodiments, the computing device 100 may determine a therapy protocol for a user based on the severity of the user's brain disease. For example, the computing device 100 may analyze the user's electroencephalogram (EEG) data to identify an abnormal area in the user's brain. If an abnormal area is identified in the user's brain, the computing device 100 may analyze the EEG corresponding to the abnormal area, calculate an index (e.g., a Z-score) corresponding to the degree of abnormality in the abnormal area, and determine a therapy protocol for the user based on the index corresponding to the degree of abnormality in the abnormal area. In other words, the computing device 100 may determine a therapy protocol for the user based on the degree of abnormality in the abnormal area. As an example, if the degree of alpha slowing in the abnormal area is severe (e.g., a Z-score of -1 or less), the therapy frequency may be set to 15 Hz as the therapy protocol, but is not limited to this example.

[0105] In various embodiments, the user's brainwave data can be analyzed to determine therapeutic areas for therapy as part of a therapy protocol for the user.

[0106] In various embodiments, the computing device 100 may determine a treatment area as a therapy protocol for a user based on a preset target. For example, the computing device 100 may predefine a target-specific treatment area, and when a target is set by a user to whom therapy is to be provided, the computing device 100 may determine a treatment area as a therapy protocol based on the predefined target-specific treatment area. For example, if the preset target is AD and the abnormality site is related to the default mode network (DMN), the computing device 100 may determine the medial prefrontal cortex, precuneus, posterior cingulate cortex, inferior parietal lobe, and hippocampus as treatment areas.

[0107] In various embodiments, the computing device 100 can analyze the user's EEG data to diagnose a brain disease for the user and determine a treatment area based on the diagnosis of the brain disease. For example, if the computing device 100 determines that the user has a specific brain disease by analyzing the user's EEG data, it can determine a region corresponding to the specific brain disease as a first treatment area, and can determine a region related to the treatment of the specific brain disease, i.e., a region that is effective when providing therapy for the specific brain disease (or a group of diseases including the relevant brain disease), as a second treatment area.

[0108] In various embodiments, the computing device 100 can analyze the user's EEG data and, if an abnormality is identified for the user, determine a therapy protocol such that therapy is provided only to the identified abnormality.

[0109] In various embodiments, when two or more abnormal regions are identified in two or more different locations of a user's brain by analyzing the user's EEG data, the computing device 100 can determine individual therapy protocols for each of the two or more abnormal regions so that individual therapies are provided for each of the two or more abnormal regions. However, without being limited thereto, when two or more abnormal regions are identified in two or more different locations of a user's brain, the computing device 100 can determine a common therapy protocol corresponding to the two or more abnormal regions.

[0110] In step S130, the computing device 100 may provide therapy to the user using the therapy protocol determined in step S120.

[0111] Here, the therapy may be PBM (Photobiomodulation). PBM can be provided at various wavelengths, such as wavelengths in the 620-780 nm range and wavelengths in the 780-1400 nm range. PBM can induce increased ATP synthesis and oxygen consumption at the cellular level and improve mitochondrial metabolism in the body. Furthermore, PBM preferably promotes neuronal cell growth and healing and improves brain damage through gene transcription processes.

[0112] In various embodiments, the computing device 100 may be connected to the therapy providing device 300 via the network 500 and provide control commands determined based on a therapy protocol to the therapy providing device 300, thereby causing the therapy providing device 300 to perform an operation of providing therapy according to the control commands. For example, if the therapy protocol determined by the computing device 100 is "(12 times for 4 weeks), (13-14 Hz), (Frontal, Global, Temporal regions), (10 minutes), (Near-infrared wavelength)," the therapy providing device 300 may provide therapy by "providing 12 therapy sessions for 4 weeks, but outputting near-infrared wavelengths of 13-14 Hz frequency in the Frontal, Global, and Temporal regions for 10 minutes," based on the control command provided by the computing device 100.

[0113] In various embodiments, the computing device 100 can provide therapy at a position corresponding to the channel for measuring brain waves through the therapy delivery device 300. For example, as shown in Fig. 6, the computing device 100 can provide therapy at a position corresponding to the position of the channel for measuring brain waves by co-locating the electrodes and PBM delivery module of the therapy delivery device 300. As a result, the computing device 100 can provide therapy at a position (treatment area) where treatment is required for the user through the therapy delivery device 300, thereby achieving the effect of providing measurement and treatment simultaneously.

[0114] The method of providing such therapy may be changed depending on the severity of the patient, the disease in question, the presence or absence of accompanying diseases, the existing therapy provision details, etc. Furthermore, the computing device 100 may decide to provide therapy to the user according to the treatment area and treatment method manually set by the controller's design, regardless of the determination of the treatment area.

[0115] 7 and 8 are diagrams comparing brain waves before and after providing therapy according to a method for providing therapy through brain wave data analysis in various embodiments.

[0116] First, as shown in Figure 7(A), Patient 1 had weak EEGs in the Alpha region and very weak power in the lateral regions. We administered therapy to the entire brain by outputting 13Hz PBM a total of 12 times over a 4-week period. As a result, as shown in Figure 7(B), we observed an increase in power in the SMR region, an improvement in Alpha power in the Temporal region, and an increase in Alpha EEG frequency.

[0117] In addition, as shown in Figure 8(A), for the second patient, whose temporal power tended to be weaker than that of the first patient, 14 Hz PBM was output only to the temporal region for four weeks, and then for the next four weeks, 14 Hz PBM and 40 Hz gamma region PBM were output to the entire region. As a result, as shown in Figure 8(B), power increased in the SMR region, alpha power in the temporal region improved, and the frequency of alpha EEG increased.

[0118] 7 and 8, in one embodiment, the present invention can improve a user's sleep problems by providing therapy to the user through improvement in the SMR region or Alpha power. That is, unlike existing SMR neurofeedback, which improves sleep problems by guiding the user to solve a specific problem, the present invention can improve sleep problems by providing direct therapy.

[0119] The method for providing therapy through analysis of EEG data has been described with reference to the flowcharts shown in the drawings. For ease of explanation, the method for providing therapy through analysis of EEG data has been illustrated and described as a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks may be performed in a different order from that illustrated and performed herein, or simultaneously. Furthermore, new blocks not shown in the present specification and drawings may be added, or some blocks may be deleted or modified. Although the embodiments of the present invention have been described above with reference to the accompanying drawings, those skilled in the art will understand that the present invention may be embodied in other specific forms without changing the technical spirit or essential characteristics thereof. Therefore, the above-described embodiments should be understood to be illustrative in all respects and not restrictive. [Explanation of symbols]

[0120] 100: Computing equipment 200: User terminal 300: Therapy delivery device 400: External Server 500: Network

Claims

1. A method performed by a computing device, comprising: acquiring electroencephalogram data of the user; determining a therapy protocol based on the acquired electroencephalogram data; and A method for providing therapy through electroencephalogram data analysis, comprising providing therapy to the user using the determined therapy protocol.

2. The step of determining the therapy protocol comprises: analyzing the acquired electroencephalogram data to determine whether or not a therapy needs to be provided to the user; and 2. The method for providing therapy through electroencephalogram data analysis according to claim 1, further comprising the step of: determining a therapy protocol for the user when it is determined that the user needs therapy; and the determined therapy protocol including at least one of information regarding the session, frequency, treatment area, delivery time, and light wavelength of the therapy to be provided to the user.

3. The step of determining the therapy protocol comprises: identifying an abnormality site based on the acquired electroencephalogram data; and 2. The method for providing therapy through analysis of electroencephalogram data according to claim 1, further comprising determining a frequency and duration of therapy for inducing the electroencephalogram corresponding to the identified abnormal area to a preset reference value as a therapy protocol for the user.

4. The step of identifying the abnormal site includes: Target setting; and 4. The method for providing therapy through electroencephalogram data analysis according to claim 3, further comprising a step of identifying an abnormal area by comparing position-specific electroencephalogram values ​​included in the acquired electroencephalogram data with position-specific electroencephalogram reference values ​​according to the set target.

5. The step of determining the therapy protocol comprises:

2. The method of claim 1, further comprising the step of analyzing the acquired electroencephalogram data and determining a therapeutic area of ​​therapy as a therapy protocol for the user.

6. The step of determining the treatment area comprises:

6. The method for providing therapy through analysis of electroencephalogram data according to claim 5, further comprising the step of, when a specific brain disease is diagnosed for the user by analyzing the acquired electroencephalogram data, determining a region where the specific brain disease is diagnosed as a first treatment area and determining a region related to the treatment of the diagnosed specific brain disease as a second treatment area.

7. The step of determining the therapy protocol comprises:

2. The method of claim 1, further comprising: determining a therapy protocol when an abnormal area of ​​the user is identified by analyzing the acquired electroencephalogram data, such that therapy is provided only to the identified abnormal area.

8. The step of determining the therapy protocol comprises:

2. The method of providing therapy through analysis of electroencephalogram data according to claim 1, further comprising the step of, when two or more abnormal areas of the user are identified by analyzing the acquired electroencephalogram data, individually determining a therapy protocol corresponding to each of the two or more identified abnormal areas.

9. The step of determining the therapy protocol comprises: analyzing the acquired electroencephalogram data to identify an abnormality site for the user; calculating an index corresponding to the degree of abnormality of the identified abnormal area based on the electroencephalogram corresponding to the identified abnormal area; and The method of claim 1 , further comprising determining a frequency of therapy as a therapy protocol for the user based on the calculated index.

10. The step of acquiring electroencephalogram data includes: and acquiring electroencephalogram data of the user measured through a plurality of channels included in an electroencephalogram measuring device according to a preset measurement protocol, The plurality of channels include:

2. The method of claim 1, wherein the distance between the channels is adjusted according to the size of the user's head.

11. processor; Network interface; memory; and a computer program that is loaded into the memory and executed by the processor, The computer program comprises: Instructions for acquiring a user's brainwave data; instructions for determining a therapy protocol based on the acquired electroencephalogram data; and A computing device for performing a method for providing therapy through analysis of electroencephalogram data, the method including instructions for providing therapy to the user using the determined therapy protocol.

12. coupled to a computing device, acquiring electroencephalogram data of the user; determining a therapy protocol based on the acquired electroencephalogram data; and A computer program stored on a recording medium readable by a computing device for executing a method for providing therapy through electroencephalogram data analysis, the method including providing therapy to the user using the determined therapy protocol.

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