Method and apparatus for assessing cognitive function using brain waves

Abstract

Disclosed are a method and apparatus for assessing a cognitive function using brain waves. The method for assessing a cognitive function includes the steps of: measuring event-related potential (ERP) signals generated in each of the left and right prefrontal lobes of a user by using a tool for assessing a cognitive function; calculating a phase-locking value (PLV) between the ERP signals; and evaluating the cognitive function of the user by means of the PLV between the ERP signals.

Description

Method and device for evaluating cognitive function using brain waves

[0001] The present invention relates to a method and device for evaluating cognitive function using brain waves, and more specifically, to a method and device for evaluating a user's cognitive function using event-related potential (ERP) signals.

[0002] As the world enters an aging society, dementia is emerging as a major social problem. Dementia is not a single disease in itself, but rather a comprehensive term that refers to a state of cognitive impairment due to various causes, making it impossible to maintain a previous level of daily life. Therefore, dementia refers to a complex symptom that occurs when a normally mature brain is damaged or destroyed by external factors such as acquired trauma or disease, resulting in a decline in cognitive functions such as perception, attention, memory, and learning, as well as higher mental functions.

[0003] Once cognitive impairment reaches a severe stage, where systemic changes are visible on CT or MRI, it becomes difficult to improve the condition, forcing patients to rely on support staff and family care for the remainder of their lives. However, in mild cognitive impairment or mild cognitive decline, where neurological decline is worsening even before systemic changes have occurred, treatment rates are high and can significantly contribute to improving quality of life and reducing economic costs. Therefore, appropriate, low-cost, non-invasive cognitive impairment risk screening and early prediction technologies are essential for preparing for a healthy aging society.

[0004] Currently, methods for assessing a user's cognitive function based on brain waves are being proposed. However, these cognitive function assessment methods primarily utilize multi-channel (23-128 channel) EEG equipment, making it difficult and inconvenient to measure the user's brain waves.

[0005] Moreover, EEG is a highly complex, nonstationary signal that continuously changes over time. Its low signal-to-noise ratio can make it difficult to separate signal from noise. This is particularly true for the elderly, who have relatively low compliance and concentration, resulting in a high level of noise and significant individual signal variability. This poses significant challenges in accurately assessing cognitive function in elderly individuals.

[0006] The present invention aims to provide a method and device for evaluating a user's cognitive function through easy and simple brain wave measurement by obtaining ERP signals of the prefrontal lobe through two channels rather than multiple channels (23 to 128 channels) and evaluating the user's cognitive function.

[0007] In addition, the present invention seeks to provide a method and device for evaluating cognitive functions that are relatively robust to noise in ERP signals by independently extracting and analyzing phase components from ERP signals that are noisy and highly variable.

[0008] However, technical challenges are not limited to the technical challenges described above, and other technical challenges may exist.

[0009] A method for evaluating cognitive function according to one embodiment of the present invention may include the steps of measuring event-related potential (ERP) signals generated from each of the left and right prefrontal lobes of a user using a cognitive function evaluation tool; calculating a phase-locking value (PLV) between the ERP signals; and evaluating the cognitive function of the user through the PLV between the ERP signals.

[0010] The method further includes a step of filtering the measured ERP signals to pass only a specific frequency band, and the calculating step can calculate PLV between ERP signals of the specific frequency band.

[0011] The above specific frequency band may be 13 to 30 Hz corresponding to the frequency band of beta waves.

[0012] The step of calculating the PLV may include the step of extracting the phase component of each of the ERP signals using a Hilbert transform; the step of determining the phase difference between the ERP signals through the phase component of each of the ERP signals; and the step of deriving the PLV between the ERP signals by applying the phase difference between the ERP signals to an exponential function.

[0013] The step of evaluating the cognitive function of the user may include evaluating the cognitive function of the user by analyzing at least one PLV variable, and the PLV variable may include at least one of (i) a maximum value of PLV in the frequency band of the beta wave, (ii) a time when PLV is at a maximum value in the frequency band of the beta wave, (iii) a PLV average value in the P200 range in the frequency band of the beta wave, (iv) a PLV average value in the P300 range in the frequency band of the beta wave, and (v) a PLV average value in the range after stimulation using the cognitive function evaluation tool in the frequency band of the beta wave.

[0014] A cognitive function evaluation device according to one embodiment of the present invention includes one or more processors; and a memory for loading or storing a program executed by the processor, wherein the program may include an operation of measuring event-related potential (ERP) signals generated in each of the left and right prefrontal lobes of a user using a cognitive function evaluation tool, an operation of calculating a phase-locking value (PLV) between the ERP signals, and an operation of evaluating the user's cognitive function through the PLV between the ERP signals.

[0015] The above processor can filter the measured ERP signals to pass only a specific frequency band, and calculate the PLV between the ERP signals of the specific frequency band.

[0016] The above specific frequency band may be 13 to 30 Hz corresponding to the frequency band of beta waves.

[0017] The processor can extract the phase component of each of the ERP signals using a Hilbert transform, determine the phase difference between the ERP signals through the phase component of each of the ERP signals, and calculate the PLV between the ERP signals by applying the phase difference between the ERP signals to an exponential function.

[0018] The processor may evaluate the cognitive function of the user by analyzing at least one PLV variable, and the PLV variable may include at least one of (i) a maximum value of PLV in the frequency band of the beta wave, (ii) a time when PLV is at a maximum value in the frequency band of the beta wave, (iii) a PLV average value in the P200 range in the frequency band of the beta wave, (iv) a PLV average value in the P300 range in the frequency band of the beta wave, and (v) a PLV average value in the range after stimulation through the cognitive function evaluation tool in the frequency band of the beta wave.

[0019] According to one embodiment of the present invention, by obtaining ERP signals of the prefrontal lobe through two channels rather than multiple channels (23 to 128 channels) and evaluating the user's cognitive function, the user's cognitive function can be evaluated through easy and simple brain wave measurement.

[0020] In addition, according to one embodiment of the present invention, by independently extracting and analyzing phase components from ERP signals with a lot of noise and high variability, it is possible to evaluate a user's cognitive function relatively robustly against noise in the ERP signal.

[0021] FIG. 1 is a diagram illustrating a configuration of a cognitive function evaluation device according to one embodiment of the present invention.

[0022] Figure 2 is a flowchart illustrating a cognitive function evaluation method according to one embodiment of the present invention.

[0023] FIG. 3 is a diagram showing PLV variables according to one embodiment of the present invention.

[0024] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Therefore, the actual implementation is not limited to the specific embodiments disclosed, and the scope of this specification includes modifications, equivalents, or alternatives within the technical concepts described in the embodiments.

[0025] Although terms such as "first" or "second" may be used to describe various components, these terms should be interpreted solely to distinguish one component from another. For example, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component.

[0026] When it is said that a component is "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but there may also be other components in between.

[0027] Singular expressions include plural expressions unless the context clearly dictates otherwise. In this document, phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. In this specification, it should be understood that the terms "comprises" or "has" and the like are intended to specify the presence of a described feature, number, step, operation, component, part, or combination thereof, but do not exclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0028] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art. Terms defined in commonly used dictionaries should be interpreted to have a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0029] Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the attached drawings, identical components are assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted.

[0030]

[0031]

[0032] FIG. 1 is a diagram illustrating a configuration of a cognitive function evaluation device according to one embodiment of the present invention.

[0033] As illustrated in FIG. 1, the cognitive function evaluation device (100) may include one or more processors (110) and a memory (120) for loading or storing a program (130) executed by the processor (110). The components included in the cognitive function evaluation device (100) of FIG. 1 are merely examples, and a person skilled in the art to which the present invention pertains will recognize that other general components may be included in addition to the components illustrated in FIG. 1.

[0034] The processor (110) controls the overall operation of each component of the cognitive function evaluation device (100). The processor (110) may be configured to include at least one of a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphics Processing Unit), an NPU (Neural Processing Unit), a DSP (Digital Signal Processor), or any other type of processor well known in the art of the present invention. In addition, the processor (110) may perform operations for at least one application or program for executing a method / operation according to various embodiments of the present invention. The cognitive function evaluation device (100) may include one or more processors.

[0035] The memory (120) stores one or more combinations of various data, commands, and information used by components (e.g., processor (110)) included in the cognitive function evaluation device (100). The memory (120) may include volatile memory and / or non-volatile memory.

[0036] The program (130) may include one or more actions in which methods / operations according to various embodiments of the present invention are implemented, and may be stored in the memory (120) in the form of software. Here, the actions correspond to commands realized in the program (130). For example, the program (130) may include instructions for performing an action of measuring event-related potential (ERP) signals generated in each of the left and right prefrontal lobes of the user using a cognitive function evaluation tool, an action of calculating a phase-locking value (PLV) between the ERP signals, and an action of evaluating the user's cognitive function through the PLV between the ERP signals.

[0037] When the program (130) is loaded into the memory (120), the processor (110) can perform methods / operations according to various embodiments of the present invention by executing a plurality of operations to implement the program (130).

[0038] The execution screen of the program (130) can be displayed through the display (140). In the case of FIG. 1, the display (140) is represented as a separate device connected to the cognitive function evaluation device (100). However, in the case of the cognitive function evaluation device (100), such as a terminal that can be carried by a user, such as a smartphone or tablet, the display (140) can be a component of the cognitive function evaluation device (100). The screen displayed on the display (140) can be before inputting information into the program or the result of executing the program.

[0039]

[0040] FIG. 2 is a flowchart illustrating a cognitive function assessment method according to an embodiment of the present invention. In one embodiment, at least one of the steps in FIG. 2 may be performed simultaneously or in parallel with other steps, and the order of the steps may be changed. Furthermore, at least one of the steps may be omitted, and other steps may be additionally performed. The steps illustrated in FIG. 2 may be performed by a processor (e.g., the processor (110) in FIG. 1) of a cognitive function assessment device (e.g., the cognitive function assessment device (100) in FIG. 1).

[0041] In step (210), the processor may measure ERP signals generated from each of the user's left and right prefrontal lobes using a cognitive function assessment tool. More specifically, the processor may measure ERP signals in response to stimulation applied by the cognitive function assessment tool from electrodes attached to each of the user's left and right prefrontal lobes.

[0042] At this time, the processor can measure ERP signals using multiple cognitive function assessment tools, depending on the type of cognitive function being assessed. For example, cognitive function assessment tools may include an n-back task for assessing working memory, a Stroop task for assessing selective attention, and a Go / Nogo task for assessing response inhibition.

[0043] In addition, the processor can measure ERP signals using various types of cognitive function assessment tools such as the Continuous Performance Test (CPT), the Integrated Visual and Auditory continuous performance task (IVA), the paced auditory serial addition test (PASAT), the categorization working memory span test (CWMS), and the Digit memory span test (DMST). However, the above cognitive function assessment tools are only examples and are not limited to the above examples.

[0044] In step (220), the processor can calculate the phase-locking value (PLV) between the ERP signals measured from each of the user's left and right prefrontal lobes. Beta oscillations, which generate oscillations of 25 to 40 Hz in the motor system of the brain involved in movement, are known to occur in the process of controlling cognitive states such as attention concentration or sensorimotor control that determines where to move.

[0045] Accordingly, the processor can filter the measured ERP signals to pass only a specific frequency band, that is, the frequency band of beta waves (13-30 Hz). For example, the processor can obtain ERP signals corresponding to the frequency band of beta waves by applying a finite impulse response band-pass filter (FIR BPF) with little phase shift to the ERP signals measured from the user's left and right prefrontal lobes, respectively.

[0046] The processor can then extract the phase components of each ERP signal by applying the Hilbert transform to the filtered ERP signals. Using these extracted phase components, the processor can then determine the phase difference between the ERP signals.

[0047] The processor can calculate the PLV between the ERP signals based on the phase difference between the ERP signals determined in this way, as shown in Equation 1 below.

[0048] <Formula 1>

[0049]

[0050] Here, Is Specific time of the second signal It represents the phase difference in . At this time, ERP signals can be divided into segments (epochs) and analyzed according to the time point at which external stimuli are given, and the nth signal among them can mean the nth segment.

[0051] This PLV indicates the degree of phase matching between specific frequency signals and can have a value between 0 and 1. That is, if PLV is 0, it indicates that there is no synchronization between the two ERP signals, and if PLV is 1, it indicates that complete synchronization between the two ERP signals has been achieved.

[0052] In step (230), the processor can evaluate the user's cognitive function through the PLV between the ERP signals. More specifically, the processor can evaluate the user's cognitive function by judging the degree of synchronization of brain wave signals using the PLV between the ERP signals. It may be a natural phenomenon that brain wave signals measured in the left and right prefrontal lobes in response to a specific stimulus are similarly synchronized. However, when the user's cognitive function is low, the degree of synchronization of brain wave signals measured in the left and right prefrontal lobes in response to a specific stimulus may be low, and the processor of the present invention can evaluate the user's cognitive function by judging the degree of synchronization of brain wave signals measured in the left and right prefrontal lobes.

[0053] At this time, the processor can evaluate the user's cognitive function by analyzing at least one PLV variable. More detailed PLV variables are described in detail in Figure 3 below.

[0054]

[0055] FIG. 3 is a diagram showing PLV variables according to one embodiment of the present invention.

[0056] A processor (e.g., the processor (110) of FIG. 1) of a cognitive function evaluation device (e.g., the cognitive function evaluation device (100) of FIG. 1) can evaluate a user's cognitive function through PLV variables derived from PLV between ERP signals. Referring to FIG. 3, the PLV variables may include at least one of (i) a maximum value of PLV in a frequency band of beta waves (PLV_B_max), (ii) a time when PLV is at a maximum value in a frequency band of beta waves (PLV_B_time), (iii) a mean value of PLV in the P200 range in a frequency band of beta waves (PLV_B_meanP200), (iv) a mean value of PLV in the P300 range in a frequency band of beta waves (PLV_B_meanP300), and (v) a mean value of PLV in a range after stimulation through a cognitive function evaluation tool in a frequency band of beta waves (PLV_B_meanP).

[0057]

[0058] The embodiments described above may be implemented using 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 be implemented using a general-purpose computer or a special-purpose computer, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and software applications running on the operating system. Furthermore, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0059] Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing device to perform a desired operation or, independently or collectively, command the processing device. The software and / or data may be stored in any type of machine, component, physical device, virtual equipment, computer storage medium, or device for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on a computer-readable recording medium.

[0060] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may store program commands, data files, data structures, etc., alone or in combination, and the program commands recorded on the medium may be those specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of program commands include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc.

[0061] 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.

[0062] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the described embodiments. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0063] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

Claims

1. A step of measuring event-related potential (ERP) signals generated from each of the user's left and right prefrontal lobes using a cognitive function assessment tool; A step of calculating a phase-locking value (PLV) between the above ERP signals; and A step of evaluating the user's cognitive function through PLV between the ERP signals. A method for assessing cognitive function, including:

2. In paragraph 1, A step of filtering the measured ERP signals to pass only a specific frequency band. Including more, The above calculation steps are: A cognitive function evaluation method for calculating PLV between ERP signals of the above specific frequency band.

3. In paragraph 2, The above specific frequency band is, A cognitive function assessment method corresponding to the frequency band of beta waves, 13 to 30 Hz.

4. In paragraph 1, The steps for calculating the above PLV are: A step of extracting the phase component of each of the ERP signals using the Hilbert transform; A step of determining a phase difference between the ERP signals through the phase component of each of the ERP signals; and A step of deriving PLV between the ERP signals by applying the phase difference between the ERP signals to an exponential function. A method for assessing cognitive function, including:

5. In paragraph 1, The step of evaluating the user's cognitive function is as follows: Evaluate the cognitive function of the user by analyzing at least one PLV variable, The above PLV variable is, A method for evaluating cognitive function, comprising at least one of (i) a maximum value of PLV in the frequency band of the beta wave, (ii) a time when PLV is at a maximum value in the frequency band of the beta wave, (iii) a mean value of PLV in the P200 range in the frequency band of the beta wave, (iv) a mean value of PLV in the P300 range in the frequency band of the beta wave, and (v) a mean value of PLV in the range after stimulation using the cognitive function evaluation tool in the frequency band of the beta wave.

6. In the cognitive function evaluation device, one or more processors; and Including a memory for loading or storing a program executed by the above processor, The above program is, A cognitive function evaluation device including an action of measuring event-related potential (ERP) signals generated from each of the left and right prefrontal lobes of a user using a cognitive function evaluation tool, an action of calculating a phase-locking value (PLV) between the ERP signals, and an action of evaluating the user's cognitive function through the PLV between the ERP signals.

7. In paragraph 6, The above processor, A cognitive function evaluation device that filters the measured ERP signals to pass only a specific frequency band and calculates PLV between ERP signals in the specific frequency band.

8. In paragraph 7, The above specific frequency band is, A cognitive function evaluation device with a frequency band of 13 to 30 Hz corresponding to beta waves.

9. In paragraph 6, The above processor, A cognitive function evaluation device that extracts the phase component of each of the ERP signals using a Hilbert transform, determines the phase difference between the ERP signals through the phase component of each of the ERP signals, and calculates the PLV between the ERP signals by applying the phase difference between the ERP signals to an exponential function.

10. In paragraph 6, The above processor, Evaluate the cognitive function of the user by analyzing at least one PLV variable, The above PLV variable is, A cognitive function evaluation device comprising at least one of (i) a maximum value of PLV in the frequency band of the beta wave, (ii) a time when PLV is at a maximum value in the frequency band of the beta wave, (iii) a PLV average value in the P200 range in the frequency band of the beta wave, (iv) a PLV average value in the P300 range in the frequency band of the beta wave, and (v) a PLV average value in the range after stimulation using the cognitive function evaluation tool in the frequency band of the beta wave.

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