KR101962258B1 - Cognitive testing device based on electroencephalogram and method for cognitive testing same - Google Patents

Abstract

The present invention provides a cognitive ability testing device which can objectively and accurately measure a cognitive ability based on a brainwave type and response time. According to an embodiment of the present invention, the cognitive ability testing device comprises: a measurement unit measuring cognitive ability information including a brainwave type and response time through a cognitive ability test based on a brainwave; a classification unit classifying the cognitive ability information into first and second classification information in accordance with a recruitment group type; a feature information extraction unit comparing the first and second classification information with the brainwave type and the response time as a comparison target, and extracting first and second feature information through the comparison; and an index list generation unit generating an index list in which the cognitive ability information is arranged in a predetermined order based on the first and second feature information.

Description

TECHNICAL FIELD [0001] The present invention relates to an apparatus and method for recognizing brain waves based on electroencephalogram (ECG)

Embodiments of the present application relate to an apparatus and method for perceptual testing.

In recent years, as Korean society has entered an aging society, brain-related diseases such as Alzheimer's disease have emerged as one of the major social issues. In addition, as the aging progresses, it naturally degrades. However, brain-related diseases are rapidly occurring due to various causes such as dementia, stroke, brain trauma, and chronic fatigue. Diagnosis of brain-related diseases is an important task because these brain-related diseases can be treated early through rapid diagnosis.

However, most of the methods for diagnosing existing brain related diseases are mostly based on the questionnaire or the computerized problem solving method. In particular, they use simple questionnaires and props to measure the perceptual cognition. In addition, there is a problem that the inspection time is long and intentionally wrong answers can be taken or images can be taken without knowing the answers, which can cause a lot of subjective influences. If the problem is exposed to the subject, There is a problem that the subjectivity of the examinee is difficult to be excluded from the cognitive evaluation.

Accordingly, there is a need for a technique for recognizing the brain change pattern by measuring objective cognitive ability, for helping early diagnosis of brain-related diseases, or for predicting the onset.

It is an object of the present invention to provide a cognition testing apparatus capable of objectively and accurately measuring cognitive ability based on brain wave type and reaction time.

A cognitive test apparatus according to an embodiment of the present application includes a measuring unit for measuring cognitive performance information including an EEG type and a reaction time through cognitive test based on brain waves, A feature information extracting unit that compares the first and second classification information with the EEG type and the reaction time as comparison objects and extracts first and second feature information through the comparison; And an index list generation unit for generating an index list in which the perceptual power information is arranged in a predetermined order based on the first and second characteristic information.

The diagnostic output unit may further include a diagnostic output unit for outputting a diagnosis result corresponding to the perceived information of the subject measured through the perception test by referring to the index list.

The apparatus may further include an updating unit updating the cognitive information of the subject according to the diagnosis result of the subject, with any one of the first and second classification information.

In the embodiment, the index list may be a list of predefined perceptions about behavior tasks predetermined according to the brain wave pattern, the perceptual information being arranged in order according to the first and second feature information, And includes diagnostic results quantified in the order of the information.

In an embodiment, the feature information extracting unit extracts first and second threshold times and first and second threshold waveforms, which are bases according to the population type, from the first and second classification information.

In an embodiment, the first characteristic information is a unit time period divided into equal time intervals in the whole interval of the first and second threshold times.

In an embodiment, the second feature information may include at least one of amplitude, peak, edge, pattern, dip, score, and flat extracted from the first and second threshold waveforms, It is a unit size section divided into sections.

In an embodiment, the index list generation unit generates a first list arranged in the order of the shorter reaction time, and generates a plurality of divided second lists based on the first feature information.

In the embodiment, the index list generation unit may generate a second list after rearranging the perceptual information of any one of the plurality of second lists based on the second characteristic information, Apply quantified diagnostic results.

In an embodiment, the classifying unit stores the perceptual power information in a storage DB to generate a storage list, and the storage list is a list storing the perceptual power information according to a measurement order.

In an exemplary embodiment, the index list generation unit may divide the first list into preset sections corresponding to the normal group, the redundant group, and the patient group, and when the perceptual information is classified into the second section corresponding to the overlapping group , And reorders the second section based on the second feature information.

In one embodiment of the present invention, the index list generator calculates and applies a quantified diagnosis result (E) to any one of the stored information items included in the index list through a quantitative index calculating formula,

(1) 이고,

(1)

Where A is a weighting index according to the population type, A is a similarity index with the first threshold time, B is a similarity index with the second threshold time, C is a similarity index with the first threshold waveform, and D Is a similarity index to the second threshold waveform.

According to one embodiment of the present application, there is provided a method of recognizing cognitive ability of a cognitive test apparatus, comprising the steps of: measuring cognitive performance information including an EEG type and a reaction time through cognitive test based on brain waves; 2 classification information, comparing the first and second classification information with the EEG type and the reaction time as comparison objects, extracting first and second characteristic information through the comparison, 1 and second feature information, and generating an index list in which the perceptual power information is arranged in an order determined, and referring to the index list, outputting a diagnosis result corresponding to the perceived information of the subject measured through the perceptual power test .

In the embodiment, the step of generating the index list may include generating a first list in which the perceptual information is arranged in the order of the shortest reaction time, generating the first list based on the first feature information, And rearranging the perceptual information included in any one of the plurality of sublists based on the second feature information.

In one embodiment, generating the index list includes generating a second list by merging any of the one and the second list, and applying a quantified cognitive power value to the second list to generate an index list.

In one embodiment of the present invention, the step of extracting the first and second characteristic information may include extracting first and second characteristic information from the first and second classification information, the first and second threshold time, And extracting a waveform. .

In one embodiment, the extracting of the first and second feature information may include setting a unit time period that is divided into equal time intervals in the entire interval of the first and second threshold times as the first feature information .

In one embodiment of the present invention, the extracting of the first and second characteristic information may include extracting the first and second characteristic information from among the amplitude, peak, edge, pattern, dip, And setting a unit size section that is divided into one to the same size section as the second feature information.

Dividing the first list into predetermined sections corresponding to the normal group, the redundant group, and the patient group; and, when the perceptual information is classified into the second section corresponding to the overlapping group, And rearranging the perceptual information included in the second section based on the information.

The method may further include updating cognitive information of the examinee to one of the first classification information and the second classification information according to a diagnosis result of the cognitive performance information of the subject.

The method of claim 1, wherein the updating comprises: transmitting a diagnostic result of the cognitive performance information of the subject to the terminal, receiving feedback information on the accuracy of the diagnostic result from the terminal, Determining whether the cognitive performance information of the subject is updated, and storing the cognitive information in any one of the first and second classification information corresponding to the diagnosis result when the update is determined.

According to the embodiment of the present invention, the brain-wave based cognitive testing apparatus and method can quantitatively diagnose cognitive ability based on neurological brain wave type and reaction time. Therefore, cognitive ability can be diagnosed more accurately and objectively.

1 is a block diagram of a cognitive test apparatus according to an embodiment of the present application.
Fig. 2 is an example for explaining the operation of the perceptual-force test apparatus of Fig. 1. Fig.
FIG. 3 is an example for illustrating the process of generating the index list.
FIG. 4 is a flowchart showing the operation of the perceptual power test apparatus of FIG. 1. FIG.
5 is a flowchart showing the operation of the feature information extracting unit of FIG.
FIG. 6 is a flowchart showing an operation according to an embodiment of the index list generation unit of FIG. 1; FIG.
7 is a flowchart showing an operation according to another embodiment of the index list generation unit of FIG.
8 is a block diagram of a cognitive testing apparatus according to another embodiment of the present application.
9 is a block diagram of a cognitive testing system in accordance with an embodiment of the present application.
Fig. 10 is a flowchart showing the operation of the perceptual-force test apparatus of Fig.
FIGS. 11 and 12 are views showing examples in which the perceptual force testing apparatus of FIG. 5 is applied.

Hereinafter, an apparatus and method for recognizing brain waves based on the present invention will be described in detail with reference to the drawings. The suffix " module " and " part " for the components used in the following description are given or mixed in consideration of ease of specification, and do not have their own meaning or role. In the present specification, the same or similar reference numerals are given to different embodiments in the same or similar configurations. As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

The drawings herein are to be understood as illustrative of the conceptual aspects of an exemplary circuit embodying the principles of the present application. That is, it should be understood that the functions shown in the figures may be substantially represented on a computer-readable medium and performed by various processes performed by the computer or processor, whether or not the computer or processor is explicitly shown.

And, each function can be provided not only with dedicated hardware, but also with the use of hardware capable of executing software in connection with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which may be shared.

In the following description of the present application, a detailed description of known technologies related to the present application will be omitted when it is determined that the gist of the present application may be unnecessarily obscured. Hereinafter, a preferred embodiment according to the present application will be described in detail with reference to the accompanying drawings.

FIG. 1 is a block diagram of a cognitive test apparatus 100 according to an embodiment of the present application, and FIG. 2 is an example for explaining the operation of the cognitive test apparatus 100 of FIG.

1 and 2, the perceptual strength testing apparatus 100 may include a measuring unit 110, a classifying unit 120, a feature information extracting unit 130, and an index list generating unit 140.

First, the measuring unit 110 can measure the cognitive power information through the brain-wave-based cognitive test. Here, the perception information may include an EEG type and a reaction time. More specifically, the measuring unit 110 can measure the EEG type through the brain-wave based cognitive test and measure the response time to the predetermined behavior task. Here, the behavioral task may be a task of measuring the response time to any one of the predetermined visual, auditory, and motor ability according to the EEG type.

That is, the measuring unit 110 can measure cognitive information including an EEG type measured through an EEG-based cognitive test and a response time to a predetermined action task according to the EEG type. Here, the brain-based cognitive test is based on the results of EEG measurements performed according to the type of brain disease-related patient group and normal army recruitment group, and the test for measuring the response time to any one of visual, auditory, Lt; / RTI >

Next, the classifying unit 120 stores the cognitive information measured through the measuring unit 110 in the storage DB 200, which will be described later, and classifies the cognitive information into the first and second classification information according to the type of the population . That is, the classifying unit 120 can store and manage the first classification information classified according to the type of the normal group and the second classification information classified according to the type of the patient group in the storage DB 200. [

Next, the feature information extracting unit 130 compares the first and second classification information classified by the classifying unit 120 with each other as the comparison target of the EEG type included in the perceptive power information and the reaction time, 1 and second feature information. For example, the feature information extracting unit 130 may extract first and second threshold times as reference based on the induction type from the first and second classification information classified by the classifying unit 120.

Here, the first threshold time means a time period in which the reaction time is the shortest in the normal group for any one behavior task, and the second threshold time means the longest reaction time in the patient group for any one behavior task can do.

In addition, the feature information extracting unit 130 may extract first and second threshold waveforms based on the induction group type from the first and second classification information classified by the classifying unit 120. Here, the first and second threshold waveforms may be waveform information indicating the greatest difference in amplitude, peak, edge, pattern, dip, bone, and flat among the EEG waveforms of the normal group and the patient group.

More specifically, the feature information extracting unit 130 extracts feature information of the first and second images classified by the classifying unit 120 using any one of frequency analysis (FFT, wavelet) and multi-scale entropy (Corelation Dimension) 2 classification information, and the first and second threshold waveforms, which are the reference according to the type of the induction group, can be extracted by separating the EEG type by frequency band. In other words, the feature information extracting unit 130 separates the noise-canceled EEG waveforms from the first and second classification information into frequency bands and separates the first and second EEG waveforms from the EEG waveforms separated according to frequency bands, The second threshold waveform can be extracted.

At this time, the feature information extracting unit 130 may divide the whole interval of the first and second threshold time into a plurality of identical time intervals, and set the size of one divided time interval as the first feature information. Here, the first feature information may be a unit time period divided into the same time period in the whole interval of the first and second threshold time periods. For example, when the first threshold time is 1 second and the second threshold value is 10 seconds, the feature information extracting unit 130 divides the whole interval of 1 second to 10 seconds into units of 2 seconds, Section can be set as the first feature information.

In addition, the feature information extracting unit 130 extracts any one of amplitude, peak, edge, pattern, dip, bone, and flat, which is the waveform information for identifying the EEG type from the first and second threshold waveforms, , The extracted one may be divided into a plurality of identical size sections, and one of the divided size sections may be set as the second characteristic information. Here, the second characteristic information may be a unit size in which the difference among the amplitude, peak, edge, pattern, dip, valley, and flat extracted from the first and second threshold waveforms is divided into the largest- Lt; / RTI >

For example, when the difference between the first and second threshold waveforms among amplitude, peak, edge, pattern, dip, bone, and flat, which is waveform information, is the largest and the magnitude difference of the amplitude is 100, The feature information extracting unit 130 may divide the entire size of 100 into the same size sections such as 1 to 5, 6 to 10, 11 to 15, Information can be set.

The feature information extracting unit 130 according to the technical idea of the present application extracts the first and second threshold time from the first and second classification information classified by the classifying unit 120, By extracting the first and second threshold waveforms, extracting the first feature information based on the first and second threshold times, and extracting the second feature information based on the first and second threshold waveforms, The index list generating unit 140 may provide a factor that allows the index list generating unit 140 to generate an objective index list of perceptions.

Next, the index list generation unit 140 can generate the index list in which the perceptual power information is arranged in the predetermined order based on the first and second feature information.

Here, the index list is a list of references to any one item of the perception strength, the memory power, the attention power, the reasoning power, the judgment power, and the concentration power. The index list is a list of the perceived power that is arranged in order according to the first and second feature information, And may include quantified diagnostic results.

More specifically, the list of indicators includes an EEG measured through the measurement unit 110 and a predetermined perceived power, memory capacity, and perceived power according to an action task including any one of visual, auditory, and athletic performance predetermined according to the EEG type, For example, the index list is a list of reference items for an item of attention, reasoning power, judgment power, and concentration, Thus, it may be a reference list for the perceived perception.

In addition, as shown in the following Tables 1 to 5, the index list generation unit 140 generates index indexes based on weighting indices according to population groups, similarity indexes with first and second threshold times, similarity with first and second threshold waveforms The exponent can be stored in advance.

Table 2 below shows an example of a similarity index with respect to the first threshold time, and Table 3 shows an example of a similarity index with the second threshold time. Table 4 is an example of the similarity index with the first threshold waveform, and Table 5 is an example of the similarity index with the second threshold waveform.

Enclosure type Weighting Index
(1 to 2) Normal group 2 Patient group One

Reaction time of cognitive information The similarity index (A) with the first threshold time (1 second)
10 to 50 0 seconds to less than 2 seconds 50 Less than 2 seconds to less than 4 seconds 40 Less than 4 seconds to less than 6 seconds 30 .
.
. .
.
.

Reaction time of cognitive information The similarity index (B) with the second threshold time (10 seconds)
1 to 10 Less than 0 second to less than 1 second One Less than 1 second to less than 2 seconds 2 Less than 2 seconds to less than 3 seconds 3 Less than 3 seconds to less than 4 seconds 4 .
.
. .
.
.

Amplitude magnitude of cognitive information The similarity index (C) of the first threshold waveform (amplitude 5)
(10 to 50) 1 or less 10 1 to less than 2 20 2 to less than 3 30 .
.
. .
.
.

Amplitude magnitude of cognitive information The similarity index (D) of the second occlusion waveform (amplitude 1)
1 to 10
Less than 0 ~ 0.5 One 0.5 to less than 1 2 1 to less than 1.5 3 1.5 to less than 2 4 2 to less than 2.5 5 .
.
. .
.
.

The index list generation unit 140 according to the embodiment can calculate the quantified diagnosis result E for the perceptual power information of the index list through the quantitative index calculation formula and apply it to the index list. In this case,

(1)이고,

(1)

A is a similarity index to the first threshold time, B is a similarity index to the second threshold time, C is a similarity index to the first threshold waveform, and D is a second threshold waveform Lt; / RTI >

For example, when one of the perceptual information included in the index list is a normal group, the reaction time is 3.1 seconds, and the amplitude of the EEG waveform is 2.3, the index list generation unit 140 generates a list (40) which is a similarity index (A) with the first threshold time, 30 which is a similarity index (B) with the second threshold time, a similarity index with the first threshold waveform (C ) And a similarity index (D) of 5 with the second critical waveform are extracted and a quantitative diagnosis result (E) 65.5 corresponding to the perceptual information of the index list is calculated through the quantitative index calculating formula, The diagnostic result (E) 65.5 for one cognitive information can be applied to the indicator list.

Hereinafter, with reference to FIG. 3, the first and second lists generated by the index list generation unit 140 and the index list will be described in more detail.

FIG. 3 is an example for illustrating the process of generating the index list.

1 to 3, the index list generation unit 140 may generate the first list 141 by arranging the recognition information in the order of short reaction time. More specifically, the index list generation unit 140 generates the index list in the storage DB 200 based on the first and second threshold times in the storage list 121, , So that the first list 141 can be generated. Here, the storage list 121 is a list in which the cognitive information measured through the measuring unit 110 is stored in the storage DB 200 in the order of measurement, and the first list 141 stores the cognitive information, And may be a list sorted by the list generating unit 140.

The index list generation unit 140 divides the first list 141 into unit time intervals based on the first feature information extracted by the feature information extraction unit 130 and generates a plurality of sub lists 143_1- 143_N and re-arranging any one of the plurality of sub-lists 143_1 to 143_N based on the second feature information extracted by the feature information extracting unit 130. [

At this time, the index list generation unit 140 can generate the index list 145 by merging the plurality of sub-lists 143_1 to 143_N rearranged. 3, the plurality of sub-lists 143_1 to 143_N are lists including at least two pieces of cognitive information from the first list 141, and the second list 145 includes a plurality of sub- And the index list 147 may be a list including diagnosis results quantified according to the cognitive information included in the second list 145. [

According to the embodiment, the index list generation unit 140 may divide the first list 141 into preset sections corresponding to the normal group, the redundant group, and the patient group. At this time, when the perceptual information is classified into the second period corresponding to the overlapping group, the surface list generating unit 140 rearranges the perceptual information included in the second period based on the second characteristic information, Lt; / RTI >

That is, the index list generation unit 140 can divide the first list 141 based on the first feature information after the recognition information is sorted according to the reaction time, have. Subsequently, the index list generation unit 140 can generate the second list 145 and the index list 147 by rearranging the plurality of sub-lists 143_1 to 143_N based on the second feature information.

The index list generation unit 140 generates the index list 147 that is rearranged after the recognition information is sorted in the predetermined order based on the first and second feature information extracted by the feature information extraction unit 130 Can be generated. Accordingly, the index list generation unit 140 generates the index list generating unit 140 for generating a list of the perceived perception, the memory, the attention, the performance, and the like for the behavioral tasks performed to confirm the EEG type of the population and the predetermined time, It is possible to provide a scale for evaluating one of the perceived power, the judgment power, and the concentration power.

In the embodiment of the technical idea of the present application, the perceptual-strength testing apparatus 100 measures the brain wave type through the brain-wave-based cognitive test and measures the brain wave type according to the measured brain wave type to the behavior task such as vision, The first and second feature information are extracted from the first and second classification information classified from the perceptual information according to the type of the induction group, , And generates a list of indexes in which the cognitive information is arranged in a predetermined order based on the index list, thereby providing a more accurate and objective evaluation scale for the cognitive ability.

FIG. 4 is a flowchart showing the operation of the perceptual-strength checking apparatus 100 of FIG.

Referring to FIGS. 1 to 4, the measuring unit 110 may measure the cognitive performance information including the brain wave type and the reaction time through the cognitive test based on the EEG in step S110.

Next, in step S120, the classifying unit 120 classifies the cognitive performance information measured through the measuring unit 110 into first and second classification information according to the type of induction group.

At this time, in step S130, the feature information extracting unit 130 compares the first and second classification information classified by the classifying unit 120 with the EEG type and the reaction time as comparison targets, and compares the first and second The first and second feature information can be extracted from the classification information.

Thereafter, in step S140, the index list generation unit 140 generates an index list in which the cognitive power information is arranged in the predetermined order based on the first and second feature information extracted through the feature information extraction unit 130 have.

5 is a flowchart showing the operation of the feature information extracting unit 130 of FIG.

Referring to FIGS. 1 to 3 and 5, the feature information extracting unit 130 extracts first and second classification information from the first and second classification information classified by the classification unit 120 in step S210, The second threshold time can be extracted.

At this time, the feature information extracting unit 130 may divide the entire interval between the first and second threshold times into a plurality of identical time intervals and set the divided unit time intervals as the first feature information, in step S220.

In step S230, the feature information extracting unit 130 extracts feature information of the first feature point extracted by the feature point extracting unit 130 from among the first feature points extracted by the feature extracting unit 130 using the FFT, wavelet, and complexity analysis (Multi-scale Entropy, Correlation Dimension) And the second classification information, and the noise-canceled EEG can be separated by frequency bands.

In step S240, the feature information extracting unit 130 may extract first and second threshold waveforms based on the induction type from the EEG waveforms separated by frequency bands.

Then, in step S250, the feature information extracting unit 130 extracts feature information such as amplitude, peak, edge, pattern, dip, score, and flat, which are waveform information for identifying an EEG type from the first and second threshold waveforms, Extracts one of the extracted feature points, divides the extracted one into a plurality of identical magnitude intervals, and sets the divided unit magnitude interval as second feature information.

FIG. 6 is a flowchart showing an operation according to an embodiment of the index list generation unit 140 of FIG.

1 to 3 and 6, in step S310, the index list generating unit 140 generates a list of indexes 121 in which the response time is shortened in the storage list 121 in which the perceptual information is stored in the order of measurement in the storage DB 200 In step S320, the first list 141 in which the perceptual information is sorted can be generated.

The index list generation unit 140 divides the first list 141 into a plurality of sub-lists 143_1 to 143_1 based on the first feature information in step S330. In step S340, At least two pieces of cognitive information contained in a sublist of the plurality of sub-lists 143_1 to 143_1 can be rearranged.

Subsequently, in step S350, the index list generation unit 140 may generate the second list 145 by merging the plurality of sub-lists 143_1 to 143_1 that have been rearranged.

Thereafter, in step S360, the index list generation unit 140 generates a diagnosis result in which the cognitive information is sequentially quantified in the second list 145 in which the cognitive power information is sorted in the predetermined order based on the first and second feature information You can create a list of applied indicators.

FIG. 7 is a flowchart showing an operation according to another embodiment of the index list generation unit 140 of FIG.

1 to 3 and 7, in step S410, the index list generation unit 140 generates a list list 121 in which the index information is stored in the storage DB 200 in the order of measurement, The recognition information can be sorted in the order of the shorter reaction time.

Next, in step S420, the index list generation unit 140 can generate the first list 141 in which the cognitive power information is sorted.

Subsequently, in step S430, the index list generating unit 140 may divide the first list into first to third sections corresponding to predetermined normal groups, redundant groups, and patient groups.

At this time, when the cognitive power information is categorized into the second section corresponding to the overlapping group in step S440, the index list generating unit 140 generates the index information based on the second feature information extracted by the feature information extracting unit 130 in step S450 So that the cognitive information contained in the redundant group can be rearranged.

Then, in step S460, the index list generating unit 140 merges the second section of the reordered redundant group between the first and third sections corresponding to the normal group and the patient group, and outputs the first and second characteristic information as a basis The index list in which the perceptual power information is arranged can be generated in the order determined as

FIG. 8 is a block diagram of a cognitive test apparatus 100_1 according to another embodiment of the present application.

8, the perceptual power testing apparatus 100_1 includes a measuring unit 110_1, a classifying unit 120_1, a feature information extracting unit 130_1, an index list generating unit 140_1, a diagnostic output unit 150_1, And an update unit 170_1. Here, the measuring unit 110_1, the classifying unit 120_1, the feature information extracting unit 130_1, and the generating unit 140_1 have the same functions as those in Figs. 1 and 2 and have the same effects, Is omitted.

According to the embodiment, the diagnostic output unit 150_1 can visualize and output the diagnosis result corresponding to the cognitive information of the subject measured through the brain-wave based cognitive testing by referring to the index list generated by the generating unit 140_1 have.

At this time, as shown in FIG. 9, the communication unit 160_1 can perform a communication function to transmit the diagnosis result output through the diagnostic output unit 150_1. The communication unit 160_1 may be a wired communication module including at least any one of a power line communication (PLC), a USB communication, an Ethernet, a serial communication, and an optical / Wireless LAN (WLAN), Digital Living Network Alliance (DLNA), Wireless Broadband (Wibro), WiMAX, HSDPA, A wireless communication module including at least one of IEEE 802.16, Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), and Wireless Mobile Broadband Service (WMBB) (Bluetooth), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, Near Field Communication (NFC), Ultra Sound Communication , USC ), A Visible Light Communication (VLC), a Wi-Fi, and a Wi-Fi Direct. That is, as shown in FIG. 5, the communication unit 160_1 can communicate with the terminal 10 held by the examinee or the administrator through the network 50. [

Next, the update unit 170_1 updates the cognitive power information of the subject based on the feedback information on the diagnosis result of the subject output through the diagnostic output unit 150_1, by using any one of the first and second classification information . ≪ / RTI >

9, the update unit 170 updates the diagnostic result of the subject output from the diagnostic output unit 150_1 to the terminal (step S502), which is connected to the network 50 via the communication unit 160_1 10), receives feedback information on the accuracy of the diagnostic result from the terminal 10, determines whether the cognitive performance information of the subject is updated, and determines whether or not the cognitive information stored in the storage DB 200 1 < / RTI > and the second classification information.

9 is a block diagram of a cognitive testing system 1000 in accordance with an embodiment of the present application.

Referring to FIG. 9, the cognitive-strength testing system 1000 may include a terminal 10, a percep- tuality testing apparatus 100_2, and a storage DB 200 held by a subject or an administrator.

First, the terminal 10 may be implemented as a computer, a portable terminal, or a television capable of being connected to or connectable to the percep- tiveness testing apparatus 100_2 via the network 50. [ For example, the computer includes a notebook computer, a desktop computer, a laptop computer, and the like on which a web browser (WEB Browser) is mounted. The portable terminal is, for example, Communication System, GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication) -2000, CDMA (Code Division Multiple Access) 2000, a W-CDMA (W-Code Division Multiple Access), a Wibro (Wireless Broadband Internet) terminal, a smart phone and the like. In addition, the television may include an Internet Protocol Television (IPTV), an Internet television (TV), a terrestrial TV, a cable TV, and the like.

In addition, the terminal 10 downloads and installs an online-based application through an App Store or an external server, receives the diagnostic result of the perception level from the perception testing apparatus 100_2 via the network 50 Outputting the feedback information, or inputting feedback information from the subject or the manager. At this time, the application may be a mobile phone of Symbian, RIMS, iPhone of Apple, Windows Mobile of Microsoft, Android of Google, The program may be a program for performing a cognitive diagnostic service based on various open operating systems such as TAGEN.

The network 50 includes a plurality of services, such as HTTP (Hyper Text Transfer Protocol), Telnet, File Transfer Protocol (FTP), Domain Name System (DNS) (Hereinafter, referred to as " Network Management Service "), a Simple Network Management Protocol (SNMP), a Network File Service (NFS) And can provide an environment in which the manager can be connected to the percep- tual-test apparatus 100_2. Meanwhile, the network 50 may be a wired or wireless Internet, or may be a core network integrated with a wired public network, a wireless mobile communication network, or a portable Internet.

The cognitive-impulse-checking apparatus 100_2 shown in Fig. 9 is the same as the features shown in Figs. 1, 2 and 8 and has the same effects as those of Figs. 1, 2 and 8, and therefore detailed description thereof will be omitted.

Next, the storage DB 200 stores cognitive power information of the collection group measured through the measurement unit 110, first and second classification data classified into the patient group classified according to the brain disease and the normal collection group, 2 classification information, the first and second lists generated through the generation unit 140, the plurality of sub-lists, and the index list, and can DB (DB) them. For example, the storage DB 200 may be a relational database management system (RDBMS) such as Oracle, Infomix, Sybase, DB2, or a database management system such as Gemston, Orion, Can be implemented for the purpose of the present application using an object-oriented database management system (OODBMS), and can have corresponding fields.

FIG. 10 is a flowchart showing the operation of the perceptual force test apparatus 100_2 of FIG.

Referring to FIGS. 9 and 10, the measuring unit 110_2 can measure the cognitive performance information including the EEG type and the reaction time through the cognitive test based on the EEG in step S510.

Next, in step S520, the classifying unit 120_2 may classify the cognitive performance information measured through the measuring unit 110_2 into first and second classification information according to the type of induction group.

At this time, in step S530, the feature information extracting unit 130_2 compares the first and second classification information classified by the classifying unit 120_2 with the EEG type and the reaction time as comparison targets, The first and second feature information can be extracted from the classification information.

Subsequently, in step S540, the index list generating unit 140_2 generates an index list in which the perceptual information is sorted in the predetermined order based on the first and second feature information extracted through the feature information extracting unit 130_2 have.

Next, in step S550, the diagnostic output unit 150_2 refers to the index list generated by the index list generating unit 140_2 to output a diagnostic result corresponding to the perceived information of the subject measured through the brain-wave based perception can do.

At this time, the communication unit 160_2 may transmit the diagnostic result of the perceived information of the subject outputted from the diagnostic output unit 150_2 to the terminal 10 connected via the network 50 in step S560.

In step S570, the update unit 170_2 receives feedback information on the accuracy of the diagnosis result from the testee or the administrator through the terminal 10, and receives the feedback information through the network 50 to the communication unit 160_2 .

In step S590, the update unit 170_2 determines whether the cognitive performance information of the subject is updated based on the feedback information. In step S590, the update unit 170_2 updates the cognitive information of the subject based on the determination result in the storage DB 200 It is possible to update one of the stored first and second classification information corresponding to the diagnosis result.

11 and 12 are views showing examples in which the perception strength testing apparatus 100_2 of FIG. 5 is applied.

5, 11, and 12, the measuring unit 110_2 includes a plurality of electrodes 111 to 11N attached to the head, and a hair band (not shown) measuring the response time for any one behavioral task Type wearable device.

More specifically, the measuring unit 110_2 measures the cognitive power information including the response time for the predetermined action according to the EEG type measured through the plurality of electrodes 111 to 11N used for the cognitive test based on brain waves, The list of indicators stored in the storage DB 200 is received through the communication unit 160_2 and the diagnostic result of the cognitive information of the examinee can be outputted by referring to the index list so that the cognitive ability Can be measured.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. 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.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

10: Terminal
50: Network
100: Cognitive testing device
110:
120:
130: Feature information extracting unit
140: Indicator list generation unit
150_1: Diagnostic output unit
160_1:
170_1:
200: Storage DB

Claims (21)

A measurement unit for measuring perceptual information about the population;
A storage DB for storing cognitive information about the population;
A classification unit that receives cognitive information on the population from the measurement unit and classifies the cognitive information about the population into the first classification information for the normal group and the second classification information for the patient group;
A feature information extracting unit for extracting first and second feature information based on a comparison result between first classification information for the normal group and second classification information for the patient group; And
And an index list generation unit that generates an index list in which the perceptual information is sorted based on the first and second feature information,
Wherein the perceptual information includes a response time to an EEG and a predetermined action task,
Wherein the measuring unit measures a response time to a predetermined behavioral task according to the EEG type and the EEG type for each subject included in the population,
Wherein the feature information extracting unit extracts a first threshold time for the normal group and a second threshold time for the patient group based on the reaction time, and based on the time difference between the first threshold time and the second threshold time, Extracts the first feature information,
Wherein the feature information extracting unit extracts a first critical waveform for the normal group and a second critical waveform for the patient group on the basis of the brain wave waveform, and extracts, based on the amplitude difference between the first critical waveform and the second critical waveform, And extracts the second characteristic information. The method according to claim 1,
And a diagnosis output unit for outputting a diagnosis result corresponding to the perceived information of the subject measured by the measuring unit with reference to the index list. 3. The method of claim 2,
Further comprising an update unit for updating the cognitive performance information of the subject according to the diagnosis result of the subject to one of the first classification information and the second classification information. The method according to claim 1,
Wherein the index list is a list of references to any one item of the perception strength, the memory power, the attention power, the reasoning power, the judgment power, and the concentration power, A cognitive test apparatus comprising diagnostic results quantified in order. delete The method according to claim 1,
Wherein the first feature information is a unit time in which a time difference between the first and second threshold times is divided into equal time intervals. The method according to claim 1,
Wherein the second feature information is a unit size that divides the amplitude difference between the first and second threshold waveforms into equal magnitude intervals. The method according to claim 1,
Wherein the index list generation unit generates a first list by sorting the perceptual information according to the reaction time and divides the first list based on the first feature information to generate a plurality of sub lists. 9. The method of claim 8,
The index list generation unit rearranges the perceptual information included in any one of the plurality of sublists based on the second feature information and then merges the plurality of sublists to generate a second list And then applying the quantified diagnostic result to the second list to generate the index list. delete delete 5. The method of claim 4,
The index list generation unit calculates and applies a quantified diagnosis result (E) for any one piece of cognitive information included in the index list through a quantitative index calculation formula,
The quantitative index calculating formula may be,

(1)
Where A is a weighting index according to the population type, A is a similarity index with the first threshold time, B is a similarity index with the second threshold time, C is a similarity index with the first threshold waveform, and D And the similarity index with the second threshold waveform. A method for recognizing a cognitive level of a cognitive test apparatus,
Measuring perceptual information on the population;
Categorizing the cognitive power information of the population into the first classification information for the normal group and the second classification information for the patient group;
Extracting first and second feature information based on a result of comparison between first classification information for the normal group and second classification information for the patient group;
Generating an index list in which the perceptual information is sorted based on the first and second feature information; And
And outputting a diagnosis result corresponding to the perceived information of the examinee with reference to the index list,
Wherein the perceptual information includes a response time to an EEG and a predetermined action task,
Wherein the extracting of the first and second feature information comprises:
Extracting a first threshold time for the normal group and a second threshold time for the patient group based on the reaction time;
Extracting the first feature information based on a time difference between the first threshold time and the second threshold time;
Extracting a first critical waveform for the normal group and a second critical waveform for the patient group based on the EEG waveform; And
And extracting the second feature information based on an amplitude difference between the first critical waveform and the second critical waveform. 14. The method of claim 13,
Wherein the generating the indicator list comprises:
Generating a first list in which the perceptual power information is arranged in the order of the shorter reaction time;
Dividing the first list into a plurality of sublists based on the first feature information;
Rearranging the perceptual information included in any one of the plurality of sublists based on the second feature information;
Merging the plurality of sub-lists to generate a second list; And
And applying the quantified cognitive power value to the second list to generate the indicator list. delete delete 14. The method of claim 13,
Wherein the first feature information is a unit time dividing a time difference between the first and second threshold times into equal time intervals. 14. The method of claim 13,
Wherein the second feature information is a unit size that divides the amplitude difference between the first and second threshold waveforms into equal magnitude intervals. delete 14. The method of claim 13,
Further comprising the step of updating the cognitive performance information of the examinee to one of the first and second classification information according to a diagnosis result of the cognitive performance information of the examinee.

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