KR20220102097A - Multiple eeg measurement and simultaneous monitoring system - Google Patents

Abstract

The present invention provides a multi-brain wave measurement and simultaneous monitoring system including a brain wave measurement device, a server, and a user system. The system may comprise: at least one brain wave measurement device (2100) including a brain wave chip measuring a brain wave and a Wi-Fi module transmitting data to a network; the sever (2500) collecting, analyzing, and storing data transmitted from a gateway (2200) which plays a role of collecting brain wave measurement data and information transmitted by the at least one brain wave measurement device (2100) and maintains performance; and the user system (2800) transmitting the analyzed information in real time to display and output monitoring results. The system, when a multi-brain wave measurement is performed, applies, to measured multi-brain waver results, one or more among a depression evaluation algorithm, an anxiety (instability) evaluation algorithm, a stress evaluation algorithm, a sleep evaluation algorithm, an ADHD evaluation algorithm, a concentration evaluation algorithm, a dementia evaluation algorithm, a brain balance evaluation algorithm, a physical fatigue evaluation algorithm, and an extreme activity index evaluation algorithm, thereby simultaneously monitoring one or more among depression, instability, stress, sleep, ADHD, concentration, dementia, brain balance, physical fatigue, and extreme activity indices for a plurality of measured persons. The present invention can monitor a plurality of persons to be measured at the same time, thereby promoting healthy life of the persons and the safety of a group.

Description

MULTIPLE EEG MEASUREMENT AND SIMULTANEOUS MONITORING SYSTEM

The present invention relates to a multiple EEG measurement and simultaneous monitoring system, and in particular, a simple configuration enables accurate multiple EEG measurement and simultaneous monitoring, and the performance of the system is improved even for simultaneous EEG measurement and simultaneous monitoring for multiple EEG measurers. It relates to a non-degrading multiple EEG measurement and simultaneous monitoring system.

EEG (Electroencephalogram) flows out of people's heads at every moment. EEG is a kind of electrical signal that occurs when a signal is transmitted between cranial nerves, which can be said to be a result of brain activity.

A device that measures EEG is called an EEG, and it can measure various biological signals such as EEG and pulse wave. Measurement techniques have been actively developed.

As an example (FIG. 1) of the prior art related to the EEG, a 'portable multi-channel EEG' (Publication No. 10-2009-0080010, publication date July 23, 2009) is known, this technology is "multi-channel In the EEG, it is about reducing power consumption by activating only the corresponding channel during the time that the EEG signal of each channel is alternately sampled by the A/D converter and shutting down the remaining channels.” Specifically, "EEG signals of each channel are bundled with a multiplexing bus at the end of the analog signal processing circuit, that is, immediately before or before the A/D converter, and managed as a logic circuit for signal selection, and time division processing is performed to generate dozens of signal lines. It is to integrate two bus lines and remove a significant portion of redundant circuits.” However, this technology only provides a technology for saving power consumption in an EEG measuring device that measures an individual's EEG, but there is a problem in that it cannot provide a technology for efficiently measuring and managing EEG for a plurality of people at once.

As another example (FIG. 2) of the prior art related to the EEG, 'EEG signal processing system including a sensor node and a plurality of sensor nodes for EEG measurement' (registration number 10-1490395, registration date January 30, 2015 ) is known, this technology " relates to a system for measuring EEG signals, and in particular to an EEG signal processing system including a sensor node and a plurality of sensor nodes for minimizing distortion of a measured analog EEG signal" to be. Specifically, "a plurality of sensor nodes that are attached to a wearable headset so as to cover a person's head and sense a person's brain waves, and EEG signals output from the plurality of sensor nodes are collected, processed and transmitted to an external device. In the EEG signal processing system including a data processing module to It is characterized in that it includes a filtering unit for removing noise, and an analog-to-digital converter for digitally converting an analog EEG signal from which noise has been removed through the filtering unit to a sampling frequency and outputting it to a data processing module". However, this technology only provides a technology that minimizes the distortion of the analog EEG signal measured by the EEG measuring device that measures an individual’s EEG, but does not provide the technology to efficiently measure and manage EEG for multiple people at once. there was

As another example of the prior art related to the EEG (Fig. 3), 'a content recommendation system and content recommendation method based on EEG/heart rate using wireless communication' (registration number 10-1576892, registration date December 07, 2015) ) is known, and this technology relates to "a content recommendation system and a content recommendation method based on EEG/heart rate using wireless communication". Specifically, "including an EEG/heart rate sensing headset and a content playback device that are interconnected through wireless communication; the EEG/heart rate sensing headset includes an EEG sensing unit sensing EEG and a heart rate sensing unit detecting a heart rate. a headset wireless communication unit for transmitting the EEG signal sensed by the EEG sensor and the heart rate signal detected by the heart rate sensor through wireless communication; a device wireless communication unit for receiving the EEG signal and the heart rate signal transmitted from the EEG/heart rate sensing headset, and at least one measured EEG variable and at least one measured heart rate variable from the EEG signal and the heart rate signal A measurement state data generating unit generating measurement state data by extracting A registration data set storage unit that is registered including a combination of a plurality of registered variables including at least one registered heart rate variable, and a state determination unit that compares the measured state data with the plurality of registered state data sets to determine a current state and a recommended content determining unit for extracting and providing pre-registered content suitable for the current state determined by the state determining unit”. However, these technologies also measure an individual's EEG/heart rate, interconnect them through wireless communication, and provide a content recommendation technology based on this. There was a problem with not being able to provide.

As another example of the prior art related to the EEG (Fig. 4), 'EEG test/training system using wireless communication and EEG test/training method using the same' (registration number 10-1813702, registration date December 22, 2017) ) is known, and this technology is "an EEG test/training system using wireless communication that can conduct EEG examination and training using wireless communication between an EEG measuring device that inspects EEG and smart terminals for EEG examination and training, and its It relates to the EEG test/training method used". Specifically, "EEG is mounted on the user's head to measure the user's brain waves that are changed by sight and hearing, wirelessly transmit them to the outside, and receive wireless signals from the outside to output voice and sound signals; Performs cognitive/learning ability test, and displays the image content of the mode selected by the user among the built-in EEG test/training contents according to the user's cognitive/learning ability test result, among the left brain, right brain activation, brain relaxation, and concentration enhancement modes and transmits sound and audio contents to the EEG wirelessly, and analyzes EEG signals from the wireless signals transmitted wirelessly from the EEG, and according to the results of the user's examination/training according to the analyzed EEG signals, a number of image and audio contents are provided to users. It is characterized in that it includes a smart terminal that selects suitable test/training content, displays it on the display unit for a set time, and transmits sound and voice content to the EEG through the communication unit. A problem that only provides an EEG test and a smart terminal for EEG examination and training, and an EEG examination/training method using wireless communication, but does not provide the technology to efficiently measure and manage EEG for multiple people at once there was

As another example of the prior art (FIG. 5) related to the EEG, 'multiple EEG measuring device and method' (registration number 10-2039752, registration date October 28, 2019) is known, this technology is It relates to an EEG measuring device and method." Specifically, "EEG electrodes located in a plurality of measurement sites, signal processing units that process the EEG signals output from the EEG electrodes in a separated state from the EEG electrodes, and EEG signals processed by the signal processing units, respectively. It includes a controller that analyzes.Thereby, it is possible to reduce the volume and weight of the EEG electrode by performing amplification and filtering of the EEG signal in a signal processing unit separated from the EEG electrode. An individual's EEG including the located EEG electrodes, signal processing units for processing EEG signals output from the EEG electrodes in a state separated from the EEG electrodes, and a controller for analyzing the EEG signals signaled by the signal processing units In the EEG measuring device that measures EEG, there was a problem in that it provided a technology to reduce the volume and weight of the EEG electrode, but could not provide a technology to efficiently measure and manage EEG for a plurality of people at once.

The present invention has been devised to solve the above-mentioned problems of the prior art.

Accordingly, the present invention is further improved to solve the problems of the prior art as described above, and relates to a system for simultaneously measuring multiple brain waves using Wi-Fi wireless communication and simultaneously monitoring data transmitted to a network and analyzing the measurement results. , Multi-EEG measurement and simultaneous monitoring of multiple measurement data without degradation of system network performance even when simultaneously monitoring the collected data by simultaneously measuring the EEG of several dozen people with a particularly simple configuration and transmitting the data under measurement to the network A monitoring system is provided.

Various problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

As an embodiment of the present invention, it provides a multiple EEG measurement and simultaneous monitoring system, including an EEG meter, a server, and a user system. The system includes at least one EEG measuring device 2100 including an EEG chip for performing EEG measurement and a Wi-Fi chip for transmitting data to a network; and a server 2500 having a data processing module 2600 for collecting, analyzing, and storing the EEG data transmitted from the at least one or more EEG 2100 and the at least one EEG 2100, and may include , in the system, when performing multiple EEG measurements, the measured multiple EEG results, for example, depression evaluation algorithm, anxiety (depression) evaluation algorithm, stress evaluation algorithm, sleep evaluation algorithm, ADHD evaluation algorithm, concentration evaluation By applying at least one or more of the algorithm, dementia evaluation algorithm, brain balance evaluation algorithm, body fatigue evaluation algorithm, or extreme behavioral index evaluation algorithm, depression, anxiety, stress, sleep, ADHD, concentration, At least one or more of dementia, brain balance, physical fatigue, or extreme behavioral index can be simultaneously monitored.

In another embodiment of the present invention, the multiple EEG measurement and simultaneous monitoring system, for example, receives and monitors the data analyzed by the data processing module in real time from the server 2500, and outputs it to the display screen 2600 It may further include a user system 2800 that does.

In another embodiment of the present invention, the at least one EEG measuring device 2100, as an example, further includes a Wi-Fi wireless transmission/reception means, and the server 2500 is a gateway for communicating with the WiFi wireless transmission/reception means of the EEG measuring device. (2200), a router 2300 that forwards the data packet transmitted from the gateway 2200 to the firewall 2400, and a firewall 2400 that filters the data packet transmitted from the router 2300 may include

In another embodiment of the present invention, the EEG measuring device 2100 may perform an EEG measurement and a method of transmitting the measured information to the server 2500 . the method; Initializing the main chip; converting the EEG chip into a measurement mode state, and the Wi-Fi module automatically scans the AP and connects to the server 2500 through the gateway 2200; Checking the lead on / off state of the EEG measuring device 2100 and confirming the state in which EEG measurement is possible when the lead on state is confirmed; When the preparation for EEG measurement is completed and the LED displays blinking blue, transmitting an EEG measurement preparation completion signal to the server 2500 through the gateway 2200; Receiving a start signal from the server 2500 through the gateway 2200, when the start signal is received, start EEG measurement, convert the measured data to raw data or FFT (Fourier change) to convert EEG data to Wi-Fi transmitting to the server 2500 through a module; If the start signal is not received, switching to the standby state until the start signal is received from the server (2500); When the EEG measurement is completed, the EEG measuring device 2100 switches to a sleep mode to reduce battery consumption while maintaining the Wi-Fi connection state, and switching the EEG chip to a standby state; receiving a wake-up signal from the server 2500 through the gateway 2200; When a wakeup signal is received, starting EEG measurement, converting the measured data to raw data or FFT (Fourier transform) and transmitting the EEG data to the server 2500 through a Wi-Fi module; automatically shutting down the power of the EEG measuring device 2100 when a predetermined time has elapsed after switching to the standby state if a wake-up signal is not received; Receiving the measurement stop signal from the server 2500 through the gateway 2200, when the measurement stop signal is received, stop the EEG measurement, and wait until a start signal is received from the server 2200 converting; If the measurement stop signal is not received, the EEG measurement is completed, the EEG meter 2100 switches to a sleep mode to reduce battery consumption while maintaining the Wi-Fi connection state, and the EEG chip is switched to standby. .

In another embodiment of the present invention, the step of checking the state of the EEG signal is an example, the step of checking the EEG signal lead on / off state (electrode attached state); It may include the step of confirming whether the EEG signal detection blinks in a predetermined color at a predetermined time interval.

In another embodiment of the present invention, the server 2500 may perform a method of instructing the operation of the EEG measuring device 2100 and executing a command for receiving EEG measurement information from the EEG measuring device 2100. have. The method includes, for example, receiving a connection signal from the EEG meter 2100 through the gateway 2200; displaying on the display means 2600 of the user system 2800 through the server whether each connected EEG measurement device 2100 can be measured; Displaying the EEG measurement possible state (lead on/off) and device information (device ID, firmware information, battery status) of the EEG measuring device 2100 on the display means 2600 of the user system 2800; Receives a ready signal from the EEG 2100 through the gateway 2200, and the user system 2800 checks the readiness of all connected EEG 2100 and sends a start signal for simultaneous start to the gateway 2200. Transmitting to the EEG (2100) through; The lead on/off state received from the EEG 2100 through the gateway 2200, the quality state of the measured data, and the measured data are displayed cumulatively at a predetermined time interval in 3D (time axis, amplitude, frequency), for a predetermined time displaying information on the measurement state and the loss state of the received data in real time on the display means 2600 of the user system 2800 through the server; It may include the step of storing the data received from the EEG meter 2100 through the gateway 2200 and the data and analysis results in the server 2500 .

In another embodiment of the present invention, the data processing module 2700 of the server 2500 analyzes the EEG measurement information measured from at least one EEG meter 2100, stores the analyzed information, and a user A method of executing a command output to the system 2800 is performed, and the method includes, for example, reading and analyzing EEG data stored in the server 2500; Depression evaluation algorithm, anxiety evaluation algorithm, stress evaluation algorithm, sleep evaluation algorithm, ADHD evaluation algorithm, concentration evaluation algorithm, dementia evaluation algorithm, brain balance evaluation algorithm, body fatigue for the index to analyze the analyzed EEG data Depression, anxiety, stress, sleep, ADHD, concentration, dementia, brain balance of a plurality of measurers by applying at least one or more algorithm logic of the evaluation algorithm or the extreme behavior index evaluation algorithm to the display means 2800 of the user system Displaying at least one or more analysis results of balance, physical fatigue, or extreme behavioral index; and storing the analysis result generated by the analyzing step in the server 2500, and automatically transmitting the EEG measurement information and/or the analysis result to a designated e-mail address.

In another embodiment of the present invention, the step of displaying the analysis result on the display means 2600 of the user system 2800 is to simultaneously display the entire EEG to measure or individually display for each EEG. may include steps.

In another embodiment of the present invention, the analysis index may include, for example, depression, anxiety, stress, suicidal ideation, dementia risk index.

In another embodiment of the present invention, the analysis result display is displayed in five levels of very serious, serious, normal, good, and very good, and includes a statistical function that can inquire the analysis result for each measured group, As for the analysis result, the data transmitted from the server may be displayed as a graph on the display means 2600 of the user system 2800 .

In the present invention, it is possible to quickly and accurately transmit all of the EEG data measured by a plurality of EEG sensors to the server simultaneously through wireless Wi-Fi, the Wi-Fi wireless reception distance is 100M, and the transmission speed can support up to 600Mbps in the case of 802.11g according to the Wi-Fi standard. Depending on the performance of the chip, more can be supported.

In the present invention, when a plurality of EEG measuring devices 2100 are connected to a router, a private IP is assigned, and data measured by a plurality of EEG measuring devices is sent to a server having a specific domain or IP address programmed in the Wi-Fi chip, and bidirectional communication is performed. In addition, data loss and traffic do not occur even if a large amount of EEG data is transmitted between multiple EEG sensors and servers.

In the present invention, as an example, it takes about 5 minutes for one person to measure EEG, and even if 50 or more measurers measure EEG at the same time, the measurement time is completed in about 5 minutes, so time and cost are reduced. It has the advantage of being very economical because it can expect significant savings.

The present invention provides a multiple EEG measurement and simultaneous monitoring system with a depression evaluation algorithm, an anxiety (depression) evaluation algorithm, a stress evaluation algorithm, a sleep evaluation algorithm, an ADHD evaluation algorithm, a concentration evaluation algorithm, a dementia evaluation algorithm, a brain balance evaluation algorithm, and body fatigue. By applying the evaluation algorithm and the extreme behavior index evaluation algorithm, it has the advantage of simultaneously monitoring the status of multiple measurers to promote the health life of the measurers and the safety of the organization.

It will be fully understood that embodiments of the present invention may provide various effects not specifically mentioned.

1, 2, 3, 4, and 5 illustrate a prior art EEG meter, system and method.
Figure 6a shows a prior art EEG measurement system of a 1:1 communication method using Bluetooth for one person measuring.
Figure 6b shows a modified EEG measurement system for a plurality of measurers by applying the EEG method of the prior art of Figure 6a.
7 shows a conceptual diagram of the EEG measurement system of the present invention.
Figure 8a shows an example of the hardware configuration of the EEG of the present invention.
8B is a Wi-Fi-based multiple EEG measurement monitoring and analysis system to which a gateway is applied, illustrating an embodiment of the present invention.
Figure 9a shows an example of the performing step of the EEG measurement of the EEG and the method of transmitting the measured information to the server in an embodiment of the multiple EEG measurement and simultaneous monitoring system of the present invention.
9B is a step of performing a method for the server to instruct the operation of the EEG and receive EEG information from the EEG in an embodiment of the multi-eEG measurement and simultaneous monitoring and analysis system of the present invention. An example is shown.
9c is a method comprising the step of analyzing, by the data processing module, EEG information measured from at least one EEG measuring device, and outputting the analyzed information in an embodiment of the multiple EEG monitoring and analysis system of the present invention; An example of the steps is shown.
10A-10D show the screen configuration of the multiple EEG measurement and simultaneous monitoring system of the present invention.
11 shows a 3D EEG analysis progress screen of the multiple EEG measurement and simultaneous monitoring system of the present invention.
12 shows a screen of measurement completion for each 3D EEG of the multiple EEG measurement and simultaneous monitoring system of the present invention.
13 shows a screen for explaining measurement results for each analysis item of the multiple EEG measurement and simultaneous monitoring system of the present invention.

Embodiments of the present invention may be modified in various forms, and the same members in each drawing are denoted by the same reference numerals, and detailed descriptions of well-known functions and configurations determined to unnecessarily obscure the gist of the present invention are omitted. It should be noted that

Another example of the prior art related to the EEG (Fig. 6a-6b) is the EEG measurement system 1000 that takes a 1:1 communication method that connects the EEG 1100 and the repeater 1200 by Bluetooth is known, In addition, the EEG measurement system 1500 that takes a 1:1 communication method for connecting the EEG measuring device 1100, the host 1200, and the server 1300 through Bluetooth has been known. The problems of this prior art are that, first, one repeater (smartphone, PC with program installed) is required for each individual, and the second is a wireless communication method, but it processes data sequentially to communicate and store data when a problem occurs. This could be slow, data not being saved, or data being lost.

Figure 7 shows a conceptual diagram of the EEG measurement system of the present invention, Figure 8a is an example of the hardware configuration of the EEG of the present invention, Figure 8b is a Wi-Fi method multiple EEG monitoring monitoring analysis system to which a gateway is applied, the present invention shows an example of

The configuration of an embodiment of the present invention with reference to FIGS. 7 and 8A-B is as follows.

Referring to Figure 8a, an example of the hardware configuration of the EEG included in the present invention is a motherboard 3100, an electrode channel 3120, a Wi-Fi communication module 3130, a lithium polymer battery 3140, a power monitoring unit (PMU) 3150 and a USB connector 3160.

Referring to FIG. 8B , an embodiment of the multiple EEG monitoring and analysis system of the present invention includes at least one EEG measuring device 2100 including an EEG chip for performing EEG measurement and Wi-Fi for transmitting data to a network, and the and a server 2500 having a data module 2700 that analyzes EEG measurement information measured from at least one EEG measurement device 2100 and outputs the analysis information, wherein the server 2500 is a data module 2600. Analyzes and stores the EEG data and information transmitted from at least one EEG measuring device 2100 by the In addition, the server 2500 may include a gateway 2200 responsible for maintaining performance, a router 2300 connected to the gateway 2200, and a firewall 2400 . In addition, the system of the present invention may further include a user system 2800 including a display means 2600 for displaying the analysis information. The at least one EEG measuring device 2100 includes a Wi-Fi wireless transmission/reception means, and the server 2500 is a gateway 2200 communicating with the WiFi wireless transmission/reception means of the EEG measuring device, and packets transmitted from the gateway 2200. It may include a router 2300 that passes through the firewall 2400 serving as a filter, and a firewall 2400 that receives a packet from the router 2300 and performs a filtering role.

Figure 9a shows an example of the performing step of the EEG measurement of the EEG and the method of transmitting the measured information to the server in an embodiment of the multiple EEG measurement monitoring analysis system of the present invention. Referring to FIG. 9A , the EEG measuring device 2100 performs EEG measurement and a method of transmitting the measured information to the server 2500, the method comprising the steps of initializing the main chip, measuring the EEG chip mode, the Wi-Fi module automatically scans the AP and connects to the server 2500 through the gateway 2200, the EEG chip is switched to the measurement mode and the lead on/off of the EEG meter 2100 Checking the status and confirming the lead-on state, confirming the state in which EEG measurement is possible Transmitting to 2500, receiving a start signal from the server 2500 through the gateway 2200, when the start signal is received, start EEG measurement, and convert the measured data to raw data or FFT (Fourier) change) and transmitting the EEG data to the server 2500 through the Wi-Fi module, if a start signal is not received, switching to a standby state until a start signal is received from the server 2500, EEG measurement When this is completed, the EEG meter 2100 switches to a sleep mode to reduce battery consumption while maintaining the Wi-Fi connection state, and the EEG chip switches to a standby state. A wakeup signal from the server 2500 is transmitted to the gateway ( 2200), starting EEG measurement when a wake-up signal is received, converting the measured data to raw data or FFT (Fourier change), and transmitting the EEG data to the server 2500 through the Wi-Fi module. , if a wakeup signal is not received, automatically shutting down the power of the EEG meter 2100 when a predetermined time has elapsed after switching to the standby state, and sending a measurement stop signal from the server 2500 to the gateway 2200 Receiving step, when the measurement stop signal is received, stop the EEG measurement, and start from the server (2200) Switching to a standby state until a signal is received, and if a measurement stop signal is not received, the EEG measurement is completed, and the EEG meter 2100 switches to a sleep mode to reduce battery consumption while maintaining the Wi-Fi connection state And the EEG chip contains the state by switching to the atmosphere.

Checking the state of the EEG signal is, for example, additionally checking the EEG signal lead on/off state (electrode attachment state), whether the EEG signal detection blinks in a predetermined color at a predetermined time interval. It may include the steps of confirming, confirming whether flashing in a predetermined color indicating the electrode attachment state, and confirming whether a predetermined color indicating whether the electrode attachment state is attached or not, blinking at predetermined time intervals. have.

9b is an example of a step of performing a method in which the server instructs the operation of the EEG and receives EEG information from the EEG in an embodiment of the multiple EEG monitoring and analysis system of the present invention. show

Referring to FIG. 9B , the server 2500 instructs the operation of the EEG measuring device 2100 , and performs a method of executing a command for receiving EEG measurement information from the EEG measuring device 2100 . The method includes the steps of receiving a connection signal from the EEG measuring device 2100 through the gateway 2200, and displaying means of the user system 2800 through the server 2500 whether it is possible to measure for each connected EEG measuring device 2100 ( 2600), the EEG measurement possible state (lead on/off) and device information (device ID, firmware information, battery status) of the EEG measuring device 2100 are displayed on the display means 2600 of the user system 2800 step, receiving the ready signal from the EEG 2100 through the gateway 2200, the user system 2800 checks the readiness of all connected EEG 2100 and sends a start signal for simultaneous start to the gateway ( 2200) through the step of transmitting to the EEG 2100, the lead on/off state received through the gateway 2200 from the EEG 2100, the quality state of the measured data, the measured data is 3D (time axis, amplitude . and storing the data and analysis results transmitted from the EEG 2100 through the gateway 2200 in the server 2500 .

Figure 9c is a step of analyzing the data and information transmitted from one or more EEG measuring devices and outputting the analyzed information in one embodiment of the real-time monitoring and EEG analysis system for multiple EEG measurement states of the present invention. An example of a step of performing a method comprising is shown.

Referring to FIG. 9C , the data processing module 2700 analyzes the EEG measurement information measured from the at least one EEG measurement device 2100 and includes a method of performing the step of outputting the analyzed information. The method includes the steps of analyzing the EEG data stored in the server 2500, and one or more analyzed EEG data for a brain index, a depression evaluation algorithm, an anxiety (positive) evaluation algorithm, a stress evaluation algorithm, a sleep evaluation algorithm, ADHD At least one of an evaluation algorithm, concentration evaluation algorithm, dementia evaluation algorithm, brain balance evaluation algorithm, body fatigue evaluation algorithm, or extreme behavior index evaluation algorithm is applied and analyzed by applying at least one or more algorithm logic (this will be described later) and the user system Depression, anxiety, stress, sleep, ADHD, concentration, dementia, brain balance of a plurality of measurers on the display means 2600 of the user system 2800 for each EEG meter 2100 on the display means 2600 of 2800 Displaying at least one or more analysis results of balance, physical fatigue, or extreme behavior index, storing all of the analysis results in the server 2500, and specifying the measurement information and/or analysis information may include the step of automatically transmitting to In addition, the step of displaying the analysis result on the display means 2600 of the user system 2800 may include, as an example, the step of simultaneously displaying the entire EEG to measure or individually displaying each EEG. The analysis index may include, for example, indexes of depression, anxiety, stress, suicidal ideation, and dementia risk. In addition, as an example, the analysis result display is displayed in five levels of very serious, severe, normal, good, and very good, and includes a statistical function that can inquire the analysis result for each measured group, and the analysis result is It may be displayed as a graph on the display means 2600 of the user system 2800 .

10a-10d shows the screen configuration of the multiple EEG measurement and simultaneous monitoring system of the present invention, and FIG. 11 shows the 3D EEG analysis progress screen of the multiple EEG measurement and simultaneous monitoring system of the present invention. 12 is a graph showing the measurement completion screen for each 3D EEG type of the multiple EEG measurement and simultaneous monitoring system of the present invention. 13 shows a screen for explaining measurement results for each analysis item of the multiple EEG measurement and simultaneous monitoring system of the present invention.

The operation of an embodiment of the present invention will be described in more detail as follows.

[Operation of the EEG meter (2100)]

The operation of the EEG measuring device 2100 employed in the present invention is as follows.

(Since the state of the equipment that multiple EEG monitors simultaneously measure EEG is the same, the operation scenario will be explained with the concept of one device)

Turn on the power by pressing the power button of all measuring instruments to be measured.

As soon as the power is turned on, the battery status is indicated by an LED.

Battery Enough: Steady Blue,

Middle of battery: solid green,

Low battery: Steady red,

Very Low: Red Flashing (0.5 sec interval)

In the standby state, the battery status is displayed. (EEG chip standby, Wifi chip sleep)

After initializing the main chip, the brainwave chip waits for measurement and the wifi module automatically scans the AP and attempts to connect.

Blinks yellow when scanning, and turns yellow when connection is complete.

When wearing the measuring instrument, check the EEG signal as follows.

-Check the EEG signal status lead on/off (electrode attachment status)

Lead on status from lead on/off: blue flashing

Lead-off status: Red blinks every 1 second

When ready for EEG measurement, the LED blinks blue and transmits a ready signal for EEG measurement from the measuring device to the server.

After monitoring the status of all measuring instruments in the server, when measurement preparation (read-on) is completed, the server sends a start command signal to the measuring instrument.

When the start command signal is recognized by the measuring device, the measuring device automatically transmits the measured EEG data to the server in real time.

The LED of the measuring instrument is displayed in blue-green blinking intervals of 1 second.

The measuring device converts the measured data to raw data or FFT and transmits the measured data to Wi-Fi.

In the measurement section, the start, open, close, and end divisions are sounded by a buzzer sound (measurement time of 5 minutes).

-Measurement start sound: “beep” (short)

-1st eye opening end/lung eye start sound: 1 minute 40 seconds “beep-beep”

-Lung eye end/2nd eye opening start sound: 3 minutes 20 seconds “beep-beep-beep”

-Measurement end sound: A “beep” (bold) sound notifies a section sound.

When the measurement is completed, the meter operates as follows.

If it is determined that there is no problem in the measurement status on the monitoring screen, the manager sends a standby command signal to each measuring instrument.

The meter maintains the Wi-Fi connection and switches to sleep mode to reduce battery consumption and switches the EEG chip to standby.

When the manager sends a wake-up signal for re-measurement, the EEG chip switches to the measurement state and the Wi-Fi remains active.

If there is no wake-up signal even after 5 minutes have elapsed since the measuring device is switched to the standby state, the power is automatically turned off.

If there is a problem in measurement or there is a need for re-measurement through the monitoring screen, the manager immediately sends a measurement stop signal, and the measuring instrument stops the measurement and switches to the standby state.

Press and hold the power button for more than 2 seconds to turn off the power.

The measuring device is charged through a charger or PC USB.

Charging time is around 2 hours (Battery is determined after checking the operation status of the first sample)

Operating time more than 2 hours

Charging LED: Green LED blinks every 2 seconds

Charging complete: Green LED lights up

[Monitoring of user system (2800)]

The monitoring of the user system 2800 employed in the present invention is as follows.

1. On the user system display screen, all EEG measurement data transmitted to the network at 1 second intervals from multiple measuring devices on the server is monitored simultaneously.

2. Monitor the following status on the user system display screen

The meter displays the number of recognized devices on the screen.

Display of EEG lead on/off electrode attachment status and device information for each measuring device (device ID, firmware information, battery status)

Enter the person's affiliation/name for all recognized measuring instruments

Simultaneous start, stop and complete command function for all measuring instruments

Control the start, stop and finish for each individual meter

Indication of lead-off status for each instrument.

Display of data measurement quality status of each measuring instrument;

By individually selecting the measuring device to be measured, the screen status can be displayed in a pop-up window.

The data of each measuring instrument is displayed in 3D state (time axis, amplitude, frequency) at 1 second intervals, and the measurement status is displayed in a graph for 5 minutes.

Check the data loss status for the data sent from the meter.

Simultaneous storage of measurement data and storage for each individual measuring device.

The stored data can be opened and the measured data can be checked.

The measurement time is 5 minutes, and the measurement period is set to open eyes (1 minute 40 seconds), piercing eyes (1 minute 40 seconds) and the second eye opening (1 minute 40 seconds).

In the measurement section, the start, open eye, close eye, and end divisions are indicated by a buzzer sound.

- Start measurement “beep” (short)

-1 The end of the first eye opening / the start of the closing eye is 1 minute 40 seconds “beep-beep”

-End of closed eye/start of second eye opening is 3 minutes 20 seconds “beep-beep-beep”

-After the measurement, a “beep” (bold) sound notifies a section sound.

The measurement time for each measuring instrument is also displayed.

It is also possible to print out the measurement results.

Comparative data before and after application of the present invention can be confirmed with respect to the measurement results. The data to be compared graphically shows the 3D measurement screen, analysis index, and display method before/after measurement.

[Operation of the server (2500)]

The operation of the server 2500 employed in the present invention is as follows.

Data transmitted from a plurality of EEG 2200 can be stably received by installing a gateway program in the server so that the transmission speed and traffic do not occur.

When transmitting the analysis data stored in the server to the user system, the data is compressed and transmitted so that the data can be transmitted quickly.

No delay or distortion occurs when multiple EEG data is monitored on the user system display screen in real time on the user system display screen.

[Operation of data processing module 2700]

The operation of the data module 2700 employed in the present invention is as follows.

1. When all the measured EEG measurements are completed, the analysis result is displayed.

2. The analysis screen is expressed by applying the algorithm logic for the analysis index to the system.

1) The analysis result is displayed at the same time, or the analysis result is displayed individually for each device.

2) The index to be analyzed is expressed as the index contents of depression, anxiety, stress, suicidal ideation, and dementia risk.

The indication method is expressed as very serious, severe, moderate, good, and very good on 5 levels.

The analysis result screen is easily understood and displayed as a graph.

Analysis result summary page contains analysis explanation, measurement result (3D, EEG analysis value (alpha, batter…) analysis result, and general opinion contents. The analysis screen is displayed and the format can be printed out.

There is a statistical function that allows you to inquire the analysis status for each measured group. It shows graphically when searching for analysis data by index and display method for affiliation and period. In case of multiple groups, full statistics function is also available.

When the measurement result data is saved in the server, the measurement data is automatically sent to the e-mail address specified for the measurement data (by date, group, number of people).

[Gateway (2200)]

Additional description of the gateway 2200 employed in the present invention is as follows.

The gateway system is located in the same subnet as the EEG 2100 and serves to collect and transmit data while communicating with a plurality of EEG bands.

Reasons for application of gateway system

If there is no gateway system, direct communication between the EEG and the server must be established. In this case, the server should be responsible for collecting and transmitting commands individually as many as the number of EEG sensors.

In this case, when the number of EEG groups increases, that is, the number of EEG instruments is 100, 200, 300... Whenever it increases in this way, there is a possibility that the performance of the server will decrease significantly.

If 500 EEG devices are attached to one server and communicate, it means that 500 simultaneous users actually connect and use one server at the same time.

In addition, since the server not only performs simple communication, but also collects and analyzes data to generate results, performance is likely to be lower.

The gateway system was applied as above in order to remove the factors that could affect the performance and divide the roles.

The effects of the present invention are as follows.

It can communicate 1:N via Wi-Fi rather than Bluetooth.

There is no need for repeaters per person.

Data can be compressed and sent to the server quickly.

By installing an additional gateway on the server, data transmission and storage can be stabilized by processing data.

When communication is made, data can be processed simultaneously.

Data loss is less than for many Bluetooth communications.

Multiple (multiple) EEG can be measured and stored at the same time in a short time.

As an embodiment of the present invention, when measuring the brain waves of 50 people for 5 minutes, it takes 5 minutes, but it takes 250 minutes for one example of a product of the prior art, and another example of a product of the prior art takes 5 minutes, but 50 repeaters (the program is installed Smartphones, PCs) are required, and data loss and instability exist.

The table below is a specification for an EEG used in an embodiment of the present invention.

A more detailed description of the algorithm logic for the brain index of the EEG data analyzed in the present invention is as follows.

● EEG test method

There are various test methods such as the lung eye test that measures the basic state of the brain, the ERP (Event Related Potential) test that examines the brain function by giving specific stimuli or changes, and the open/closed eye revitalization method. It is assumed that the method of "annual resurrection" is used. In the 2-channel EEG, Ch1 measures the left brain (Fp1) of the frontal lobe, and Ch2 measures the right brain (Fp2).

● EEG measurement conditions

EEG is a very minute signal in the unit of 1 millionth (micro) volt, and it is amplified and analyzed, so the accuracy of the measuring device is important, but the condition and environment of the subject are very important. EEG analysis can be performed accurately only when the measurement is accurate.

① Steady state: The first condition for EEG measurement is to measure in a steady state. Even if you measure EEG when you are happy, sad, or not physically and mentally stable, you cannot accurately evaluate the condition of the subject.

② Awakening state: When measuring EEG, it should be measured in a clear awakening state. In a state where it is difficult to wake up due to drowsiness or taking drugs, even if the brain wave is measured, the condition of the subject cannot be accurately evaluated.

③ Lung eye condition: When evaluating the basic brain function of a person, it is generally evaluated as an EEG of the lung eye condition. In the state of winding the body, the examination should be carried out as freely as possible without moving the body.

④ No movement or blinking during eye opening: If you move or blink during open eye, noise waves are mixed, and it is difficult to accurately read EEG unless the noise waves are artificially removed. Since noise waves are not signals from the brain, but signals from the body or other physical signals, accurate EEG readings are possible only when noise waves are minimized.

⑤ Other measurement conditions: In order to accurately measure EEG, the following conditions must be met.

- The part where the active electrode is attached (in case of 2 channel, the location of the forehead part Fp1, Fp2) and the location where the ground electrode and the reference electrode are attached must be wiped clean with a wet tissue, etc. to remove cosmetics, grease, sweat, etc. before measuring.

- After wearing the headband, at least 30 seconds to 1 minute should have time to stabilize physically and mentally with breathing. If the test is performed immediately after wearing, it may not be stable, and the EEG may need time to stabilize.

- Measurement should be carried out in a quiet place where there is no floating population and there is no noise or vibration.

- During the open eye examination, if the gaze is measured while looking down at a 45 degree angle, it reduces eye dryness and fatigue, thereby reducing blinking.

- If there are devices such as air conditioners, TVs, and heaters in the measurement area, they may be affected by electromagnetic waves, so check and measure them in advance.

- If the data processing capacity of the EEG device or the processing speed of the computer (smartphone) is slow, there may be a delay in EEG data processing and data may be collected in the form of miscellaneous waves, so this should be considered in advance.

- Noise can be generated by the computer's AC current, contact resistance, etc., so this must be taken into account.

● Opening and closing eye resurrection method inspection method

The commonly used open-eye revitalization test is in the form of “open eye EEG - open eye EEG - open eye EEG” and the measurement time varies from 30 seconds to 5 minutes. Therefore, it is reasonable to remove the first 15 seconds and the last 5 seconds of the data measured for 1 minute for both open and lung eyes, and then use the intermediate 40 seconds of data for analysis.

In the case of other existing programs, there is a method of “1st open eye examination - lung eye examination - 2nd open eye examination”, but the EEG of the 1st open eye examination is not used for analysis, but tends to be used as “time for examination preparation”, and data Since it is rather confusing, it is necessary to shorten the examination time by conducting the open eye examination only once after the lung eye examination.

● Collection of EEG data

1) EEG data is collected every second by obtaining amplitude values for each frequency from 1Hz to 30Hz through FFT transformation of the data collected in Ch1 and Ch2. Even if data is collected up to 128 Hz or 256 Hz, more than 30 Hz is not used for analysis.

2) For the collected data, the average amplitude value is calculated for each [frequency band].

Note) The method of classifying frequency bands may differ from researcher to researcher.

3) Alpha wave collects data on closed eyes every second from Ch1 and Ch2, respectively. Among them, only the amplitude data from 16 seconds to 55 seconds in the middle are used except for the data of the first 15 seconds and the last 5 seconds.

4) EEG data during open eyes are collected in the same way as 1), 2), and 3).

● Depression Rating Algorithm

1) Data extraction

① Sum Ch1 and Ch2 values of alpha wave in closed eyes and find the average value

Example) 16 sec ~ 55 sec amplitude sum of Ch1 (left brain) alpha wave (8Hz ~ 12.99Hz) = 520 ÷ 40 = 13

Ch2 (right brain) Alpha wave (8 Hz to 12.99 Hz) 16 sec to 55 sec Amplitude Sum = 440 ÷ 40 = 11

※ In general, if the Ch1 (left brain) value is high by this method, it is evaluated as depressed and introverted.

② Calculate 40 [left brain alpha value - right brain alpha value] every second for 40 seconds in the lungs.

③ Find the number of positive values (the left-brain alpha value is greater) and the number of negative values (the greater right-brain alpha value).

Ex) Find the alpha wave value of Ch1 and Ch2 in units of every second, calculate which one was higher for each second, and then find the number of times that each channel prevailed.

※ Rather than evaluating depression using only the average amplitude value, the validity and reliability of the evaluation can be increased by partially reflecting the number of dominant occurrences. Unless it is done, it can only be reflected in the EEG analysis.

2) Depression Rating Algorithm

① [Average amplitude of left brain alpha wave - Average amplitude of right brain alpha wave] ÷ Average amplitude of left brain alpha wave = ?

Example) In the table above, [13-11] ÷ 13 = 15.3% (Left brain dominates by 15.3% in average amplitude → Depression evaluation)

② [number of times left brain dominance - number of times right brain dominance] ÷ number of times left brain dominance = ?

Example) In the table above, [23-17] ÷ 23 = 26.1% (If it is negative, the right brain dominates overall, indicating a cheerful and extroverted tendency)

③ Given a weight, calculate the value of [① × 80%] + [② × 20%] and use it as the depression evaluation index.

Example) Based on the table above, [15.3% × 80%] + [26.1% × 20%] = 12.24% + 5.2% = 17.5%

3) Evaluation index by algorithm

The general EEG evaluation for depression is calculated in the manner of ① above to evaluate whether it is depression or not, and the counselor's experience and knowledge are given according to the level of the level. evaluation indicators were developed according to the So, when the value of ③ above is X

① if X>10.0% then : A state in which depression does not easily appear, does not show up when a depressed situation comes, and the depression does not last long and can be overcome by oneself

※ If the right brain is dominant, the value is negative, so if it is less than 0 when expressed as a graph, it should be expressed as 0.

※ In actual programming, all decimal places should be reflected so that there is no omission by the inequality sign formula.

② if 10.01%<X<20.0% then: Occasionally depressed or introverted. Depression does not last long and is not severe enough to interfere with daily life.

③ if 20.01%<X<35.0% then: Because there are many cases of depression and deep trouble, it is necessary to quickly solve the troublesome problem or need mental health management situation

④ if 35.01%<X then: In daily life, it is easy to lose motivation and to fall into negative thoughts due to frequent depression and worries, so you need help from people around you and experts quickly.

● Anxiety (Positive) Assessment Algorithm

1) Data extraction

① Obtain the values per second and average of left and right brain alpha waves in the lungs. (Same as depression)

② Find the maximum and minimum values of the left and right brains, respectively, among 40 seconds of data.

Ex) In the table of item ① above, the maximum value for the left brain is 18 and the minimum value is 11, and the maximum value for the right brain is 16 and the minimum value is 8.

③ Find the average of the maximum deviation of the left and right brains. → The larger this value, the more unstable it is.

Ex) In the table of item ① above, the maximum deviation of the left brain is 18 (maximum value)-11 (minimum value) = 7 (maximum deviation), and the maximum deviation of the right brain is 16-8 = 8. Therefore, the mean maximum deviation of wah and right brain is (7+8)/2=7.5.

④ Find the ratio of deviation between the left brain and the right brain. → The larger this value, the greater the left/right deviation, so it is unstable. Ex) In the table of ① above, the deviation ratio between the left and right brain is [high value (13) - low value (11)] ÷ high value (13) = 2/13 = 15.4%.

⑤ Find the average intensities of the left and right brains of delta, theta, and alpha waves.

⑥ Find the ratio of delta wave/alpha wave and theta wave/alpha wave ratio. (Yes)

⑦ Calculate the average EEG intensity and the weight of alpha waves in the left and right brains of the entire frequency band. (Example)

2) Anxiety (Positive) Evaluation Algorithm

① Above 2)-③ [(Maximum deviation of left brain) + (Maximum deviation of right brain)] ÷ 2 to find the average maximum deviation of left and right brain. (Maximum deviation of left brain 7, maximum deviation of right brain 8, average 7.5) → The larger the maximum deviation, the more unstable it is. If the maximum deviation is 5 or less, it is very stable, if it is 10 or less, it is stable, if it is 15 or more, it is unstable, and if it is 20 or more, it can be evaluated as very unstable. However, since the intensity of the amplitude may be different for each EEG device to be measured, it may be more reasonable to compare the maximum deviation with the average intensity of the alpha wave and evaluate it as a ratio value.

That is, the maximum deviation of the left brain alpha wave above 7 / the average of the left brain alpha wave 13 = 53.8%

The maximum deviation of the upper right brain alpha wave is 8 / The average of the right brain alpha wave is 11 = 72.7%, so the maximum deviation ratio of the right brain is high, so it can be seen that the right brain is more unstable than the left brain.

However, in order to evaluate the left and right as a single value for instability, the left and right averages are taken and the maximum deviation reflection value is (53.8% + 72.7%) / 2 = 63.25%.

If the maximum deviation exceeds the average value, it becomes more unstable. Therefore, if it is lower than 100%, the level of instability is evaluated as low, and if it is higher than 100%, the level of instability is evaluated as high. However, even if the maximum deviation of the alpha wave is less than the average value, it cannot be said that there is no complete instability. Therefore, it is better to set this reflection value to 0 only when the maximum deviation for instability is 50% or less, and to reflect it to instability if it is more than that.

Therefore, the evaluation of the maximum deviation is [large deviation reflected value - 50% = Y], and if the value is negative, it is close to stable, and if it is +, the value is reflected in the instability evaluation.

Ex) When the maximum deviation of the left brain alpha wave is 7, the average value is 13, the maximum deviation of the right brain is 8, and the average value is 11, the maximum deviation of the left brain is 7/13=53.8%, and the maximum deviation of the right brain is 8/11=72.7% and the average is 63.25%, so it is calculated as 63.25% - 50% = 13.25%. If this value is negative, the maximum deviation is less than 50% of the average, so it is stable, so the reflected value is set to 0, and if it is +, it is reflected as an unstable evaluation value. (If it is 100%, the maximum deviation value is 10 times the average value of the alpha wave, so it is extremely unstable)

② Above 2)-④ Deviation ratio between left and right brain = 15.4% → The larger the deviation ratio, the more unstable (stable).

If this deviation ratio exceeds the maximum of 50%, it can be considered that the left and right brain asymmetry is high and very unstable, so multiply the calculated value by 2 to get 15.4% × 2 = 30.8%. do. (If it is 100%, the left and right brains are completely asymmetric and extremely unstable)

③ Above 2)-⑥ Delta/alpha wave ratio = X → If the ratio is higher than 50%, it is unstable (unstable).

Therefore, if 50% - X is a negative value (if the delta wave is higher than the alpha wave), it is added to the analysis value in ①, and if it is +, the delta wave is not high because the delta wave is not high. (Add 10% to instability)

Therefore, the weight is reflected as much as [Value of ① × 10%] + [Value of ② × 10%].

Ex) [Value of ① 13.25% × 10%] + [Value of ② 30.8% × 10%] = 4.405% is reflected as this value.

④ Above 2)-⑥ Ratio of theta wave/alpha wave = Y → If the ratio is higher than 50%, it is unstable (unstable).

Therefore, if 50% - Y is negative (if theta wave is higher than alpha wave), it is added to the analysis value of ①, and if it is +, theta wave is not high because theta wave is not high. Add 10% to instability)

Therefore, the weight is reflected as much as [Value of ① × 10%] + [Value of ② × 10%].

Ex) [Value of ① 13.25% × 10%] + [Value of ② 30.8% × 10%] = 4.405% is reflected as this value.

⑤ Above 2)-⑦ Left and right brain average EEG intensity and proportion of alpha wave of the entire frequency band = When doing Z → If the proportion of alpha wave is lower than 50%, it is unstable.

Therefore, if Z - 50% is negative, the intensity of the alpha wave is less than 50%, so it affects the instability.

Therefore, the weight is reflected as much as [Value of ① × 10%] + [Value of ② × 10%].

Ex) [Value of ① 13.25% × 10%] + [Value of ② 30.8% × 10%] = 4.405% is reflected as this value. In the table above, since the proportion of alpha waves is 55.6%, this value becomes 0.

⑥ Evaluate the above ① + ② + ③ + ④ + ⑤ as the sum of the respective weights.

[Value of ① × 35% ] + [Value of ② × 35%) ] + [ Value of ③ × 10% ] +[ Value of ④ × 10% ] + [Value of ④ × 10%]

Yes)

3) Evaluation index by algorithm

The general EEG evaluation for anxiety (depression) is calculated in the manner of ① and ② above to evaluate whether or not anxiety is present, and counseling is conducted according to the experience and knowledge of the counselor according to the high or low level. In this analysis, items ③, ④, ⑤ It is intended to increase the reliability and validity of the analysis by partially additionally reflecting the So, when the value of ⑥ above is X

① if X>10.0% then : emotional. They are emotionally stable, so they can overcome external circumstances and difficulties on their own.

※ In actual programming, all decimal places should be reflected so that there is no omission by the inequality sign formula.

② if 10.01%<X<25.0% then : Sometimes emotionally and emotionally unstable (uneasy) does not last long, but it is a state in which you can overcome it yourself by effort. It can affect you, so you need to make an effort to overcome it.

③ if 25.01%<X<40.0% then : emotional. Because emotionally unstable (unsettled) can have a lot of impact on learning and work, it is a condition in which uneasy (unsettled) factors are discovered through consultation with a professional or people around them and they need to be resolved quickly or mental health management

④ if 40.1%<X then : emotional. It is a state of the brain that is emotionally unstable and has a great impact on daily life, so it is necessary to seek help from people and experts around you as soon as possible.

● Stress Assessment Algorithm

1) Data extraction

Stress is divided into mental stress and physical stress, and can be divided into left brain and right brain values. Mental stress is evaluated by the absolute intensity of high beta wave in the lungs, and physical stress is evaluated by the intensity of delta waves in the lungs. However, since there is a separate analysis item called physical fatigue, the evaluation focuses on mental stress here.

① Obtain the unit value and average value for each second of the left and right brain high beta waves in the lungs.

② Obtain the unit value and average value of left and right brain alpha waves in the lungs every second. (Same as depression)

2) Stress Assessment Algorithm

① Mental stress assessment

A) Absolute value evaluation

The average value of the high beta waves of the left and right brains is calculated. Generally, if the value is less than 0.5, there is no stress, if it is less than 1, there is little stress, if it is more than 1, there is stress, and if it is more than 2, it is evaluated as high stress. On the other hand, in other programs, the optimal intensity of alpha waves in closed eyes is considered to be 5 to 10. However, since the absolute intensity of the measured value may be different depending on the EEG device, it is evaluated as an alpha wave relative value as follows.

B) Alpha wave relative value evaluation

Since the absolute intensity of the measured value may be different depending on the EEG device, the high beta wave and the alpha wave value can be compared and mental stress or mental antistress can be evaluated according to the ratio. In the above example, since the stable high beta wave is less than 0.5 and the alpha wave is between 5 and 10, when comparing the high beta wave 0.5 and 7.5, which is the median value of the alpha wave, the ratio is 6.666%. Relative values can be evaluated.

In the table above, the left and right average intensity of high beta wave is [(0.9 + 0.825) ÷ 2] = 0.8625 and the average intensity of alpha wave is [(13+11) ÷ 2] = 12, so [0.8625 ÷ 12] = 7.1875% .

Therefore, since the value is close to 7%, it can be evaluated that the mental stress is small.

Therefore, the algorithm for evaluating mental stress is [L/R average intensity of high beta wave in closed eye ÷ Average left/right intensity of alpha wave in closed eye] = X

(X-7%) = mental stress assessment value, (in the example above, 7.1875% - 7% = 0.1875%)

However, since the absolute value of high beta wave is too small, there is not much difference in the value calculated according to the individual EEG measurement value. That is, 0.1875% × 3 = 0.5625%

By performing calculations in this way, the following values can be calculated according to the change in the high beta value. In general, no matter how high the high beta value in the closed eye, it has rarely been seen that it exceeds 3.0, so it is considered reasonable to evaluate the stress by applying this formula.

On the other hand, if the high beta wave value is applied as the appropriate intensity of the alpha wave, it is as follows.

If the final mental stress value is negative, it is set to 0, and if it exceeds 100, the highest value is set to 100%.

3) Evaluation index by algorithm

In this analysis, mental stress should be analyzed as one data, so the mental stress value was evaluated by averaging the left and right brains.

That is, when the above ①-B) [L/R average intensity of high beta wave in closed eye ÷ Left/right average intensity of alpha wave in closed eye] = X (X-7%)×3 = Mental stress evaluation value, and that When the value is X

① if X>15.0% then: It is good to maintain this level well because there is not much stress and it is the optimal state to overcome it well even if there is stress.

※ In actual programming, all decimal places should be reflected so that there is no omission by the inequality sign formula.

② if 15.01%<X<30.0% then: There is stress and sometimes it is a state that can affect daily life, so appropriate rest and leisure activities to relieve stress are necessary

③ if 30.01%<X<45.0% then: A lot of stress affects daily life and sometimes impulsive tendencies may appear, so it is necessary to try to improve stress through sufficient rest and leisure activities.

④ if 45.01%<X then: The stress level is very high, so the body and mind are tired, the nerves are sensitive, and impulsive tendencies may appear frequently.

● Sleep Assessment Algorithm

1) Data extraction

As the EEG characteristic related to sleep, it can be evaluated as the [disappearance rate], which is the typical “lung eye - change of EEG due to open eye”, and the representative among them is the loss rate of alpha wave. Alpha waves are resting and relaxing brain waves, which increase in the pulmonary eye and decrease or suppress in the open eye. This phenomenon is called “alpha-blocking”. Therefore, it is the primary evaluation related to sleep to check whether the alpha-blocking phenomenon appears normally. In addition, if a phenomenon in which the loss rate of delta and theta waves is reversed (a phenomenon that appears weak in open eye and strong in closed eye) occurs, this is also It is assessed as having problems with sleep duration and quality of sleep over a long period of time.

Therefore, EEG data is extracted as follows to obtain the value of the loss rate.

① Obtain the values per second and average of left and right brain alpha waves in the lungs.

In the same way, the unit value and average value of the left and right brain alpha waves during open eye are obtained every second.

② Obtain the unit value and average value for each second of the left and right brain delta waves in the closed eye.

In the same way, the values per second and average values of the left and right brain delta waves during open eye are obtained.

③ Obtain the unit value and average value of left and right brain theta waves in the lungs every second.

Obtain the unit value and average value of left and right brain theta waves every second during open eye.

2) Sleep Assessment Algorithm

Whether or not the loss rates of alpha, delta, and theta waves are reversed, sleep-related EEG analysis can be evaluated. The change (disappearance rate) of EEG in the open/closed eyes can be summarized as follows.

On the other hand, the brain waves should be evaluated separately from the left brain and the right brain, but in this analysis, a total of six variables including the left and right brains, alpha waves, delta waves, and theta waves should be evaluated as one value, so the values of the left and right brains are analyzed using the average value. decide to do

① Extract the data between 16 and 55 seconds in each of the closed and open eyes of the alpha wave to obtain the average amplitude value, and calculate the loss rate as [(lung eye-open eye) ÷ lung × 100]. After calculating the left and right brain values, the average value is calculated.

② Delta wave calculates the rate of disappearance by finding the ratio [(open eye-lung eye) ÷ open eye × 100]. After calculating the left and right brain values, the average value is calculated.

③ Calculate the loss rate by calculating the ratio of theta wave [(open eye-lung eye) ÷ open eye × 100]. After calculating the left and right brain values, the average value is calculated.

Calculation of loss rate ex) All values are average values of left and right brain.

The dissipation rate obtained in this way can be evaluated with the weights of alpha wave 40%, delta wave 30%, and theta wave 30%, and the minimum and maximum values from -50 to +50.

evaluation example)

3) Evaluation index by algorithm

EEG evaluation for sleep is basically evaluated by the loss rate of alpha waves, delta waves, and theta waves, and the values can vary from minus to plus 50 or more. It is possible to evaluate with an evaluation interval. When the above evaluation value is X

① if X>-10.0% then: Since there are many sleep problems, sleep problems may have an effect in daily life, so a lot of effort is needed to get a healthy and sufficient sleep.

② if -10.0%<X<10.0% then: Sleep is not a big problem, but the brain is tired and sometimes sleepy during the day or it is difficult to concentrate, so efforts are needed to get a healthy and sufficient sleep

③ if 10.01%<X<45.0% then: I am having a healthy sleep and there is no problem with sleep, so it would be good if I could keep the status quo

④ if 45.01%<X then: The sleep problem is not great, but the brain is tired and sometimes it may be difficult to be distracted or concentrate in daily life, so efforts are needed to get a healthy and sufficient sleep.

● ADHD Assessment Algorithm

ADHD is an abbreviation of Attention-Deficit Hyperactivity Disorder (ADHD) and is a name called a disease. (Example) Distraction, lack of impulse control, impulsivity

1) Data extraction

The characteristics of EEG related to ADHD are that the absolute intensity of theta wave is strong, the ratio of [theta wave ÷ beta wave] is high, and the ratio of [theta wave ÷ SMR] is high and shows left-right asymmetry. Other EEG analysis methods may be added, but the following EEG is used for analysis based on the data extracted from the 2-channel EEG device. To evaluate as one value, the left and right brain values are summed and averaged.

① Obtain the values per second and average values of the left and right brain theta waves in the lungs.

② Calculate the value in units of seconds and the average value of the left and right brain SMRs in the closed eye.

③ Calculate the unit value and average value of the left and right brain low beta waves in the lungs every second.

④ Obtain the values in units of seconds and average values of left and right brain alpha waves in the lungs.

This value is used to evaluate left-right brain imbalance.

2) ADHD Assessment Algorithm

① Evaluation according to the theta wave absolute intensity value

This evaluation is not used in the analysis of the present invention because the intensity of the EEG may be different depending on the device.

② Calculate the theta wave/beta wave ratio value in the closed eye.

In previous studies, ADHD is known to have a ratio of theta wave:beta wave exceeding 3:1. Beta waves are analyzed using low beta waves.

In the above table, the average intensity of theta wave is (5.25+5.38) ÷ 2 = 5.315 and the average intensity of the low beta wave is (1.62 + 1.63) ÷ 2 = 1.625, so 5.315 ÷ 1.625 = 3.27.

How much higher this value is than 3 can evaluate the degree of ADHD.

Example) (3.27 - 3) ÷ 3 = 9%

③ Calculate the theta wave:SMR ratio value in the closed eye.

In other programs, this ratio value is also used for “distraction” analysis, which evaluates how distracted the brain is. This ratio is also used as an index to evaluate how well the brain can pay attention without being distracted. In general, since SMR has a larger amplitude than beta wave, the ratio of theta wave:SMR can be evaluated with a weight of 50% by calculating a value based on 2.5:1.

In the above table, the average intensity of theta wave is (5.25+5.38) ÷ 2 = 5.315 and the average intensity of SMR is (2.1+2.3) ÷ 2 = 2.2, so 5.315 ÷ 2.2 = 2.4.

④ If the calculated value of ② above and the calculated value of ③ are weighted by 50% respectively to calculate the evaluation value, it can be used as an index to evaluate the degree of ADHD according to the height of the value.

⑤ To evaluate the left and right brain intensity asymmetry, the ratio is calculated by subtracting the lower value from the higher value of the average value of the left brain of alpha wave and the average value of the right brain of the alpha wave. It is additionally reflected to calculate the final evaluation value.

Ex) Above [(Average of left brain alpha wave (high value) 13) - (Average of right brain alpha wave in closed eye (low value) 11] ÷ Average of left brain alpha wave (high value) 13) = 2 ÷ 13 = 15.3%

The final evaluation value is calculated by multiplying the value in ④ above by 15.3% and adding the calculated value.

evaluation example)

For the evaluation of this number, the degree of ADHD can be evaluated by setting the reference value to 3. If the evaluation value is 3 or less, it is evaluated that the possibility of impulsive behavior is low without being distracted. For easy expression in 100% ratio, the reference value is 3 and [(final evaluation value - 3) ÷ reference value] is calculated, [(3.24 - 3] ÷ 3] = 8.0%, which is unlikely to be ADHD.

3) Evaluation index by algorithm

When the above evaluation value is X

① if X>15% then: Less likely to be distracted and impulsive. It would be better to keep the current status quo.

② if 15.01%<X<25.0% then: It is a state of the brain that can sometimes be distracted, have difficulty concentrating, and sometimes act impulsively.

③ if 25.1%<X<35% then: A condition that requires counseling and treatment along with efforts to improve it, as it is usually distracting, it is difficult to concentrate, and sometimes, it is difficult to control one's own behavior, so impulsive behavior lim

④ if 35.1%<X then: It may be very distracting, unable to concentrate, and it may be difficult to control one's own impulsive behavior, so counseling and treatment are urgently needed to improve it.

● Concentration Assessment Algorithm

Concentration can be exercised in a state where the basic brain state is comfortable, pleasant, and not distracted, so it can be evaluated as a state in which there is no sleep problem, the intensity of theta wave is not high, and SMR and beta wave are activated to some extent. In other programs, the concentration is evaluated by measuring the dynamic ability of the brain with a low-beta-wave feedback test.

On the other hand, studies on concentration only mention whether the related EEG showed a significant increase before and after a specific intervention (training), and there are few studies that suggest standard values.

1) Data extraction

Combining these previous studies and EEG analysis experience, as mentioned above, concentration can be evaluated as theta wave, SMR, and beta wave. We want to calculate the value and evaluate it with a weight in a ratio of 40:60. The ratio of theta wave:SMR indicates the degree of distraction or selective concentration (attention), so it is better to reflect 40% of this number because if you are distracted, you cannot concentrate, and theta wave:low beta wave is used as an index to evaluate concentration, so 60% is reflected I would like to Therefore, the following data are required for analysis.

① Obtain the values per second and average values of the left and right brain theta waves in the lungs.

② Calculate the value in units of seconds and the average value of the left and right brain SMRs in the closed eye.

③ Calculate the unit value and average value of the left and right brain low beta waves in the lungs every second.

2) Concentration Evaluation Algorithm The concentration evaluation algorithm is basically the same as evaluating theta wave: SMR and theta wave: low beta among the evaluation algorithms for ADHD, and the opposite interpretation is used instead.

① Calculate the theta wave:beta wave ratio value in the closed eye.

In previous studies, it was said that it was difficult to concentrate when the ratio of theta wave:beta wave was high. can be evaluated on Beta waves are analyzed using low beta waves.

In the above table, the average intensity of theta wave is (5.25+5.38) ÷ 2 = 5.315 and the average intensity of the low beta wave is (1.62 + 1.63) ÷ 2 = 1.625, so 5.315 ÷ 1.625 = 3.27.

The higher the value, the more difficult it is to concentrate, and the lower the value, the higher the concentration.

③ Calculate the theta wave:SMR ratio value in the closed eye.

In other programs, this ratio value is also used for “distraction” analysis, which evaluates how distracted the brain is. This ratio is also used as an index to evaluate how well the brain can pay attention without being distracted. This value is evaluated as a specific gravity of 40%.

In the above table, the average intensity of theta wave is (5.25+5.38) ÷ 2 = 5.315 and the average intensity of SMR is (2.1+2.3) ÷ 2 = 2.2, so 5.315 ÷ 2.2 = 2.4. The higher the value, the lower the ability to focus selectively, and the lower the value, the higher the selective focus.

④ If the evaluation value is calculated by placing 60%:40% weight on the calculated value of ① and ②, respectively, it can be used as an index to evaluate the degree of concentration according to the height of the value.

evaluation example)

3) Algorithmic evaluation index Since the lower the value, the higher the concentration. Therefore, the minimum value is 0 and the maximum value is 5, so that the section can be divided and the degree of concentration can be evaluated. When the above evaluation value is X

① if 3.51<X then : Usually very distracted and difficult to concentrate, often resulting in poor learning or work efficiency

② if 2.81<X<3.5 then: often distracted and difficult to concentrate

③ if 2.01<X<2.8 then: no problems paying attention or concentrating well

④ if X>2.0 then: It is a brain state that can pay attention well and concentrate very well.

● Dementia Assessment Algorithm

It is believed that dementia should not be used as the name of a disease unless it is a medical person or a medical institution, and it is better to use names such as “decline in cognitive function” and “general decrease in brain function”. A common point mentioned in various EEG studies related to dementia is that patients with dementia have 1) stronger theta waves than normal people, 2) stronger delta waves than normal people, and 3) inactive beta waves during cognitive activity. being analyzed. Even with the developer's experience, theta wave and delta wave amplitude are high in dementia patients or persons with reduced cognitive function, and the alpha-blocking phenomenon does not appear well. impossible.

1) Data extraction

In order to evaluate the risk of dementia, theta wave, which is classically widely used, is first reflected, and the delta wave is partially reflected. Even when the alpha wave power is high during open eyes, it can be evaluated that there are sleep problems and brain fatigue problems, so the alpha wave power is reversed. It reflects this as a phenomenon and reflects brain distraction and fatigue by reflecting theta wave/SMR.

① Obtain the values per second and average values of the left and right brain theta waves in the lungs. If this value is large, it is evaluated that there is a risk of dementia.

② Obtain the unit value and average value for each second of the left and right brain delta waves in the closed eye. If this value is large, it is evaluated that it has an effect on dementia.

③ Calculate the value in units of seconds and the average value of left and right brain alpha waves in the lungs. If the alpha wave is high even during open eyes, it is evaluated as low activity.

④ Obtain the unit value and average value of left and right brain alpha waves every second during open eye. Analyze the reversal phenomenon of opening and closing eyes.

⑤ Calculate the value in units of seconds and the average value of the left and right brain SMRs in the lungs. In comparison with the theta wave in the lung eye, the degree of brain fatigue and distraction is evaluated by the ratio value.

⑥ Find the power value for each frequency from 1 to 13Hz, and find the frequency in which frequency band the dominant frequency is formed. When a dominant frequency is formed in the theta wave band (if the amplitude is high), it can be evaluated that it is close to the risk of dementia. In the table below, 9Hz is the dominant frequency of the subject.

※ Even at the age of 80, in healthy elderly people, the amplitude of 9Hz alpha wave is dominant.

2) Dementia evaluation algorithm

The biggest difference in EEG between dementia patients and normal people is theta wave. However, as mentioned in the second study above, theta wave is not a disease-specific (pathognomic) characteristic that appears only in dementia patients, but is a characteristic that appears in all people with reduced overall brain function (ADHD, tics, autism, brain aging, etc.) High theta waves as a result of EEG tests cannot be definitively diagnosed as dementia. Nevertheless, the following analysis algorithm can be created to analyze the risk of dementia with EEG data.

① Theta wave overestimation. (Theta wave: Alpha wave)

Whether theta wave is excessive or not, the intensity of the signal may be different for each EEG device, so it is reasonable to compare it with the alpha wave in the lungs and evaluate it as a ratio value. Since the theta wave in the lung eye should be less than the alpha wave, if it exceeds 50%, it can be evaluated as theta wave excess.

That is, the average intensity of the theta wave in the upper lung is (3.88 + 3.70) ÷ 2 = 3.79, and the average intensity of the alpha wave is (13 + 11) ÷ 2 = 12. So (3.79 ÷ 12) × 100 = 31.58%. The higher this value exceeds 50%, the higher the theta wave, so attention is distracted and the brain's information processing ability is lowered. The weight of this value is given the most and the risk of dementia is evaluated.

② Delta wave overestimation. (delta wave: alpha wave)

Delta waves are brain waves that appear during deep sleep or coma. Reflecting the research result that the delta wave significantly increases in dementia patients, the ratio of the average delta wave to the alpha wave in the closed eye can be partially used to evaluate the risk of dementia.

That is, the average intensity of the delta wave in the closed eye is (5.25 + 5.38) ÷ 2 = 5.315 and the average value of the alpha wave is 12, so 5.315 ÷ 12 × 100 = 44.29%. If this ratio exceeds 50%, the delta wave is high and can be partially reflected in the risk of dementia.

③ Reversal evaluation of alpha wave loss rate (alpha mean for closed eyes - mean alpha for open eyes) ÷ mean alpha for closed eyes The alpha wave becomes stronger when the eyes are closed and weakens when the eyes are opened (alpha blocking phenomenon) to be a normal EEG change. Only then, the basic rhythm (goyuri rhythm) is formed to dominate the alpha wave. In addition, if the alpha wave disappearance rate is reversed, the alpha wave becomes stronger when the eyes are opened. Therefore, in a previous study, the increase in alpha wave during eye opening was also related to dementia. .

That is, the average alpha wave during closed eye is 12, and the average alpha wave during open eye is (9.4 + 9.1) ÷ 2 = 9.25, and the loss rate is calculated as (lung eye mean - OPEN average) ÷ lung mean × 100 (12 - 9.25) ÷ 12 100 = 22.9%. If this value is negative, the alpha wave is higher during open eye, which may cause sleep problems, as well as make the brain tired all the time and reduce cognitive function. If this value is positive, it is normal, and if it is negative, it is partially reflected in the risk of dementia.

④ Theta wave: SMR ratio evaluation

Theta wave is an EEG that occurs predominantly during light sleep, and this EEG appears excessively high during wakefulness, stability, and the lungs. Since SMR is an EEG that is activated in the state of attention and arousal, this ratio value can be calculated and partially reflected in the dementia risk assessment. In the above table, the average intensity of theta wave is (3.88 + 3.70) ÷ 2 = 3.79 and the average intensity of SMR is (2.1 + 2.3) ÷ 2 = 2.2, so 3.79 ÷ 2.2 = 1.72. If this ratio exceeds 2.5, the theta wave is high, so it can be evaluated that the brain is fatigued, it is difficult to maintain arousal, and the risk of dementia is higher.

⑤ Dominant frequency evaluation

The dominant frequency of a normal person at rest, arousal, and closed eyes should be an alpha wave (8~12.99Hz). If the dominant frequency is formed in the theta wave band lower than this, it can be seen as aging and functional deterioration of the brain. Therefore, in the table of ⑥ above, it can be evaluated that the dominant frequency is formed lower than 8 Hz, and the risk of dementia or symptoms becomes more severe as 7 → 6 → 5 → 4 Hz. If the dominant frequency is formed in the theta wave band in this way, naturally, the amplitude of theta wave is high, so theta; The ratio of SMR is bound to increase as well.

⑥ There can be various analysis methods for EEG, but if one is not good, it will affect other analysis. For example, when theta wave is high, theta:alpha ratio, theta:SMR ratio, dominant frequency, etc. are inevitably affected. Each of the five values evaluated above is weighted, and dementia risk can be evaluated comprehensively. If the algorithm is created and calculated in the above way, the dementia risk can be evaluated as follows. Set the minimum value to 0% and the maximum value to 100%.

Case 1)

Case 2)

Case 3)

3) Evaluation index by algorithm

Dementia is a phenomenon in which memory, language ability, calculation ability, visual/spatial ability, etc. are uniformly lost overall due to a marked decrease in memory and cognitive function, resulting in mental devastation and a decrease in intelligence. Although the representative characteristic of EEG related to dementia is excessive theta wave, the algorithm was written by reflecting the characteristics of other EEGs in which brain function deteriorates. Therefore, when X is the final value for evaluating dementia risk according to Case 1) analysis of 3)-⑥ above, it can be evaluated that the higher the value, the higher the dementia risk. However, rather than using this as a diagnosis of dementia, it would be reasonable to use it as an evaluation index for dementia risk, cognitive function, memory, and deterioration of brain function.

① if X>5% then: The risk of dementia is assessed as normal brain function

② if 5.01%<X<15.0% then: Brain function is somewhat lowered, so it is sometimes evaluated that functions such as memory, cognitive ability, and learning ability are somewhat inferior

③ if 15.01%<X<30.0% then: Brain function is significantly lowered, so functions such as memory, cognition, and learning are often evaluated as markedly deteriorated

④ if 30.01%<X50.0% then: The overall brain function is remarkably deteriorated and it is often difficult to perform memory, cognition, learning, and work, so quick improvement is required.

⑤ if 50.01%<X then: The brain function is so severely degraded that it is often difficult to perform memory, cognition, learning, and work, so prompt action is needed to improve it.

● Brain Balance Assessment Algorithm

Brain Balance Balance can be replaced with "left and right brain balance" in other words. Each part of the brain has different roles, and although the left and right brains are separated from each other, they are connected by a bundle of nerves called the “corpus callosum,” which creates mental and physical activities while exchanging information quickly. The evaluation algorithm for left and right brain balance can be evaluated by the intensity symmetry of the brain waves and the coherence of the brain waves, which are usually evaluated due to the very poor academic data.

1) Data extraction

In general, left-right balance (balance) is evaluated by the symmetry and synchronicity of alpha waves in the lungs. In this analysis, in order to increase differentiation, validity, and reliability, the symmetry and synchronicity of alpha waves are the basis, and the intensity symmetry of SMR, low beta, and high beta is analyzed.

① Obtain the values per second and average of left and right brain alpha waves in the lungs. (century symmetry)

② Evaluate the increase/decrease every second of left and right brain alpha waves in the lungs. (Coherence - concurrency)

A value of 1 is given if the amplitudes of the left and right brains increase or decrease simultaneously, and a value of 0 is given if they are different.

This data is evaluated from the 15th and 16th seconds.

③ Obtain the value in units of seconds and the average value of the left and right brain SMRs in the lungs.

④ Obtain the unit value and average value of the left and right brain low beta waves in the lungs every second.

⑤ Obtain the unit value and average value for each second of the left and right brain high beta waves in the lungs.

2) Brain balance evaluation algorithm

① Calculate the symmetric value of left and right brain alpha waves in the closed eye.

[(lower value among left and right brain) ÷ (higher value among left and right brain)] × 100 to find the intensity symmetry value.

Using the data in the table above as an example, 11 ÷ 13 × 100 = 84.6%

The higher this value, the better the left and right brain intensity symmetry.

② Evaluate the coherence (simultaneity) of left and right brain alpha waves in the lungs.

The number of times the left and right brains increase or decrease together during the 40 seconds of EEG measurement in the lungs is added up and compared with the measurement time of 40 seconds, and the ratio value is calculated.

Using the data in the above table as an example, 35 ÷ 40 × 100 = 87.5% because it increased or decreased 35 times in 40 seconds.

③ Find the left and right brain intensity symmetry values of SMR in closed eyes.

[(lower value among left and right brain) ÷ (higher value among left and right brain)] × 100 to find the intensity symmetry value.

Taking the SMR values in the table above as an example, 2.1 ÷ 2.3 × 100 = 91.3%

④ Find the left and right brain intensity symmetry of the low beta wave in the closed eye.

[(lower value among left and right brain) ÷ (higher value among left and right brain)] × 100 to find the intensity symmetry value.

Taking the low beta value in the table above as an example, 1.62 ÷ 1.63 × 100 = 99.4%

⑤ Find the left and right brain intensity symmetry values of high beta waves in the closed eye.

[(lower value among left and right brain) ÷ (higher value among left and right brain)] × 100 to find the intensity symmetry value.

Using the high beta values in the table above as an example, 0.7 ÷ 0.8 × 100 = 87.5%

⑥ By giving weight to the values of ①, ②, ③, ④, ⑤ above, the brain balance is finally evaluated.

3) Evaluation index by algorithm

The evaluation of this number can be evaluated from 0% to 100%. In general, the left and right brains are separated from each other and play different roles, so it is difficult to achieve 100% perfect balance. % or more, it is evaluated as normal, but the same algorithm as above is used to evaluate the left and right brain balance as one indicator.

When the final value above is X

① if 90.1%<X then: The left and right brain balance is very good, the brain usage efficiency is good and the emotional stability is very good

② if 80.1%<X<90% then: Left and right brain balance is good, so brain usage efficiency is good and emotionally stable.

③ if 70.1%<X<80% then: The left/right brain balance is at a normal level, and if you put more effort, you can increase the brain usage efficiency.

④ if X>70% then: It is necessary to recover the brain condition through effort because the brain usage efficiency may decrease due to the balance between the left and right brains.

● Body fatigue evaluation algorithm

Body fatigue is evaluated by the intensity of delta waves in the lungs by using the term “physical stress” in EEG analysis.

1) Data extraction

Stress is divided into mental stress and physical stress, and can be divided into left brain and right brain values. Mental stress is evaluated by the absolute intensity of high beta wave in the lungs, and physical stress is evaluated by the intensity of delta waves in the lungs.

Since the amplitude of delta waves greatly increases sensitively to the mixing of miscellaneous waves such as body movement and blinking, brain waves must be analyzed in consideration of this.

① Obtain the values per second and average values of left and right brain delta waves in the lungs.

② Obtain the unit value and average value of left and right brain alpha waves in the lungs every second. (Same as depression)

2) Physical stress assessment algorithm

① Physical stress assessment

A) Absolute value evaluation

In general, for physical stress, the average value of the delta wave of the left and right brains is obtained, and if the value is less than 5, there is no stress, if it is less than 10, there is little stress, if it is more than 10, there is stress, and if it is 20 or more, it is evaluated as high stress. However, since the value is different depending on the characteristics of the EEG device, the physical stress is evaluated with the relative value with the alpha wave as follows.

B) Alpha wave relative value evaluation

Since the absolute intensity of the measured value may be different depending on the EEG device, compare the delta wave and the alpha wave value and evaluate the physical stress or physical antistress according to the ratio.

Since the intensity of the delta wave in the closed eye should be less than 50% of the alpha wave, the ratio of delta wave/alpha wave can be obtained and physical stress can be evaluated according to the value.

C) In the table above, the left and right average intensity of delta wave is [(5.25+5.325)÷2] = 5.2875, and the average intensity of alpha wave is [(13+11)÷2] = 12, so [5.2875 ÷ 12] = 44.0625% becomes

Therefore, since the value is less than 50%, it can be evaluated that the physical stress is small.

However, even if the value is less than 50%, it cannot be said that there is no physical stress at all.

Algorithm for evaluating physical stress is [Left/right average intensity of delta wave in closed eye ÷ Left/right average intensity of alpha wave in closed eye] = X

(X-40%) = Physical stress assessment value

If this value is negative, it is set to 0, and if it exceeds 100, the highest value is set to 100%.

In the example above), 44.0625% - 40% = 4.0625% is the physical stress evaluation value.

If the average value of the left and right of the delta wave is 13 and the average value of the alpha wave is 12

[(13÷12) × 100 ] - 40% = 68.3%. This can be evaluated that the delta wave, which should be 40% lower than the alpha wave, is higher than the alpha wave, indicating that physical tension and stress are high.

3) Evaluation index by algorithm

It is common to evaluate the physical stress by separating the left and right brain values separately, but in this analysis, the stress needs to be analyzed with one data, so the physical stress value was evaluated by averaging the left and right brains.

That is, when the above ①-C) [L/R average intensity of the delta wave in the closed eye ÷ the average intensity of the left/right alpha wave in the closed eye] = X (X-40%) = Physical stress evaluation value

① if X>15.0% then: It is good to maintain this level well because there is not much stress and it is the optimal state to overcome it well even if there is stress.

※ In actual programming, all decimal places should be reflected so that there is no omission by the inequality sign formula.

② if 15.01%<X<30.0% then: Due to stress, it is a state that can sometimes affect daily life, so appropriate rest and leisure activities to relieve stress are necessary.

③ if 30.01%<X<45.0% then: A lot of stress affects daily life and sometimes impulsive tendencies may appear, so it is necessary to make efforts to improve stress through sufficient rest and leisure activities.

④ if 45.01%<X then: The stress level is very high, so the body and mind are tired, the nerves are sensitive, and impulsive tendencies may appear frequently.

● Extreme behavior index evaluation algorithm

“Extreme behavior” is influenced by various factors such as family, heredity, surrounding environment, economic life, social life, etc. It will happen because you can't control (regulate) it. Even in the same situation, different people behave differently because their brains are different in accepting and thinking about reality. So far, there has been no study that analyzed the brain waves of people with extreme behaviors (eg, suicide), and it is impossible to predict “there will be extreme behaviors in the future” using brain waves. Or, it should be meaningful that the people around you can help the person to overcome difficulties.

1) Data extraction

Combining the above prior studies, the EEG characteristics of people with suicidal thoughts can be summarized as “very depressed, very anxious, very distracted, very impulsive, and very stressed.” Since these evaluations were all calculated above, the extreme behavior index is calculated by additionally reflecting “negative behavioral tendency”.

① Reflect the depression index. Depression index is 3-1. The number calculated by the depression evaluation algorithm is used.

② It reflects the instability (sustainability) index. Anxiety index is 3-2. The numerical value calculated by the insecurity (stable) evaluation algorithm is used.

③ Distracted and impulsive 3-4. The number calculated by the ADHD evaluation algorithm is used.

④ Stress is 3-3. Mental stress and 3-9. Take advantage of physical fatigue (physical stress).

⑤ Use the following data to evaluate negative behavioral tendencies.

a) Obtain the left and right average values of alpha waves in the closed eye.

B) Obtain the left and right average values of low beta waves in the lungs.

C) Find the alpha:low beta of the left brain and alpha:low beta of the right brain, and calculate the difference ratio between the two values by [(right brain value - left brain value) ÷ right brain value × 100].

Case 1)

Case 2)

⑥ The values of ①, ②, ③, ④, and ⑤ above are given weights for final evaluation.

Each person experiences and learns differently in their lifetime, so even if any one of the above items is extremely high, extreme behavior may occur. However, from the point of view of the characteristics of EEG, depression, impulsivity, and negative tendencies were evaluated as having more influence on extreme choices among the above items, and instability and stress were also partially reflected to increase the rationality of the evaluation.

3) Evaluation index by algorithm

Each person experiences and learns differently in their lifetime, so even if any one of the above items is extremely high, they may have extreme behavior. However, from the point of view of the characteristics of EEG, depression, impulsivity, and negative tendencies were evaluated as having more influence on extreme choices among the above items, and instability and stress were also partially reflected to increase the rationality of the evaluation. In addition, in the process of leading a life in the harsh modern society, everyone has some degree of depression, anxiety, impulsivity, and stress, so it is necessary to be careful in the evaluation. When the value of ⑥ above is X

① if X>10.0% then: The extreme behavior index is low, so it is evaluated as no risk

② if 10.01%<X<20.0% then: Brain function sometimes decreases, resulting in decreased motivation and depressed or impulsive thoughts. Therefore, efforts to relieve stress and improve mental health are necessary.

③ if 20.01%<X<40.0% then: The brain function for making rational and efficient judgments is significantly reduced, and there is a risk of frequent depressive or impulsive behavior, so professional or professional treatment is needed.

④ if 40.01%<X then: The brain function for rational and efficient thinking seems to be in a very severely degraded state, so treatment is needed to improve mental health as soon as possible.

The above description describes one example in the present invention, and may be modified in various forms without departing from the spirit of the present invention.

In the above, the present invention has been described in detail with reference to specific examples, but since the examples are only for illustrating the present invention, substituted, added and modified embodiments are also provided below without departing from the technical spirit of the present invention. It should be regarded as belonging to the scope of protection of the present invention as defined by the claims appended hereto.

1000 The prior art EEG system consisting of one measuring instrument and a repeater, 1100 EEG measuring instrument, 1200 repeater, 1300 Prior art server 1500 The prior art EEG system consisting of multiple measuring instruments and multiple repeaters, 2000 Multiple measuring instruments and EEG of the present invention consisting of a plurality of repeaters, 2100 EEG of the present invention, 2200 gateway, 2300 router, 2400 firewall, 2500 server, 2600 display means, 2700 data processing module, 2800 user system, 3100 main chip, 3120 electrode channel , 3130 Wi-Fi communication module, 3140 lithium polymer battery, 3150 power monitoring unit (PMU), 3160 USB connector

Claims (10)

In a multiple EEG measurement and simultaneous monitoring system, including an EEG, a server and a user system, the system comprises:
At least one EEG measuring device 2100 including an EEG chip for performing EEG measurement and a Wi-Fi chip for transmitting data to a network; and
A server 2500 having a data processing module 2600 for collecting, analyzing, and storing the EEG data transmitted from the at least one or more EEG 2100 and the at least one EEG 2100 includes a server 2500,
In the system, when multiple EEG measurements are performed, a depression evaluation algorithm, an anxiety (depression) evaluation algorithm, a stress evaluation algorithm, a sleep evaluation algorithm, an ADHD evaluation algorithm, a concentration evaluation algorithm, a dementia evaluation algorithm, By applying at least one or more of the brain balance evaluation algorithm, the body fatigue evaluation algorithm, or the extreme behavior index evaluation algorithm, it can reduce depression, anxiety, stress, sleep, ADHD, concentration, dementia, brain balance, Characterized in that at least one or more of the physical fatigue or extreme behavior index is monitored simultaneously.
Multiple EEG measurement and simultaneous monitoring system. According to claim 1, wherein the multiple EEG measurement and simultaneous monitoring system receives the data analyzed by the data processing module in real time from the server (2500), monitors, and outputs the user system (2800) to the display screen (2600) characterized in that it further comprises
Multiple EEG measurement and simultaneous monitoring system. According to claim 1, wherein the at least one EEG measuring device 2100 further comprises a Wi-Fi wireless transmission and reception means, the server 2500 is a gateway 2200 for communicating with the WiFi wireless transmission and reception means of the EEG, the gateway ( A router 2300 that transmits data packets transmitted from 2200) to the firewall 2400, and a firewall 2400 that filters the data packets transmitted from the router 2300, characterized in that it further comprises doing
Multiple EEG measurement and simultaneous monitoring system. According to claim 1, wherein the EEG meter (2100) performs the EEG measurement and a method of transmitting the measured information to the server (2500), the method;
Initializing the main chip;
converting the EEG chip into a measurement mode state, and the Wi-Fi module automatically scans the AP and connects to the server 2500 through the gateway 2200;
Checking the lead on / off (lead on / off) state of the EEG measuring device 2100 and confirming the EEG measurement possible state when the lead on (lead on) state is confirmed;
When the preparation for EEG measurement is completed and the LED displays blue blinking, transmitting an EEG measurement preparation completion signal to the server 2500 through the gateway 2200;
Receiving a start signal from the server 2500 through the gateway 2200, when the start signal is received, start EEG measurement, convert the measured data to raw data or FFT (Fourier change) to convert EEG data to Wi-Fi transmitting to the server 2500 through a module;
If the start signal is not received, switching to the standby state until the start signal is received from the server (2500);
When the EEG measurement is completed, the EEG measuring device 2100 switches to a sleep mode to reduce battery consumption while maintaining the Wi-Fi connection state, and switching the EEG chip to a standby state;
receiving a wake-up signal from the server 2500 through the gateway 2200;
When a wakeup signal is received, starting EEG measurement, converting the measured data to raw data or FFT (Fourier transform) and transmitting the EEG data to the server 2500 through a Wi-Fi module;
automatically shutting down the power of the EEG measuring device 2100 when a predetermined time has elapsed after switching to the standby state if a wake-up signal is not received;
Receiving the measurement stop signal from the server 2500 through the gateway 2200, when the measurement stop signal is received, stop the EEG measurement, and wait until a start signal is received from the server 2200 converting;
If the measurement stop signal is not received, the EEG measurement is completed, the EEG meter 2100 switches to a sleep mode to reduce battery consumption while maintaining the Wi-Fi connection state, and the EEG chip is switched to standby, including a state to do
Multiple EEG measurement and simultaneous monitoring system. According to claim 4, wherein the step of checking the state of the EEG signal
Checking the EEG signal lead on/off state (electrode attachment state), checking whether the EEG signal detection flickers in a predetermined color at a predetermined time interval, and flickering in a predetermined color indicating the electrode attachment state A step of confirming, and checking whether a predetermined color indicating whether the electrode is attached or not is flickering at a predetermined time interval.
Multiple EEG measurement and simultaneous monitoring system. According to claim 1, wherein the server 2500 performs a method of instructing the operation of the EEG measuring device 2100, and executing a command for receiving EEG information from the EEG measuring device 2100, the method comprising;
Receiving a connection signal from the EEG meter 2100 through the gateway 2200;
displaying on the display means 2600 of the user system 2800 through the server whether each connected EEG measurement device 2100 can be measured;
Displaying the EEG measurement possible state (lead on/off) and device information (device ID, firmware information, battery status) of the EEG measuring device 2100 on the display means 2600 of the user system 2800;
Receives a ready signal from the EEG 2100 through the gateway 2200, and the user system 2800 checks the readiness of all connected EEG 2100 and sends a start signal for simultaneous start to the gateway 2200. Transmitting to the EEG (2100) through;
The lead on/off state received from the EEG 2100 through the gateway 2200, the quality state of the measured data, and the measured data are displayed cumulatively at a predetermined time interval in 3D (time axis, amplitude, frequency), for a predetermined time displaying information on the measurement state and the loss state of the received data in real time on the display means 2600 of the user system 2800 through the server;
Data received from the EEG meter 2100 through the gateway 2200 and the data and analysis results comprising the step of storing the data and analysis results in the server 2500
Multiple EEG measurement and simultaneous monitoring system. The method of claim 1,
The data processing module 2700 of the server 2500 analyzes the EEG measurement information measured from at least one EEG meter 2100, stores the analyzed information, and outputs a command to the user system 2800 performing a method of executing, said method comprising:
Reading and analyzing the EEG data stored in the server 2500;
Depression evaluation algorithm, anxiety (depression) evaluation algorithm, stress evaluation algorithm, sleep evaluation algorithm, ADHD evaluation algorithm, concentration evaluation algorithm, dementia evaluation algorithm, brain balance evaluation algorithm, body fatigue for the index to analyze the analyzed EEG data Depression, anxiety, stress, sleep, ADHD, concentration, dementia, brain of each of a plurality of measurers by applying at least one or more algorithm logic of the evaluation algorithm or the extreme behavior index evaluation algorithm to the display means 2800 of the user system Displaying at least one or more analysis results of balance balance, physical fatigue, or extreme behavior index; and
Storing the analysis result generated by the analyzing step in the server 2500, and automatically transmitting the EEG measurement information and/or the analysis result to a designated e-mail address.
Multiple EEG measurement and simultaneous monitoring system. According to claim 7, wherein the step of displaying the analysis result on the display means (2600) of the user system (2800),
It characterized in that it comprises the step of simultaneously displaying the entire EEG to measure or individually displaying for each EEG measuring instrument.
Multiple EEG measurement and simultaneous monitoring system. The method of claim 7, wherein the analytical index is
characterized by indices of depression, anxiety, stress, suicidal ideation, and dementia risk
Multiple EEG measurement and simultaneous monitoring system. 8. The method of claim 7, wherein the analysis result display is displayed in five levels of very serious, severe, normal, good, and very good, and includes a statistical function that can inquire the analysis result for each measured group, and the analysis result is displayed as a graph on the display means 2600 of the user system 2800
Multiple EEG measurement and simultaneous monitoring system.

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