KR20140103560A - Electroencephalogram detection apparatus of hair band type providing sleep electroencephalogram learning memory consolidation analysis function of brain and method thereof - Google Patents

Abstract

The present invention relates to a hair band type electroencephalogram (EEG) detection apparatus and a terminal for providing a function of analyzing sleep electroencephalogram learning memory consolidation, and a method thereof, which comprise the following: a hair band configured to be mounted on the forehead of an examinee during a usual day or before the examinee goes to bed; a tightening tool configured to fasten the hair band to fit a length of a circumstance of the head on which the hair band is mounted; an EEG detection apparatus including at least one EEG detection sensor configured to detect EEG signals through electrodes attached on the forehead of the examinee; and a terminal including a sleep and learning manager application program (SLM app) configured to output electroencephalogram by transmitting the EEG signals to the terminal through Bluetooth, ZigBee, RF communications, detect an optimal sleeping hour when learning is consolidated by detecting a delta wave of ranging between 0.2 to 4 Hz closely related to learning memory consolidation in a brain during sleep and electrooculogram (EOG) immediately following the delta wave at least two times, and predict a time when the first or second EOG is detected right after a learning memory consolidation related brainwave is detected as a wake time. According to the present invention, a hair band type EEG detection apparatus is mounted on the forehead of an examinee before the examinee goes to bed and brainwaves measured by multiple EEG detection electrodes while the examinee sleeps are transmitted to an SLM app in a computer, a smartphone, or a tablet PC through Bluetooth, ZigBee, or RF communications. Thus, it is possible to search optimal sleeping hours allowing a study amount to be retained at the maximum and predict a wake time to minimize fatigue.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a brain-type electroencephalogram measuring apparatus, a terminal and a method thereof,

More particularly, the present invention relates to a hair band type EEG measuring device, a terminal and a method thereof, and more particularly, to a hair band type EEG measuring device that attaches a hair band type EEG measuring device to a head of a subject before sleeping, Electroencephalogram (EEG) Electroencephalogram (EEG) Electroencephalogram (EEG) signals are transmitted to the brain wave analysis application program (SLM App) of a terminal such as a computer, a smart phone or a tablet PC through Bluetooth or ZigBee or RF wireless communication. Memory Retension (Consolidation) of the brain is optimized through the analysis of EOG (ElectroOCULOGRAPHY) and EOG (Delta wave in the range of 0.2 ~ 4Hz) among Sleep EEG which changes according to the user's sleep cycle A brain band type EEG device and a terminal for providing sleep enhancement analysis memory enhancement analysis function for predicting sleep time and optimal wake time And a method thereof.

Electroencephalogram (EEG) is the first electrophysiologist in the United States to record the electrochemistry of the brain in 1875 as a galvanometer for the first time by a physiologist, R. Keaton, It is a bioelectric signal measured noninvasively by an electrode attached to the surface.

Hans Berger (Hans Berger), who first detected human brain waves, recorded two platinum electrodes inserted under the skull defects of a patient who had a head trauma and could be recorded even after putting an electrode on the scalp And it is named as "electroencephalogram" such as electrocardiogram (ECG) or electromyogram (EMG) and its brain wave is called "berger rhythm".

Hans Berger was the first to discover EEG in the human scalp and then to record EEG during sleep (first spindle detection), hypoxia, partial and total brain damage, and epileptic seizure.

A human-based EEG study began in 1920, using a string-galvanometer for measurement in his early EEG studies, and a dual coil galvanometer in 1926, (nonpolarizable) electrodes were used to record human brain waves and reported for the first time in 1929. In the first record, the electrodes were attached to the frontal lobe and the occipital lobe to record a single-channel EEG for 1 to 3 minutes.

The electrical activity of the brain reflected in brain waves is determined by neurons, glia cells, and blood-brain barrier, which are mainly caused by nerve cells. The glial cells that occupy half of the brain weight regulate the flow of ions and molecules in synapses where neurons are connected, and maintain, support, and maintain the structure of neurons. The blood-brain barrier plays a role in selecting only the necessary substances among the various substances in the cerebral blood vessels. Changes in EEG caused by the glial cells and the blood-brain barrier slowly take place slowly. On the other hand, changes in the EEG caused by the activity of neurons are large, fast, and various.

Electroencephalography (EEG) is a microscopic bioelectricity generated when a signal is generated between the nervous system and the nervous system. Electrode is used to measure the electrical potential difference between the brain surface and the delta δ) waves, theta waves, alpha waves, beta waves, and gamma waves.

The EEG can be classified according to its frequency and amplitude. EEG emanating from the human brain has a frequency of 0 to 30 Hzdml and an amplitude of about 20 to 200 μV.

EEG waves are artificially delta-delta (0.2 ~ 3.99 Hz), theta-theta waves (4 ~ 7.99 Hz), alpha-alpha waves (8 ~ 12.99 Hz) ~ 29.99 Hz) and gamma-gamma waves (30 ~ 50 Hz)

Delta waves have a frequency of 0.2 to 4 Hz and amplitudes of 20 to 200 V and are mainly present in normal sleeping conditions or in neonates. It is known that the delta wave state produces a large amount of growth hormone. The fact that teenagers are tall when they are asleep has these scientific facts on the back.

Theta waves have a frequency of 4 to 8 Hz and amplitudes of 20 to 100 V, appearing emotionally stable or before falling into the water. So, the Ctapa is often called the boundary state of crust and dream. In this state, the image of the mind is often accompanied by an unexpected dream-like image, and the image leads to a vivid memory.

The alpha wave has a frequency of 8 to 13 Hz and an amplitude of 20 to 60 V, and appears in relaxed states like meditation, helping to relieve stress and improve concentration. If we close our eyes and relax our body, the activity of EEG relaxes the speed. At this time, our brain produces alpha papa, and the brain becomes alpha state. People in healthy, stress-free conditions tend to have a high level of activity in ALPHA. Using these ALPA's, 'MC Square' learning aids have been developed. Some researchers are even more subdivided into Low Alpha, Middle Alpha, and High Alpha.

Beta waves have a frequency of 13 to 30 Hz and an amplitude of 2 to 20 V and appear when they are active, such as in tension or excitement. It helps to improve mobility, and it is the brain wave when consciousness is awake. Beta waves appear mainly when we open our eyes, walk, get excited, and focus on the outside world. Beta waves appear predominantly in the frontal lobe, appearing in all conscious activities, such as when awake, when speaking, and especially in the case of anxiety, tension, and complex computation.

The gamma wave has a frequency of 30 to 50 Hz and an amplitude of 2 to 20 V, and appears mainly when excited. Gamma waves are vibrated more rapidly than beta waves, and are reported to be more emotionally irritated, and are related to high cognitive information processing such as reasoning and judgment.

Delta waves, theta waves, alpha waves, beta waves, and gamma waves are frequency regions of brain waves that are classified as handy. Researchers who want to analyze the EEG characteristics of a specific state usually first observe the power spectrum distribution that shows the distribution of power for each frequency component of 0-50Hz and then analyze the frequency components to determine the power spectrum distribution Each part of the measurement shows a slightly different aspect.

The cerebral cortex under the surface of the head is largely divided into frontal lobe, parietal lobe, temporal lobe, occipital lobe. For example, in the occipital lobe corresponding to the head of the head, the primary visual cortex is responsible for primary visual information processing, and the parietal cortex near the crown is the somatic cortex responsible for movement / sensory information processing.

CT, Functional MRI, PET, and MEG are used to measure the structure of the brain. F-MRI and PET have advantages in spatial resolution compared to EEG, but time resolution is lower than EEG It is not possible to see changes in the brain within. In addition, these devices are not only very expensive, but also have a special measuring room. Therefore, unless the brain function measuring device which is low in cost and excellent in both time and spatial resolution is developed, the brain wave measuring device analyzes the brain function (Brain function) Analyze the function of the brain that can not be observed.

Recently, brain scientists have been studying the theory that the brain keeps activity while sleeping, the blood of the brain is still increasing, and that sleep is an active phenomenon of the brain caused by the needs of the brain, It is closely related to the memory process that allows students to memorize learned information during the day. During a sleeping "dream" sleep (REM sleep), a brain specialist at Seoul National University Hospital recovers brain fatigue by increasing protein synthesis, but chooses the information learned during the day to avoid unnecessary information (REM) sleep because it stores it in long-term memory. At bedtime, the entorhinal cortex will take the necessary rest when sleeping in a non-REM sleeping state. REM sleep Deprivation of sleep deprives you of cognitive skills related to memory and sleeps enough. REM sleep, which is increased in the second half of sleep, saves complex information and ideas and regains vitality. If you sleep as much as possible, you will make your brain healthy while you sleep.

According to UCLA researchers in Nature NeuroScience, in the sleep or unconscious state, some areas of the human brain consolidate memory in the learning memory of the brain during sleep, and neurons in all areas of the brain ) Actvation at the same time, and lack of sleep has a detrimental effect on health, including learning and memory problems.

The brain wave measuring device evaluates the essential ability required for learning by measuring the cognitive intensity, cognitive velocity, concentration, and left / right brain activity detected from the brain waves by detecting and analyzing the user's brain waves in the university hospital and the clinic, (ADHD), attention deficit disorder (ADD), depression, learning disability, anxiety disorder, insomnia, autism, and dementia. do.

However, the existing EEG sensing device detects the optimal time for reinforcement of the learning memory by analyzing the characteristics of the EEG according to the sleep cycle (90 minutes 1 cycle), or detecting the optimal sleep time for minimizing fatigue when awakening And predicted the optimum wake time, and did not provide the morning call function.

SUMMARY OF THE INVENTION An object of the present invention to solve the problems of the prior art is to attach a hair band type EEG apparatus to a head of a subject before taking a sleep and measure EEG signals from at least one EEG (Electroencephalogram) , A / D conversion and amplification, and the measured data is transmitted to the brain wave analysis application (SLM App) of a computer, a smart phone, and a tablet PC through Bluetooth or ZigBee or RF wireless communication, (EEG) of the user's sleep stage by measuring the Delta wave of the user's brain. Among them, Delta wave in the range of 0.2 ~ 4Hz and EOG (Electrooculography) which are closely related to the brain learning area consolidation , The degree of safety: the signal that appears during the movement of the eye in the REM sleep phase) to determine the optimal sleeping time (memory retension, consolidation) To detect and provide awoke from his sleep when tired to predict the optimal Wake Time is minimized, which provides enhanced analysis of sleep EEG study of memory function, a hair band type EEG device and the terminal.

Another object of the present invention is to provide a method of measuring a brain band type EEG that provides a sleep enhancement memory enhancement analysis function.

In order to accomplish the object of the present invention, a hair band type EEG measuring apparatus for providing a sleep EEG learning memory strengthening analysis function is characterized in that a hair band type EEG measuring apparatus includes a hair band attached to a forehead region of a subject's head before going to bed, An electroencephalogram measuring device having a tightening device that tightens the band to a circumferential length of the wearer's head, and at least one EEG detecting sensor for detecting an EEG signal from the electrodes attached to the forehead of the subject's head; And an EEG signal transmitted from the EEG device to a terminal through wireless communication to output an EEG signal and analyzing a sleep EEG according to a user's sleep stage measured at the time of sleeping, Memory) EEG detection program that detects the Delta wave in the range of 0.2 ~ 4Hz and the following EOG more than 2 times and sets the wake time when the 1st or 2nd EOG immediately after it is detected. App).

The at least one EEG detecting sensor automatically detects an EOG or a subsequent EOG at least two times after a delay of less than 4 Hz, and then activates an alarm to wake at a time when the first EOG or second EOG immediately after it is detected, or And an alarm is activated to wake up at a time set by the terminal.

The wireless communication transmits / receives an EEG signal to / from an EEG analysis application program (SLM App) of the terminal by using any one of Bluetooth, ZigBee or RF wireless communication from the EEG measurement device, (Eg, Sleep EEG associated with enhancement of learning, history of sleep state by individual / date / time of day, score of quality of sleep (eg, delta wave twice And the delta wave at 60 Hz and the delta wave at 20 Hz). After detecting the delta wave of 4 Hz or less and the following EOG more than 2 times, the first or second EOG An optimal sleep time in which learning is enhanced upon detection, and an alarm setting time).

The EEG apparatus includes at least one EEG detecting electrode measuring an EEG signal from a head surface and using a fiber electrode; An EEG signal detector for detecting an EEG signal measured from an EEG detecting electrode attached to each measurement position; An amplifier for amplifying a fine EEG signal from the EEG signal detecting unit and removing Gaussian noise; EEG waves of LPF, BPF1, BPF2, BPF3, and HPF using LPF, BPF, and HPF were classified into EEGs with frequencies ranging from 0 to 50 Hz. A filter for filtering the sleep EEG using a low pass filter of 4 Hz or less when the button is set to detect sleep EEG by a button; An A / D converter for converting a filtered analogue EEG signal into a digital EEP signal; And controls the EEG signal to be filtered according to the user's button setting. The EEG signal measured by the EEG of the terminal is transmitted through the wireless communication while being stored in real time by receiving the EEG signal from the A / D converter ; A power supply unit for supplying a predetermined driving voltage to the EEG device; A storage unit for storing the measured EEG signal as an entrance and for storing the self-button or the wake-point information set by the terminal; And a wireless communication unit for transmitting the measured EEG signal to an EEG analysis application program (SLM App) of the terminal using Bluetooth or ZigBee or RF wireless communication.

The EEG apparatus includes a power ON / OFF button, and includes a delta wave (LPF, 4 Hz or less), theta wave (BPF1, 4 to 8 Hz), an alpha wave (BPF2, 8 to 13 Hz), a beta wave (BPF3, 30 Hz), and a gamma wave (HPF, 30 Hz or more).

The EEG apparatus further includes an LED unit for displaying the color of the EEPROM when the EEPROM is turned on or off or when the EEPROM is detected.

The EEG measuring apparatus analyzes sleep EEG of the user's sleep stage measured from at least one EEG electrode (fiber electrode) of the hair band type EEG measuring apparatus at bedtime, and calculates a Delta wave in the range of 0.2 to 4 Hz Has a self button for setting a wake time when the first or second EOG immediately after the detection of the EOG after the completion of the detection of the EOG is completed is counted by the terminal; And an alarm generating unit for generating an alarm to wake up at a predetermined morning call time.

In order to achieve the other object of the present invention, a method of measuring a brain-type EEG that provides a function of enhancing sleep-EEG learning memory includes the steps of: (a) analyzing at least one EEG (Electroencephalogram, (EEG signal) measured from a bioelectrical signal generated upon activation of a brain cell of a hippocampus through an enthhhinal cortex of the subject's brain at bedtime from a detecting electrode ; (b) EEG signal amplification and Gaussian noise elimination; low-pass filtering (LPF) to filter the delta waves below 4 Hz used as sleeping EEG; sleep EEG filtering, filtering A / D conversion; (c) transmitting the measured EEG signal from the brain band type EEG device to a terminal such as a computer, a smart phone, or a tablet PC through Bluetooth, ZigBee or RF wireless communication; (d) Analyzing sleep EEG measured at bedtime by sleep application, and it was found that EEG waves in the range of 0.2 ~ 4Hz, which are closely related to brain memory consolidation, Detects an EOG according to the EEG twice or more, detects an optimal sleep time for which learning is enhanced, performs an alarm function when the first or second EOG is detected immediately after completion of confirmation of EEG related brain enhancement, and displays an EEG signal .

Preferably, the sleep band type EEG measurement method for providing the sleep EEG learning memory intensification analysis function comprises: (e) measuring a sleep wake-up EEPROM of the sleeping EEPROM according to a preset wake-up time or a wake- To generate an alarm.

The EEG monitoring device, the EEG device, and the terminal and method for providing a sleep EEG learning memory intensification analysis function according to the present invention are characterized in that a hair band type EEG measuring device is attached to the subject's head before sleeping, EEG signals measured from one or more EEG (Electroencephalogram) detection electrodes are transmitted to an EEG application program (SLM App) of a computer, a smart phone, or a tablet PC via Bluetooth or ZigBee communication, It analyzes the EOG that follows immediately after the wave and detects the best sleep time that the learning memory is strengthened and predicts the optimum wake time.

The EEG signal measured from at least one EEG detecting electrode (fiber electrode) is transmitted to a terminal (PC, Smart Phone, Tablet PC, etc.) via Bluetooth communication while sleeping by wearing a hair band type EEG measuring device. ), And the Sleep EEG of the subject is analyzed by Sleep App of the terminal (PC, Smart Phone, Tablet PC) to extract the optimal Wake Time. Based on Delta wave (0.2 ~ 4Hz range) and EOG, we propose sleeping time minimizing fatigue, searching for optimal sleeping time to maximize the amount of learning, and suggesting optimal sleeping time for each individual.

FIG. 1 is a block diagram of a brain wave measuring system using a hairband type EEG detecting sensor that provides a sleeping brain-learning memory reinforcing analysis function according to the present invention.
2 is an internal configuration diagram of an EEG detecting apparatus using a hairband type EEG detecting sensor according to an embodiment of the present invention.
FIG. 3 is a system configuration diagram for analyzing a brain wave of a subject measured by a hair band type brain-wave measuring device by transmitting it to a brain wave analysis application program (Sleep App) of a computer, a smart phone, or a tablet PC through Bluetooth communication.
FIG. 4 is a diagram illustrating an EEG sleep cycle. FIG.
5 is a flowchart illustrating an EEG measurement method using a hairband type EEG detection sensor according to the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a block diagram of a brain wave measuring system using a hairband type EEG detecting sensor that provides a sleeping brain-learning memory reinforcing analysis function according to the present invention.

A brain band type EEG measurement system that provides a sleep enhancement memory enhancement analysis function includes a hair band 10 attached to the forehead of a subject immediately before bedtime; A tightening device (20) for tightening the hair band (10) in accordance with the circumferential length of the worn hair; And at least one EEG detecting sensor (30) for detecting an EEG signal from electrodes (Electrodes) attached to the forehead region of the subject's head. And an EEG signal detected from at least one EEG detecting sensor 30 of the hair band type brain-wave measuring device is transmitted to a terminal (computer, smartphone, tablet PC, another portable terminal) via Bluetooth, ZigBee or RF wireless communication (Electroencephalogram) output by EEG application (SLM App) and the activation of hippocampal neurons (neurons) through the cerebral enthohinal cortex. After measuring the bioelectrical signal, EEG encoding was used to measure the sleep EEG measured at bedtime. EEG (Electrooculography, REL: REM), which is closely related to brain memory consolidation, (Rapid Eye Movement) The most important feature in the sleep phase, which is the signal that appears when the eye moves. And a terminal having a brain wave analysis application (App SLM) (70) for detecting the time and estimate the time is detected enhance study related EEG confirmation completion immediately after the first or the second of the EOG to the Wake Time.

At least one EEG detecting sensor 30 uses a fiber electrode for EEG detection without attaching a metal electrode to which a conductive gel is applied to a measurement region of the head.

The at least one EEG detecting sensor 30 detects an EOG of a Delta wave in the range of 0.2 to 4 Hz at the time of going to bed at least twice to detect an optimal sleep time in which the learning memory of the brain is consolidated, The wake time information is transmitted to the terminal so as to wake up when the first or second EOG is detected immediately after completion of the related EEG confirmation, and the wired terminal is automatically paired via wireless communication to operate an alarm to the connected terminal or wake up at a time set by the terminal And an alarm is activated.

The user can use the EEG of the hair band type EEG and the terminal to analyze the EOG of the Delta wave in the range of 0.2 to 4 Hz at bedtime and the first or second EOG Wake Point, and it wakes up listening to the alarm automatically generated by the EEG application (SLM App) of the terminal.

The wireless communication is characterized in that an EEG signal is transmitted and received to a terminal (a computer, a smart phone, a tablet PC, a separate portable terminal) by using a communication method of Bluetooth, ZigBee, or RF wireless communication from a hair band type EEG device. .

1. SLM (Sleep and Learning Manager) Product Features

1) An EEG signal detected from at least one EEG detecting electrode (fiber electrode) attached to the inner surface of an EEG measurement device worn on the forehead of a subject's head at the time of sleeping by wearing a hair band type EEG measurement device, (Sleep EEG) measured by an application of a terminal (PC, Smart Phone, Tablet PC) through Bluetooth and A / D conversion, and analyzing the user's usual sleep EEG,

- Based on periodic analysis of sleep, sleep time minimizes fatigue

- After waking up after sleeping with a brain band type EEG device, a wake-up time set by the user on the terminal (PC, Smart Phone, Tablet PC) or Delta wave of 4Hz or less, The alarm is automatically activated to wake up when the second or second EOG is detected, or the alarm is output so that the user wakes up at the time set by the terminal

2) Search for optimal sleep time to maximize learning memory

      - Delta wave of less than 4 Hz and immediately after detection of EOG followed by more than two times, EOG is detected when the first or second EOG is detected,

2.1.2 How to use SLM

① Memory Retention (Consolidation)

② Brain frequency Dictation and Transmission (SLM APP)

③ Personal Data Analysis and Extracting the Perfect Time

④ Calling to the User

2. Main features

1) Attached EEG (SLM sensor) in the form of a hairband before going to the surface of the water

2) Sleep EEG transmission and analysis in real time while taking sleep

3) Extraction of time to maximize memory renting (Consolidation)

4) The user can adjust the sleep time according to the time suggested by SLM (Sleep Learning Manager)

5) Sleep EEG related to learning, sleep history by individual / date / time of day, score on sleep quality (eg delta wave, and later on) by EEG analysis application (SLM App) EOG followed by EOG more than 2 times, 100 points, delta wave, followed by EOG 60 times, and delta wave 20 points), Delta wave of 4Hz or less and EOG followed by detection more than 2 times Second or second EOG detection)

2 is an internal configuration diagram of an EEG detecting apparatus using a hairband type EEG detecting sensor according to an embodiment of the present invention.

Hair band type sleep and learning manager (SLM) is a system that attaches a hair band type EEG measurement device to the subject's head before going to bed and detects at least one EEG (Electroencephalogram) attached to the forehead part of the head at bedtime The measured EEG signal of the head surface from the electrode is transmitted to the SLM App 37 of a terminal such as a computer, a smart phone, or a tablet PC through Bluetooth or ZigBee wireless communication to obtain the cerebral cortex, EEG encoding EEG measured from bioelectrical signals generated when the hippocampus is activated through sleep is closely related to learning memory consolidation of sleep among sleep EEG measured at bedtime Delta waves in the range of 0.2 ~ 4Hz and EOG following the EEG are detected twice or more to detect the optimal sleep time that enhances brain learning at sleep When the wet strengthening related EEG (dalta wave of NREM-4 step) Check the completion immediately after the first or the second of EOG is detected predicts Wake Time.

FIG. 3 is a system configuration diagram for analyzing a brain wave of a subject measured by a hair band type brain-wave measuring device by transmitting it to a brain wave analysis application program (Sleep App) of a computer, a smart phone, or a tablet PC through Bluetooth communication.

The hair band type EEG apparatus includes a plurality of EEG electrodes 31, an EEG signal detecting unit 32, an amplifier 33, a filter 34, an A / D converter 35, 36, a button unit 37, an LED unit 38, a power supply unit 40, a storage unit 43, and a wireless communication unit 44.

If the wireless communication unit is not used, the hair band type brain-wave measuring apparatus may be connected to the terminal by wire using a USB connection unit or by using a serial interface connection unit.

The power supply unit 40 includes a power supply unit 41 that receives an AC 220V power supply and an AC / DC converter 42 that converts an AC 220V power supply to a predetermined DC drive voltage (DC 5V).

At least one EEG detecting electrode (31) for attaching a hair band type EEG measuring device to the forehead of a subject's head before going to bed, measuring an EEG signal from the head surface at bedtime and using a fiber electrode; An EEG signal detecting unit 32 for detecting an EEG signal measured from an external wave detecting electrode attached to each measurement position; An amplifier 33 for amplifying a fine EEG signal from the EEG signal detecting unit 32 and removing Gaussian noise; (LPF, 4 Hz or less), theta waves (BPF1, 4 to 8 Hz), alpha waves (BPF2, 8 to 13 Hz), and beta waves using the LPF, BPF, (BPF 3, 13 to 30 Hz) and a gamma wave (HPF, 30 Hz or more), and is configured to detect a sleep EEG by a button. A filter (34) for filtering the sleep EEG using a filter; An A / D converter 35 for converting the filtered analogue EEG signal into a digital EEP signal; And controls the EEG frequency to be filtered according to the user's button setting. The EEG signal measured by the EEG signal of the terminal is stored in real time while being received by the A / D converter 35 from the A / D converter 35 A control unit (36) for controlling the transmission of the data; (LPF, 4Hz or less), theta wave (BPF1, 4 to 8Hz), alpha wave (BPF2, 8 to 13Hz), Beta wave (BPF3, 13 to 30Hz) (37) for controlling the filter setting by frequency range of the brain waves of the high frequency (HPF, 30 Hz or more); An LED unit 38 for displaying a color when the power of the EEG measurement apparatus is turned on / off or when a sleeping brain wave is sensed; A power supply unit 40 for providing a predetermined driving voltage to the EEG apparatus; A storage unit 43 for temporarily storing the measured EEG signal, and storing the self-button or the wake-point information set by the terminal; And a wireless communication unit 44 for transmitting the measured EEG signal to the EEG analysis application program (SLM App) 70 of the terminal using Bluetooth or ZigBee wireless communication.

The terminal (PC, smart phone, Tablet PC) includes a wireless communication unit 45 receiving the EEG signal measured by the headband EEG apparatus via Bluetooth or ZigBee wireless communication; A microcomputer 46 for controlling each function; An EEG analysis application program (SLM App) 70 and a memory 48 for storing EEG signals measured by time period; An LCD display unit 47 for outputting an analyzed brain wave signal; At sleep, the EEG is closely related to brain memory learning memory consolidation among sleep EEGs measured from at least one EEG electrode (fiber electrode) of a brain band type EEG measurement device. Delta waves in the range of 0.2 to 4 Hz EOG is detected more than once to detect the optimal sleep time to enhance the learning and when the first or second EOG is detected immediately after confirmation of brain enhancement (NREM-4 step delta wave) An alarm generating unit 49 for generating an alarm to wake up at a pre-set morning call time designated by itself or / and a terminal; (SLM App) 70 outputs a user's brain wave signal and outputs a user's brain wave data (sleep EEG related to learning, sleep / (Eg, delta wave, followed by more than two EOG occurrences of 100, delta wave, followed by one EOG of 60, and delta wave of 20, respectively) , Delta wave of 4Hz or less and following EOG is detected more than 2 times, and the first or second EOG immediately after the alarm detection time), Delta wave of 4Hz or less and EOG following immediately more than 2 times are detected The optimum sleep time and alarm setting time associated with the reinforcement of the learning memory set on the basis of the first or second EOG).

FIG. 4 is a diagram illustrating an EEG sleep cycle. FIG.

SLEEP CYCLE of the EEG sleep cycle is classified as follows.

(1) Wake: Alpha (8-12Hz), beta (13-30Hz)

(2) drawsy

(3) nREM1: irregular Theta: 3.5 ~ 7.5hz, eyelid movement and eye movement

(4) nREM2: irregular Theta: 3.5 ~ 7.5hz, sleep spindles: 12 ~ 14hz

               K complexes: sharp waveform

(5) nREM3: delta (~ 4 Hz)

(6) nREM4: delta (~ 4 Hz)

(7) Rapid Eye Movement (REM): sensitive to external stimuli,

The sleep stages are classified as AWAKE, REM, NREM-1, NREM-2, NREM-3 and NREM-4 and are classified into 3, 4, 5, 6, Repeat.

NREM-3, and NREM-4. In the embodiment of the present invention, the sleep band type brain-wave measuring apparatus measures the NREM-4 phase delta wave at a cycle of 90 minutes per cycle , Rapid Eye Movement (REM) Detects the EOG that occurs when the eye moves while sleeping in the sleep phase. As a result, if the Delta wave of 4Hz or less and the EOG immediately following the EOG are detected more than once, the detected first or second EOG The optimum sleep time and alarm setting time associated with the reinforcement of the learning memory set as a reference).

5 is a flowchart illustrating an EEG measurement method using a hairband type EEG detection sensor according to the present invention.

A method of measuring EEG using a hair band type EEG detection sensor is a method in which a user puts a hair band type brain wave measuring device on the subject's head after setting a Wake point of a sleeping brain wave of a brain wave measuring device by a button, Hair band type EEG (Electroencephalogram) detection electrode of at least one EEG (Electroencephalogram) detection electrode of the subject when the hippocampus is activated through activation of the hippocampus through the enthhhinal cortex of the subject Detecting (S10) an EEG signal measured from the electrical signal; The EEG signal is amplified, Gaussian noise is eliminated, and the LPF (Low Pass Filter) is used to filter the 4Hz or less delta wave used as the sleeping brain wave. (S20); (S30) of transmitting an EEG signal measured from a brain band type EEG measurement device to an EEG analysis application program (SLM App) 70 of a terminal such as a computer, a smart phone, or a tablet PC via Bluetooth or ZigBee wireless communication; Analyzing sleep EEG measured at bedtime (S40) with sleep EEG including delta waves of 0.2 to 4 Hz or less; By analyzing EEG waveforms at each step of the brain, we can detect the Delta wave in the range of 0.2 ~ 4Hz and the EOG immediately after the EEG, which are closely related to brain memory consolidation, The first or second EOG immediately after confirmation of brain waves (NREM-4 stage delta wave) associated with brain learning enhancement at the time of sleep is detected and the time (first or second EOG time immediately after the Delta wave below Hz) Detecting an optimum sleep time as a reference when the sensed time is detected, and displaying the EEG signal (S50); And a wake-up point of a sleeping brain wave set by a button of the EEG measuring device set by a wireless communication via a terminal or a user when an EOG generated immediately after a delta wave of 4 Hz or less is detected more than twice in the NREM- (S54) automatically generating an alarm at the terminal at an optimal sleep time associated with the reinforcement of the learning memory, which is set based on the detected first or second EOG.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be taken in conjunction with the present invention. The present invention can be variously modified or modified.

10: Hair band 20: Fastening device
30: EEG detection sensor 70: EEG analysis application (SLM App)

Claims (10)

A brain band type brain wave measuring apparatus for providing a sleep enhancement memory enhancement analysis function,
A hair band attached to the forehead of the subject's head before bedtime or before bedtime, a tightening device tightened to the circumference of the head with the hair band worn, and an EEG signal from the electrodes attached to the forehead of the subject's head A brain wave measuring device having at least one brain wave detecting sensor for detecting a brain wave; And
The EEG signal transmitted from the EEG device is transmitted to the terminal through wireless communication to output an EEG signal. The EEG signal, which is closely related to the learning memory consolidation of the brain among the sleep EEG measured at bedtime, EEG is detected more than twice to detect the optimal sleep time to enhance the learning and EEG analysis application which predicts the wake time when the first or second EOG is detected immediately after completion of EEG confirmation Sleep App);
A brain-type electroencephalogram measuring apparatus for providing a sleep-brain-learning memory enhancement analysis function. The method according to claim 1,
The at least one EEG detecting sensor
A brain band type electroencephalogram measuring apparatus which provides a function of reinforcing sleeping brain training memory, characterized by using a fiber electrode for EEG detection without attaching a conductive electrode to the measurement region of the head. The method according to claim 1,
The terminal
When sleeping, it detects the delta wave of 4Hz or less and the EOG immediately following it more than once, activates the alarm automatically to wake up when the 1st or 2nd EOG is detected, or activates the alarm to wake at the time set by the terminal And a brain-type brain-wave measuring device that provides a sleep enhancement analysis memory function. The method according to claim 1,
The wireless communication transmits and receives an EEG signal to the EEG analysis application program (SLM App) of the terminal by using any one of Bluetooth or ZigBee communication from the EEG measurement device. The EEG analysis application (SLM App) (Sleep EEG related to learning, history of sleep state by individual / date / time of day, score on the quality of sleep (e.g. delta wave and subsequent EOG) And the delta wave at 60 Hz and the delta wave at 20 Hz). After detecting the delta wave of 4 Hz or less and the following EOG more than 2 times, the first or second EOG And an alarm setting time at the time of detection) is outputted. The method according to claim 1,
The EEG measuring device
At least one EEG detecting electrode measuring an EEG signal from a head surface and using a fiber electrode;
An EEG signal detector for detecting an EEG signal measured from an external wave detecting electrode attached to each measurement position;
An amplifier for amplifying a fine EEG signal from the EEG signal detecting unit and removing Gaussian noise;
EEG waves of LPF, BPF1, BPF2, BPF3, and HPF using LPF, BPF, and HPF are classified into EEGs with frequencies ranging from 0 to 50 Hz. A filter for filtering the sleep EEG using a low pass filter of 4 Hz or less when the button is set to detect sleep EEG by a button;
An A / D converter for converting a filtered analogue EEG signal into a digital EEP signal;
And controls the EEG signal to be filtered according to the user's button setting. The EEG signal measured by the EEG of the terminal is transmitted through the wireless communication while being stored in real time by receiving the EEG signal from the A / D converter ;
A power supply unit for supplying a predetermined driving voltage to the EEG device;
A storage unit for storing the measured EEG signal as an entrance and for storing the self-button or the wake-point information set by the terminal; And
A wireless communication unit for transmitting the measured EEG signal to an EEG analysis application program (SLM App) of the terminal using Bluetooth or ZigBee or RF wireless communication;
A brain-type electroencephalogram measuring apparatus for providing a sleep-brain-learning memory enhancement analysis function. The method according to claim 1,
The EEG measuring device
(LPF, 4Hz or less), theta wave (BPF1, 4 to 8Hz), alpha wave (BPF2, 8 to 13Hz), Beta wave (BPF3, 13 to 30Hz) And a button unit for controlling the filter setting for each frequency range of the brain waves of the brain (HPF, 30 Hz or more). The method according to claim 1,
The EEG measuring device
A brain band type EEG measuring device which provides a memory reinforcement analysis function of a sleep EEG learning memory which further includes an LED section for displaying the color when the EEG power is turned on or off or when a sleep EEG is detected. The method according to claim 1,
The EEG measuring device
(Sleep EEG) measured from at least one EEG electrode (fiber electrode) of a hair band type EEG apparatus at bedtime was measured, and a Delta wave ranging from 0.2 to 4 Hz and a subsequent EOG were detected twice or more Further comprising a self-button for setting a wake time when the detected first or second EOG is detected, and an alarm generating unit for generating an alarm to wake up at a pre-set morning call time, Hair band type EEG measuring device that provides enhancement analysis function. A method of measuring a brain band type EEG that provides a sleep enhancement memory enhancement analysis function,
(a) a hair band attached to the head at the bed at the time of sleeping from at least one EEG (Electroencephalogram) detecting electrode of the EEG apparatus; Detecting an EEG signal (EEG signal) measured from a bioelectrical signal generated when a neuron (nerve cell) is activated;
(b) EEG signal amplification and Gaussian noise elimination; low-pass filtering (LPF) to filter the delta waves below 4 Hz used as sleeping EEG; sleep EEG filtering, filtering A / D conversion;
(c) transmitting the measured EEG signal from the brain band type EEG device to a terminal such as a computer, a smart phone, or a tablet PC through Bluetooth, ZigBee or RF wireless communication;
(d) Sleep EEG, which is a sleep EEG with a delta wave of 0.2 to 4 Hz or less, is measured by sleep application of a terminal. Detection of Delta wave in the range of 0.2 ~ 4Hz and EOG following immediately after EEG is closely related to memory consolidation. Detection of the optimal sleep time with enhanced learning. Performing an alarm function and displaying an EEG signal when a first EOG is detected;
Wherein the brain EEG brain imaging method comprises the steps of: 10. The method of claim 9,
The method of claim 1, further comprising the step of automatically generating an alarm at a pre-set morning call time or a wake point of a sleeping brain wave set by a user by a button of the brain wave measuring device through the terminal, How to measure.

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