KR20170096290A - Control mehtod for ventilating system using brain wave - Google Patents

Abstract

The present invention provides a method of driving a ventilation system capable of discharging air using brainwaves. According to an embodiment of the present invention, the method of driving a ventilation system using brainwaves includes: a brainwave receiving step of receiving users brainwaves; a smell determination step of determining whether a device is driven by detecting the brainwaves for recognizing the smell from the received brainwaves; a menu displaying step of displaying, on the display, a menu for driving the device; a selection determination step of selecting the device to be driven in a manner that compares a change in brainwaves with a pre-stored pattern and determines whether the change is the same as the pre-stored pattern; and a device driving step of driving the selected device.

Description

TECHNICAL FIELD [0001] The present invention relates to a ventilation system using a brain wave,

The present invention relates to a driving method of a ventilation system, and more particularly, to a driving method of a ventilation system using an EEG.

Generally, brain waves are biological signals that directly or indirectly reflect human consciousness or unconscious state, and refers to a wavelength having a frequency of 30 Hz or less with a potential difference of tens of microvolts measured in all regions of human scalp.

These EEGs are classified into a delta wave, a theta wave, an alpha wave, a beta wave, and a gamma wave by frequency band. Delta waves are typically EEG waves with a frequency of less than 4 Hz and are typical of normal sleep states. Theta waves are those with frequencies in the range of 4 to 8 Hz and are often present when the state is mentally disturbed or distracted, often in adolescents with learning disabilities .

The alpha wave is an electroencephalogram with a frequency of about 8 to 12 Hz, which is generally stable when the mental state is stable, and the eye is closed and taking a relaxed psychological state. Alpha waves also occur when there is a high degree of concentration to separate from the surrounding situation, or when psychological stabilization has occurred due to meditation. Gamma wave is an EEG having a frequency of 30 to 50 Hz and appears in an excited state.

Beta waves refer to the EEG with a frequency of about 12 to 30 Hz, which is mainly observed when a little tension or attention is paid. Beta waves are widespread throughout the brain when exercising, learning, or performing tasks. The beta wave is divided into an SMR wave having a frequency of 12 to 15 Hz, an intermediate beta wave having a frequency of 15 to 18 Hz, and a high-beta wave having a frequency of 20 Hz or more. Beta waves are more stressful when exposed to stress such as anxiety or tension.

When attention is paid, SMR wave appears. When concentrated and normal activities are performed, middle beta waves with a frequency of 15 to 18 Hz appear in the left brain, and Kobe beat exceeding 20 Hz appears when tension and anxiety continue.

The present invention provides a driving method of a ventilation system capable of discharging air using brain waves.

According to an aspect of the present invention, there is provided a method of operating a ventilation system using an EEG, the method comprising: receiving an EEG signal to receive a user's EEG; detecting an EEG to recognize an odor from the received EEG; A menu display step of displaying a menu for driving the device, a selection judgment step of comparing the change of the brain waves with the pre-stored pattern and judging whether the brain waves are coincident or not and selecting a device to be driven, Step < / RTI >

The smell determining step may include a cognitive EEG search step of determining whether Kobe beat or gamma wave continuously appears for a predetermined time.

In addition, the cognitive EEG search step may store a characteristic EEG pattern of the EEG appearing when the user assumes an unpleasant smell, and may determine whether the received EEG matches the stored EEG pattern.

In addition, the smell determining step may include a question generating step of generating and displaying a question, and a positive judgment step of judging occurrence of positive EEG or negative EEG for the question.

In addition, the positive judgment step may be performed by using a bias, which means a direction of rotation in a phase space composed of EEG components measured at two places, and a synchronization rate, which is a variable indicating the degree of simultaneous increase or decrease of EEG measured at two places So that it can judge a positive or negative intention.

In addition, the menu display step may include a menu moving step of moving the menu according to the change of brain waves.

In addition, in the menu moving step, the concentration factor obtained by dividing the value obtained by adding the SMR wave and the middle beta wave to the separator is calculated. When the concentration is increased, the menu is moved to the right. When the concentration factor and Kobe beat increase, Can be moved.

In addition, the selection determination step may determine that the patterns match when the increase and decrease of the alpha waves are repeated twice within the set period.

In addition, in the selection determination step, it is determined that the pattern matches when the activity of the left parietal lobe becomes higher and the activity of the left / right frontal lobe becomes lower in the determination of the activity change of the alpha .

The ventilation system driving method may further include a drive confirmation step of determining a change in brain waves by crossing a picture and a character when it is determined that the patterns match in the pattern determination step.

The driving confirmation step may include a display step of sequentially displaying characters and pictures on a display.

In addition, the driving confirmation step may include a step of determining a change in brain waves when a picture is presented, and a step of determining a change in brain waves when a character is presented.

As described above, according to the present invention, the air can be discharged by operating the ventilation system using the change of EEG.

1 is a view for explaining a ventilation system according to a first embodiment of the present invention.
2 is a block diagram illustrating a ventilation system using EEG according to a first embodiment of the present invention.
3 is a flowchart illustrating a method of driving a ventilation system using an EEG according to the first embodiment of the present invention.
4 is a view illustrating a menu display unit of a ventilation system using EEG according to the first embodiment of the present invention.
FIG. 5 is a block diagram illustrating a ventilation system using EEG according to a second embodiment of the present invention.
6 is a flowchart illustrating a method of driving a ventilation system using EEG according to a second embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention can be variously modified and may have various embodiments, and specific embodiments will be described in detail with reference to the drawings. It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

Terms including ordinals, such as first, second, etc., may be used to describe various elements, but the elements are not limited to these terms. The terms are used only for the purpose of distinguishing one component from another.

For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.

FIG. 1 is a view for explaining a ventilation system according to a first embodiment of the present invention, and FIG. 2 is a configuration diagram illustrating a ventilation system using an EEG according to a first embodiment of the present invention.

1 and 2, a ventilation system 100 using an EEG according to the first embodiment includes an EEG receiving unit 10, an EEG analyzing unit 20, an odor determining unit 30, (40), and a device moving part (50).

The ventilator system 100 using the EEG according to the first embodiment uses a brain wave to use the brain wave measuring device 20 by the user 110 in the home so that the hood 130 for ventilation, the window 140, (30) and the like. The brain wave measuring device 120 may be formed in various forms such as a hat, a headset, and the like.

The EEG receiving unit 10 attaches a plurality of electrodes made of a cap or the like to a user's scalp and receives the user's brain waves through the electrodes. For example, a ground electrode among a plurality of electrodes is disposed at the center of a user's scalp, and a right-brain electrode and a left-brain electrode are attached to the scalp of the user on both sides thereof. At this time, the EEG receiving unit 10 selects and receives an EEG for the frontal region of the user except the EEG or the frontal region. The EEG receiving unit 10 may have a band shape and a hat shape.

The EEG analysis unit 20 extracts the alpha waves, beta waves, theta waves, and gamma waves from the received EEG. The EEG analysis unit 20 includes an amplification module 21, a filter module 22, and an AD conversion module 23.

The amplification module 21 has an amplifier therein and amplifies the brain waves of several tens of μV to several tens of mV received by the brain-wave receiving unit 10 through the amplifier to 3 to 5 V to facilitate the analysis of the received brain waves.

The filter module 22 includes a plurality of analog filters to filter various noise included in the brain waves amplified by the amplification module 21. At this time, the filter is composed of a high pass filter, a band pass filter, a band stop filter, and a low pass filter. Such a high-pass filter primarily removes noise due to DC voltage, respiration, body movement, eye blinking, and the like, and the band-pass filter filters the EEG having a frequency band range to be measured. The band-stop filter removes noise due to the power supply of 50Hz or 60Hz, and the low-pass filter limits the width of the brain waves to prevent distortion and prevents distortion occurring when the brain waves are restored. The AD conversion module 23 digitizes the electroencephalogram in the analog state extracted through the filter module 22.

The smell judgment unit (30) detects an EEG for recognizing the smell and judges whether the apparatus is driven or not. The smell determining unit 30 includes a cognitive EEG search module 31, a question generating module 32, and a probing module 33. The cognitive EEG search module 31 recognizes the stress brain waves generated in the user's brain when the user smells an unpleasant smell. The cognitive EEG search module 31 determines whether Kobe beat or gamma wave continuously appears for a preset time. The cognitive EEG search module 31 determines that an unpleasant smell has occurred due to cooking or the like when Kobe beat or gamma wave continues for 10 seconds or more.

The cognitive EEG search module 31 may store the characteristic EEG pattern of the EEG appearing when the user assumes an unpleasant smell and judge whether or not the received EEG matches the stored EEG pattern. For this purpose, the user can store the EEG generated in a situation where an unpleasant smell appears.

The question generation module 32 generates a question by displaying a character on the display screen or generating a sound. The question can be answered by asking whether you want to venture.

The positive decision module 33 diagnoses the occurrence of positive EEG or negative EEG for the question. Since the change of the beta wave in the negative EEG wave rather than the positive EEG wave is definite, the positive decision module 33 can judge whether or not the negative wave pattern of the EEG occurs. The positive decision module 33 calculates the temporal and spatial correlation of brain waves to determine whether the brain waves are positive or negative. The affirmative decision module 33 can determine the positive and negative by calculating the bias and synchronization rate and using it as a logic decision variable of the neural network. Here, the bias refers to the direction of rotation in the phase space composed of EEG components measured at two places, and the synchronization rate is a variable indicating the degree of simultaneous increase or decrease of EEG measured at two places.

로 정의되며, 여기서, 상기 s1과 s2는 두 위치에서 측정된 뇌파 신호, 상기 H(x)는 x가 음수이거나 0일 때에는 0, 양수일 때는 1의 값을 갖는 스텝 함수, 및 상기 w는 동기율을 계산하는 구간 크기이다.The synchronization rate

(X) is a step function having a value of 0 when x is negative or 0, and a positive value when x is positive, and w is a step function having a synchronization rate .

로 정의되며, 여기서 상기 P(t)는 편향성, 상기 벡터 s는 s1과 s2로 이루어진 2차원 벡터, 상기 단위 벡터 θ는 s1과 s2로 이루어지는 위상공간에서의 원점을 중심으로 시계 반대 방향으로 회전하는 방향의 단위 벡터이다. On the other hand,

, Wherein P (t) is biased, the vector s is a two-dimensional vector consisting of s1 and s2, and the unit vector [theta] is rotated counterclockwise about the origin in the phase space consisting of s1 and s2 Direction unit vector.

The positive decision module 33 determines the positive and negative by assigning the synchronization rate and the bias to the variables of the classification method such as the neural network and the linear decision function. The synchronization rate and bias can be used individually, and both variables can be input. The neural network consists of a multilayer perceptron with an input layer, a hidden layer, and a node level output layer, and is already learned for logic judgment. The affirmative judgment module 33 transmits an affirmative signal to the drive selection section 40 when it is determined to be affirmative.

The drive selection unit 40 compares the change of brain waves with a pre-stored pattern, and determines whether or not the EEG coincides with the pre-stored pattern to select a device to be driven.

The drive selection unit 40 compares the increase / decrease of the alpha wave, beta wave, theta wave, and gamma wave among the EEG signals when an affirmative signal is received, and compares the change of the extracted EEG with the pre-stored pattern to determine coincidence . The drive selection unit 40 includes a menu display module 41, a menu movement module 42, and a selection determination module 43.

As shown in FIG. 4, the menu display module 41 displays a menu for selecting a device on the display screen. The menu movement module 42 moves the menu according to the change of brain waves so that the user can select the device.

The menu movement module 42 calculates the concentration factor obtained by dividing the value obtained by adding the SMR wave and the middle beta wave into the cepa and moves the menu to the right when the concentration is increased and the menu to the left when the concentration factor and Kobe beat are increased Can be moved. When the user gazes at the menu, the degree of concentration increases. As the concentration increases, the value of the concentration factor obtained by dividing the sum of the SMR wave and the middle beta wave by the separator increases. When concentration increases, the menu moves to the right by default. On the other hand, if you give power to your hand or face in the state of increased concentration, Kobe beat increases, and when the concentration increases, Kobe beat increases, the menu moves to the left.

The selection determination module 43 determines whether the received EEG pattern matches the pre-stored pattern and the received EEG changes, and generates a signal for selecting a device to be driven in the menu. The selection determination module 43 determines that the patterns match if the increase and decrease of the alpha waves are repeated two or more times within the set period. When the user repeats the operation of closing and closing the eyes at intervals of 3 seconds, the alpha wave is increased in the closed eye and the alpha wave is decreased in the closed eye. The change pattern of the alpha wave is stored, It is possible to judge whether or not these patterns match.

The selection decision module 43 determines that the pattern matches when the activity of the left parietal lobe is increased and the activity of the left / right frontal lobe is lowered in the determination of the activity change of the alpha wave at least twice within a predetermined time It can be judged. The activity of the left parietal lobe is increased and the activity of the left / right frontal lobe becomes very low. Such a change pattern of the alpha waves is stored, and the selection judgment module 43 compares the pattern matching Can be determined.

In addition, the selection determination module 43 can determine that the patterns match when the increase of the SMR wave and the middle beta wave is repeated twice or more within a predetermined period. When the user recognizes the picture or the character, the SMR wave and the intermediate beta wave increase, and the selection judgment module 43 can judge whether the pattern matches or not.

The device moving unit 50 activates the device selected by the drive selection unit 40 when a signal for selecting the device is received. The device moving part 50 may include a hood operation module for operating a hood installed in the kitchen, a window opening module for opening the window, and a cleaner operation module for operating the air cleaner. Here, operating the hood means operating the fan installed in the hood.

Hereinafter, a method of driving the ventilation system using EEG according to the first embodiment of the present invention will be described. 3 is a flowchart illustrating a method of driving a ventilation system using an EEG according to the first embodiment of the present invention.

1 to 3, the method for driving the ventilation system using the EEG according to the first embodiment includes the steps of receiving an EEG wave (step S101), analyzing an EEG wave (step S102), determining an odor (step S103) (S104), a selection determination step (S105), and a device activation step (S106).

The EEG receiving step S101 is performed by the EEG receiving unit 10 and attaches a plurality of electrodes to the user's scalp and receives the user's brain waves through the electrodes. For example, when the ground electrode of the plurality of electrodes is disposed at the center of the user's scalp, and both right and left brain electrodes are attached to the scalp of the user in the same number on both sides thereof, (S101) may be selected by receiving an EEG for the frontal region of the user or an EEG for the region excluding the frontal lobe.

The EEG analysis step (S102) extracts the alpha waves, beta waves, theta waves, and gamma waves from the received EEG. The EEG analysis step S102 includes an amplification step, a filtering step, and an AD conversion step.

The amplifying step is performed by the amplifying module 21 and amplifies the brain waves of several tens of μV to tens of mV received by the EEG receiving unit 10 to 3 to 5 V by using an amplifier.

The filtering step is performed by the filter module 123 and filters various noise included in the EEG amplified by using a plurality of analog filters. In this case, the filter may be a high pass filter, a band pass filter, a band stop filter, or a low pass filter. The filtering step includes a high-pass filtering step of first removing noise due to DC voltage, respiration, body motion, eye blinking, and the like using a high-pass filter, and an EEG And a band pass filtering step of filtering the received signal.

In the filtering step, a band-stop filtering step of removing noise due to power supply of 50 Hz or 60 Hz using a band-stop filter and a bandpass filtering of a low-pass filter are used to prevent distortion, And a low-pass filtering step for preventing the low-pass filter. The A / D conversion step digitizes the EEG in the analog state using the A / D conversion module 125.

In the smell judgment step (S103), an EEG recognizing the smell is detected to judge whether or not the apparatus is driven. The smell determining step S103 includes a cognitive EEG search step, a question generating step, and a positive judgment step. The cognitive EEG search step recognizes the stress brain waves that occur in the user's brain when an unpleasant smell is given. The cognitive EEG search step determines whether Kobe beat or gamma waves are continuously appearing for a predetermined time. The cognitive brain wave search step judges that an unpleasant smell occurs due to cooking or the like when the Kobe beat or gamma wave persists for more than 10 seconds.

In addition, the cognitive EEG search step stores the characteristic EEG pattern of the EEG appearing when the user takes an unpleasant smell and judges whether or not the received EEG matches the stored EEG pattern. For this purpose, the user can store the EEG generated in a situation where an unpleasant smell appears.

The question generating step generates a question in such a manner that a character is displayed on the display screen or a sound is generated. The question can be answered by asking whether you want to venture.

The positive judgment stage diagnoses the occurrence of positive EEG or negative EEG for the question. Since the change of the beta wave in the negative EEG rather than the positive EEG is clear, the judgment step can judge only whether the negative pattern of the EEG occurs. In addition, the positive judgment step calculates the temporal and spatial correlation of brain waves to determine whether they are positive brain waves or negative brain waves. In the affirmative judgment step, positive and negative can be judged by calculating the bias and synchronization rate and using it as a logical decision variable of the neural network. Here, the bias refers to the direction of rotation in the phase space composed of EEG components measured at two places, and the synchronization rate is a variable indicating the degree of simultaneous increase or decrease of EEG measured at two places.

로 정의되며, 여기서, 상기 s1과 s2는 두 위치에서 측정된 뇌파 신호, 상기 H(x)는 x가 음수이거나 0일 때에는 0, 양수일 때는 1의 값을 갖는 스텝 함수, 및 상기 w는 동기율을 계산하는 구간 크기이다.The synchronization rate

(X) is a step function having a value of 0 when x is negative or 0, and a positive value when x is positive, and w is a step function having a synchronization rate .

로 정의되며, 여기서 상기 P(t)는 편향성, 상기 벡터 s는 s1과 s2로 이루어진 2차원 벡터, 상기 단위 벡터 θ는 s1과 s2로 이루어지는 위상공간에서의 원점을 중심으로 시계 반대 방향으로 회전하는 방향의 단위 벡터이다. On the other hand,

, Wherein P (t) is biased, the vector s is a two-dimensional vector consisting of s1 and s2, and the unit vector [theta] is rotated counterclockwise about the origin in the phase space consisting of s1 and s2 Direction unit vector.

In the affirmative judgment step, positive and negative are judged by assigning the synchronization rate and bias to the variables of the classification method such as the neural network and the linear decision function. The synchronization rate and bias can be used individually, and both variables can be input. The neural network consists of a multilayer perceptron with an input layer, a hidden layer, and a node level output layer, and is already learned for logic judgment. If it is determined in the affirmative decision step, the positive signal is transmitted to the signal generation unit.

In the menu display step S104, when a positive signal is received, a menu is displayed so that the apparatus can be driven on the display, and a cursor for selecting a menu or a menu by an electroencephalogram is moved. The menu display step S104 may include a displaying step and a menu moving step.

As shown in FIG. 4, the display step displays a menu for selecting a device on the display screen. The menu moving step moves the menu according to the change of brain waves so that the user can select the device.

In the menu move step, the concentration factor is calculated by dividing the value obtained by adding the SMR wave and the middle beta wave into the separator, moving the menu to the right when the concentration is increased, and moving the menu to the left when the concentration factor and Kobe beat increase have. When the user gazes at the menu, the degree of concentration increases. As the concentration increases, the value of the concentration factor obtained by dividing the sum of the SMR wave and the middle beta wave by the separator increases. When concentration increases, the menu moves to the right by default. On the other hand, if you give your hand or face strength while the concentration is increased, the Kobe beat increases. In the menu movement phase, when the concentration increases and Kobe beat increases, the menu moves to the left.

The selection decision step S105 generates a signal for selecting a device to be driven in the menu by judging whether the received EEG pattern matches the pre-stored pattern and the received EEG change. The selection determination step S105 determines that the patterns match when the increase and decrease of the alpha waves are repeated two or more times within the set period of stunning. When the user repeats the operation of closing the eyes at intervals of 3 seconds, the alpha wave is increased in the closed eye and the alpha wave is decreased in the closed eye. The change pattern of the alpha wave is stored and the selection decision step (S105) It is possible to judge whether or not these patterns match.

In the selection decision step S105, when the activity of the left parietal lobe is increased and the activity of the left / right frontal lobe is lowered in determining the change in activity of the alpha wave, It can be judged. In the case of reading a character or thinking of a character in the head, the activity of the left parietal lobe becomes high and the activity of the frontal lobe becomes very low. The change pattern of the alpha waves is stored. In the selection judgment step (S105) Can be determined.

In addition, the selection determination step (S105) can determine that the patterns match when the increase of the SMR wave and the middle beta wave is repeated twice or more within a preset period. If the user recognizes the picture or the character, the SMR wave and the middle beta wave increase, and the selection decision step S105 can determine whether the pattern matches or not.

The device operation step S106 activates the device selected by the drive selection unit 40 when a signal for selecting the device is received. The device activation step S106 may include a hood activation module for activating a hood installed in the kitchen, a window opening module for opening the window, and a cleaner operation module for activating the air cleaner. Here, operating the hood means operating the fan installed in the hood.

Hereinafter, a ventilation system using EEG according to a second embodiment of the present invention will be described. FIG. 5 is a block diagram illustrating a ventilation system using EEG according to a second embodiment of the present invention.

5, the ventilating system 100 using the EEG according to the second embodiment includes an EEG receiving unit 10, an EEG analyzing unit 20, an odor determining unit 30, a driving selecting unit 40, A device moving part 50, and a driving confirmation part 80. [

The ventilator system 100 using the EEG according to the second embodiment is configured such that the user 110 in the home uses the brain wave measuring instrument 120 to perform a ventilating operation using the hood 130, (150), and the like. The brain wave measuring device 120 may be formed in various forms such as a hat, a headset, and the like.

The EEG receiving unit 10 attaches a plurality of electrodes made of a cap or the like to a user's scalp and receives the user's brain waves through the electrodes. For example, a ground electrode among a plurality of electrodes is disposed at the center of a user's scalp, and a right-brain electrode and a left-brain electrode are attached to the scalp of the user on both sides thereof. At this time, the EEG receiving unit 10 selects and receives an EEG for the frontal region of the user except the EEG or the frontal region. The EEG receiving unit 10 may have a band shape and a hat shape.

The EEG analysis unit 20 extracts the alpha waves, beta waves, theta waves, and gamma waves from the received EEG. The EEG analysis unit 20 includes an amplification module 21, a filter module 22, and an AD conversion module 23.

The amplification module 21 has an amplifier therein and amplifies the brain waves of several tens of μV to several tens of mV received by the brain-wave receiving unit 10 through the amplifier to 3 to 5 V to facilitate the analysis of the received brain waves.

The filter module 22 includes a plurality of analog filters to filter various noise included in the brain waves amplified by the amplification module 21. At this time, the filter is composed of a high pass filter, a band pass filter, a band stop filter, and a low pass filter. Such a high-pass filter primarily removes noise due to DC voltage, respiration, body movement, eye blinking, and the like, and the band-pass filter filters the EEG having a frequency band range to be measured. The band-stop filter removes noise due to the power supply of 50Hz or 60Hz, and the low-pass filter limits the width of the brain waves to prevent distortion and prevents distortion occurring when the brain waves are restored. The AD conversion module 23 digitizes the electroencephalogram in the analog state extracted through the filter module 22.

The smell judgment unit (30) detects an EEG for recognizing the smell and judges whether the apparatus is driven or not. The smell determining unit 30 includes a cognitive EEG search module 31, a question generating module 32, and a probing module 33. The cognitive EEG search module 31 recognizes the stress brain waves generated in the user's brain when the user smells an unpleasant smell. The cognitive EEG search module 31 determines whether Kobe beat or gamma wave continuously appears for a preset time. The cognitive EEG search module 31 determines that an unpleasant smell has occurred due to cooking or the like when Kobe beat or gamma wave continues for 10 seconds or more.

The cognitive EEG search module 31 may store the characteristic EEG pattern of the EEG appearing when the user assumes an unpleasant smell and judge whether or not the received EEG matches the stored EEG pattern. For this purpose, the user can store the EEG generated in a situation where an unpleasant smell appears.

The question generation module 32 generates a question by displaying a character on the display screen or generating a sound. The question can be answered by asking whether you want to venture.

The positive decision module 33 diagnoses the occurrence of positive EEG or negative EEG for the question. Since the change of the beta wave in the negative EEG wave rather than the positive EEG wave is definite, the positive decision module 33 can judge whether or not the negative wave pattern of the EEG occurs. The positive decision module 33 calculates the temporal and spatial correlation of brain waves to determine whether the brain waves are positive or negative. The affirmative decision module 33 can determine the positive and negative by calculating the bias and synchronization rate and using it as a logic decision variable of the neural network. Here, the bias refers to the direction of rotation in the phase space composed of EEG components measured at two places, and the synchronization rate is a variable indicating the degree of simultaneous increase or decrease of EEG measured at two places.

로 정의되며, 여기서, 상기 s1과 s2는 두 위치에서 측정된 뇌파 신호, 상기 H(x)는 x가 음수이거나 0일 때에는 0, 양수일 때는 1의 값을 갖는 스텝 함수, 및 상기 w는 동기율을 계산하는 구간 크기이다.The synchronization rate

(X) is a step function having a value of 0 when x is negative or 0, and a positive value when x is positive, and w is a step function having a synchronization rate .

로 정의되며, 여기서 상기 P(t)는 편향성, 상기 벡터 s는 s1과 s2로 이루어진 2차원 벡터, 상기 단위 벡터 θ는 s1과 s2로 이루어지는 위상공간에서의 원점을 중심으로 시계 반대 방향으로 회전하는 방향의 단위 벡터이다. On the other hand,

, Wherein P (t) is biased, the vector s is a two-dimensional vector consisting of s1 and s2, and the unit vector [theta] is rotated counterclockwise about the origin in the phase space consisting of s1 and s2 Direction unit vector.

The positive decision module 33 determines the positive and negative by assigning the synchronization rate and the bias to the variables of the classification method such as the neural network and the linear decision function. The synchronization rate and bias can be used individually, and both variables can be input. The neural network consists of a multilayer perceptron with an input layer, a hidden layer, and a node level output layer, and is already learned for logic judgment. The affirmative judgment module 33 transmits an affirmative signal to the drive selection section 40 when it is determined to be affirmative.

The drive selection unit 40 compares the change of brain waves with a pre-stored pattern, and determines whether or not the EEG coincides with the pre-stored pattern to select a device to be driven.

The drive selection unit 40 compares the increase / decrease of the alpha wave, beta wave, theta wave, and gamma wave among the EEG signals when an affirmative signal is received, and compares the change of the extracted EEG with the pre-stored pattern to determine coincidence . The drive selection unit 40 includes a menu display module 41, a menu movement module 42, and a selection determination module 43.

The menu display module 41 displays a menu for selecting the device on the display screen. The menu movement module 42 moves the menu according to the change of brain waves so that the user can select the device.

The menu movement module 42 calculates the concentration factor obtained by dividing the value obtained by adding the SMR wave and the middle beta wave into the cepa and moves the menu to the right when the concentration is increased and the menu to the left when the concentration factor and Kobe beat are increased Can be moved. When the user gazes at the menu, the degree of concentration increases. As the concentration increases, the value of the concentration factor obtained by dividing the sum of the SMR wave and the middle beta wave by the separator increases. When concentration increases, the menu moves to the right by default. On the other hand, if you give power to your hand or face in the state of increased concentration, Kobe beat increases, and when the concentration increases, Kobe beat increases, the menu moves to the left.

The selection determination module 43 determines whether the received EEG pattern matches the pre-stored pattern and the received EEG changes, and generates a signal for selecting a device to be driven in the menu. The selection determination module 43 determines that the patterns match if the increase and decrease of the alpha waves are repeated two or more times within the set period. When the user repeats the operation of closing and closing the eyes at intervals of 3 seconds, the alpha wave is increased in the closed eye and the alpha wave is decreased in the closed eye. The change pattern of the alpha wave is stored, It is possible to judge whether or not these patterns match.

The selection decision module 43 determines that the pattern matches when the activity of the left parietal lobe is increased and the activity of the left / right frontal lobe is lowered in the determination of the activity change of the alpha wave at least twice within a predetermined time It can be judged. The activity of the left parietal lobe is increased and the activity of the left / right frontal lobe becomes very low. Such a change pattern of the alpha waves is stored, and the selection judgment module 43 compares the pattern matching Can be determined.

In addition, the selection determination module 43 can determine that the patterns match when the increase of the SMR wave and the middle beta wave is repeated twice or more within a predetermined period. When the user recognizes the picture or the character, the SMR wave and the intermediate beta wave increase, and the selection judgment module 43 can judge whether the pattern matches or not.

If the drive selection unit 40 determines that the patterns match, the drive confirmation unit 80 presents a figure and a letter to each other to determine whether the device is to be finally driven. The driving confirmation unit 80 includes a picture display module 81 for presenting a picture, a character display module 82 for displaying characters, and a change in brain waves when a picture is presented, And an EEG change determining module 83 for determining EEGs.

The picture display module 81 displays pictures on a TV or other display panel. The picture may be displayed on the screen or in the form of an LED lamp. The figure can be made up of figures, and it can be composed of figure painting, watercolor painting, oil painting and so on.

The character display module 82 displays a picture on a TV or other display panel. The picture may be displayed on the screen or in the form of an LED lamp. The letters can be made up of one word, or a word or a sentence.

The EEG change determination module 83 determines a change in the EEG when the picture is presented and a change in the EEG when the character is presented. The EEG change determination module 83 can determine that the intensity of the setter wave is lowered and the intensity of the SMR wave and the middle beta wave are increased when the picture is presented. , It can be judged that there is a willingness to drive the intensity of the cetapha and the intensity of the middle beta wave to be lower than that of the case where the picture is presented.

In addition, the EEG change determination module 83 reduces the intensity of the alpha wave in the right hemisphere, does not change in the left hemisphere, and decreases in the left hemisphere when presenting the letters, It can be judged that there is a willingness to drive that there is no change of the alpha wave in the right hemisphere.

When a person recognizes a picture and a character, they show different EEG patterns. In the case of recognizing a picture and a character, the intensity of the seta is reduced compared to the background EEG. In the case of recognizing a picture, the SMR wave and the middle beta wave The intensity increases significantly. The intensity of the SMR wave and the middle beta wave can be measured by the intensity of the voltage. In addition, when a person recognizes a character, the intensity of a seta wave increases and the intensity of a middle beta wave becomes significantly lower than when a picture is presented.

In addition, judging the change of the ALPHA rather than the type of ALPA, the intensity of the ALPHA is decreased in the left hemisphere, the ALPA is not changed in the right hemisphere, During the task, the intensity of the alpha wave is decreased in the right hemisphere and there is no change in the alpha wave in the left hemisphere.

The device operation unit 50 activates the device selected by the drive selection unit 40 when the drive confirmation unit 80 receives a signal. The device moving part 50 may include a hood operation module for operating a hood installed in the kitchen, a window opening module for opening the window, and a cleaner operation module for operating the air cleaner. Here, operating the hood means operating the fan installed in the hood.

Hereinafter, a method of driving a ventilation system using an EEG according to a second embodiment of the present invention will be described. 6 is a flowchart illustrating a method of driving a ventilation system using EEG according to a second embodiment of the present invention.

Referring to FIGS. 5 and 6, the method for driving the ventilation system using the EEG according to the first embodiment includes the steps of receiving an EEG wave (step S201), analyzing an EEG wave (step S202), determining an odor (step S203) (S204), a selection determination step (S205), a driving confirmation step (S206), and a device activation step (S207).

The EEG receiving step S201 is performed by the EEG receiving unit 10 and attaches a plurality of electrodes to the user's scalp to receive the user's brain waves through the electrodes. For example, when the ground electrode of the plurality of electrodes is disposed at the center of the user's scalp, and both right and left brain electrodes are attached to the scalp of the user in the same number on both sides thereof, (S201) can select and receive brain waves for the frontal region of the user or brain waves for the region excluding the frontal lobe.

The EEG analysis step (S202) extracts the alpha waves, beta waves, theta waves, and gamma waves from the received EEG. The EEG analysis step S202 includes an amplification step, a filtering step, and an AD conversion step. The amplifying step is performed by the amplifying module 21 and amplifies the brain waves of several tens of μV to tens of mV received by the EEG receiving unit 10 to 3 to 5 V by using an amplifier.

The filtering step is performed by the filter module 123 and filters various noise included in the EEG amplified by using a plurality of analog filters. In this case, the filter may be a high pass filter, a band pass filter, a band stop filter, or a low pass filter. The filtering step includes a high-pass filtering step of first removing noise due to DC voltage, respiration, body motion, eye blinking, and the like using a high-pass filter, and an EEG And a band pass filtering step of filtering the received signal.

In the filtering step, a band-stop filtering step of removing noise due to power supply of 50 Hz or 60 Hz using a band-stop filter and a bandpass filtering of a low-pass filter are used to prevent distortion, And a low-pass filtering step for preventing the low-pass filter. The A / D conversion step digitizes the EEG in the analog state using the A / D conversion module 125.

In the smell determining step (S203), an EEG recognizing the smell is detected to determine whether the apparatus is driven. The smell judgment step (S203) includes a cognitive EEG search step, a question generating step, and a positive judgment step. The cognitive EEG search step recognizes the stress brain waves that occur in the user's brain when an unpleasant smell is given. The cognitive EEG search step determines whether Kobe beat or gamma waves are continuously appearing for a predetermined time. The cognitive brain wave search step judges that an unpleasant smell occurs due to cooking or the like when the Kobe beat or gamma wave persists for more than 10 seconds.

In addition, the cognitive EEG search step stores the characteristic EEG pattern of the EEG appearing when the user takes an unpleasant smell and judges whether or not the received EEG matches the stored EEG pattern. For this purpose, the user can store the EEG generated in a situation where an unpleasant smell appears.

The question generating step generates a question in such a manner that a character is displayed on the display screen or a sound is generated. The question can be answered by asking whether you want to venture.

The positive judgment stage diagnoses the occurrence of positive EEG or negative EEG for the question. Since the change of the beta wave in the negative EEG rather than the positive EEG is clear, the judgment step can judge only whether the negative pattern of the EEG occurs. In addition, the positive judgment step calculates the temporal and spatial correlation of brain waves to determine whether they are positive brain waves or negative brain waves. In the affirmative judgment step, positive and negative can be judged by calculating the bias and synchronization rate and using it as a logical decision variable of the neural network. Here, the bias refers to the direction of rotation in the phase space composed of EEG components measured at two places, and the synchronization rate is a variable indicating the degree of simultaneous increase or decrease of EEG measured at two places.

로 정의되며, 여기서, 상기 s1과 s2는 두 위치에서 측정된 뇌파 신호, 상기 H(x)는 x가 음수이거나 0일 때에는 0, 양수일 때는 1의 값을 갖는 스텝 함수, 및 상기 w는 동기율을 계산하는 구간 크기이다.The synchronization rate

(X) is a step function having a value of 0 when x is negative or 0, and a positive value when x is positive, and w is a step function having a synchronization rate .

로 정의되며, 여기서 상기 P(t)는 편향성, 상기 벡터 s는 s1과 s2로 이루어진 2차원 벡터, 상기 단위 벡터 θ는 s1과 s2로 이루어지는 위상공간에서의 원점을 중심으로 시계 반대 방향으로 회전하는 방향의 단위 벡터이다. On the other hand,

, Wherein P (t) is biased, the vector s is a two-dimensional vector consisting of s1 and s2, and the unit vector [theta] is rotated counterclockwise about the origin in the phase space consisting of s1 and s2 Direction unit vector.

In the affirmative judgment step, positive and negative are judged by assigning the synchronization rate and bias to the variables of the classification method such as the neural network and the linear decision function. The synchronization rate and bias can be used individually, and both variables can be input. The neural network consists of a multilayer perceptron with an input layer, a hidden layer, and a node level output layer, and is already learned for logic judgment. If it is determined in the affirmative decision step, the positive signal is transmitted to the signal generation unit.

In the menu display step S204, when an affirmative signal is received, a menu is displayed so that the apparatus can be driven on the display, and a cursor for selecting a menu or a menu by an electroencephalogram is moved. The menu display step S204 may include a displaying step and a menu moving step.

As shown in FIG. 4, the display step displays a menu for selecting a device on the display screen. The menu moving step moves the menu according to the change of brain waves so that the user can select the device.

In the menu move step, the concentration factor is calculated by dividing the value obtained by adding the SMR wave and the middle beta wave into the separator, moving the menu to the right when the concentration is increased, and moving the menu to the left when the concentration factor and Kobe beat increase have. When the user gazes at the menu, the degree of concentration increases. As the concentration increases, the value of the concentration factor obtained by dividing the sum of the SMR wave and the middle beta wave by the separator increases. When concentration increases, the menu moves to the right by default. On the other hand, if you give your hand or face strength while the concentration is increased, the Kobe beat increases. In the menu movement phase, when the concentration increases and Kobe beat increases, the menu moves to the left.

The selection determination step S205 generates a signal for selecting a device to be driven in the menu by determining whether the received EEG pattern matches the pre-stored pattern and the received EEG change. In the selection decision step S205, it is determined that the patterns match when the increase and decrease of the alpha waves are repeated twice or more within the stipulated period. When the user repeats the operation of closing and closing the eyes at intervals of 3 seconds, the alpha wave is increased in the closed eye and the alpha wave is decreased in the closed eye. The change pattern of the alpha wave is stored and the selection decision step (S205) It is possible to judge whether or not these patterns match.

In the selection decision step S205, when the activity of the left parietal lobe is increased and the activity of the left / right frontal lobe is lowered in the determination of the activity change of the alpha wave, It can be judged. When the character is read or the character is considered in the head, the activity of the left parietal lobe becomes high and the activity of the frontal lobe becomes very low. The change pattern of the alpha waves is stored. In the selection decision step S205, Can be determined.

In addition, the selection decision step S205 may determine that the patterns match when the increase of the SMR wave and the middle beta wave is repeated twice or more within a predetermined period. If the user recognizes the picture or the character, the SMR wave and the middle beta wave increase, and the selection decision step S205 can determine whether the pattern matches or not.

If it is determined that the patterns match in the selection step (S206), the driving confirmation step (S206) crosses the pictures and characters to determine whether there is a ventilation intention finally. The driving confirmation step (S206) includes a display step of displaying a picture or a character on the screen, and an EEG change determination step of determining a change of the EEG when the picture is presented and a change of the EEG when the character is presented.

The display step displays pictures or characters on a TV or other display panel. Pictures or characters may be displayed on the screen or in the form of LED lamps. The figure can be made up of figures, and it can be composed of figure painting, watercolor painting, oil painting and so on. The letters can be made up of one word, or a word or a sentence.

The EEG decision step determines the EEG changes when presenting pictures and the EEG changes when presenting text. In the step of judging the EEG change, it can be judged that the intensity of the setter wave is lowered and the intensity of the SMR wave and the middle beta wave are increased when the picture is presented. In the determination step of the EEG change, , It can be judged that there is a willingness to drive the intensity of the seta waves and the intensity of the middle beta waves to be lower than when the pictures are presented.

In the step of judging the EEG change, the intensity of the alpha wave is decreased in the right hemisphere while the intensity of the alpha wave is not changed in the left hemisphere. When the letter is presented, the intensity of the alpha wave is decreased in the left hemisphere, It can be judged that there is a willingness to drive that there is no change of ALPHA.

When a person recognizes a picture and a character, they show different EEG patterns. In the case of recognizing a picture and a character, the intensity of the seta is reduced compared to the background EEG. In the case of recognizing a picture, the SMR wave and the middle beta wave The intensity increases significantly. The intensity of the SMR wave and the middle beta wave can be measured by the intensity of the voltage. In addition, when a person recognizes a character, the intensity of a seta wave increases and the intensity of a middle beta wave becomes significantly lower than when a picture is presented.

In addition, judging the change of the ALPHA rather than the type of ALPA, the intensity of the ALPHA is decreased in the left hemisphere, the ALPA is not changed in the right hemisphere, During the task, the intensity of the alpha wave is decreased in the right hemisphere and there is no change in the alpha wave in the left hemisphere.

The device activation step S207 activates the selected device when it is determined that the device is to be operated in the drive confirmation step S206. The device operation step S207 may include a hood operation module for operating a hood installed in the kitchen, a window opening module for opening the window, and a cleaner operation module for activating the air cleaner. Here, operating the hood means operating the fan installed in the hood.

As described above, preferred embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms have been used, they have been used only in a general sense to easily describe the technical contents of the present invention and to facilitate understanding of the invention , And are not intended to limit the scope of the present invention. It is to be understood by those skilled in the art that other modifications based on the technical idea of the present invention are possible in addition to the embodiments disclosed herein.

100, 200: ventilation system
10: EEG receiver
20: EEG analysis unit
21: Amplification module
22: Filter module
23: AD conversion module
30: odor judgment unit
31: Cognitive EEG search module
32: Question Generation Module
33: Probe decision module
40:
41: Menu display module
42: Menu movement module
43: Selection decision module
50:
51: Hood operation module
52: Window opening module
53: Cleaner operation module
80:
81: Picture display module
82: Character display module
83: EEG change judgment module

Claims (12)

An EEG receiving step of receiving a user's EEG;
An odor determination step of determining whether an apparatus is operating by detecting an EEG that recognizes an odor from a received EEG;
A menu display step of displaying a menu so that the apparatus can be driven on the display;
Selecting a device to be driven by comparing a change in brain waves with a pre-stored pattern in the menu and determining whether the EEG is consistent; And
An operation step of activating the selected device;
And a controller for controlling the operation of the ventilation system.
The method according to claim 1,
Wherein the smell determining step includes a cognitive EEG search step of determining whether Kobe beat or gamma wave continuously appears for a preset time period.
3. The method of claim 2,
Wherein the cognitive EEG search step stores a characteristic EEG pattern of an EEG appearing when the user assumes an unpleasant smell and determines whether or not the received EEG matches the stored EEG pattern.
3. The method of claim 2,
Wherein the smell determining step comprises a question generating step of generating and displaying a question, and a positive judgment step of judging occurrence of positive EEG or negative EEG for the question.
The method of claim 3,
Wherein the positive determination step comprises determining a positive direction using a synchronization rate, which is a variable indicating a direction of rotation in a phase space composed of EEG components measured at two places and a variable indicating a degree of simultaneous increase or decrease of EEG measured at two places, Wherein the controller determines whether a physician or a physician is negative.
The method according to claim 1,
Wherein the menu display step includes a menu moving step of moving a menu according to a change of brain waves.
The method according to claim 6,
In the menu moving step, the concentration factor obtained by dividing the value obtained by adding the SMR wave and the middle beta wave to the separator is calculated. When the concentration is increased, the menu is moved to the right, and when the concentration factor and Kobe beat increase, the menu is moved to the left Wherein the ventilating system comprises:
The method according to claim 6,
Wherein the selection decision step determines that the patterns match if the increase and decrease of the alpha waves are repeated twice within the set period of stunning.
The method according to claim 6,
In the selection judgment step, it is determined that the pattern matches when the activity of the left parietal lobe is increased and the activity of the left / right frontal lobe is lowered in the determination of the activity change of the alpha wave twice or more within a predetermined time A method of driving a ventilation system using an EEG.
The method according to claim 1,
Wherein the ventilation system driving method further comprises a driving confirmation step of determining a brain wave change by crossing a picture and a letter when it is determined that the patterns match in the pattern determination step.
11. The method of claim 10,
Wherein the driving confirmation step includes a display step of sequentially displaying characters and pictures on a display.
12. The method of claim 11,
Wherein the driving confirmation step includes an EEG change determination step of determining an EEG change when a picture is presented and a EEG change when a character is presented.

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