KR101842709B1 - Smart device authentication system using brain wave - Google Patents

Smart device authentication system using brain wave Download PDF

Info

Publication number
KR101842709B1
KR101842709B1 KR1020160017399A KR20160017399A KR101842709B1 KR 101842709 B1 KR101842709 B1 KR 101842709B1 KR 1020160017399 A KR1020160017399 A KR 1020160017399A KR 20160017399 A KR20160017399 A KR 20160017399A KR 101842709 B1 KR101842709 B1 KR 101842709B1
Authority
KR
South Korea
Prior art keywords
eeg
wave
sound wave
feature
unit
Prior art date
Application number
KR1020160017399A
Other languages
Korean (ko)
Other versions
KR20170096289A (en
Inventor
송지성
박수조
송형석
고한구
이승열
최유현
Original Assignee
한양대학교 에리카산학협력단
송지성
박수조
송형석
고한구
이승열
최유현
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 한양대학교 에리카산학협력단, 송지성, 박수조, 송형석, 고한구, 이승열, 최유현 filed Critical 한양대학교 에리카산학협력단
Priority to KR1020160017399A priority Critical patent/KR101842709B1/en
Publication of KR20170096289A publication Critical patent/KR20170096289A/en
Application granted granted Critical
Publication of KR101842709B1 publication Critical patent/KR101842709B1/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • A61B5/0476—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Software Systems (AREA)
  • Computer Hardware Design (AREA)
  • Accounting & Taxation (AREA)
  • Neurology (AREA)
  • Human Computer Interaction (AREA)
  • Neurosurgery (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Dermatology (AREA)
  • Biomedical Technology (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The present invention provides a smart device authentication system capable of authenticating using EEG.
A smart device authentication system using EEG according to an embodiment of the present invention includes an EEG measuring unit for measuring brain waves, an EEG transmitting unit for transmitting measured EEG waves, a sound wave generating unit for generating a sound wave in a smart device, A characteristic storage unit for storing the characteristic EEG waves, a feature deciding unit for comparing the received EEG with the stored feature EEGs, and an unlocking unit for unlocking the smart device when the EEG coincides with each other.

Description

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

The present invention relates to a smart device authentication system, and more particularly, to a smart device authentication 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 areas on 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. The delta wave is a brain wave with a frequency of less than 4Hz and typically appears in a normal sleep state. Theta wave is an EEG having a frequency of about 4 to 8 Hz, which is mainly observed when the state is disturbed or distracted. .

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 smart device authentication system capable of authenticating using EEG.

A smart device authentication system using EEG according to an embodiment of the present invention includes an EEG measuring unit for measuring brain waves, an EEG transmitting unit for transmitting measured EEG waves, a sound wave generating unit for generating a sound wave in a smart device, A characteristic storage unit for storing the characteristic EEG waves, a feature deciding unit for comparing the received EEG with the stored feature EEGs, and an unlocking unit for unlocking the smart device when the EEG coincides with each other.

Here, the smart device authentication system may further include a status display unit for displaying the status of the user to the smart device, and the status display unit may display an unstable status when the kobe tarp increases, , And the concentrated state can be displayed when the SMR wave increases.

The smart device authentication system further includes an alarm canceling unit for stopping the alarm sound when the SMR wave and the middle beta wave are continuously received for a predetermined time or longer by measuring the intensity of the SMR wave and the middle beta wave when the alarm sound is generated .

The smart device authentication system may further include a settlement authentication unit for determining whether the EEG received in the electronic settlement matches the stored EEG pattern and performing payment authentication.

In addition, the sound wave generator may generate a sound wave having a non-audible frequency.

In addition, the sound wave generator may generate a sound wave having a frequency of 1 Hz to 30 Hz.

In addition, the sound wave generator may generate a sound wave having a frequency of 20 kHz to 500 kHz.

In addition, the sound wave generator may include a uniform sound wave module that generates sound waves having a uniform frequency.

The sound wave generating unit may include a frequency converting module for generating a sound wave while changing a frequency.

The sound wave generating unit may include a gap generating module for generating a sound wave intermittently so as to generate a gap in which a sound wave is not generated.

Also, the smart device authentication system may further include a feature storage unit for storing a change pattern of an alpha wave generated by a sound wave, wherein the feature storage unit compares a user's brain wave generated in response to a sound wave with a brain wave of a pre- And a feature extraction module for extracting a user's brain wave feature.

The feature storage unit may include an average determination module for determining a feature repeatedly appearing in a user's brain wave inputted a plurality of times, and a storage module for storing a user's brain wave feature as a feature brain wave.

The feature determination unit may include a reaction time determination module that compares the received EEG in response to a sound wave and the stored feature EEG, and determines a degree of similarity in response to the stimulus.

 The feature determination unit may include a peak value determination module that compares the received EEG in response to a sound wave and the stored feature EEG, and determines whether or not the maximum intensity of the EEG matches.

The feature determination unit may include a reaction frequency determination module that compares the received EEG in response to a sound wave and the stored feature EEG, and determines whether a change in EEG appears according to a frequency change of a sound wave.

The feature determining unit may include a resting period determining module that compares the received EEG in response to a sound wave and the stored feature EEG, and determines whether the pattern of the noise EEG appears during a rest period during which no sound waves are generated.

As described above, according to the present invention, it is possible to provide a smart device authentication system that is more secure and can be easily unlocked by storing a user's brain wave characteristic and comparing the received EEG with stored EEG to unlock the smart device.

1 is a schematic view for explaining a smart device authentication system using brain waves according to a first embodiment of the present invention.
2 is a block diagram illustrating a smart device authentication system using brain waves according to a first embodiment of the present invention.
FIG. 3 is a flowchart illustrating a smart device authentication method using an EEG according to the first embodiment of the present invention.
4 is a block diagram illustrating a smart device authentication system using EEG according to a second embodiment of the present invention.
FIG. 5 is a flowchart illustrating a smart device authentication method using an 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 schematic diagram for explaining a smart device authentication system using an EEG according to a first embodiment of the present invention, and FIG. 2 is a diagram illustrating a smart device authentication system using an EEG according to a first embodiment of the present invention FIG.

1 and 2, the smart device authentication system 101 using the EEG according to the first embodiment includes an EEG measuring unit 10, an EEG transmitting unit 30, and an authenticating unit 20 .

The EEG measuring unit 10 comprises an apparatus for receiving an EEG attached to a user. The EEG measuring unit 10 may attach a plurality of electrodes to the scalp of the user 110 to measure the brain waves of the user 80 through the electrodes. In addition, the brain-wave measuring unit 10 can indirectly measure brain waves using ultraviolet rays, infrared rays, or the like. The EEG measuring unit 10 may be a headset 120, a hair band, or a pair of glasses, and may be a hat. The brain wave transmitting unit 30 transmits the brain wave to the authentication unit 20 of the smart device 130 using wireless communication.

The authentication unit 20 includes a sound wave generating unit 21, a feature storage unit 23, a feature determination unit 24, a status display unit 25, an unlocking unit 26, and an alarm canceling unit 28.

The authentication unit 20 may be an application installed in the smart device 130. The smart device 130 may be a portable device such as a smart phone, a tablet PC, a smart watch, or a smart glass, and may be an electronic device having a wireless communication function.

The sound wave generating unit 21 may generate a sound in the smart device, but may generate a sound wave having an invisible frequency. The sound wave generator 21 generates a sound wave through a speaker installed on a door or a wall, and can generate a sound wave when the user presses a button or when it is determined that a person is positioned on the proximity sensor.

The sound wave generator 21 can generate sound waves having a frequency of 1 Hz to 30 Hz and sound waves having frequencies of 20 kHz to 500 kHz. When an ultrasonic wave having a frequency in the non-audible range is generated, the alpha wave increases, and the sound wave generating part generates a sound wave which can not be heard but can change the brain waves. In addition, even when a sound wave having a frequency of a very low non-audible frequency is generated, the alpha wave is increased.

The sound wave generating unit 21 may include a uniform sound wave module 211, a frequency conversion module 212, and a gap generation module 213. The uniform sound wave module 211 generates a sound wave having a uniform frequency without changing the frequency.

The frequency conversion module 212 generates a sound wave and changes the frequency of the sound wave. The gap generation module 213 generates a sound wave intermittently to generate a gap that is a section in which sound waves are not generated.

Meanwhile, the feature storage unit 23 stores a feature EEG capable of unlocking the authentication unit 20 in the smart device. The feature storage unit 23 may store a change pattern of an alpha wave generated by a sound wave.

The feature storage unit 23 includes a feature extraction module 231 that compares a user's brain wave generated in response to a sound wave with brain waves of an ordinary user and extracts a user's brain wave feature, An average determination module 232 for determining a feature to be displayed, and a storage module 233 for storing a user's brain wave feature as a feature brain wave.

The feature extraction module 231 compares the average brain waves of the general public with the brain waves of the user and extracts a portion of the user's brain wave that is different from that of the ordinary human. For example, the feature extraction module may determine whether a user's brain wave exhibits a strong intensity at a measurement frequency to calculate a frequency band having a characteristic peak value.

On the other hand, the average determination module 232 determines characteristics repeatedly appearing in the user's brain waves inputted a plurality of times. Even if the feature is calculated by the feature extraction module 231, the one-time appearance may be different depending on time and situation. Therefore, the average determination module 232 extracts the user's brain wave characteristics commonly appearing in various situations. The storage module 233 stores the EEG patterns of the user extracted from the feature extraction module 231 and the average determination module 232 as feature EEGs.

The feature determination unit 24 compares the EEG received in response to the sound wave with the stored feature EEG to determine whether the EEG is coincident or not. In particular, the feature determination unit 24 may extract an alpha wave among brain waves and determine whether the extracted alpha wave is consistent with the stored pattern. The feature determination unit 24 includes a reaction time determination module 241, a peak value determination module 242, a reaction frequency determination module 243, and a pause determination module 244.

The reaction time determination module 241 compares the inputted EEG with the stored feature EEG, determines whether or not the time corresponding to the stimulus matches, and determines whether the input EEG matches the feature EEG. Here, " matching time " does not necessarily mean exactly the same thing, but means that the time difference is within a predetermined range.

The peak value determination module 242 compares the input EEG with the stored feature EEG, compares the maximum intensity of the EEG in a predetermined frequency band, and determines whether the input EEG matches the feature EEG.

The reaction frequency determination module 243 compares the received EEG in response to the sound wave and the stored EEG, and determines whether the EEG changes according to the frequency change of the sound wave.

Also, the idle period determination module 244 compares the received EEG in response to the sound waves and the stored feature EEG, and determines whether or not the patterns of the noise EEG appear during the idle period in which the sound waves are not generated.

The unlocking unit 26 releases the smart device lock when it is determined that the EEG stored in the feature deciding unit 24 and the received EEG coincide with each other.

The status display unit 25 can display the status of the screen user of the smart device in color or characters. The state display unit 25 displays an unstable state when the Kobe wave is increased, a stable state when the ALFA wave is increased, and a concentrated state when the SMR wave is increased.

When the alarm sound is generated, the alarm canceling unit 28 measures the intensity of the SMR wave and the middle beta wave, and stops the alarm sound when the SMR wave and the middle beta wave are continuously received for a predetermined time or more. When the alarm sounds and the user wakes up, the SMR wave and the middle beta wave rise, focusing on the alarm sound. The alarm releasing unit 28 can release the alarm when the user recognizes that the user is awakened even before the user releases the alarm of the smart device by touching. The alarm cancellation unit 28 can release the alarm when the state of increasing the SMR wave and the intermediate beta wave is maintained for 5 to 20 seconds.

Hereinafter, a smart device authentication method using EEG according to the first embodiment of the present invention will be described. FIG. 3 is a flowchart illustrating a smart device authentication method using an EEG according to the first embodiment of the present invention.

Referring to FIG. 3, the smart device authentication method using EEG according to the first embodiment includes storing EEG waves S101, S102, EEG comparison steps S103, S104, A status display step S105, and an alarm canceling step S106.

In the EEG storage step (S101), the received EEG is received a plurality of times in response to a sound wave, and a feature is extracted from a user's EEG and stored in a smart device as a feature EEG. The EEG storage step (S101) can store the characteristics of the ALPHA waves that change in response to the sound waves.

The EEG storage step (S101) includes a feature extraction step of extracting a user's EEG characteristics by comparing an input EEG of the user and pre-stored EEGs of the user, and an average determination step of determining repeated features of the EEG input And a storing step of storing the user's brain wave characteristic as a characteristic brain wave.

The sound wave generation step (S102) can generate a sound in the smart device, and generate a sound wave having an invisible frequency. The sound wave generation step (S102) generates a sound wave through a speaker installed on a door or a wall, and can generate a sound wave when the user presses a button or when it is determined that a person is positioned on the proximity sensor.

The sound wave generation step (S102) can generate a sound wave having a frequency of 1 Hz to 30 Hz and a sound wave having a frequency of 20 kHz to 500 kHz. When an ultrasonic wave having a frequency in the non-audible range is generated, the alpha wave increases, and the sound wave generating part generates a sound wave which can not be heard but can change the brain waves. In addition, even when a sound wave having a frequency of a very low non-audible frequency is generated, the alpha wave is increased.

The sound wave generating step (S102) may include at least one of a uniform sound wave step, a frequency converting step, and a gap generating step. The uniform sound wave step generates a sound wave having a uniform frequency without changing the frequency.

The frequency conversion step generates a sound wave and changes the frequency of the sound wave. The gap generation step generates a sound wave intermittently to generate a gap that is a section in which sound waves are not generated.

In the EEG comparison step (S103), the EEG received in response to the sound wave is compared with the feature EEG stored in the smart device to determine coincidence. In the EEG comparison step (S103), the pattern of the ALPHA wave that changes by the sound wave can be compared with the stored characteristic EEG.

The EEG comparison step S103 may include a reaction time determination step of comparing the EEG received in response to the sound wave with the stored EEG, and determining whether the time corresponding to the stimulus is matched.

The EEG comparison step S103 compares the EEG received in response to the sound wave with the stored EEG, compares the maximum intensity of the EEG in a predetermined frequency band, and determines whether the input EEG matches the characteristic EEG Step < / RTI >

The EEG comparison step S103 may include a peak value determination step of determining whether or not the maximum intensity of the received EEG is consistent with the sound wave. The EEG comparison step S103 may include a noise determination step of comparing the EEG received in response to the sound wave with the stored EEG, and determining whether a pattern of the EEG exhibits a minute intensity below a predetermined intensity .

The unlocking step S104 releases the lock of the smart device when it is determined that the received EEG coincides with the stored feature EEG.

The status of the screen user of the smart device can be displayed in color or text. The state display step (S105) displays an unstable state when the kobe wave is increased, a stable state when the alpha wave is increased, and a concentrated state when the SMR wave is increased.

The alarm releasing step (S106) measures the intensity of the SMR wave and the middle beta wave when the alarm sound is generated, and stops the alarm sound when the SMR wave and the middle beta wave are continuously received for a predetermined time or more. When the alarm sounds and the user wakes up, the SMR wave and the middle beta wave rise, focusing on the alarm sound. The alarm releasing step (S106) can cancel the alarm if the user recognizes that the user is awakened even before the user releases the alarm of the smart device by touching. In the alarm releasing step (S106), the alarm can be released when the state of increasing the SMR wave and the intermediate beta wave is maintained for 5 to 20 seconds.

Hereinafter, a smart device authentication system using EEG according to a second embodiment of the present invention will be described. 4 is a block diagram illustrating a smart device authentication system using EEG according to a second embodiment of the present invention.

Referring to FIG. 4, the smart device authentication system 102 using the EEG according to the second embodiment includes an EEG measuring unit 10, an EEG transmitting unit 30, and an authenticating unit 20.

The EEG measuring unit 10 comprises an apparatus for receiving an EEG attached to a user. The EEG measuring unit 10 may attach a plurality of electrodes to the scalp of the user 110 to measure the brain waves of the user 80 through the electrodes. In addition, the brain-wave measuring unit 10 can indirectly measure brain waves using ultraviolet rays, infrared rays, or the like. The EEG measuring unit 10 may be a headset 120, a hair band, or a pair of glasses, and may be a hat. The brain wave transmitting unit 30 transmits the brain wave to the authentication unit 20 of the smart device 130 using wireless communication.

The authentication unit 20 includes a sound generator 21, a feature storage unit 23, a feature determination unit 24, a status display unit 25, an unlocking unit 26, a payment authentication unit 27, (28).

The authentication unit 20 may be an application installed in the smart device 130. The smart device 130 may be a portable device such as a smart phone, a tablet PC, a smart watch, or a smart glass, and may be an electronic device having a wireless communication function.

The sound wave generating unit 21 may generate a sound in the smart device, but may generate a sound wave having an invisible frequency. The sound wave generator 21 generates a sound wave through a speaker installed on a door or a wall, and can generate a sound wave when the user presses a button or when it is determined that a person is positioned on the proximity sensor.

The sound wave generator 21 can generate sound waves having a frequency of 1 Hz to 30 Hz and sound waves having frequencies of 20 kHz to 500 kHz. When an ultrasonic wave having a frequency in the non-audible range is generated, the alpha wave increases, and the sound wave generating part generates a sound wave which can not be heard but can change the brain waves. In addition, even when a sound wave having a frequency of a very low non-audible frequency is generated, the alpha wave is increased.

The sound wave generating unit 21 may include a uniform sound wave module 211, a frequency conversion module 212, and a gap generation module 213. The uniform sound wave module 211 generates a sound wave having a uniform frequency without changing the frequency.

The frequency conversion module 212 generates a sound wave and changes the frequency of the sound wave. The gap generation module 213 generates a sound wave intermittently to generate a gap that is a section in which sound waves are not generated.

Meanwhile, the feature storage unit 23 stores a feature EEG capable of unlocking the authentication unit 20 in the smart device. The feature storage unit 23 may store a change pattern of an alpha wave generated by a sound wave.

The feature storage unit 23 includes a feature extraction module 231 that compares a user's brain wave generated in response to a sound wave with brain waves of an ordinary user and extracts a user's brain wave feature, An average determination module 232 for determining a feature to be displayed, and a storage module 233 for storing a user's brain wave feature as a feature brain wave.

The feature extraction module 231 compares the average brain waves of the general public with the brain waves of the user and extracts a portion of the user's brain wave that is different from that of the ordinary human. For example, the feature extraction module may determine whether a user's brain wave exhibits a strong intensity at a measurement frequency to calculate a frequency band having a characteristic peak value.

On the other hand, the average determination module 232 determines characteristics repeatedly appearing in the user's brain waves inputted a plurality of times. Even if the feature is calculated by the feature extraction module 231, the one-time appearance may be different depending on time and situation. Therefore, the average determination module 232 extracts the user's brain wave characteristics commonly appearing in various situations. The storage module 233 stores the EEG patterns of the user extracted from the feature extraction module 231 and the average determination module 232 as feature EEGs.

The feature determination unit 24 compares the EEG received in response to the sound wave with the stored feature EEG to determine whether the EEG is coincident or not. In particular, the feature determination unit 24 may extract an alpha wave among brain waves and determine whether the extracted alpha wave is consistent with the stored pattern. The feature determination unit 24 includes a reaction time determination module 241, a peak value determination module 242, a reaction frequency determination module 243, and a pause determination module 244.

The reaction time determination module 241 compares the inputted EEG with the stored feature EEG, determines whether or not the time corresponding to the stimulus matches, and determines whether the input EEG matches the feature EEG. Here, " matching time " does not necessarily mean exactly the same thing, but means that the time difference is within a predetermined range.

The peak value determination module 242 compares the input EEG with the stored feature EEG, compares the maximum intensity of the EEG in a predetermined frequency band, and determines whether the input EEG matches the feature EEG.

The reaction frequency determination module 243 compares the received EEG in response to the sound wave and the stored EEG, and determines whether the EEG changes according to the frequency change of the sound wave.

Also, the idle period determination module 244 compares the received EEG in response to the sound waves and the stored feature EEG, and determines whether or not the patterns of the noise EEG appear during the idle period in which the sound waves are not generated.

The unlocking unit 26 releases the smart device lock when it is determined that the EEG stored in the feature deciding unit 24 and the received EEG coincide with each other.

The settlement authentication unit 27 searches for an EEG pattern at the time of electronic payment using a smart device and performs settlement authentication. The settlement authentication unit 27 generates a sound wave having a non-audible frequency at the time of electronic payment, receives a user's brain wave to be compared with the brain waves stored in the feature storage unit 23, .

The status display unit 25 can display the status of the screen user of the smart device in color or characters. The state display unit 25 displays an unstable state when the Kobe wave is increased, a stable state when the ALFA wave is increased, and a concentrated state when the SMR wave is increased.

When the alarm sound is generated, the alarm canceling unit 28 measures the intensity of the SMR wave and the middle beta wave, and stops the alarm sound when the SMR wave and the middle beta wave are continuously received for a predetermined time or more. When the alarm sounds and the user wakes up, the SMR wave and the middle beta wave rise, focusing on the alarm sound. The alarm releasing unit 28 can release the alarm when the user recognizes that the user is awakened even before the user releases the alarm of the smart device by touching. The alarm cancellation unit 28 can release the alarm when the state of increasing the SMR wave and the intermediate beta wave is maintained for 5 to 20 seconds.

Hereinafter, a smart device authentication method using an EEG according to a second embodiment of the present invention will be described. FIG. 5 is a flowchart illustrating a smart device authentication method using an EEG according to a second embodiment of the present invention.

Referring to FIG. 5, the smart device authentication method using EEG according to the second embodiment includes an EEG storage step S201, a sound wave generating step S202, a brain wave comparing step S203, an unlocking step S204 ), A status display step S205, a payment authentication step S206, and an alarm releasing step S207.

In the brain wave storing step (S201), the received user's brain wave is received a plurality of times in response to the sound wave, and the feature is extracted from the user's brain wave and stored in the smart device as a characteristic brain wave. The EEG storage step (S201) can store the characteristics of the ALPHA waves that change in response to the sound waves.

The EEG storage step S201 includes a feature extraction step of extracting a user's EEG feature by comparing an input EEG of the user and pre-stored EEGs of the user, and an average determination step of determining repeated features of the EEG input of the user And a storing step of storing the user's brain wave characteristic as a characteristic brain wave.

The sound wave generation step (S202) may generate a sound in the smart device, but generate a sound wave having an audible frequency. The sound wave generation step (S202) generates a sound wave through a speaker installed on a door or a wall, and can generate a sound wave when the user presses a button or when it is determined that a person is positioned on the proximity sensor.

The sound wave generation step (S202) can generate a sound wave having a frequency of 1 Hz to 30 Hz and a sound wave having a frequency of 20 kHz to 500 kHz. When an ultrasonic wave having a frequency in the non-audible range is generated, the alpha wave increases, and the sound wave generating part generates a sound wave which can not be heard but can change the brain waves. In addition, even when a sound wave having a frequency of a very low non-audible frequency is generated, the alpha wave is increased.

The sound wave generation step S202 may include at least one of a uniform sound wave step, a frequency conversion step, and a gap generation step. The uniform sound wave step generates a sound wave having a uniform frequency without changing the frequency.

The frequency conversion step generates a sound wave and changes the frequency of the sound wave. The gap generation step generates a sound wave intermittently to generate a gap that is a section in which sound waves are not generated.

In the EEG comparison step (S203), the EEG received in response to the sound wave is compared with the feature EEG stored in the smart device to determine coincidence. In the EEP comparison step (S203), the pattern of the ALPHA wave that changes by the sound wave can be compared with the stored characteristic EEP.

The EEG comparison step S203 may include a reaction time determination step of comparing the EEG received in response to the sound wave with the stored EEG, and determining whether the time corresponding to the stimulus is matched.

The EEG comparison step (S203) compares the EEG received in response to the sound wave with the stored EEG, compares the maximum intensity of the EEG in a predetermined frequency band, and determines whether the input EEG matches the characteristic EEG Step < / RTI >

The EEG comparison step S203 may include a peak value determination step of determining whether the maximum intensity of the received EEG is consistent with the sound wave. The EEG comparison step S203 may include a noise determination step of comparing the received EEG in response to a sound wave and the stored feature EEG to determine whether or not a pattern of a noise EEG exhibits a minute intensity below a predetermined intensity .

In the unlocking step S204, if it is determined that the received EEG coincides with the stored characteristic EEG, the smart device is unlocked.

The status of the screen user of the smart device can be displayed in color or text. In the state display step (S205), an unstable state is displayed when the Kobe beat is increased, a stable state is displayed when the alpha wave is increased, and a concentrated state is displayed when the SMR wave is increased.

The payment authentication step (S206) determines whether the EEG received at the time of electronic payment matches the stored EEP pattern and performs payment authentication. The payment authentication step (S206) generates a sound wave having a non-audible frequency at the time of electronic payment, receives a user's brain wave in response to the electronic wave and compares the user's brain wave with the brain waves stored in the feature storage unit (23) .

In the alarm releasing step (S207), when the alarm sound is generated, the intensity of the SMR wave and the middle beta wave is measured and the alarm sound is stopped when the SMR wave and the middle beta wave are continuously received for a predetermined time or more. When the alarm sounds and the user wakes up, the SMR wave and the middle beta wave rise, focusing on the alarm sound. The alarm releasing step (S207) can release the alarm when the user recognizes that the user is awakened even before the user releases the alarm of the smart device by touching. In the alarm releasing step S207, the alarm can be released when the state of increasing the SMR wave and the middle beta wave is maintained for 5 to 20 seconds.

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: Smart Device Authentication System
10: EEG measurement unit
20:
21: sound wave generator
23: Feature storage unit
24:
25: Status indicator
26:
27:
28:
30: EEG transmission unit

Claims (16)

An EEG measuring unit for measuring EEG;
An EEG transmitting unit for transmitting the measured EEG; And
A feature storage unit for storing a feature EEG capable of releasing the lock; a feature determination unit for comparing the received EEG with the stored feature EEG to determine whether the EEG is coincident; And an unlocking unit for unlocking the device,
Wherein the feature storage unit includes a feature extraction module for extracting a user's brain wave feature by comparing a user's brain wave generated in response to a sound wave and a brain wave of an ordinary user; An average determination module for determining a feature repeatedly appearing in a user's brain wave inputted a plurality of times; And a storage module for storing the EEG characteristics of the user as a feature EEG.
The method according to claim 1,
Wherein the smart device authentication system further comprises a status display unit for displaying a status of a user to the smart device, wherein the status display unit displays an unstable state when Kobe beat increases, displays a stable state when an ALPA increases, And the concentrated state is displayed when the wave is increased.
The method according to claim 1,
The smart device authentication system further includes an alarm canceling unit for stopping the alarm sound when the SMR wave and the middle beta wave are continuously received for a predetermined time or longer by measuring the intensity of the SMR wave and the middle beta wave when the alarm sound is generated A smart device authentication system using brain waves.
The method according to claim 1,
Wherein the smart device authentication system further comprises a settlement authentication unit for determining whether the brain waves received at the time of electronic settlement coincide with the stored brain wave pattern and performing settlement authentication.
The method according to claim 1,
Wherein the sound wave generator generates a sound wave having a non-audible frequency.
6. The method of claim 5,
Wherein the sound wave generator generates a sound wave having a frequency of 1 Hz to 30 Hz.
6. The method of claim 5,
Wherein the sound wave generator generates a sound wave having a frequency of 20 kHz to 500 kHz.
6. The method of claim 5,
Wherein the sound wave generating unit includes a uniform sound wave module for generating sound waves having a uniform frequency.
6. The method of claim 5,
Wherein the sound wave generator includes a frequency conversion module for generating a sound wave while changing a frequency.
6. The method of claim 5,
Wherein the sound wave generating unit includes a gap generating module for generating a gap which is a section in which a sound wave is not generated by generating a sound wave intermittently.
delete delete The method according to claim 1,
Wherein the feature determination unit includes a reaction time determination module that compares the received EEG in response to a sound wave and stored feature EEG, and determines a similarity of time in response to a stimulus.
The method according to claim 1,
Wherein the feature determination unit includes a peak value determination module that compares the received EEG in response to a sound wave and the stored feature EEG, and determines whether or not the maximum intensity of EEG coincides with each other.
The method according to claim 1,
Wherein the feature determining unit includes a reaction frequency determining module that compares the received EEG in response to a sound wave and the stored feature EEG to determine whether a change in the EEG appears according to a frequency change of a sound wave, Authentication system.
The method according to claim 1,
Wherein the feature determining unit includes a pause determining module that compares the received EEG in response to a sound wave and the stored feature EEG to determine whether a pattern of a noise EEG appears during a rest period during which a sound wave is not generated, Device authentication system.
KR1020160017399A 2016-02-15 2016-02-15 Smart device authentication system using brain wave KR101842709B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020160017399A KR101842709B1 (en) 2016-02-15 2016-02-15 Smart device authentication system using brain wave

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1020160017399A KR101842709B1 (en) 2016-02-15 2016-02-15 Smart device authentication system using brain wave

Publications (2)

Publication Number Publication Date
KR20170096289A KR20170096289A (en) 2017-08-24
KR101842709B1 true KR101842709B1 (en) 2018-03-28

Family

ID=59758255

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020160017399A KR101842709B1 (en) 2016-02-15 2016-02-15 Smart device authentication system using brain wave

Country Status (1)

Country Link
KR (1) KR101842709B1 (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108519810B (en) * 2018-03-07 2021-04-09 Oppo广东移动通信有限公司 Electronic device, brain wave unlocking method and related product
CN108596625B (en) * 2018-03-26 2022-04-26 Oppo广东移动通信有限公司 Electronic device, equipment control method and related product

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011251058A (en) * 2010-06-03 2011-12-15 Panasonic Corp Method and apparatus of measuring auditory steady-state response

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011251058A (en) * 2010-06-03 2011-12-15 Panasonic Corp Method and apparatus of measuring auditory steady-state response

Also Published As

Publication number Publication date
KR20170096289A (en) 2017-08-24

Similar Documents

Publication Publication Date Title
CN109464130B (en) Sleep assisting method, system and readable storage medium
US12023133B2 (en) Method of measuring physiological parameter of subject in contactless manner
KR100975590B1 (en) A detection method of sleepiness
KR101842709B1 (en) Smart device authentication system using brain wave
US20140213908A1 (en) Portable electronic device having heart rate monitoring function
US20220012324A1 (en) Wearable device having high security and stable blood pressure detection
Ohtsuki et al. Biometrie authentication using hand movement information from wrist-worn PPG sensors
KR101842707B1 (en) Control mehtod for security system using brain wave
KR20170096288A (en) Smart device authentication mehtod using brain wave
KR20170096274A (en) Emotion expression system using brain wave
KR101769473B1 (en) Security system using brain wave
KR101831757B1 (en) Smart phone application and smart phone pairing apparatus using using brain wave
KR101769481B1 (en) Control mehtod for security system using brain wave
KR20170096281A (en) Security system using brain wave
KR101727183B1 (en) Home care system using brain wave
KR101727175B1 (en) Smart phone pairing method using brain wave
KR101464066B1 (en) apparatus and method for measuring living body information
KR101769475B1 (en) Controlling mehthod for electronic equipment using pattern ctreation part
KR101612037B1 (en) Electronic equipment control device using brain wave
KR101664218B1 (en) Electronic equipment control device using brain wave
KR101664029B1 (en) Control mehthod for electronic equipment using brain wave
KR101727193B1 (en) Control mehthod for electronic device having brain wave pattern synchronization part
KR101728469B1 (en) Electronic equipment control device having brain wave pattern synchronization part
KR101664027B1 (en) Control method for electronic equipment using brain wave and activation pattern
KR20170096272A (en) Emotion expression method using brain wave

Legal Events

Date Code Title Description
A201 Request for examination
E902 Notification of reason for refusal
E701 Decision to grant or registration of patent right
GRNT Written decision to grant