CN110575165A - APP used for brain monitoring and intervention in cooperation with EEG equipment - Google Patents
APP used for brain monitoring and intervention in cooperation with EEG equipment Download PDFInfo
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- CN110575165A CN110575165A CN201910909488.2A CN201910909488A CN110575165A CN 110575165 A CN110575165 A CN 110575165A CN 201910909488 A CN201910909488 A CN 201910909488A CN 110575165 A CN110575165 A CN 110575165A
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- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
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- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M21/02—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
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- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
- A61M2021/0027—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the hearing sense
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- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
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- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
- A61M2021/0055—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus with electric or electro-magnetic fields
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Abstract
The invention discloses an APP (application) matched with an EEG (electroencephalogram) device for brain monitoring and intervention, which comprises a head sleeve, a mobile phone, a cloud server and a brain monitoring APP, wherein brain electrodes are installed on two sides of the top in the head sleeve through bolts, reference electrodes are installed on two sides in the head sleeve, an A processor and an A Bluetooth module are installed on the top of the head sleeve through an installation box, the A processor is electrically connected with the A Bluetooth module, the reference electrodes and the brain electrodes, the mobile phone is provided with a B Bluetooth module, a B processor, a network controller, a brain monitoring APP and a camera, and the brain monitoring APP is provided with an image recognition system and a data analysis module. The head-mounted EEG equipment is adopted to read the brain waves of the user in real time, the real electroencephalogram state of the user is obtained by data screening and algorithm to judge the emotion and attention of the user and the physical state of the user in sleep before and during sleep, and the accuracy, the real-time performance and the practicability are guaranteed.
Description
Technical Field
The invention belongs to the technical field of EEG brain monitoring, and particularly relates to an APP used for brain monitoring and intervention in cooperation with EEG equipment.
Background
The conventional App on the market cannot monitor and display real-time sleep electroencephalogram data, the displayed data are the previous report results, the next sleep does not have a sufficient effective sleep aiding effect, part of the APPs used by matched hardware display data obtained by heartbeat, pulse, respiration, body temperature and body movement, the relevance of life behaviors such as sleep and the like is not visual and close enough, the real-time body data state cannot be displayed for a user to check, only past records exist, and the current monitoring condition does not exist; in the sleep intervention, only results are reported through previous inaccurate analysis, and one or more fixed sleep-assisting schemes can be given: the sleeping aid helps sleep in modes of movement, smell, vision, massage, light and sound, but neglects the individual differentiation and cannot generate a fundamentally effective sleeping aid effect.
Disclosure of Invention
The invention aims to provide an APP (application) matched with an EEG (electroencephalogram) device for brain monitoring and intervention use, and aims to solve the problems that the existing APP on the market proposed in the background technology cannot monitor and display real-time sleep electroencephalogram data, the displayed data are all previous report results, and do not have a sufficient and effective sleep-assisting effect on next sleep, and part of the APPs used with matched hardware display data obtained by heartbeat, pulse, respiration, body temperature and physical movement, the relevance of life behaviors such as sleep and the like is not visual and close enough, and the real-time body data state cannot be displayed for a user to check, and only past records are available, and the current monitoring condition is not available; in the sleep intervention, only results are reported through previous inaccurate analysis, and one or more fixed sleep-assisting schemes can be given: the sleeping aid helps sleep in modes of movement, smell, vision, massage, light and sound, but neglects the individual differentiation and cannot generate a fundamentally effective sleeping aid effect.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a close EEG equipment, includes headgear, cell-phone, high in the clouds server and brain monitoring APP, bolt installation brain electricity electrode is passed through to top both sides in the headgear, both sides installation reference electrode in the headgear, installation box installation A treater and A bluetooth module are passed through at the headgear top, A treater and A bluetooth module, reference electrode and brain electricity electrode electricity are connected, the cell-phone has B bluetooth module, B treater, network controller, brain monitoring APP and camera, brain monitoring APP possesses image recognition system and data analysis module, B treater and camera, bluetooth module, brain monitoring APP, image recognition system and network controller electricity are connected, network controller and high in the clouds server signal connection.
Further, the APP of the EEG equipment matched with the EEG equipment for brain monitoring and interventional use comprises the following operation steps:
Step 101: opening the APP, and completing login and registration processes to enter a page; 5 items of basic information are required to be input or the next operation is required to be entered when the page is entered for the first time;
Step 102: the Bluetooth module B9 of the mobile phone 2 is matched with the head sleeve 1, and 3 options are needed to be completed when the head sleeve 1 is connected for the first time and sleep-aiding power is used; after the pairing connection is completed, after the person wears the headgear, the page displays real-time electroencephalogram data changes of the wearer, detects corresponding electroencephalogram data along with the behavior activities of the wearer, analyzes and processes the data, and continuously displays the change conditions of the data; according to the analysis result of the brain waves collected currently, obtaining a sleep analysis report which respectively corresponds to the personal emotion, attention and original brain wave data;
step 103: clicking a page sleep-aiding button to enter a sleep-aiding page, wherein the sleep-aiding page also displays the current electroencephalogram data, clicking a setting interface can perform sleep parameter setting operation, and matching acoustic wave sleep aiding and micro-magnetic sleep aiding are performed by analyzing the electroencephalogram data and the real sleep condition and body state; if the single electroencephalogram monitoring equipment is worn, when the sleep-aiding function cannot be applied, electroencephalogram data of a wearer can be monitored in real time and displayed on an application interface; the headgear clicks the 'long press ends sleep' button, so that the sleep-aiding function can be ended and the sleep self-evaluation can be carried out;
Step 104: after the sleep aid is finished, the user can independently return to the home page to check the sleep aid report, and when the hardware equipment is not taken off, the user can still click the data at the bottom to check the historical sleep aid record and the detailed sleep aid report.
Further, the image recognition system has an image recognition module, an image repository, a history data repository, an account database, an image processing module, and an image transmission module.
Furthermore, the brain monitoring APP maintains the pairing relationship among the equipment, the data processing process and the message queue through a resource management component, ensures the coordination and cooperation of the three, dynamically adjusts the pairing relationship to adapt to user login and logout at any time under a multi-user environment, ensures automatic data acquisition during multi-user sleep through background scheduling and management of the cloud server, and completes large-scale service support and data collection.
further, the data analysis method designed in step 102 is to complete analysis of the sleep data of the user, give a sleep stage report, and complete self-correction of the collected big data, and the cloud server stores two sets of data collected during sleep in the background, one set of the data is a feature formed by analyzing the time domain and the frequency domain of the original EEG waveform data at intervals of 1 minute, and the other set of the data is the original EEG waveform data stored at intervals of 1 minute and is formed by segmenting the sleep stage based on the feature data and using the existing fixed rule.
further, the analysis mode in step 102 is based on five sleep modes of falling asleep, light sleep, middle sleep, deep sleep and rapid eye movement sleep (REM), the non-falling asleep criterion is that Alpha wave (Alpha brain wave) and beta wave (beta wave) are more than 50% in 10 minutes, and the light sleep criterion is that: theta wave (Saita brain wave) accounts for more than 50% in 10 minutes, and the standard for judging the middle sleep is as follows: the Delta wave (Delta wave) accounts for more than 35% in 10 minutes, and the deep sleep judgment standard is as follows: delta wave (Delta wave) accounts for more than 50% in 10 minutes, and the judgment standard of rapid eye movement sleep (REM) is as follows: the ratio of Theta wave (saita brain wave) to Alpha wave (Alpha brain wave) exceeds 50% in 10 minutes (wherein the ratio of Alpha wave (Alpha brain wave) exceeds 10%), and the frequency range of Alpha wave (Alpha brain wave) is as follows: 8-13Hz, the frequency range of the beta wave (beta wave) is as follows: 13-30Hz, and the frequency range of the Theta wave (Saita brain wave) is as follows: 4-8Hz, the frequency range of the Delta wave (Delta wave) is as follows: 1-4 Hz.
Further, the sleep stage is given by a fixed rule, and when both the fixed rule and the network model are available, the sleep stage is given by the fixed rule and the network model together (when the confidence of the judgment given by the network model is higher than a threshold, the result given by the network model is used, otherwise, the result given by the rule is used).
Compared with the prior art, the invention has the beneficial effects that: can realize showing the data feedback of monitoring, intervention sleep fast in real time, let the user reach the purpose of real-time supervision and real-time intervention sleep through this APP can combine supporting hardware, solve and can not look over and know accurate real-time brain electric data and go to intervene the pain point of sleep according to real-time data to can show different individual data according to individual difference, in addition analysis and help sleep.
Drawings
Fig. 1 is a schematic diagram of the connection of an APP for use with an EEG device for brain monitoring and intervention according to the present invention.
fig. 2 is a schematic diagram of a headset structure of an APP for use with EEG equipment for brain monitoring and intervention according to the present invention.
fig. 3 is a schematic diagram of the connection of a device module for use with an EEG apparatus for brain monitoring and intervention according to the present invention.
Fig. 4 is a schematic diagram of a process for generating a brain monitoring APP interface of an APP for use in conjunction with EEG equipment for brain monitoring and intervention according to the present invention.
Fig. 5 is a schematic diagram of an image recognition system of APP for brain monitoring and intervention in cooperation with an EEG device according to the present invention.
fig. 6 is a schematic diagram of a brain monitoring APP module of an APP for use with EEG equipment for brain monitoring and intervention according to the present invention.
Fig. 7 is a schematic diagram of an emotion detection interface of a brain monitoring APP of an APP for use with EEG equipment for brain monitoring and intervention according to the present invention.
Fig. 8 is a schematic view of an attention detection interface of a brain monitoring APP of an APP for use with EEG equipment for brain monitoring and intervention according to the present invention.
Fig. 9 is a schematic diagram of a sleep-aid interface for a brain monitoring APP for use with an EEG apparatus for brain monitoring and intervention of the present invention.
fig. 10 is a schematic diagram of an APP setup and basic information interface for use with an EEG device for brain monitoring and intervention according to the present invention.
Fig. 11 is a schematic diagram of a mental health monitoring report interface for a brain monitoring APP for use with an EEG device for brain monitoring and intervention according to the present invention.
Fig. 12 is a schematic diagram of a brain performance detection report interface for a brain monitoring APP of an APP for use with EEG equipment for brain monitoring and intervention according to the present invention.
Fig. 13 is a schematic diagram of a general brain-benefit game report interface for a brain-monitoring APP of an APP for use with EEG equipment for brain monitoring and intervention according to the present invention.
Fig. 14 is a schematic diagram of a sleep aid data report for a brain monitoring APP for use with an EEG apparatus for brain monitoring and intervention in accordance with the present invention.
In the figure: 1. a headgear; 2. a mobile phone; 3. a cloud server; 4. an electroencephalogram electrode; 5. a reference electrode; 6. a, a processor; 8. a, a Bluetooth module; 9. b, a Bluetooth module; 10. an image recognition system; 11. b, a processor; 12. brain monitoring APP; 13. a network controller; 14. a camera is provided.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1-17, an EEG device, includes headgear 1, cell-phone 2, cloud server 3 and brain monitoring APP12, bolt installation brain electrode 4 is passed through to top both sides in the headgear 1, both sides installation reference electrode 5 in the headgear 1, installation box installation a treater 6 and a bluetooth module 8 are passed through at headgear 1 top, a treater 6 is connected with a bluetooth module 8, reference electrode 5 and brain electrode 4 electricity, cell-phone 2 has B bluetooth module 9, B treater 11, network controller 13, brain monitoring APP12 and camera 14, brain monitoring APP12 possesses image recognition system 10 and data analysis module, B treater 11 is connected with camera 14, bluetooth module 9, brain monitoring APP12, image recognition system 10 and network controller 13 electricity, network controller 13 and cloud server 3 signal connection.
The APP used for brain monitoring and intervention by matching with the EEG equipment comprises the following operation steps:
Step 101: opening the APP, and completing login and registration processes to enter a page; 5 items of basic information are required to be input or the next operation is required to be entered when the page is entered for the first time;
step 102: the Bluetooth module B9 of the mobile phone 2 is matched with the head sleeve 1, and 3 options are needed to be completed when the head sleeve 1 is connected for the first time and sleep-aiding power is used; after the pairing connection is completed, after the person wears the headgear, the page displays real-time electroencephalogram data changes of the wearer, detects corresponding electroencephalogram data along with the behavior activities of the wearer, analyzes and processes the data, and continuously displays the change conditions of the data; according to the analysis result of the brain waves collected currently, obtaining a sleep analysis report which respectively corresponds to the personal emotion, attention and original brain wave data;
step 103: clicking a page sleep-aiding button to enter a sleep-aiding page, wherein the sleep-aiding page also displays the current electroencephalogram data, clicking a setting interface can perform sleep parameter setting operation, and matching acoustic wave sleep aiding and micro-magnetic sleep aiding are performed by analyzing the electroencephalogram data and the real sleep condition and body state; if the single electroencephalogram monitoring equipment is worn, when the sleep-aiding function cannot be applied, electroencephalogram data of a wearer can be monitored in real time and displayed on an application interface; the headgear clicks the 'long press ends sleep' button, so that the sleep-aiding function can be ended and the sleep self-evaluation can be carried out;
Step 104: after the sleep aid is finished, the user can independently return to the home page to check the sleep aid report, and when the hardware equipment is not taken off, the user can still click the data at the bottom to check the historical sleep aid record and the detailed sleep aid report.
In this embodiment, as shown in fig. 1, the optical character recognition engine is configured to convert the content of the screenshot page into text, and the account database is configured to store the text.
The brain monitoring APP12 maintains the pairing relationship among the equipment, the data processing process and the message queue through a resource management component, ensures the coordination and cooperation of the three, dynamically adjusts the pairing relationship to adapt to user login and logout at any time under a multi-user environment, ensures automatic data acquisition during multi-user sleep through background scheduling and management of the cloud server 3, and completes large-scale service support and data collection.
In this embodiment, as shown in fig. 2 and 3, the brain monitoring APP12 maintains the pairing relationship among the device, the data processing process, and the message queue through a resource management component, and ensures that the three coordinate and cooperate, and the pairing relationship is dynamically adjusted, so as to adapt to user login and login performed at any time in a multi-user environment.
The data analysis method designed in step 102 is used for analyzing the sleep data of the user, giving a sleep stage report, and performing self-correction on the acquired big data, the cloud server 3 stores two sets of data acquired during sleep in the background, one set of data is formed by analyzing the time domain and the frequency domain of the original EEG waveform data at intervals of 1 minute, the other set of data is formed by dividing the sleep stage by the original EEG waveform data stored at intervals of 1 minute and by using the existing fixed rule based on the feature data.
in this embodiment, as shown in fig. 2 and 3, the cloud server 3 stores two sets of data acquired during sleep in the background, one set of data is a feature formed based on time domain and frequency domain analysis of original EEG waveform data at intervals of 1 minute, and the other set of data is original EEG waveform data stored at intervals of 1 minute, and based on the feature data, segmentation of sleep stages is formed by using an existing fixed rule.
Wherein the analysis mode in step 102 is based on five sleep modes of falling asleep, light sleep, middle sleep, deep sleep and rapid eye movement sleep (REM), the non-falling asleep criterion is that Alpha wave (Alpha brain wave) and beta wave (beta wave) account for more than 50% in 10 minutes, and the light sleep criterion is that: theta wave (Saita brain wave) accounts for more than 50% in 10 minutes, and the standard for judging the middle sleep is as follows: the Delta wave (Delta wave) accounts for more than 35% in 10 minutes, and the deep sleep judgment standard is as follows: delta wave (Delta wave) accounts for more than 50% in 10 minutes, and the judgment standard of rapid eye movement sleep (REM) is as follows: the ratio of Theta wave (saita brain wave) to Alpha wave (Alpha brain wave) exceeds 50% in 10 minutes (wherein the ratio of Alpha wave (Alpha brain wave) exceeds 10%), and the frequency range of Alpha wave (Alpha brain wave) is as follows: 8-13Hz, the frequency range of the beta wave (beta wave) is as follows: 13-30Hz, and the frequency range of the Theta wave (Saita brain wave) is as follows: 4-8Hz, the frequency range of the Delta wave (Delta wave) is as follows: 1-4 Hz.
In this embodiment, as shown in fig. 2, 3, and 4, the analysis mode analyzes and judges the brain waves based on five sleep modes, i.e., sleep-in, light sleep, middle sleep, deep sleep, and rapid eye movement sleep (REM).
The sleep stage is given by a fixed rule, and when both the fixed rule and the network model can be used, the sleep stage is given by the fixed rule and the network model together (when the confidence coefficient of judgment given by the network model is higher than a threshold value, a result given by the network model is used, otherwise, the result given by the rule is used).
In this embodiment, as shown in fig. 3 and 4, the sleep stages are given by fixed rules, and when both the fixed rules and the network model are available, the sleep stages are given by the fixed rules and the network model together.
Example 2
As shown in fig. 1-14, an EEG device, includes headgear 1, cell-phone 2, cloud server 3 and brain monitoring APP12, bolt installation brain electrode 4 is passed through to top both sides in the headgear 1, both sides installation reference electrode 5 in the headgear 1, headgear 1 top is through installation box installation a treater 6 and a bluetooth module 8, a treater 6 is connected with a bluetooth module 8, reference electrode 5 and brain electrode 4 electricity, cell-phone 2 has B bluetooth module 9, B treater 11, network controller 13, brain monitoring APP12 and camera 14, brain monitoring APP12 possesses image recognition system 10 and data analysis module, B treater 11 is connected with camera 14, bluetooth module 9, brain monitoring APP12, image recognition system 10 and network controller 13 electricity, network controller 13 and cloud server 3 signal connection.
The APP used for brain monitoring and intervention by matching with the EEG equipment comprises the following operation steps:
Step 101: opening the APP, and completing login and registration processes to enter a page; 5 items of basic information are required to be input or the next operation is required to be entered when the page is entered for the first time;
Step 102: the Bluetooth module B9 of the mobile phone 2 is matched with the head sleeve 1, and 3 options are needed to be completed when the head sleeve 1 is connected for the first time and sleep-aiding power is used; after the pairing connection is completed, after the person wears the headgear, the page displays real-time electroencephalogram data changes of the wearer, detects corresponding electroencephalogram data along with the behavior activities of the wearer, analyzes and processes the data, and continuously displays the change conditions of the data; according to the analysis result of the brain waves collected currently, obtaining a sleep analysis report which respectively corresponds to the personal emotion, attention and original brain wave data;
Step 103: clicking a page sleep-aiding button to enter a sleep-aiding page, wherein the sleep-aiding page also displays the current electroencephalogram data, clicking a setting interface can perform sleep parameter setting operation, and matching acoustic wave sleep aiding and micro-magnetic sleep aiding are performed by analyzing the electroencephalogram data and the real sleep condition and body state; if the single electroencephalogram monitoring equipment is worn, when the sleep-aiding function cannot be applied, electroencephalogram data of a wearer can be monitored in real time and displayed on an application interface; the headgear clicks the 'long press ends sleep' button, so that the sleep-aiding function can be ended and the sleep self-evaluation can be carried out;
step 104: after the sleep aid is finished, the user can independently return to the home page to check the sleep aid report, and when the hardware equipment is not taken off, the user can still click the data at the bottom to check the historical sleep aid record and the detailed sleep aid report.
In this embodiment, as shown in fig. 1, a head-mounted EEG device is used to read a user's brain waves in real time, and the real electroencephalogram state of the user is obtained by data screening and an algorithm to determine the emotion and attention of the user and the physical state of the user during sleep before and during sleep, so that accuracy, real-time performance, and practicability are better guaranteed.
The image recognition system 10 has an image recognition module, an image repository, a history data repository, an account database, an image processing module, an image transmission module, and an optical character recognition engine.
In this embodiment, as shown in fig. 1, the optical character recognition engine is configured to convert the content of the screenshot page into text, and the account database is configured to store the text.
The brain monitoring APP12 maintains the pairing relationship among the equipment, the data processing process and the message queue through a resource management component, ensures the coordination and cooperation of the three, dynamically adjusts the pairing relationship to adapt to user login and logout at any time under a multi-user environment, ensures automatic data acquisition during multi-user sleep through background scheduling and management of the cloud server 3, and completes large-scale service support and data collection.
In this embodiment, as shown in fig. 2 and 3, the brain monitoring APP12 maintains the pairing relationship among the device, the data processing process, and the message queue through a resource management component, and ensures that the three coordinate and cooperate, and the pairing relationship is dynamically adjusted, so as to adapt to user login and login performed at any time in a multi-user environment.
the data analysis method designed in step 102 is used for analyzing the sleep aid data of the user, giving a sleep aid staging report, performing self-correction on the acquired big data, storing two sets of data acquired during sleep aid in the background of the cloud server 3, analyzing and forming characteristics based on time domain and frequency domain of original EEG waveform data at intervals of 1 minute, storing the original EEG waveform data at intervals of 1 minute, and forming segmentation of the sleep aid staging by using the existing fixed rule based on the characteristic data.
In this embodiment, as shown in fig. 2 and 3, the cloud server 3 stores two sets of data collected during sleep assistance in the background, one set of data is a feature formed by analyzing a time domain and a frequency domain of original EEG waveform data at intervals of 1 minute, and the other set of data is original EEG waveform data stored at intervals of 1 minute, and segmentation of sleep stages is formed by using an existing fixed rule based on the feature data.
Wherein the analysis mode in step 102 is based on five sleep modes of falling asleep, light sleep, middle sleep, deep sleep and rapid eye movement sleep (REM), the non-falling asleep criterion is that Alpha wave (Alpha brain wave) and beta wave (beta wave) account for more than 50% in 10 minutes, and the light sleep criterion is that: theta wave (Saita brain wave) accounts for more than 50% in 10 minutes, and the standard for judging the middle sleep is as follows: the Delta wave (Delta wave) accounts for more than 35% in 10 minutes, and the deep sleep judgment standard is as follows: delta wave (Delta wave) accounts for more than 50% in 10 minutes, and the judgment standard of rapid eye movement sleep (REM) is as follows: the ratio of Theta wave (saita brain wave) to Alpha wave (Alpha brain wave) exceeds 50% in 10 minutes (wherein the ratio of Alpha wave (Alpha brain wave) exceeds 10%), and the frequency range of Alpha wave (Alpha brain wave) is as follows: 8-13Hz, the frequency range of the beta wave (beta wave) is as follows: 13-30Hz, and the frequency range of the Theta wave (Saita brain wave) is as follows: 4-8Hz, the frequency range of the Delta wave (Delta wave) is as follows: 1-4 Hz.
In this embodiment, as shown in fig. 2, 3, and 4, the analysis mode analyzes and judges the brain waves based on five sleep modes, i.e., sleep-in, light sleep, middle sleep, deep sleep, and rapid eye movement sleep (REM).
The sleep stage is given by a fixed rule, and when both the fixed rule and the network model can be used, the sleep stage is given by the fixed rule and the network model together (when the confidence coefficient of judgment given by the network model is higher than a threshold value, a result given by the network model is used, otherwise, the result given by the rule is used).
In this embodiment, as shown in fig. 3 and 4, the sleep stages are given by fixed rules, and when both the fixed rules and the network model are available, the sleep stages are given by the fixed rules and the network model together.
the working principle and the using process of the invention are as follows: the head-mounted EEG equipment is adopted to read the brain waves of the user in real time, the real electroencephalogram state of the user is obtained by data screening and algorithm to judge the emotion and attention of the user and the physical state of the user in sleep before and during sleep, and the accuracy, the real-time performance and the practicability are guaranteed.
although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The utility model provides an EEG equipment, includes headgear (1), cell-phone (2), cloud server (3) and brain monitoring APP (12), a serial communication port, bolt installation brain electricity electrode (4) are passed through to top both sides in headgear (1), both sides installation reference electrode (5) in headgear (1), installation box installation A treater (6) and A bluetooth module (8) are passed through at headgear (1) top, A treater (6) are connected with A bluetooth module (8), reference electrode (5) and brain electricity electrode (4) electricity, cell-phone (2) have B bluetooth module (9), B treater (11), network controller (13), brain monitoring APP (12) and camera (14), brain monitoring APP (12) possess image recognition system (10) and data analysis module, B treater (11) and camera (14), Bluetooth module (9), brain monitoring APP (12), image recognition system (10) and network controller (13) electricity are connected, network controller (13) and cloud server (3) signal connection.
2. An APP for brain monitoring and interventional use with an EEG device according to claim 1, characterized in that: the operation steps are as follows:
Step 101: opening the APP, and completing login and registration processes to enter a page; 5 items of basic information are required to be input or the next operation is required to be entered when the page is entered for the first time;
Step 102: the Bluetooth module B9 of the mobile phone 2 is matched with the head sleeve 1, and 3 options are needed to be completed when the head sleeve 1 is connected for the first time and sleep-aiding power is used; after the pairing connection is completed, after the person wears the headgear, the page displays real-time electroencephalogram data changes of the wearer, detects corresponding electroencephalogram data along with the behavior activities of the wearer, analyzes and processes the data, and continuously displays the change conditions of the data; according to the analysis result of the brain waves collected currently, obtaining a sleep analysis report which respectively corresponds to the personal emotion, attention and original brain wave data;
Step 103: clicking a page sleep-aiding button to enter a sleep-aiding page, wherein the sleep-aiding page also displays the current electroencephalogram data, clicking a setting interface can perform sleep parameter setting operation, and matching acoustic wave sleep aiding and micro-magnetic sleep aiding are performed by analyzing the electroencephalogram data and the real sleep condition and body state; if the single electroencephalogram monitoring equipment is worn, when the sleep-aiding function cannot be applied, electroencephalogram data of a wearer can be monitored in real time and displayed on an application interface; the headgear clicks the 'long press ends sleep' button, so that the sleep-aiding function can be ended and the sleep self-evaluation can be carried out;
Step 104: after the sleep aid is finished, the user can independently return to the home page to check the sleep aid report, and when the hardware equipment is not taken off, the user can still click the data at the bottom to check the historical sleep aid record and the detailed sleep aid report.
Further, the image recognition system has an image recognition module, an image repository, a history data repository, an account database, an image processing module, and an image transmission module.
3. An EEG device according to claim 1, characterized in that: the image recognition system (10) has an image recognition module, an image repository, a history data repository, an account database, an image processing module, and an image transmission module.
4. APP for brain monitoring and interventional use with an EEG device according to claim 2, characterized in that: the brain monitoring APP (12) maintains the pairing relationship among the equipment, the data processing process and the message queue through a resource management component, ensures the coordination and cooperation of the three, dynamically adjusts the pairing relationship to adapt to user login and logout at any time under a multi-user environment, ensures automatic data acquisition during multi-user sleep through background scheduling and management of the cloud server (3), and completes large-scale service support and data collection.
5. APP for brain monitoring and interventional use with an EEG device according to claim 2, characterized in that: the data analysis method is designed in the step 102, analysis of the user sleep data is completed, a sleep stage report is given, self-correction of the collected big data is completed, the cloud server (3) stores two sets of data collected in the sleep period in the background, one set of data is formed by analyzing the time domain and the frequency domain of the original EEG waveform data at intervals of 1 minute, the other set of data is formed by dividing the sleep stage by the original EEG waveform data stored at intervals of 1 minute and by using the existing fixed rule based on the feature data.
6. APP for brain monitoring and interventional use with an EEG device according to claim 2, characterized in that: the analysis mode in step 102 is based on five sleep modes of falling sleep, light sleep, middle sleep, deep sleep and rapid eye movement sleep (REM), the non-falling sleep determination criterion is that Alpha wave (Alpha brain wave) and beta wave (beta wave) account for more than 50% in 10 minutes, and the light sleep determination criterion is that: theta wave (Saita brain wave) accounts for more than 50% in 10 minutes, and the standard for judging the middle sleep is as follows: the Delta wave (Delta wave) accounts for more than 35% in 10 minutes, and the deep sleep judgment standard is as follows: delta wave (Delta wave) accounts for more than 50% in 10 minutes, and the judgment standard of rapid eye movement sleep (REM) is as follows: the ratio of Theta wave (saita brain wave) to Alpha wave (Alpha brain wave) exceeds 50% in 10 minutes (wherein the ratio of Alpha wave (Alpha brain wave) exceeds 10%), and the frequency range of Alpha wave (Alpha brain wave) is as follows: 8-13Hz, the frequency range of the beta wave (beta wave) is as follows: 13-30Hz, and the frequency range of the Theta wave (Saita brain wave) is as follows: 4-8Hz, the frequency range of the Delta wave (Delta wave) is as follows: 1-4 Hz.
7. APP for brain monitoring and interventional use with an EEG device according to claim 2, characterized in that: the sleep stages are given by fixed rules, and when both the fixed rules and the network model are available, the sleep stages are given by the fixed rules and the network model together (when the confidence of the judgment given by the network model is higher than a threshold, the result given by the network model is used, otherwise, the result given by the rules is used).
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Application publication date: 20191217 |