CN111938673A - Anxiety state detection and feedback system based on EEG signal - Google Patents
Anxiety state detection and feedback system based on EEG signal Download PDFInfo
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Abstract
The invention discloses an anxiety state detection and feedback system based on an EEG signal. The system comprises three parts, namely a specially-made portable EEG signal monitoring device, an EEG signal analysis system and an anxiety state analysis algorithm. The working principle of the platform is as follows: (1) the method comprises the following steps that a specially-made portable EEG signal monitoring device collects EEG data of a user and transmits the EEG data into an EEG signal analysis system in real time; (2) the EEG signal analysis system receives the data, decodes and preprocesses the data, and then performs signal identification through a brain wave data machine learning model by applying an anxiety state analysis algorithm; (3) and when the system identifies the signal data characteristic that the user enters the anxiety state, the anxiety state is fed back to the equipment and the terminal in real time for feedback.
Description
Technical Field
The invention belongs to the technical field of EEG signal identification, and particularly relates to an anxiety state detection and feedback system based on an EEG signal.
Background
Anxiety is a common emotional state, and uncontrollable anxiety states, known as anxiety disorders, are a wide range of mental health disorders. Anxiety states occur in association with environmental factors, personality traits, and other factors, and occur with intense brain activity abnormalities. In particular, abnormalities in brain activity during anxiety will be reflected in abnormalities in the EEG signal. Studies have shown that EEG signal abnormalities of an anxiety state may be reflected in multiple dimensions, including frequency, power spectrum, coupling relationships, and the like. With the continuous improvement of the EEG signal identification and analysis technology, the EEG brain wave monitoring technology of the anxiety state is more mature, and the multidimensional signal characteristics of the anxiety state can be accurately analyzed based on the machine learning algorithm to judge whether the user is in the anxiety state.
The existing EEG-based monitoring method generally uses 64 or 128 multi-channel large-scale EEG equipment, and is inconvenient to use and high in cost; in addition, the existing method has no parallel module for feeding back and updating the feature database, so that the detection accuracy of the anxiety state is not high, and the anxiety state is difficult to apply to conventional auxiliary medical diagnosis and treatment.
Disclosure of Invention
The invention aims to provide an anxiety state detection and feedback system based on an EEG signal, which aims to solve the problems that the existing anxiety EEG detection technology uses multi-channel equipment, is inconvenient and high in cost, and provides a more accurate anxiety characteristic database construction algorithm based on continuous feedback updating. The system is designed based on the EEG dynamic characteristic signals, can monitor the brain waves of a user, feeds back whether the user enters an anxiety state or not in real time, feeds back to equipment and an external terminal, further feeds back to monitors such as hospitals and the like, and realizes the function of assisting medical diagnosis and treatment.
In order to achieve the above purpose, the invention provides the following technical scheme: an anxiety state detection and feedback system based on EEG signals. The method comprises a set of steps of collecting and transmitting EEG signals of a user, judging and feeding back the anxiety state, wherein the steps are realized by three parts of specially-made EEG signal monitoring equipment, an EEG signal processing system and an anxiety state analysis algorithm. The method comprises the following steps of collecting and transmitting EEG signals of a user, judging and feeding back the anxiety state:
the first step is as follows: collecting electroencephalogram signals through a specially-made portable EEG signal monitoring device;
the second step is that: amplifying and coding the signals, and transmitting the signals to an EEG signal analysis system;
the third step: the EEG signal analysis system decodes, preprocesses and extracts features, and applies a machine learning algorithm to analyze signals to judge whether the real-time EEG signals reach the features of the anxiety state;
the fourth step: and when the monitored signal reaches the anxiety state characteristic, recording the anxiety state data and feeding back the anxiety state data to the specially-made portable EEG equipment and the external terminal for prompting and state display.
Preferably: the specific flow of the signal acquisition and transmission step in the first step and the second step is as follows:
the first step is as follows: electrode arrays of the special EEG signal monitoring equipment worn by a user are positioned on 4 forehead parts and 1 left ear part and the right ear part of the head part and are respectively used for acquiring forehead brain wave signals and reference brain wave signals;
the second step is that: the specially-made EEG signal monitoring equipment amplifies and codes signals through an external transmission part thereof, integrates the signals into high-frequency digital signals and transmits the high-frequency digital signals to an EEG signal analysis system.
Preferably: the third step of carrying out signal decoding, preprocessing, feature extraction and analysis discrimination comprises the following calculation processes:
the first step is as follows: decoding the high-frequency digital signal to restore the high-frequency digital signal into a multi-channel brain wave signal;
the second step is that: filtering and denoising the signal by a regression method, a self-adaptive filtering method and an independent component analysis method, and removing interference and noise;
the third step: performing time domain and frequency domain parameter extraction and characteristic change on the processed brain wave data, and classifying;
the fourth step: calculating the characteristic combination of a time domain and a frequency domain by using a random forest machine learning algorithm to obtain parameters of different dimensions of the anxiety state;
the fifth step: and establishing and continuously perfecting a parameter characteristic library of the anxiety state, and comparing and analyzing whether the real-time brain wave signal characteristic parameters reach the threshold value of the anxiety state.
Preferably: and the fourth step of recording the data when the monitored signal reaches the anxiety state characteristic, and feeding back the user and the terminal comprises the following specific flows:
the first step is as follows: the EEG signal analysis system analyzes the brain wave signal characteristics of a user in real time, and when the characteristic parameters reach an anxiety state threshold, data and states of corresponding stages are recorded to a parameter characteristic library;
the second step is that: feeding back the anxiety signal to a tailored EEG signal monitoring device;
the third step: after receiving the anxiety state signal, the special EEG signal monitoring equipment controls the equipment light source to light up to prompt the user of anxiety;
the fourth step: sending real-time data of the anxiety state of the user to external terminals of monitors such as hospitals and the like, displaying the state and applying the state to auxiliary diagnosis and treatment support;
the fifth step: and regularly applying the state data of each user, updating and perfecting the algorithm of the anxiety characteristics, and constructing a more perfect parameter characteristic library.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention is designed based on the dynamic EEG signal characteristics, and the brain wave model characteristics of the anxiety state are analyzed by analyzing the signal data through a machine learning algorithm, so that the signal data characteristics of the user entering the anxiety state can be rapidly and accurately judged;
(2) according to the specially-made EEG signal monitoring equipment, the electrode array arrangement is reasonably carried out, so that enough brain wave information can be obtained, and the reliability of EEG signal monitoring is ensured;
(3) the invention is convenient to wear, is convenient to apply to monitoring data to a monitor in real time in daily life, and realizes long-time monitoring and auxiliary diagnosis and treatment functions.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of a tailored EEG signal monitoring device of the present invention;
FIG. 3 is a data interaction diagram of EEG signal acquisition, transmission and feedback of the present invention;
FIG. 4 is a flow chart of an application of the EEG signal analysis system of the present invention for signal analysis, processing and analysis;
FIG. 5 is a graph illustrating data parameter characteristics of an anxiety state EEG signal in accordance with the present invention;
FIG. 6 is a schematic diagram of an anxiety analysis algorithm of the present invention;
fig. 7 is a schematic diagram of the principle of the present invention.
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.
As shown in fig. 1, the present invention provides a technical solution: an anxiety state detection and feedback system based on EEG signals comprises a set of steps of collecting and transmitting EEG signals of a user, judging and feeding back the anxiety state, and specifically comprises the following steps:
the first step is as follows: collecting electroencephalogram signals through a specially-made portable EEG signal monitoring device;
the second step is that: amplifying and coding the signals, and transmitting the signals to an EEG signal analysis system;
the third step: the EEG signal analysis system decodes, preprocesses and extracts features, and applies a machine learning algorithm to analyze signals to judge whether the real-time EEG signals reach the features of the anxiety state;
the fourth step: and when the monitored signal reaches the anxiety state characteristic, recording the anxiety state data and feeding back the anxiety state data to the specially-made portable EEG equipment and the external terminal for prompting and state display.
In this embodiment, preferably, as shown in fig. 2 and fig. 3, the specific flow of the signal acquisition and transmission step in the first step and the second step is as follows:
the first step is as follows: electrode arrays of the special EEG signal monitoring equipment worn by a user are positioned on 4 forehead parts and 1 left ear part and the right ear part of the head part and are respectively used for acquiring forehead brain wave signals and reference brain wave signals;
the second step is that: the specially-made EEG signal monitoring equipment amplifies and codes signals through an external transmission part thereof, integrates the signals into high-frequency digital signals and transmits the high-frequency digital signals to an EEG signal analysis system.
In this embodiment, preferably, as shown in fig. 4 to 6, the third step of performing the signal decoding, preprocessing, feature extraction, and analysis and discrimination steps includes:
the first step is as follows: decoding the high-frequency digital signal to restore the high-frequency digital signal into a multi-channel brain wave signal;
the second step is that: filtering and denoising the signal by a regression method, a self-adaptive filtering method and an independent component analysis method, and removing interference and noise;
the third step: performing time domain and frequency domain parameter extraction and characteristic change on the processed brain wave data, and classifying;
the fourth step: calculating the characteristic combination of a time domain and a frequency domain by using a random forest machine learning algorithm to obtain parameters of different dimensions of the anxiety state;
the fifth step: and establishing and continuously perfecting a parameter characteristic library of the anxiety state, and comparing and analyzing whether the real-time brain wave signal characteristic parameters reach the threshold value of the anxiety state.
In this embodiment, preferably, as shown in fig. 7, the fourth step, when it is monitored that the signal reaches the anxiety state feature, recording the data, and feeding back the user and the terminal includes the following specific steps:
the first step is as follows: the EEG signal analysis system analyzes the brain wave signal characteristics of a user in real time, and when the characteristic parameters reach an anxiety state threshold, data and states of corresponding stages are recorded to a parameter characteristic library;
the second step is that: feeding back the anxiety signal to a tailored EEG signal monitoring device;
the third step: after receiving the anxiety state signal, the special EEG signal monitoring equipment controls the equipment light source to light up to prompt the user of anxiety;
the fourth step: sending real-time data of the anxiety state of the user to external terminals of monitors such as hospitals and the like, displaying the state and applying the state to auxiliary diagnosis and treatment support;
the fifth step: and regularly applying the state data of each user, updating and perfecting the algorithm of the anxiety characteristics, and constructing a more perfect parameter characteristic library.
Claims (4)
1. Anxiety state detection and feedback system based on EEG signal, its characterized in that: the method comprises the steps of collecting, transmitting, judging and feeding back the EEG signals of a user, wherein the steps of collecting, transmitting, judging and feeding back the EEG signals of the user comprise the following steps:
the first step is as follows: collecting electroencephalogram signals through a specially-made portable EEG signal monitoring device;
the second step is that: amplifying and coding the signals, and transmitting the signals to an EEG signal analysis system;
the third step: the EEG signal analysis system decodes, preprocesses and extracts features, and applies a machine learning algorithm to analyze signals to judge whether the real-time EEG signals reach the features of the anxiety state;
the fourth step: and when the monitored signal reaches the anxiety state characteristic, recording the anxiety state data and feeding back the anxiety state data to the specially-made portable EEG equipment and the external terminal for prompting and state display.
2. The EEG signal based anxiety state detection and feedback system according to claim 1 wherein: the specific flow of the signal acquisition and transmission step in the first step and the second step is as follows:
the first step is as follows: electrode arrays of the special EEG signal monitoring equipment worn by a user are positioned on 4 forehead parts and 1 left ear part and the right ear part of the head part and are respectively used for acquiring forehead brain wave signals and reference brain wave signals;
the second step is that: the specially-made EEG signal monitoring equipment amplifies and codes signals through an external transmission part thereof, integrates the signals into high-frequency digital signals and transmits the high-frequency digital signals to an EEG signal analysis system.
3. The EEG signal based anxiety state detection and feedback system according to claim 1 wherein: the third step of carrying out signal decoding, preprocessing, feature extraction and analysis discrimination comprises the following calculation processes:
the first step is as follows: decoding the high-frequency digital signal to restore the high-frequency digital signal into a multi-channel brain wave signal;
the second step is that: filtering and denoising the signal by a regression method, a self-adaptive filtering method and an independent component analysis method, and removing interference and noise;
the third step: performing time domain and frequency domain parameter extraction and characteristic change on the processed brain wave data, and classifying;
the fourth step: calculating the characteristic combination of a time domain and a frequency domain by using a random forest machine learning algorithm to obtain parameters of different dimensions of the anxiety state;
the fifth step: and establishing and continuously perfecting a parameter characteristic library of the anxiety state, and comparing and analyzing whether the real-time brain wave signal characteristic parameters reach the threshold value of the anxiety state.
4. The EEG signal based anxiety state detection and feedback system according to claim 1 wherein: and the fourth step of recording the data when the monitored signal reaches the anxiety state characteristic, and feeding back the user and the terminal comprises the following specific flows:
the first step is as follows: the EEG signal analysis system analyzes the brain wave signal characteristics of a user in real time, and when the characteristic parameters reach an anxiety state threshold, data and states of corresponding stages are recorded to a parameter characteristic library;
the second step is that: feeding back the anxiety signal to a tailored EEG signal monitoring device;
the third step: after receiving the anxiety state signal, the special EEG signal monitoring equipment controls the equipment light source to light up to prompt the user of anxiety;
the fourth step: sending real-time data of the anxiety state of the user to external terminals of monitors such as hospitals and the like, displaying the state and applying the state to auxiliary diagnosis and treatment support;
the fifth step: and regularly applying the state data of each user, updating and perfecting the algorithm of the anxiety characteristics, and constructing a more perfect parameter characteristic library.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113180694A (en) * | 2021-04-07 | 2021-07-30 | 北京脑陆科技有限公司 | Data real-time labeling method and system based on EEG signal |
| CN113180662A (en) * | 2021-04-07 | 2021-07-30 | 北京脑陆科技有限公司 | EEG signal-based anxiety state intervention method and system |
| CN113180661A (en) * | 2021-04-07 | 2021-07-30 | 北京脑陆科技有限公司 | Method and system for regulating and controlling anxiety state based on EEG signal |
| CN113208617A (en) * | 2021-04-06 | 2021-08-06 | 北京脑陆科技有限公司 | Anxiety state nerve regulation and control method and system based on EEG signal |
| CN113208618A (en) * | 2021-04-06 | 2021-08-06 | 北京脑陆科技有限公司 | Excrement and urine excretion early warning method and system based on EEG signal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102715902A (en) * | 2012-06-15 | 2012-10-10 | 天津大学 | Emotion monitoring method for special people |
| CN106725454A (en) * | 2016-12-22 | 2017-05-31 | 蓝色传感（北京）科技有限公司 | The assessment system and method for anxiety degree are assessed using EEG signals |
| CN106725458A (en) * | 2016-12-31 | 2017-05-31 | 深圳市达实智控科技股份有限公司 | One kind is detected based on brain wave and notifies system |
| US20190038203A1 (en) * | 2016-03-04 | 2019-02-07 | Brainsview Inc. | System, process, and devices for real-time brain monitoring in panic and anxiety disorder |
| CN110604565A (en) * | 2019-08-02 | 2019-12-24 | 北京脑陆科技有限公司 | Brain health screening method based on portable EEG equipment |
-
2020
- 2020-08-21 CN CN202010846303.0A patent/CN111938673A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102715902A (en) * | 2012-06-15 | 2012-10-10 | 天津大学 | Emotion monitoring method for special people |
| US20190038203A1 (en) * | 2016-03-04 | 2019-02-07 | Brainsview Inc. | System, process, and devices for real-time brain monitoring in panic and anxiety disorder |
| CN106725454A (en) * | 2016-12-22 | 2017-05-31 | 蓝色传感（北京）科技有限公司 | The assessment system and method for anxiety degree are assessed using EEG signals |
| CN106725458A (en) * | 2016-12-31 | 2017-05-31 | 深圳市达实智控科技股份有限公司 | One kind is detected based on brain wave and notifies system |
| CN110604565A (en) * | 2019-08-02 | 2019-12-24 | 北京脑陆科技有限公司 | Brain health screening method based on portable EEG equipment |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113208617A (en) * | 2021-04-06 | 2021-08-06 | 北京脑陆科技有限公司 | Anxiety state nerve regulation and control method and system based on EEG signal |
| CN113208618A (en) * | 2021-04-06 | 2021-08-06 | 北京脑陆科技有限公司 | Excrement and urine excretion early warning method and system based on EEG signal |
| CN113180694A (en) * | 2021-04-07 | 2021-07-30 | 北京脑陆科技有限公司 | Data real-time labeling method and system based on EEG signal |
| CN113180662A (en) * | 2021-04-07 | 2021-07-30 | 北京脑陆科技有限公司 | EEG signal-based anxiety state intervention method and system |
| CN113180661A (en) * | 2021-04-07 | 2021-07-30 | 北京脑陆科技有限公司 | Method and system for regulating and controlling anxiety state based on EEG signal |
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