CN113331840B - Depression mood brain wave signal identification system and method - Google Patents

Abstract

The invention provides a system and a method for recognizing brain wave signals of depressed emotions, which comprises a control device and a brain wave acquisition device; the brain wave acquisition device is in communication connection with the control device; the brain wave acquisition device acquires brain wave signals of the tested person, and the control device identifies whether the brain wave signals have the characteristics of the brain wave signals of the depressed emotion or not, so that whether the depressed emotion occurs in the tested person or not is judged. The method is beneficial to improving the accuracy of identifying whether the tested person has the depressed emotion, saving the time for identifying whether the tested person has the depressed emotion, expanding the popularization range of identifying whether the tested person has the depressed emotion and improving the efficiency of identifying whether the tested person has the depressed emotion.

Description

Depression mood brain wave signal identification system and method

Technical Field

The invention relates to the technical field of brain wave signal identification systems for emotions, in particular to a brain wave signal identification system and method for depression emotion.

Background

Currently, in the monitoring and diagnosis of depressive moods and depression, testing is performed primarily by means of questionnaires, using the PHQ-9 specified by the national ministry of health and fitness or the academically more accepted hamilton scale; a doctor or psychological consultant will typically make a diagnosis by oral consultation on the basis of these two scales. This process is highly subjective. On one hand, the testee who fills in the questionnaire for the first time is not easy to understand the character expression in the questionnaire, so that misfilling is caused; the tested person with a plurality of filling experiences can decide real answer or false answer according to the needs of the person because of the recognition mechanism in the person, so that the result is deeply influenced by the subjectivity of the tested person; on the other hand, doctors or psychological consultants rely more on personal experience when analyzing the scale results and consultation of the tested person, and the subjective nature of depression diagnosis is brought by the degree and quality of experience.

At present, scientific research on the characteristic recognition of brain wave signals of depression is very lacked in academic research on depression recognition. In commercial applications, in products or patent applications for identifying depression by brain wave signals, there is no difference in brain wave signal characteristics used as a reference for depression characteristics, and the difference is in attempts to find the depression brain wave signal characteristics by various computer tools, such as neural networks, genetic networks, artificial intelligence based on big data, and the like. Based on the characteristics of the brain wave signals of depression discovered by computer tools, the degree of credibility is not scientifically verified. Therefore, this approach is not really effective in identifying depression.

The prior Chinese patent publication No. CN109918556A discloses a method for identifying depressed emotions by combining social relations and microblog text characteristics of microblog users, and the method is used for identifying the depressed emotions users by a machine learning method through a microblog user social relation network and microblog text data release. And (4) marking a characteristic label of the depressed emotion on the microblog text, and performing word segmentation by using a word segmentation device and removing stop words at the same time. And extracting words related to the depressed mood as characteristic words by using chi-square test to extract characteristic values. After the feature words are selected, the weight value of each feature word is calculated for each microblog text, and meanwhile, the microblog text is mapped to one feature vector. And training a text classification model of the depressed emotion according to the feature vectors. And calculating the final depression emotion result of the user according to the average probability calculated according to the first N highest probabilities and the obtained PageRank (pi) by using a model fusion method. According to the method for identifying the depressed emotion by integrating the social relationship of the microblog users and the microblog text characteristics, the identification precision of the depressed emotion can be further enhanced.

For the prior art, the inventor thinks that the accuracy rate of identifying whether the tested person has the depressed emotion is low, the time consumption for identifying whether the tested person has the depressed emotion is long, the range of identifying whether the tested person has the depressed emotion is small, and the efficiency of identifying whether the tested person has the depressed emotion is low.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a system and a method for identifying brain wave signals of depressed emotions.

The brain wave signal identification system for the depressed mood provided by the invention comprises a control device and a brain wave acquisition device;

the brain wave acquisition device is in communication connection with the control device;

the brain wave acquisition device acquires brain wave signals of the tested person, and the control device identifies whether the brain wave signals have the characteristics of the brain wave signals of the depressed emotion or not, so that whether the depressed emotion occurs in the tested person or not is judged.

Preferably, the control device comprises a central controller, a TGAM module, a filtering module, a storage unit and an analysis and identification module;

the TGAM module, the filtering module, the storage unit and the analysis and identification module are respectively in communication connection with the central controller;

The brain wave acquisition device is in communication connection with the TGAM module;

the TGAM module receives brain wave signals at a brain wave acquisition device and transmits the brain wave signals to the central controller;

the filtering module is used for filtering the brain wave signals received by the central controller;

the storage unit is at least used for storing a depression emotion characteristic recognition model;

the analysis and recognition module compares the collected brain wave signals with the stored depression emotion feature recognition model for analysis, and recognizes whether the brain wave signal features of depression emotion exist or not.

Preferably, the brain wave collecting device adopts a non-invasive single dry electrode.

Preferably, a wireless transmission module is embedded in the brain wave acquisition device;

and the wireless transmission module establishes communication connection with the TGAM module.

Preferably, the data collected by the brain wave collecting device are brain wave signals directly reflecting cognitive psychological indexes of the human, and at least comprise alpha waves, beta waves, theta waves, delta waves, gamma waves, concentration degree and relaxation degree.

Preferably, the model for identifying depressed mood features comprises at least an alpha wave value, a beta wave value, a theta wave value, a delta wave value, a gamma wave value, a concentration value and a relaxation value.

Preferably, the control device determines whether the subject has a depressed mood and then obtains an analysis result, the control device is connected with a display unit for receiving and displaying the analysis result from the control device in a communication manner, and the display unit displays the brain wave signal of the subject.

Preferably, the brain wave signal characteristic of the depressed mood displayed in the display unit is, for example, a brain wave signal characteristic of an M-shaped depressed mood.

A method for recognizing brain wave signals of depressed mood comprises the following steps:

step 1: collecting brain wave signals of a tested person;

step 2: and identifying whether the brain wave signal has the characteristics of the brain wave signal of the depressed emotion or not so as to judge whether the depressed emotion occurs in the tested person or not.

Preferably, the step 2 comprises:

step 2.1: receiving collected brain wave signals;

step 2.2: filtering the brain wave signals;

step 2.3: and comparing the collected brain wave signals with the stored depression emotion feature identification model to identify whether the brain wave signal features of depression emotion exist or not.

Compared with the prior art, the invention has the following beneficial effects:

1. the method is beneficial to improving the accuracy rate of identifying whether the tested person has the depressed emotion, saving the time for identifying whether the tested person has the depressed emotion, expanding the popularization range of identifying whether the tested person has the depressed emotion and having the effect of improving the efficiency of identifying whether the tested person has the depressed emotion;

2. The brain wave acquisition device is connected with the control device through the wireless transmission module, so that the brain wave acquisition device and the control device are communicated and interacted, and the brain wave acquisition device is simpler in structure and convenient to install and use by adopting a wireless transmission mode.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:

fig. 1 is a system framework diagram of a brain wave signal recognition system for depressed mood;

fig. 2 is a display diagram of a brain wave signal recognition system for depression emotion displayed in a display unit;

FIG. 3 is a brain wave signal feature diagram for M-shaped depressed mood;

fig. 4 is a flowchart of a method for identifying brain wave signals for depressed mood.

Detailed Description

The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.

The embodiment of the invention discloses a brain wave signal identification system for depression emotion, which comprises a control device 10, a brain wave acquisition device 20 and a display unit 30, as shown in fig. 1. The control device 10 includes a central controller 110, a TGAM module 120, a filtering module 130, a storage unit 140, and an analysis and identification module 150. The TGAM module 120, the filtering module 130, the storage unit 140, and the analysis and identification module 150 are each communicatively coupled to the central controller 110. The brain wave acquiring apparatus 20 is, for example, a cubad brain wave instrument, the control apparatus 10 realizes its functions by a computer, and the display unit 30 performs display by the computer.

The brain wave collecting device 20 is in communication connection with the control device 10, the brain wave collecting device 20 adopts non-invasive single dry conductive electrodes, and does not need to be coated with conductive paste or conductive liquid, and the number of the non-invasive single dry conductive electrodes is two. The brain wave acquisition device 20 is embedded with a wireless transmission module 210, executes a biofeedback instruction in the monitoring process, and performs data communication with a multi-channel brain wave receiver in a wireless mode, wherein the wireless working frequency is 2.4G. The wireless transmission module 210 is, for example, a WIFI module, a bluetooth module, etc., and the wireless transmission module 210 establishes a communication connection with the TGAM module 120. The sampling frequency of the brain wave collecting device 20 is 512HZ, and the signal precision is 0.25 uV. The brain wave acquisition device 20 acquires brain wave signals of a subject, and the data acquired by the brain wave acquisition device 20 are brain waves directly reflecting cognitive psychological indexes of the subject, and at least include alpha waves, beta waves, theta waves, delta waves, gamma waves, concentration degrees and relaxation degrees.

The brain wave collecting device 20 is provided with a rechargeable lithium battery. The rechargeable lithium battery is arranged on the brain wave acquisition device 20 and used for supplying power to the brain wave acquisition device 20, and a power supply does not need to be connected from the outside when the rechargeable lithium battery is used, so that the brain wave acquisition device 20 is more convenient to use.

The TGAM module 120 receives the brain wave signals at the brain wave collecting device 20 through the wireless transmission module 210 and transmits the brain wave signals to the central controller 110. The filtering module 130 performs filtering processing on the brain wave signals received by the central controller 110, and the brain wave signals are normalized by the filtering module 130. A storage unit 140 at least for storing a depression mood feature recognition model; the model for identifying the depressed mood features at least comprises an alpha wave value, a beta wave value, a theta wave value, a delta wave value, a gamma wave value, a concentration value and a relaxation value. The analysis and recognition module 150 compares the collected brain wave signals with the stored depression emotion feature recognition model, and recognizes whether the brain wave signals have the brain wave signal features of depression emotion, thereby determining whether the subject has the depression emotion, obtaining an analysis result, and transmitting the analysis result to the central controller 110.

The central controller 110 is communicatively connected with a display unit 30, and the display unit 30 is, for example, a mobile phone APP, PC software, and the like. The display unit 30 receives the display analysis result from the central controller 110, and the display unit 30 displays the brain wave signal of the subject.

The storage depression emotion feature recognition model in the storage unit 140 has high confidence level, and the confidence level test and the double blind experiment are respectively carried out.

And (3) reliability experiments, wherein 20 testees diagnosed with depressed emotions are tested, and the brain wave signal characteristics of the depressed emotions are found in the monitored brain wave signals.

And (3) testing 10 tested persons with a confirmed depressed mood and 10 tested persons without a confirmed depressed mood through a validity test, and finding that the brain wave signal characteristics of the depressed mood exist in the brain wave signals of the tested persons with the confirmed depressed mood and the brain wave signal characteristics of the depressed mood do not exist in the brain wave signals of the tested persons without the confirmed depressed mood.

In a double blind experiment, 68 subjects were randomly recruited. On one hand, whether the depressed emotion exists in the tested person is identified through a traditional psychological consultation diagnosis method (questionnaire + psychological projection technology) to obtain a first depressed emotion result, and on the other hand, whether the depressed emotion exists in the brain wave signals of the tested person is identified through a depressed emotion brain wave signal identification system to obtain a second depressed emotion result. The first depressed mood outcome and the second depressed mood outcome were compared and the results were 91.2% consistent (if the first depressed mood outcome and the second depressed mood outcome were both considered a depressed mood or no depressed mood for a test subject, the two outcomes were considered consistent, otherwise they were inconsistent).

The brain wave signal characteristic of a depressed mood displayed in the display unit 30 is, for example, the brain wave signal characteristic of an M-like depressed mood. As shown in fig. 2, the lateral direction of the diagram indicates time in seconds, the vertical direction indicates a time value corresponding to time, and the M-shaped depressed mood brain wave signal characteristic is among the elliptically-circled filament-shaped brain wave signal characteristics, that is, the filament-shaped brain wave signal characteristic is also the depressed mood brain wave signal characteristic.

As shown in fig. 3, the brain wave signal characteristics of M-shaped depressed mood are determined as follows:

the brain wave signals of the continuous five-second duration form an M-shaped brain wave signal characteristic, and time values of the continuous five-second duration are D1, D2, D3, D4 and D5, and D1< D2, D2> D3, D3< D4 and D4> D5. D1 represents a time value of the first second of the continuous five-second time, D2 represents a time value of the second of the continuous five-second time and corresponds to the first peak of the M-shaped brain wave signal, D3 represents a time value of the third second of the continuous five-second time and corresponds to the valley of the M-shaped brain wave signal, D4 represents a time value of the fourth second of the continuous five-second time and corresponds to the second peak of the M-shaped brain wave signal, and D5 represents a time value of the fifth second of the continuous five-second time.

And | D2-D4| ≦ the first time value difference and | D1-D5| ≦ the second time value difference (note | D2-D4| ≦ the first time value difference representing that the absolute value of the difference between D2 and D4 is less than or equal to the first time value difference and | D1-D5| ≦ the second time value difference representing that the absolute value of the difference between D2 and D4 is less than or equal to the second time value difference). The range of the first time value difference is 0-3, and the range of the second time value difference is 0-10; the first time value difference and the second time value difference are arbitrarily combined within the range of the first time value difference and the range of the second time value difference. If the above conditions are simultaneously satisfied, the brain wave signal is a brain wave signal characteristic of M-like depressed mood. The time value, the first time value difference, and the second time value difference represent any one of an alpha wave value, a beta wave value, a theta wave value, a delta wave value, a gamma wave value, a concentration value, and a relaxation value. For example, the first time value difference is 3, the second time value difference is 10; the first time value difference is 2 and the second time value difference is 9; the first time value difference is 1 and the second time value difference is 8.

The specific algorithm is as follows:

as shown in fig. 1, the system and method for identifying brain wave signals of depressed mood realize the functions of collecting, filtering, amplifying, a/D converting, data processing and analyzing brain wave signals based on the TGAM module 120, and output the parameters of the brain wave signals through the UART standard interface. When the system and the method for identifying the brain wave signals of the depressed mood are used, the brain wave acquisition device 20 is worn on the head of a tested person, so that the non-invasive single-dry electrode is positioned at the forehead of the tested person, the forehead brain wave signals of the tested person are acquired, the acquired brain wave signals are transmitted to the TGAM module 120, and the acquired brain wave signals are filtered through the filtering module 130 to remove harmonic waves; the central controller 110 calls the depression emotion feature recognition model stored in the storage module 140, and the model is entered into the analysis recognition module 150 to be compared with the collected brain wave signals for analysis, so as to recognize whether the collected brain wave signals have the brain wave signal features of depression emotion, judge whether the tested person has depression emotion, obtain an analysis result, and transmit the analysis result back to the central controller 110, and further transmit the analysis result to the display unit 30 for display by the central controller 110.

The embodiment of the invention also discloses a method for identifying the brain wave signals of the depressed mood, which comprises the following steps as shown in figure 4:

s1: the receiving brain wave acquisition device acquires forehead brain wave signals of the testee;

s2: filtering the collected brain wave signals;

s3: comparing the collected brain wave signals with a stored depression emotion feature identification model, identifying whether the brain wave signal features of depression emotion exist or not, and judging whether the depression emotion exists or not;

s4: and outputting a judgment result and displaying the judgment result.

Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.

The foregoing description has described specific embodiments of the present invention. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (7)

1. A brain wave signal identification system for depressed emotion is characterized by comprising a control device (10) and a brain wave acquisition device (20);

the brain wave acquisition device (20) is in communication connection with the control device (10);

the brain wave acquisition device (20) acquires brain wave signals of the testee, and the control device (10) identifies whether the brain wave signals have the brain wave signal characteristics of a depressed mood so as to judge whether the testee has the depressed mood;

the control device (10) comprises a central controller (110), a TGAM module (120), a filtering module (130), a storage unit (140) and an analysis and identification module (150);

the TGAM module (120), the filtering module (130), the storage unit (140) and the analysis and identification module (150) are respectively in communication connection with the central controller (110);

The brain wave acquisition device (20) is in communication connection with the TGAM module (120);

the TGAM module (120) receives brain wave signals at a brain wave acquisition device (20) and transmits the brain wave signals to the central controller (110);

the filtering module (130) is used for filtering the brain wave signals received by the central controller (110);

the storage unit (140) is at least used for storing a depression emotion characteristic recognition model;

the analysis and recognition module (150) compares the collected brain wave signals with a stored depression emotion feature recognition model for analysis, and recognizes whether the brain wave signal features of depression emotion exist or not;

the control device (10) judges whether the tested person has depressed emotion and then obtains an analysis result, a display unit (30) for receiving and displaying the analysis result from the control device (10) is connected to the control device (10) in a communication mode, and the display unit (30) displays the brain wave signal of the tested person;

the brain wave signal characteristic of the depressed mood displayed in the display unit (30) is a brain wave signal characteristic of an M-shaped depressed mood, being a brain wave signal of a duration of 5 consecutive seconds, the time values of the duration of five consecutive seconds being D1, D2, D3, D4 and D5, and D1< D2, D2> D3, D3< D4 and D4> D5, D1 representing the time value of the first second of the duration of five consecutive seconds, D2 representing the time value of the second of the duration of five consecutive seconds and corresponding to the first peak of the brain wave signal of the M-shaped, D3 representing the time value of the third of the duration of five seconds and corresponding to the trough of the brain wave signal of the M-shaped, D4 representing the time value of the fourth of the duration of five seconds and corresponding to the second peak of the brain wave signal of the M-shaped, and D5 representing the time value of the fifth second of the duration of five seconds.

2. The depressed mood brain wave signal recognition system according to claim 1, characterized in that the brain wave collecting device (20) employs a non-invasive single-lead dry electrode.

3. The depressed mood brain wave signal recognition system as claimed in claim 2, wherein the brain wave collecting device (20) is embedded with a wireless transmission module (210);

the wireless transmission module (210) establishes a communication connection with the TGAM module (120).

4. The depressed mood brain wave signal recognition system as claimed in claim 1, wherein the brain wave collecting means (20) collects data which are brain wave signals directly reflecting the cognitive psychological indexes of the human, including at least α -wave, β -wave, θ -wave, δ -wave, γ -wave, concentration and relaxation.

5. The system according to claim 1, wherein the model for recognizing the characteristics of the depressed mood comprises at least an alpha wave value, a beta wave value, a theta wave value, a delta wave value, a gamma wave value, a concentration value and a relaxation value.

6. A depressed-mood brain wave signal recognition method applied to a depressed-mood brain wave signal recognition system as claimed in any one of claims 1 to 5, comprising the steps of:

Step 1: collecting brain wave signals of a tested person;

and 2, step: and identifying whether the brain wave signal has the characteristics of the brain wave signal of the depressed emotion or not so as to judge whether the depressed emotion occurs in the tested person or not.

7. The method as claimed in claim 6, wherein the step 2 comprises:

step 2.1: receiving collected brain wave signals;

step 2.2: filtering the brain wave signals;

step 2.3: and comparing the collected brain wave signals with the stored depression emotion feature identification model to identify whether the brain wave signal features of depression emotion exist or not.

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