CN110123314B - Method for judging brain concentration and relaxation state based on electroencephalogram signals - Google Patents
Method for judging brain concentration and relaxation state based on electroencephalogram signals Download PDFInfo
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Abstract
The invention relates to a method for judging the state of brain concentration and relaxation based on electroencephalogram signals, which comprises the following steps: collecting an electroencephalogram signal; analyzing and processing the electroencephalogram signals to obtain a plurality of brain waves with different frequency bands, and calculating the concentration and the relaxation of the brain; establishing a discrimination model of a concentration state and a relaxation state, and extracting a characteristic value D by using the discrimination model; and comparing the characteristic value D with a threshold value, judging the state to be in the concentrated state if the D is greater than the threshold value, and judging the state to be in the relaxed state if the D is less than the threshold value. The method analyzes the forehead electroencephalogram signal to obtain the concentration degree and the relaxation degree, judges the concentration and relaxation states through the analysis of the concentration degree and the relaxation degree, and can effectively assist medical equipment and daily wearable equipment in monitoring the brain state.
Description
Technical Field
The invention relates to an electroencephalogram signal processing technology, in particular to a method for judging a state that a brain is concentrated and relaxed based on an electroencephalogram signal.
Background
The brain-computer interface (BCI) technology is developed in the early stage for military purposes, and is intended to perform such tasks as battle operations by means of conscious remote control of a robot, and then gradually developed in the medical field, so as to find a method for curing dyskinesia through interdisciplinary studies such as neuroscience, signal detection, machine learning and the like, and is popular in the entertainment industry, particularly in the field of virtual control.
At present, electroencephalogram technology is used as an important basic subject in brain science research and is widely applied to the research fields of medicine, neuro-tube science, psychology, active rehabilitation, brain-computer interfaces and the like. The electroencephalogram technology research is mainly applied to the medical field, such as epilepsy, induction of electroencephalogram, psychology, psychiatry and the like. Researchers have conducted research on rehabilitation robots, which stimulate brain electrical activity to promote rehabilitation of patients. In addition, the electroencephalogram technology can be combined with the fields of virtual reality and lie detection.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for judging the brain concentration and relaxation state based on electroencephalogram signals.
The technical scheme for solving the problems is as follows: the method for judging the state of brain concentration and relaxation based on the electroencephalogram signals comprises the following steps:
s1, acquiring electroencephalogram signals;
s2, analyzing and processing the electroencephalogram signals to obtain a plurality of brain waves of different frequency bands, and calculating the concentration and the relaxation of the brain;
s3, establishing a distinguishing model of a concentration state and a relaxation state, and extracting a characteristic value D by using the distinguishing model;
s4, the feature value D is compared with a threshold value, and if D > the threshold value, it is determined as being in the attentive state, and if D < the threshold value, it is determined as being in the relaxed state.
In a preferred embodiment, in step S3, the concentration degree is x, the relaxation degree is y, the feature value is D, and the discriminant model is established as follows:
and smoothing the discriminant model.
In a preferred embodiment, the brainwaves of α, β, θ and four different frequency bands are obtained in step S2, the brainwave powers of α, β, θ and each frequency band are analyzed, concentration and relaxation are analyzed, the power density of α waves and β waves generated when the brain is active is used as the detection criterion of concentration, and the power density of waves and θ waves in a relaxed state is used as the detection criterion of relaxation.
Compared with the prior art, the invention achieves the following technical effects:
1. further analysis on original concentration degree and degree of relaxation algorithm's basis, according to electroencephalogram's concentration degree and degree of relaxation, it is in and concentrates on or relax the state to analyze out the brain, can effectively assist medical equipment and daily wearable equipment monitoring brain state.
2. Modeling the concentration degree and the relaxation degree of the brain, and calculating by using the characteristic value D; the characteristic value D well utilizes the inverse relation between the concentration degree and the relaxation degree, and the two indexes are combined, so that more information can be provided than a single index, and the concentration and relaxation state implied in the concentration degree and the relaxation degree is represented.
3. The method is different from the traditional pure software and pure hardware processing method, but adopts a mode of combining hardware and software processing, the electroencephalogram signal acquisition and transmission are completed by adopting hardware at the early stage, the concentration state and the relaxation state are judged by adopting a software algorithm at the later stage, the cost is saved by adopting the software algorithm for judgment at the later stage compared with the pure hardware method, and the efficiency is improved by adopting the hardware preprocessing method at the early stage compared with the pure software processing method.
Drawings
FIG. 1 is an overall flow chart of the state determination of the present invention;
FIG. 2 is a block diagram of electroencephalogram signal acquisition and processing according to the present invention;
FIG. 3 is a flowchart of an iterative process of concentration and relaxation;
FIG. 4 is a flowchart of the determination of the concentration state and the relaxation state;
fig. 5 is a complete flow chart of a preferred embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, but the embodiments of the present invention are not limited thereto.
The invention uses an EEG module to collect and process EEG signals; transmitting the acquired signal to a mobile phone end through Bluetooth; obtaining brain waves of alpha, beta, theta and four frequency bands by using a classical power spectrum method; determining concentration degree x and relaxation degree y by brain waves of four frequency bands; modeling the concentration degree and the relaxation degree, extracting a characteristic value D, comparing the characteristic value D with a threshold value, and judging the concentration state of the brain. As shown in fig. 1, the method specifically comprises the following steps:
s1, collecting electroencephalogram signals
As shown in fig. 2, the electroencephalogram acquisition sensor adopts a flexible electrode, and the electroencephalogram preprocessing module adopts a TGAM module; the flexible electrode dual-lead mode and the TGAM module are used for completing electroencephalogram signal collection, and then the electroencephalogram signals are transmitted to the intelligent external equipment (the mobile phone end in the embodiment) through Bluetooth adaptation. Specifically, a reference electrode is placed at the back of the ear, a flexible electrode is placed at the forehead to collect 1-50HZ electroencephalogram signals, then the signals are transmitted to a TGAM module to carry out preprocessing such as denoising, amplification and A/D conversion on the electroencephalogram signals, original electroencephalogram data and electroencephalogram characteristic values are output, and the signals are transmitted to the mobile phone end through Bluetooth adaptation. The reference electrode is arranged at the back of the ear, so that the noise interference of the electroencephalogram signal is small, and the signal is purer.
S2, calculating concentration degree and relaxation degree
And (3) adopting the app on the mobile phone terminal as an analysis processing tool to analyze and process the electroencephalogram signals to obtain brain waves of alpha, beta, theta and four different frequency bands, and further calculating the concentration degree and the relaxation degree of the brain.
When the power density spectrum is calculated on the app of the mobile phone end, a classical power density spectrum formula is used:
wherein, omega is the frequency band of the power spectral density, N is the number of sampling points of the frequency band, XNFor the Fourier transformed spectrum, GpIs a power spectrum.
Analyzing the power of alpha, beta, theta and brain waves of each wave band, analyzing the concentration and the relaxation, taking the power density of alpha waves (8-13HZ) and beta waves (>14HZ) generated when the brain is active as the detection standard of the concentration, taking the power density of waves (0.5-3HZ) and theta waves (4-8HZ) in the relaxed state as the detection standard of the relaxation, and finally removing the correlation between the concentration and the relaxation to judge the state of the brain. The method has the advantages of small calculation amount and capability of acquiring electroencephalogram information in real time.
In the embodiment, the sum sigma of the power of the wave of 0.5-8HZ and the power of the theta wave is calculated according to the characteristics of enhancing the theta wave and the wave band brain wave based on relaxation and enhancing the alpha wave and the beta wave when focusing on0.5HZ＜f＜8HZMultiplying the ratio of G (j omega) to the total power by 100 to obtain the value Y of the degree of relaxation>Sum of power of alpha wave and beta wave of 8HZf＞8HZThe ratio of G (j ω) to the total rate is multiplied by 100 as the value X of concentration, i.e.
S3, establishing a distinguishing model of the concentration state and the relaxation state
Setting the concentration degree as x, the relaxation degree as y and the characteristic value as D, and establishing a discrimination model as follows:
smoothing the discrimination model, and when n is 1, D < -1 > -D < 0 >; when n ≠ 1:
d [ n ] ═ max { D [ n ], D [ n-1] } D [ n ], or
D[n]＝max{D[n],D[n-1]}+D[n]
It is better to adopt multiplication smoothing when the measuring time is shorter, and it is better to adopt addition smoothing when the measuring time is longer.
The smoothing processing is carried out on the characteristic value D, the fluctuation of the electroencephalogram signal can be eliminated, the smoothing processing can be carried out for multiple iterations, the separation degree of the D values in the state of concentration and the state of relaxation is larger, and the processing process is shown in figure 3. The feature value D in the relaxed state and the concentration state is smoothed to reflect the brain concentration relaxed state more effectively, and the different states can be distinguished.
This embodiment establishes a basic model
And extracting the characteristic, calculating by using the D as a characteristic value, and then performing smooth calculation on the characteristic value D to eliminate the jitter and the noise of the characteristic value D. Because the characteristic value D well utilizes the concentration degree and the releaseThe two indexes are combined to provide more information than a single index, and the concentration and relaxation state implied in the concentration and the relaxation is shown.
S4, judging the state of concentration and relaxation
The feature value D of the discriminant model is compared with a threshold, and if D > the threshold, it is determined to be in the attentive state, and if D < the threshold, it is determined to be in the relaxed state, as shown in fig. 4. The criteria for adjusting the concentration and relaxation states may be varied by adjusting the threshold values to suit different monitoring requirements.
The method has the advantages that the information in the complex electroencephalogram signals can be conveniently extracted through simple operation, and compared with the traditional electroencephalogram analysis method, the method greatly reduces the operation amount, so that the concentration state or the relaxation state can be displayed in real time.
The flexible electrode is fixed at the forehead, the ear clip is used for fixing the reference electrode at the back of the ear, the electrode is connected with the TGAM module, the TGAM module is connected with the Bluetooth, data are transmitted to the smart phone through the Bluetooth, and the smart phone is used for calculating. Preprocessing the data by using app in the mobile phone, analyzing the instant concentration degree and the instant release degree, putting the concentration degree and the release degree into a discriminant model, calculating a characteristic value D, performing three cycles on the characteristic value D, outputting the characteristic value D, comparing the characteristic value D with a threshold value, and judging the states of concentration and release, as shown in fig. 5.
In this embodiment, in order to enable the app at the mobile phone end to have good interaction with the user, the app meets the following requirements: the Bluetooth wireless intelligent terminal has a good user interaction interface, displays the Bluetooth connection condition in real time, prompts the user to connect quality, visualizes the collected brain wave data, displays the concentration degree and the release degree in real time, displays the current state of the user in real time, and can record and store the state of the user. When the Bluetooth wireless communication device is used, firstly, a Bluetooth button is clicked to connect, Bluetooth starts to connect, a signal quality column above the Bluetooth starts to display the current signal quality immediately, the Bluetooth wireless communication device starts to work when the Bluetooth is successfully connected and clicks a start button, and stops working when a stop button is clicked; after the work is started, the concentration degree and the relaxation degree are displayed on the left side of the corresponding column; the current score is Average, a clock is arranged in the middle of the interactive interface to display the monitoring time, and the concentration degree bar can instantly display the current concentration/distraction state. In addition, the app additionally expands the functions of playing music and ringing.
The method further analyzes on the basis of the original concentration and relaxation algorithm, and analyzes that the brain is in a concentrated or relaxed state according to the concentration and relaxation of the electroencephalogram signals; an app is designed by the algorithm to provide good interaction for the user, and the method can be widely used for monitoring the brain state in the medical field and can help the user to monitor the learning and working state of the user in real time.
The foregoing description of specific embodiments of the present invention has been presented. 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 suggested to one skilled in the art, but it is intended to cover all modifications and alterations falling within the scope of the present disclosure as defined in the appended claims without departing from the spirit thereof.
Claims (8)
1. The method for judging the state that the brain is concentrated and relaxed based on the electroencephalogram signals is characterized by comprising the following steps:
s1, acquiring electroencephalogram signals;
s2, analyzing and processing the electroencephalogram signals to obtain a plurality of brain waves of different frequency bands, and calculating the concentration and the relaxation of the brain;
s3, establishing a distinguishing model of a concentration state and a relaxation state, and extracting a characteristic value D by using the distinguishing model;
s4, comparing the characteristic value D with a threshold value, judging the state to be concentrated if the D is greater than the threshold value, and judging the state to be relaxed if the D is less than the threshold value;
in step S3, the concentration degree is x, the relaxation degree is y, the feature value is D, and the discriminant model is established as follows:
the smoothing process of the discrimination model comprises the following steps:
when n is 1, D-1 is D0;
when n ≠ 1, D [ n ] ═ max { D [ n ], D [ n-1] } D [ n ], or D [ n ] ═ max { D [ n ], D [ n-1] } + D [ n ];
the smoothing process performs multiple iterations to make the separation of the D values in the concentration and relaxation states greater.
2. The method according to claim 1, wherein the brainwaves of α, β, θ and four different frequency bands are obtained in step S2, the brainwave powers of α, β, θ and each frequency band are analyzed, concentration and relaxation degree are analyzed, the power density of α and β waves generated when brain activity is active is used as a detection criterion of concentration degree, and the power density of waves and θ waves in a relaxed state is used as a detection criterion of relaxation degree.
3. The method according to claim 2, wherein the brain wave power of each band is analyzed by calculating a power density spectrum.
4. The method of claim 3, wherein the power density spectrum is calculated using the classical power density spectrum formula:
wherein, omega is the frequency band of the power spectral density, N is the number of sampling points of the frequency band, XNFor the Fourier transformed spectrum, GpIs a power spectrum.
5. The method of claim 2, wherein the sum of the powers of the 0.5-8HZ wave and the theta wave is Σ0.5HZ＜f＜8HzMultiplying the ratio of G (j omega) to the total power by 100 to obtain the value Y of the degree of relaxation>Sum of power of alpha wave and beta wave of 8HZf＞8HZThe ratio of G (j ω) to total power is multiplied by 100 as the value X of concentration, i.e.
6. The method of claim 1, wherein in step S1, a reference electrode is placed on the back of the ear and a flexible electrode is placed on the forehead to acquire 1-50HZ of electroencephalogram signals.
7. The method of claim 1, wherein in step S1, the acquired brain electrical signals are transmitted to a TGAM module for pre-processing.
8. The method of claim 7, wherein the pre-processing comprises denoising, scaling, and A/D conversion.
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| CN113080998B (en) * | 2021-03-16 | 2022-06-03 | 北京交通大学 | Electroencephalogram-based concentration state grade assessment method and system |
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| CN113729731B (en) * | 2021-09-06 | 2022-12-06 | 上海觉觉健康科技有限公司 | System and method for recognizing brain consciousness state based on electroencephalogram signals |
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