CN110123314A - Judge that brain is absorbed in the method for relaxation state based on EEG signals - Google Patents

Abstract

The present invention relates to judging that brain is absorbed in the method for relaxation state based on EEG signals, comprising steps of acquisition EEG signals；EEG signals are analyzed and processed, the E.E.G of multiple and different frequency ranges is obtained, calculate the focus and allowance of brain；The discrimination model for being absorbed in state and relaxation state is established, extracts characteristic value D using discrimination model；Characteristic value D is compared with threshold value, is judged as absorbed state if D>threshold value, if D<threshold value is judged as relaxation state.This method carries out forehead EEG signal to analyze focus and allowance, is absorbed in by analytical judgment to focus and allowance and relaxation state, can effectively auxiliary medical equipment and daily wearable device monitor brain states.

Description

Method for judging the focused and relaxed state of the brain based on EEG signals

technical field

The invention relates to an electroencephalogram signal processing technology, in particular to a method for judging the concentration and relaxation state of the brain based on the electroencephalogram signal.

Background technique

The early research of brain-computer interface (BCI) technology was for military purposes, intending to remotely control robots through consciousness to complete tasks such as combat, and then gradually developed in the medical field, in order to find a cure through interdisciplinary research such as neuroscience, signal detection, and machine learning. approach to people with movement disabilities and has gained popularity in the entertainment industry, especially in the field of virtual manipulation.

At present, EEG technology, as an important basic subject in brain science research, has been widely used in medicine, neuromanagement, psychology, active rehabilitation, brain-computer interface and other research fields. EEG technology research is mainly used in the medical field, such as epilepsy, induced EEG and psychology, psychiatry, etc. Scholars have conducted research on rehabilitation robots, which promote the rehabilitation of patients by stimulating brain electricity. In addition, EEG technology can also be combined with virtual reality and polygraph fields.

Contents of the invention

In order to solve the problems existing in the prior art, the present invention provides a method for judging the concentration and relaxation state of the brain based on EEG signals. The method models the degree of concentration and relaxation, extracts eigenvalues, and analyzes whether the brain is focused or relaxed. state, which can effectively assist medical equipment and daily wearable devices to monitor the state of the brain.

The technical solution of the present invention to solve the above-mentioned problems is as follows: the method for judging the state of brain focus and relaxation based on EEG signals comprises the following steps:

S1, collecting EEG signals;

S2. Analyze and process the EEG signal, obtain brain waves of multiple different frequency bands, and calculate the degree of concentration and relaxation of the brain;

S3. Establish a discriminant model for the focused state and the relaxed state, and use the discriminant model to extract the characteristic value D;

S4. Comparing the characteristic value D with a threshold value, if D>threshold value, it is judged as a focused state, and if D<threshold value, it is judged as a relaxed state.

In a preferred embodiment, in the step S3, set the degree of concentration as x, the degree of relaxation as y, and the feature value as D, and establish the discriminant model as:

Smooth the discriminative model.

In a preferred embodiment, in step S2, brain waves of four different frequency bands α, β, θ, and δ are obtained, and the brain wave power of each band of α, β, θ, and δ is analyzed to analyze the degree of concentration and relaxation, The power density of α wave and β wave generated when the brain is active is used as the detection standard of concentration, and the power density of δ wave and θ wave in the relaxed state is used as the detection standard of relaxation.

Compared with the prior art, the present invention has achieved the following technical effects:

1. On the basis of the original concentration and relaxation algorithm, further analysis, according to the concentration and relaxation of EEG signals, it can be analyzed that the brain is in a state of concentration or relaxation, which can effectively assist medical equipment and daily wearable devices to monitor the state of the brain .

2. Model the concentration and relaxation of the brain, and use the eigenvalue D to calculate; since the eigenvalue D makes good use of the inverse relationship between concentration and relaxation, combining the two indicators can provide More informative than a single indicator, showing the state of focused relaxation implied in concentration and relaxation.

3. The present invention is different from the traditional pure software and pure hardware processing methods, but adopts the combination of hardware and software processing. In the early stage, hardware is used to complete the collection and transmission of EEG signals, and in the later stage, software algorithms are used to monitor the focused state and the relaxed state. Discrimination, the later use of software algorithm for discrimination saves costs compared to the pure hardware method, and the early use of hardware preprocessing method improves the efficiency compared with the pure software processing method.

Description of drawings

Fig. 1 is the overall flowchart of state judgment of the present invention;

Fig. 2 is a block diagram of EEG signal acquisition and processing of the present invention;

Fig. 3 is the flow chart of iterative processing of degree of concentration and degree of relaxation;

Fig. 4 is the flow chart of judging focused state and relaxed state;

Figure 5 is a complete flowchart of a preferred embodiment of the present invention.

Detailed ways

The present invention will be described in further detail below with reference to the accompanying drawings and examples, but the embodiments of the present invention are not limited thereto.

The present invention uses the EEG module to collect and process the EEG signals; transmits the collected signals to the mobile terminal through Bluetooth; uses the classical power spectrum method to obtain the brain waves of four frequency bands α, β, θ, and δ; Wave determines the degree of concentration x and the degree of relaxation y; model the degree of concentration and relaxation, extract the characteristic value D, compare the characteristic value D with the threshold value, and determine the concentration state of the brain. As shown in Figure 1, it specifically includes the following steps:

S1. Acquisition of EEG signals

As shown in Figure 2, the EEG acquisition sensor uses flexible electrodes, and the EEG preprocessing module adopts the TGAM module; the EEG signal acquisition is completed by using the flexible electrode dual-lead method and the TGAM module, and then transmitted to the smart external device through Bluetooth adaptation (This embodiment is the mobile terminal). Specifically, a reference electrode is placed on the back of the ear, and a flexible electrode is placed on the forehead to collect 1-50HZ EEG signals, which are then transmitted to the TGAM module to perform preprocessing such as denoising, amplification, and A/D conversion on the EEG signals, and output the original EEG signals. Wave data and EEG characteristic values are transmitted to the mobile phone through Bluetooth adaptation. The reference electrode is placed behind the ear, the noise interference of the EEG signal is small, and the signal is more pure.

S2. Calculating concentration and relaxation

The app on the mobile phone is used as an analysis and processing tool to analyze and process the EEG signals to obtain brain waves in four different frequency bands of α, β, θ, and δ, and further calculate the degree of concentration and relaxation of the brain.

When calculating the power density spectrum on the mobile app, use the classical power density spectrum formula:

Among them, ω is the frequency band of power spectral density, N is the number of sampling points in this frequency band, X _N is the spectrum after Fourier transform, and G _p is the power spectrum.

Analyze the brain wave power of each band of α, β, θ, δ, analyze the degree of concentration and relaxation, and take the power density of α wave (8-13HZ) and β wave (>14HZ) generated when the brain is active as the The detection standard of concentration, the power density of delta wave (0.5-3HZ) and theta wave (4-8HZ) in the relaxed state is used as the detection standard of relaxation, and finally the correlation between concentration and relaxation is removed to judge the brain status. The advantage of this method is that the amount of calculation is small, and the EEG information can be obtained in real time.

In this embodiment, based on the enhancement of θ and δ band brain waves during relaxation, and the enhancement of α and β brain waves during concentration, the sum of the power of 0.5-8HZ δ wave and θ wave ∑ _0.5HZ<f<8HZ G The ratio of (jω) to the total power is multiplied by 100 as the value Y of the degree of relaxation, and the sum of the power of f>8HZ α wave and β wave ∑ _f>8HZ The ratio of G(jω) to the total rate is multiplied by 100 as concentration The value of degree X, i.e.

S3. Establish a discriminant model for focused state and relaxed state

Let the degree of concentration be x, the degree of relaxation be y, and the characteristic value be D. The discriminant model is established as follows:

Smooth the discriminant model, when n=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]

Multiplicative smoothing is better for short measurement times, and additive smoothing is better for longer measurement times.

Smoothing the eigenvalue D can eliminate fluctuations in the EEG signal. The smoothing process can be iterated multiple times to make the D value in the focused state and the relaxed state more separated. The processing process is shown in Figure 3. Smoothing the eigenvalue D in the relaxed state and the focused state can more effectively reflect the focused and relaxed state of the brain, and help to distinguish different states.

This example establishes the basic model Extract features, use D as the eigenvalue for calculation, and then smooth the eigenvalue D to eliminate the jitter and noise of the eigenvalue D. Since the eigenvalue D makes good use of the inverse relationship between concentration and relaxation, the combination of the two indicators can provide more information than a single indicator, showing the implicit relationship between concentration and relaxation. state of focused relaxation.

S4. Judging the state of focus and relaxation

Compare the characteristic value D of the discriminant model with the threshold value, if D>threshold value, it is judged as a state of concentration, and if D<threshold value, it is judged as a state of relaxation, as shown in Figure 4. The standard for adjusting the state of concentration and relaxation can be changed by adjusting the threshold, so as to be suitable for different monitoring requirements.

The advantage of the algorithm of the present invention is that the information in complex EEG signals can be easily extracted through simple calculations, which greatly reduces the amount of calculation compared with traditional EEG analysis methods, so it can display the focused state or relaxed state in real time .

In this embodiment, the flexible electrode is fixed on the forehead, and the reference electrode is fixed on the back of the ear using ear clips. The electrode is connected to the TGAM module, and the TGAM module is connected to Bluetooth. The data is transmitted to the smartphone through Bluetooth, and the smartphone is used for calculation. Use the app on the mobile phone to preprocess the data, analyze the instant concentration and relaxation, put the concentration and relaxation into the discriminant model to calculate the characteristic value D, and output the characteristic value D after three cycles of the characteristic value D And compare it with the threshold value to judge the state of focus and relaxation, as shown in Figure 5.

In this embodiment, in order to enable the app on the mobile phone to have a good interaction with the user, the app meets the following requirements: have a good user interface, display the Bluetooth connection status in real time, remind the user of the connection quality, visualize the collected brain wave data, and real-time Display the degree of concentration and relaxation, display the current state of the user in real time, and be able to record and save the user's state. When you need to use it, first click the connect Bluetooth button, and the Bluetooth will start to connect, and the signal quality bar above will immediately display the current signal quality. When the Bluetooth connection is successful, click the start button to start working, and click the stop button to stop working; start working In the future, the degree of concentration and relaxation will be displayed on the left side of the corresponding column; the current score is Average, and there is a clock in the middle of the interface to display the monitoring time, and the column of concentration level will instantly display the current state of concentration/distraction. In addition, the app also expands the functions of playing music and ringing.

The method of the present invention further analyzes on the basis of the original degree of concentration and degree of relaxation algorithm, and according to the degree of concentration and degree of relaxation of EEG signals, it is analyzed that the brain is in a state of concentration or relaxation; and an app is designed by the algorithm to provide users with good Interaction, can be widely used in brain status monitoring in the medical field, and can help users monitor their own learning and working status in real time.

Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above-mentioned specific embodiments, and those skilled in the art can think of various variations or modifications, but without departing from the spirit of the present disclosure, all modifications and replacements made will fall within It is within the scope of the present disclosure as defined by the appended claims.

Claims (10)

1. The method for judging the focused relaxation state of the brain based on EEG signals, is characterized in that, comprising the following steps: S1, collecting EEG signals; S2. Analyze and process the EEG signal, obtain brain waves of multiple different frequency bands, and calculate the degree of concentration and relaxation of the brain; S3. Establish a discriminant model for the focused state and the relaxed state, and use the discriminant model to extract the characteristic value D; S4. Comparing the characteristic value D with a threshold value, if D>threshold value, it is judged as a focused state, and if D<threshold value, it is judged as a relaxed state. 2. method according to claim 1, it is characterized in that, in step S3, set degree of concentration as x, degree of relaxation as y, feature value D, set up discriminant model as: Smooth the discriminative model. 3. The method according to claim 1, characterized in that, in step S2, brainwaves of four different frequency bands of α, β, θ, and δ are obtained, and the brainwave powers of each waveband of α, β, θ, and δ are analyzed, and carried out For the analysis of concentration and relaxation, the power density of α wave and β wave generated when the brain is active is used as the detection standard of concentration, and the power density of δ wave and θ wave in the relaxed state is used as the detection standard of relaxation. 4. The method according to claim 3, characterized in that the brain wave power of each band of α, β, θ, δ is analyzed by calculating the power density spectrum. 5. The method according to claim 4, wherein the power density spectrum is calculated using the classical power density spectrum formula: Among them, ω is the frequency band of power spectral density, N is the number of sampling points in this frequency band, X _N is the spectrum after Fourier transform, and G _p is the power spectrum. 6. The method according to claim 3, characterized in that, the power sum of the delta wave and theta wave of 0.5-8HZ ∑ _0.5Hz<f<8HZ G(jω) and the ratio of total power are multiplied by 100 as relaxation The value Y of the degree of concentration, multiply the ratio of the power sum of the α wave and the β wave of f>8HZ ∑ _f>8HZ G(jω) to the total rate by 100 as the value X of the degree of concentration, that is 7. The method according to 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 collect 1-50HZ EEG signals. 8. The method according to claim 1, characterized in that in step S1, the collected EEG signals are transmitted to the TGAM module for preprocessing. 9. The method according to claim 8, wherein the preprocessing includes denoising, amplification and A/D conversion. 10. The method according to claim 2, characterized in that, smoothing the discriminant model is: When n=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].