Mental comfort assessment method and system based on electroencephalogram signals

Abstract

The invention relates to a mental comfort assessment method and system based on electroencephalogram signals. An existing evaluation method is poor in effect. According to the method, whole-course electroencephalogram signal collection is carried out on the sleep process of a user, a data packet is formed, the data packet is divided into sleep fragment packets, and the sleep depth corresponding to each sleep fragment packet and the energy intensity of various brain waves are obtained; various brain waves are combined and compared pairwise and are associated with corresponding sleep depths to form associated parameters for evaluating the mental comfort, so that a sensitive brain wave combination for evaluating the mental comfort of the user is obtained. According to the method, the electroencephalogram signals of the user are collected, the intensities of various kinds of brain waves are compared, correlation parameters are formed, the sensitive brain wave combination is selected from the correlation parameters through comparison, the sensitive brain wave combination serves as a detection object with high correlation with the mental comfort, the mental comfort evaluation accuracy is improved, and the sensitivity of the mental comfort evaluation is improved. The detection difficulty is reduced, the operation and acquisition are convenient, and the use experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of sleep, and in particular to a method and system for evaluating mental comfort. Background Art

[0002] Comfort is a fundamental attribute of sleep products and a key factor affecting sleep quality, playing a central role in their design and evaluation. Existing technologies typically use sleep quality as a proxy for comfort to evaluate the effectiveness of sleep products, but these results cannot guide product optimization. Sleep quality is affected by a variety of factors, including not only the functional characteristics of the product itself but also a person's psychological state, lifestyle habits, ambient noise, and light intensity. This means that even if a product significantly improves the sleep quality of certain users, it's difficult to determine whether this is entirely due to the product's comfort design, rather than the influence of other external variables.

[0003] Compared to sleep quality, comfort is a more direct indicator that is more closely linked to the product. Comfort includes both mental and physical comfort. By objectively and standardizedly quantifying mental comfort, not only can product effectiveness be quickly assessed, eliminating the time and cost of a full night of sleep quality testing, but it can also provide direction for product optimization. However, existing commonly used mental comfort assessment techniques include subjective questionnaires and physiological assessments, but both have shortcomings:

[0004] When using subjective questionnaires, users complete questionnaires to subjectively rate comfort. This method relies on individual perceptions, is susceptible to recall bias and subjective cognition, and cannot provide real-time quantitative assessments. When using physiological assessments, simplified methods based on physiological indicators such as body movement and heart rate variability can indirectly reflect comfort levels. However, these indicators have little variability at rest, resulting in insufficient assessment accuracy and inability to sensitively reflect real-time changes in comfort. Consequently, existing technologies are unable to identify test subjects that strongly correlate with mental comfort, which not only affects the accuracy of mental comfort assessments, but also increases the difficulty of testing and compromises the user experience. Summary of the Invention

[0005] In order to address the shortcomings of the existing technology, the present invention provides a mental comfort assessment method and system based on EEG signals. The method collects the user's EEG signals, compares the intensities of various brain waves and forms correlation parameters. Sensitive brain wave combinations that have a strong correlation with mental comfort are selected from the correlation parameters. This not only improves the accuracy of mental comfort assessment, but also reduces the difficulty of detection, facilitates operation and acquisition, and improves the user experience.

[0006] The present invention is implemented in the following way: a method for evaluating mental comfort based on EEG signals, which collects EEG signals throughout the user's sleep process and forms data packets, divides the data packets into sleep segment packets, calculates the sleep depth corresponding to each sleep segment packet and the energy intensity of various brain waves, compares various brain waves in pairs and associates them with the corresponding sleep depth to form correlation parameters for evaluating mental comfort, thereby obtaining a sensitive brain wave combination for judging the user's mental comfort. By collecting the user's EEG signals, comparing the intensities of various brain waves and forming correlation parameters, a sensitive brain wave combination is selected from the correlation parameters through comparison, and the sensitive brain wave combination is used as a detection object with a strong correlation with mental comfort, which not only improves the accuracy of mental comfort assessment, but also reduces the difficulty of detection, facilitates operation and acquisition, and improves the user experience.

[0007] Preferably, the user's sleep duration is T, and the data packet includes frequency parameters f and amplitude X arranged in sequence along the sleep duration. The data packet is processed by the following steps:

[0008] In the first step, the data packet is divided into y sleep segment packets according to the unit time t, where y=T / t.

[0009] The second step is to classify the data in each sleep segment package according to the EEG sleep staging algorithm, and obtain the sleep depth value D corresponding to each sleep segment package and record it in the data set. The value range of D is 1-4, and from small to large, it represents the awake state, rapid eye movement state, light sleep state, and deep sleep state;

[0010] The third step is to calculate the frequency point range f corresponding to each brain wave c , f c =(f cL / Δf,f cH / Δf), Δf is the frequency resolution of brain waves, c ranges from 1 to 5, and from small to large, they represent δ waves, θ waves, α waves, β waves and γ waves respectively, f cH is the upper limit of the frequency of each brain wave, f cL is the lower limit of the frequency of each brain wave;

[0011] The fourth step is to calculate the brainwave signal strength S[f c ] to calculate, using S[f c ] Calculate the energy intensity of each brain wave in each sleep segment package and form a data set;

[0012] The fifth step is to group the δ waves, θ waves, α waves, β waves and γ waves in each sleep segment into two groups and calculate the brain wave intensity ratio R i , recorded in the data set, i represents the number of combinations;

[0013] The sixth step is to combine the sleep depth value D corresponding to each sleep segment package in the data set j and brain wave intensity ratio R i , calculate the ratio of each brain wave intensity R i The corresponding correlation parameter W i ;

[0014] The seventh step is to calculate the correlation parameters W i Comparison is made to obtain a maximum value, and the brain wave intensity combination associated with the maximum value is set as the sensitive brain wave combination.

[0015] The collected EEG signals contain multiple brain waves, including delta, theta, alpha, beta and gamma waves. Since the changes in the direction of various brain waves are the same and the data are similar when the human body is in different sleep states, a single brain wave cannot be used as an effective basis for judging mental comfort. Therefore, the five brain waves are compared in pairs and the user's sleep depth value D at the time of each brain wave collection is calculated. j Fusion is performed to form 20 brain wave intensity ratios R i , and then calculate the corresponding correlation parameter W i To compare and obtain the maximum value, and the comparison relationship between the two brain waves corresponding to the maximum value is used as the sensitive brain wave combination. In the above method, the brain wave intensity ratio R is obtained by grouping each type of brain wave into two groups. i , effectively increasing the differences between brain wave data, thereby improving data sensitivity, facilitating use and comparison, and also collecting the user's sleep depth value D at each brain wave j As a coefficient for fusion, it ensures that the EEG signal collected when the user's sleep depth is deeper can obtain a larger correlation parameter W i , thereby enhancing the strong correlation between the sensitive brain wave combination and the user's deep sleep, and further improving the accuracy of the sensitive brain wave combination in detecting mental comfort.

[0016] Preferably, the sampling frequency of the sampling device is FS times per second, and the number of sampling points in a detection period t is N, where N=t*FS. Increasing the number of sampling points increases the amount of data, thereby improving the accuracy of obtaining sensitive brain wave combinations.

[0017] Preferably, in the third step, Δf = FS / N. Δf is the frequency resolution of the brainwave, which is used to reflect the accuracy of brainwave acquisition. Substituting N = t * FS, we get Δf = 1 / t, which means that the smaller the parameter t, the higher the frequency resolution of the brainwave, which can be used to obtain more accurate values when used for calculations. The intensity of the acquired brainwaves is weighted by judging the resolution.

[0018] Preferably, the frequency of the delta wave is 0.5-4 Hz, the frequency of the theta wave is 4-8 Hz, the frequency of the alpha wave is 8-12 Hz, the frequency of the beta wave is 12-30 Hz, and the frequency of the gamma wave is 30-100 Hz. The frequency point ranges of various brain waves are set so as to calculate the frequency point ranges f of various brain waves. c , including the upper frequency limit f of each brain wave cH and the lower frequency limit f cL .

[0019] Preferably, in the fourth step, Where X(n) represents the amplitude at each sampling point, and e is the base of the natural logarithm. The signal strength at each sampling point in each of the y sleep segments is calculated sequentially to provide data for calculating the energy intensity of each brain wave.

[0020] Preferably, in the fourth step, the intensity of each brain wave is calculated based on the data in a single sleep segment package. The data set includes y data sets corresponding to each sleep segment package, and each data set contains the energy intensity corresponding to each brain wave. The energy intensity of the five different frequency bands of brain waves in the y sleep segment packages is calculated in sequence to calculate the ratio R i Provide data support.

[0021] Preferably, in the fifth step, the energy intensities of the delta wave, theta wave, alpha wave, beta wave and gamma wave are combined in pairs to form a ratio R i The numerator and denominator of i is 1-20, and the data set includes y groups of data corresponding to each sleep segment package, each data group contains the energy intensity corresponding to each brain wave and a ratio R of up to 20 combinations. i The five brain waves are combined in pairs to form 10 combinations, and the two types of brain waves in each combination are alternately used as the numerator and denominator to form two brain wave intensity ratios, so that the five brain waves are combined in pairs to form 20 brain wave intensity ratios R i , to ensure the correlation parameter W in the later calculation i By obtaining the maximum value, the brainwave combination with the greatest difference can be obtained, which is convenient for the subsequent numerical comparison operation and can effectively improve the accuracy. The data package includes y data groups. Each data group is calculated based on the data in the corresponding sleep segment package and obtains the energy intensity corresponding to each brainwave and the ratio R of 20 combinations. i , calculate the ratio R for each data group i The data all come from the corresponding fragmented sleep package to ensure that the data sources do not overlap.

[0022] Preferably, in the sixth step, Among them, R ijrepresents R in the jth sleep segment package i , MR ij Indicates the number of R in the jth sleep segment package i The median of j Indicates the sleep depth in the jth sleep segment packet, MD j Indicates a full night of sleep D j The median of the sleep segment data. Based on the data in each sleep segment package, the ratio R is calculated. i , through the sleep depth value D corresponding to the sleep segment packet j Perform bundle calculations to obtain common data that can be directly compared and calculated between different sleep segment packages, and then calculate the correlation parameters W i , correlation parameter W i There are 20, and the ratio of the intensity of the 20 brain waves is R i Associate one by one.

[0023] A system for running the method includes a data acquisition unit, a processing unit, and a storage unit. The data acquisition unit is used to collect the user's brain wave signals during sleep and generate raw data to be transmitted to the processing unit. The processing unit receives the raw data from the data acquisition unit and performs calculations according to a preset program to obtain a sensitive brain wave combination for judging the user's mental comfort. The storage unit is used to store the running program, the raw data from the data acquisition unit, the calculated data set, and the sensitive brain wave combination obtained by comparison. When calculating, the processing unit performs rational grouping calculations on the data, compares each type of brain wave in pairs, and performs universal calculations in conjunction with the sleep depth value, so that the correlation parameter W used to obtain the sensitive brain wave combination is obtained. i It can connect the data obtained during the user's entire sleep process, thereby ensuring a strong correlation between sensitive brain wave combinations and the user's mental comfort.

[0024] The beneficial effects of the present invention are as follows: by collecting the user's EEG signals, comparing the intensities of various types of brain waves and forming correlation parameters, a sensitive brain wave combination is selected through comparison in the correlation parameters, and the sensitive brain wave combination is used as a detection object that has a strong correlation with mental comfort, which not only improves the accuracy of mental comfort assessment, but also reduces the difficulty of detection, facilitates operation and acquisition, and improves the user experience. In addition, EEG signals are the clinical standard for judging sleep depth, so mental comfort must be strongly correlated with sleep depth. From the perspective of product application, the purpose of sleep products is to help users fall asleep quickly or deepen their sleep depth. Therefore, using indicators that are strongly correlated with "sleep depth" as evaluation indicators for mental relaxation products is also consistent with theory and practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1is a schematic diagram of the step structure of the method;

[0026] Figure 2 is a structural diagram of the system; DETAILED DESCRIPTION

[0027] The essential features of the present invention are further described below with reference to the accompanying drawings and specific implementation methods.

[0028] Example 1:

[0029] This embodiment provides a method for evaluating mental comfort based on EEG signals.

[0030] like Figure 1 The method shown collects EEG signals throughout the user's sleep process and forms data packets. The data packets are segmented into sleep segment packets. The sleep depth and energy intensity of each type of brainwave corresponding to each sleep segment packet are calculated. The various brainwaves are compared in pairs and correlated with the corresponding sleep depth to form correlation parameters for assessing mental comfort. This method obtains sensitive brainwave combinations for judging the user's mental comfort. By collecting the user's EEG signals and comparing the intensities of the various brainwaves to form correlation parameters, sensitive brainwave combinations are selected from the correlation parameters through comparison. The sensitive brainwave combinations are used as detection targets with a strong correlation with mental comfort. This not only improves the accuracy of mental comfort assessment, but also reduces the difficulty of detection, facilitates operation and acquisition, and enhances the user experience.

[0031] In this embodiment, the user's sleep duration is T, and the data packet includes frequency parameters f and amplitude X arranged in order of sleep duration. The sampling frequency of the sampling device is FS times per second, and the number of sampling points in a detection period t is N, where N = t*FS. The data packet is processed through the following steps:

[0032] In the first step, the data packet is divided into y sleep fragment packets based on the unit time t, where y = T / t. By setting an appropriate t, both the data volume and the fault tolerance rate are taken into account. By reducing the unit time t, the number of sleep fragment packets is increased, thereby improving the accuracy of data analysis, making it easier to find sensitive brain wave combinations that are strongly related to the user's mental comfort, and effectively controlling the number of errors, thereby meeting the fault tolerance rate requirements.

[0033] The second step is to classify the data within each sleep segment package using an EEG sleep staging algorithm, and obtain a sleep depth value D corresponding to each sleep segment package. This value is recorded in the data set. D ranges from 1 to 4, representing the awake state, rapid eye movement state, light sleep state, and deep sleep state, from small to large. Using the existing EEG sleep staging algorithm, the data within each sleep segment package is calculated and analyzed to determine the user's sleep state at the time the sleep segment package was collected. This is then used to determine the importance of the data within the sleep segment package in obtaining sensitive brainwave combinations. The importance of each sleep segment package is then separated and identified, increasing the proportion of data from the user's deep sleep state, which is conducive to finding sensitive brainwave combinations that are strongly associated with the user's mental comfort.

[0034] The third step is to calculate the frequency point range f corresponding to each brain wave c , f c =(f cL / Δf,f cH / Δf), Δf is the frequency resolution of brain waves, c ranges from 1 to 5, and from small to large, it represents delta wave, theta wave, alpha wave, beta wave and gamma wave respectively, f cH is the upper limit of the frequency of each brain wave, f cL is the lower limit of the frequency of each brain wave.

[0035] Specifically, Δf=FS / N. Substituting N=t*FS, we get Δf=1 / t, which means that the smaller the parameter t is, the higher the frequency resolution of the brain wave is, and more accurate values can be obtained when used for calculations. The intensity of the acquired brain waves is weighted by judging the resolution.

[0036] Specifically, the frequency of the delta wave is 0.5-4 Hz, the frequency of the theta wave is 4-8 Hz, the frequency of the alpha wave is 8-12 Hz, the frequency of the beta wave is 12-30 Hz, and the frequency of the gamma wave is 30-100 Hz. Each type of brain wave has a corresponding upper frequency limit f cH and the lower frequency limit f cL , which makes it easier to calculate the frequency range of each brain wave and provide data support for subsequent calculations.

[0037] The fourth step is to calculate the brainwave signal strength S[f c ] to calculate, using S[f c ]The energy intensity of each brain wave in each sleep segment package is calculated and a data set is formed.

[0038] Specifically, Here, X(n) represents the amplitude at each sampling point, and e is the base of the natural logarithm. Because the amplitudes of individual EEG waves within each sleep segment vary, combining the corresponding frequency ranges yields the signal strength of each EEG wave within each sleep segment, providing data for subsequent calculations. This formula effectively incorporates the amplitude differences of each EEG wave within each sleep segment, thereby reflecting the corresponding signal strength and effectively increasing the energy intensity differences obtained in subsequent calculations.

[0039] Specifically, based on the data in a single sleep segment package, the intensity of each brain wave is calculated. Using the signal strength S[f c ] to calculate the energy intensity of each brain wave in each sleep segment package, and through the energy intensity changes, find the type of brain wave that is strongly associated with the user's mental comfort. The data set includes y groups of data corresponding to each sleep segment package, and each data group contains the energy intensity corresponding to each brain wave. This ensures that each sleep segment package calculates the energy intensity of each brain wave based on its own data, ensures that the data between each sleep data packet is processed independently, and prevents the data collected when the user is in different sleep states from cross-influencing each other. It also increases the energy intensity difference by increasing the signal strength difference, thereby facilitating the acquisition of sensitive brain wave combinations through comparison.

[0040] The fifth step is to group the δ waves, θ waves, α waves, β waves and γ waves in each sleep segment into two groups and calculate the brain wave intensity ratio R i , recorded in the data set, i represents the number of combinations.

[0041] Specifically, the energy intensities of the δ wave, θ wave, α wave, β wave and γ wave are combined in pairs to form a ratio R i The numerator and denominator of i is 1-20, and the data set includes y groups of data corresponding to each sleep segment package, each data group contains the energy intensity corresponding to each brain wave and a ratio R of up to 20 combinations. i The five brain waves are combined in pairs to form 10 combinations, and the two types of brain waves in each combination are alternately used as the numerator and denominator to form two brain wave intensity ratios, so that the five brain waves are combined in pairs to form 20 brain wave intensity ratios R i , to ensure the correlation parameter W in the later calculation i By obtaining the maximum value, the brainwave combination with the greatest difference can be obtained, which is convenient for the subsequent numerical comparison operation and can effectively improve the accuracy. The data package includes y data groups. Each data group is calculated based on the data in the corresponding sleep segment package and obtains the energy intensity corresponding to each brainwave and the ratio R of 20 combinations. i , calculate the ratio R for each data group i The data all come from the corresponding fragmented sleep package to ensure that the data sources do not overlap.

[0042] The sixth step is to combine the sleep depth value D corresponding to each sleep segment package in the data set j and brain wave intensity ratio R i , calculate the ratio of each brain wave intensity R i The corresponding correlation parameter W i .

[0043] Specifically, Among them, R ij represents R in the jth sleep segment package i , MR ij Indicates the number of R in the jth sleep segment package i The median of j Indicates the sleep depth in the jth sleep segment packet, MD j Indicates a full night of sleep D j The median of the sleep depth value D j As the corresponding ratio R of each sleep segment package i The weight parameters are processed so that each sleep segment packet calculates the ratio R i There is a basis for combining them, so that the brain wave data obtained by each sleep segment package can be combined with each other to form a correlation parameter W related to the user's entire sleep process. i , which makes it easier to obtain sensitive brain wave combinations through comparison.

[0044] Specifically, the correlation parameter W i and the ratio R i One by one, and there are 20 of them, the correlation parameter W i The corresponding ratio R in each data group i The data obtained after unified combined calculation is used to reflect the ratio R i Correlation with the user's overall sleep state.

[0045] The seventh step is to calculate the correlation parameters W i Compare and obtain the maximum value, and set the brain wave intensity combination associated with the maximum value as the sensitive brain wave combination. i The larger the value, the stronger the correlation between the corresponding brainwave intensity combination and the user's sleep, and is designated as a sensitive brainwave combination. Using sensitive brainwave combinations as test targets with a strong correlation to mental comfort not only improves the accuracy of mental comfort assessments, but also reduces detection difficulty, facilitating operation and acquisition, and enhancing the user experience.

[0046] For example, assuming the sampling rate is FS = 200 Hz, the number of sampling points in each sleep segment is 30 * 200 = 6000, and the data packet is processed through the following steps:

[0047] The first step is to monitor the user's EEG signals during a full night of sleep lasting 8 hours. Because the original EEG signal is a time series waveform, the amplitude is expressed as the vertical height between the peak and the trough in the waveform. Therefore, the amplitude can be directly calculated based on the original waveform (calculating the difference between the peak and the trough or the absolute amplitude relative to the baseline). The amplitude has a corresponding value at each sampling point. Taking t = 30 seconds as the unit, the whole night of sleep is divided into (8*60*60) / 30 = 960 sleep segments, and 960 sleep segment packages are formed.

[0048] The second step is to define the upper frequency limit f of each type of brain wave cH and the lower frequency limit f cL The frequency bands below 0.5HZ and greater than 100HZ are eliminated. The former is because ultra-slow waves and artifacts need to be excluded, and the latter is because components >100Hz can hardly be reliably detected by the detection equipment. The range of 0.5-100HZ is the frequency band related to sleep scenes.

[0049] The third step is to calculate the frequency resolution of the EEG signal, Δf = 200 / (200*30) = 1 / 30, and calculate the frequency point range corresponding to different brain wave frequency bands: δ wave (0.5-4Hz), 0.5*30~4*30=15~120; θ wave (4-8Hz), 4*30~8*30=120~240; α wave (8-12Hz), 8*30~12*30=240~360; β wave (12-30Hz), 12*30~30*30=360~900; γ wave (30-100HZ), 30*30~100*30=900~3000.

[0050] The frequency range below 0.5HZ (0-15) and the frequency range above 100HZ (3000-6000) are not considered.

[0051] The fourth step is to convert the amplitude of each sampling point into signal strength according to the formula in 960 sleep segment packets, because each sampling point corresponds to an amplitude value. c Corresponding to an X(n), the data contained in each sampling point at this time includes the sleep depth and the signal strength of each frequency point corresponding to the delta wave, theta wave, alpha wave, beta wave and gamma wave (frequency point 15 to frequency point 3000, a total of 2985 data points);

[0052] Based on the signal strength of each brain wave at each frequency point, the formula is used to calculate the sum of the signal strengths of the five brain wave frequencies in each sleep segment package. At this time, each sleep segment contains six data points: the sleep depth value and the signal strength of each frequency band of the delta wave to the gamma wave;

[0053] The fifth step is to calculate the signal strength P of each frequency band of δ wave, θ wave, α wave, β wave and γ wave c The ratio R between the two i There are 20 types in total. At this moment, each sleep segment contains the sleep depth, the signal intensity of each frequency band of delta wave, theta wave, alpha wave, beta wave and gamma wave, and the signal intensity ratio R of each frequency band of delta wave, theta wave, alpha wave, beta wave and gamma wave. i A total of 26 data items;

[0054] Step 6: Calculate the median sleep depth (1) and the median of the signal intensity ratio of each frequency band of δ wave to γ wave in 960 sleep segments. Then use the corresponding formula to calculate the median of 20 items and R i Corresponding W i ,(R ij -MR ij ) means starting from the first sleep segment, using the R i Subtract the corresponding median until the end of the 960th sleep segment, (D j -MD j ) means starting from the first sleep segment, using D j Subtract the corresponding median until the 960th sleep segment ends. Each sleep segment contains 20 ratios R i The median number of the total number of medians obtained in one night is 20*960=19200.

[0055] Step 7: Compare 20 Ws i Size, select the R corresponding to the maximum value i As a sensitive brainwave combination, it is used to achieve a relaxing effect on the mind.

[0056] Example 2:

[0057] Compared with the first embodiment, this embodiment provides a system.

[0058] like Figure 2 The system shown is used to run the method, and includes a data acquisition unit, a processing unit, and a storage unit. The data acquisition unit is used to collect the EEG signals of the user during sleep and form raw data to be transmitted to the processing unit. The processing unit receives the raw data from the data acquisition unit and performs calculations according to a preset program to obtain a sensitive brain wave combination for judging the user's mental comfort. The storage unit is used to store the running program, the raw data from the data acquisition unit, the calculated data set, and the sensitive brain wave combination obtained by comparison. When calculating, the processing unit performs rational grouping calculations on the data, compares each type of brain wave in pairs, and uses the sleep depth value to perform universal calculations, so that the correlation parameter W for obtaining the sensitive brain wave combination is obtained. iIt can connect the data obtained during the user's entire sleep process, thereby ensuring a strong correlation between sensitive brain wave combinations and the user's mental comfort.

[0059] The features and effects of the method described in this embodiment are consistent with those of the first embodiment and will not be described in detail.

Claims

1. A method for evaluating mental comfort based on EEG signals, characterized in that: The EEG signals of the user's entire sleep process are collected and formed into data packets, which are then divided into sleep segment packets. The sleep depth corresponding to each sleep segment packet and the energy intensity of various brain waves are calculated. The various brain waves are compared in pairs and correlated with the corresponding sleep depth to form correlation parameters for evaluating mental comfort, thereby obtaining a sensitive brain wave combination for judging the user's mental comfort.

2. The method for evaluating mental comfort based on EEG signals according to claim 1, characterized in that: The user's sleep duration is T. The data packet includes frequency parameters f and amplitude X arranged in the order of the sleep duration. The data packet is processed by the following steps: In the first step, the data packet is divided into y sleep segment packets according to the unit time t, where y=T / t. The second step is to classify the data in each sleep segment package according to the EEG sleep staging algorithm, and obtain the sleep depth value D corresponding to each sleep segment package and record it in the data set. The value range of D is 1-4, and from small to large, it represents the awake state, rapid eye movement state, light sleep state, and deep sleep state; The third step is to calculate the frequency point range f corresponding to each brain wave c , f c =(f cL / Δf,f cH / Δf), Δf is the frequency resolution of brain waves, c ranges from 1 to 5, and from small to large, they represent δ waves, θ waves, α waves, β waves and γ waves respectively, f cH is the upper limit of the frequency of each brain wave, f cL is the lower limit of the frequency of each brain wave; The fourth step is to calculate the brainwave signal strength S[f c ] to calculate, using S[f c ] Calculate the energy intensity of each brain wave in each sleep segment package and form a data set; The fifth step is to group the δ waves, θ waves, α waves, β waves and γ waves in each sleep segment into two groups and calculate the brain wave intensity ratio R i , recorded in the data set, i represents the number of combinations; The sixth step is to combine the sleep depth value D corresponding to each sleep segment package in the data set j and brain wave intensity ratio R i , calculate the ratio of each brain wave intensity R i The corresponding correlation parameter W i ; The seventh step is to calculate the correlation parameters W i Comparison is made to obtain a maximum value, and the brain wave intensity combination associated with the maximum value is set as the sensitive brain wave combination.

3. The method for evaluating mental comfort based on EEG signals according to claim 2, characterized in that: The sampling frequency of the sampling device is FS times per second, and the number of sampling points in a detection cycle t is N, N=t*FS.

4. The method for evaluating mental comfort based on EEG signals according to claim 3, characterized in that: In the third step, Δf = FS / N.

5. The method for evaluating mental comfort based on EEG signals according to claim 2, characterized in that: The frequency of the delta wave is 0.5-4 Hz, the frequency of the theta wave is 4-8 Hz, the frequency of the alpha wave is 8-12 Hz, the frequency of the beta wave is 12-30 Hz, and the frequency of the gamma wave is 30-100 Hz.

6. The method for evaluating mental comfort based on EEG signals according to claim 4, characterized in that: In the fourth step, Wherein, X(n) represents the amplitude at each sampling point, and e is the base of the natural logarithm.

7. The method for evaluating mental comfort based on EEG signals according to claim 6, characterized in that: In the fourth step, the intensity of each brain wave is calculated based on the data in a single sleep segment package. The data set includes y data groups corresponding to each sleep segment package, and each data group contains energy intensity corresponding to each brain wave.

8. The method for evaluating mental comfort based on EEG signals according to claim 7, characterized in that: In the fifth step, the energy intensities of the delta wave, theta wave, alpha wave, beta wave and gamma wave are combined in pairs to form ratios R i The numerator and denominator of i is 1-20, and the data set includes y groups of data corresponding to each sleep segment package, each data group contains the energy intensity corresponding to each brain wave and the ratio R of 20 combinations i .

9. The method for evaluating mental comfort based on EEG signals according to claim 8, characterized in that: In the sixth step, Among them, R ij represents R in the jth sleep segment package i , MR ij Indicates the number of R in the jth sleep segment package i The median of j Indicates the sleep depth in the jth sleep segment packet, MD j Indicates a full night of sleep D j of the median.

10. A system, characterized in that: Used to execute the method according to any one of claims 1 to 9, comprising: A data acquisition unit, used to collect the user's EEG signals during sleep and generate raw data for transmission to the processing unit; A processing unit receives the raw data from the data acquisition unit and performs calculations according to a preset program to obtain a sensitive brain wave combination for judging the user's mental comfort; The storage unit is used to store the running program, the original data from the data acquisition unit, the data set obtained by calculation, and the sensitive brain wave combination obtained by comparison.

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