Method for evaluating brain function excitation inhibition state

Abstract

The invention discloses a brain function excitation inhibition state evaluation method, which comprises the steps of detection tool selection, signal acquisition, preprocessing, characteristic parameter extraction and result evaluation, the detection tools comprise electroencephalogram, functional near infrared spectrum imaging and functional magnetic resonance imaging, the detection tools are selected according to the adaptability of a patient, and a treatment strategy is customized in a personalized manner. The method has high pertinence, and the brain function state of the patient can be rapidly evaluated; by extracting a plurality of characteristic parameters as an evaluation basis, the dynamic attributes of the brain are comprehensively considered, and the accuracy and comprehensiveness of a detection result are ensured; and meanwhile, based on assignment of characteristic parameters, dynamic changes of the brain regions can be evaluated in real time during interaction between the brain regions, and the accuracy and reliability of a detection result are ensured by deeply considering and integrating dynamic characteristics of the brain.

Description

Technical Field

[0001] The present invention belongs to the technical field of brain function status assessment, relates to the assessment of children's brain function status, and specifically relates to a method for assessing the excitement and inhibition status of brain function. Background Art

[0002] In recent years, the incidence of autism spectrum disorder (ASD) in children has continued to rise. As a neurodevelopmental disorder, ASD has diverse and complex clinical symptoms. Patients face significant obstacles in social interaction and communication, and are often accompanied by repetitive and stereotyped behaviors. It is particularly noteworthy that the pathogenesis of ASD involves multiple factors such as genetics, environment, and immunity, and is extremely complex. This complexity causes individual differences in symptom presentation, disease progression, and treatment response, which is referred to as "heterogeneity." This heterogeneity not only poses a challenge to the diagnosis of ASD, but also brings considerable difficulties to the formulation of clinical treatment and intervention strategies.

[0003] In studies in multiple interdisciplinary fields such as genetics, neurotransmitters, and the microbiome-brain-gut axis, it has been widely confirmed that ASD patients generally have an imbalance in excitation / inhibition (E / I). This imbalance may significantly affect an individual's sensory processing, memory formation, social interaction, emotion regulation, and other neurocognitive functions, thereby inducing a series of clinical symptoms of ASD. More importantly, these cross-disciplinary research results point to a core view: E / I imbalance may be the "ultimate common path" leading to the onset of ASD. In addition, relevant clinical trials have also verified the effectiveness of E / I imbalance as a therapeutic target for ASD. Through targeted intervention in the E / I imbalanced cortical area, the E / I balance state of specific cortical areas can be effectively restored, thereby producing a certain therapeutic effect on the symptoms of ASD patients. It is worth noting that the E / I index does not depend on external stimulation, but is closely related to the continuous fluctuation of synaptic input in the resting state, and regulates the generation of field potentials in this process, which provides a new perspective and direction for the treatment of ASD.

[0004] Therefore, how to quickly and accurately detect whether there is E / I imbalance in the cortical area is the key to developing personalized treatment strategies for ASD patients. However, current detection methods face two major challenges. First, the lack of a standardized E / I imbalance detection mechanism has greatly limited the direct comparison and verification of research results. Second, many existing evaluation methods fail to fully consider the dynamic properties of the brain when evaluating the E / I imbalance state of brain function. Given the real-time dynamic changes in the interaction between brain regions, its balance state should also be manifested as a dynamic fluctuation pattern. Therefore, when constructing a detection method, it is necessary to deeply consider and incorporate the dynamic characteristics of the brain to ensure the accuracy and reliability of the test results. So far, no researcher has proposed a detection method that can effectively solve the above problems. Summary of the invention

[0005] In view of the above problems, the main purpose of the present invention is to design a method for evaluating the excitation-inhibition state of brain function, which solves the problems of accuracy and reliability in detecting E / I imbalance in the patient's cortical area by incorporating dynamic fluctuation information of the brain.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A method for evaluating the excitation and inhibition state of brain function, the method comprising the selection of detection tools, signal acquisition, preprocessing, feature parameter extraction and evaluation results;

[0008] The specific steps include:

[0009] Selecting detection tools according to the degree of cooperation of the assessor, the detection tools include electroencephalogram, functional near infrared spectroscopy imaging, and functional magnetic resonance imaging;

[0010] Collect signals from different detection tools separately;

[0011] The signal acquisition of the electroencephalogram is: recording the electrical activity signals of the brain by placing conductive electrodes on the surface of the assessor's scalp; the signal acquisition of the functional near-infrared spectroscopy imaging is: recording the concentration change signals of oxyhemoglobin and deoxyhemoglobin in different areas of the cerebral cortex by placing multiple probes on the surface of the assessor's scalp; the signal acquisition of the functional magnetic resonance imaging is: based on the blood oxygen level dependence effect, monitoring the change signals of blood oxygen level during brain activity;

[0012] Preprocess the signals collected by each detection tool;

[0013] The signal preprocessing of the electroencephalogram includes: filtering, processing noise, and removing artifacts; the signal preprocessing of the functional near-infrared spectroscopy imaging includes: processing artifacts, filtering, segmentation, baseline comparison, data conversion, and conversion of optical density into blood oxygen concentration data; the signal preprocessing of the functional magnetic resonance imaging includes: correction, standardization, filtering, regression, and smoothing;

[0014] The characteristic parameters of each detection tool are extracted from the preprocessed signal, and the excitation and inhibition states of the evaluator's brain function under different detection tools are evaluated by the characteristic parameters. The evaluation results include inhibition state, intermediate state and excitation state.

[0015] The characteristic parameters of the electroencephalogram include: EEG phase synchronization, sample entropy, index of the power spectrum density 1 / f curve, E / I index extracted from detrended fluctuation analysis, α power, fast wave / slow wave ratio; the characteristic parameters of the functional near-infrared spectroscopy imaging include: changes in the concentrations of oxyhemoglobin and deoxyhemoglobin; the characteristic parameters of the functional magnetic resonance imaging include: amplitude of whole-brain hemodynamic activity and whole-brain functional connectivity characteristics;

[0016] By combining the evaluation results of various characteristic parameters of the same detection tool, the final brain function excitation and inhibition state of the evaluator can be obtained.

[0017] As a further description of the present invention, the relationship between the EEG phase synchronization and the excitation and inhibition state of brain function includes the following:

[0018] The EEG signal is passed through a bandpass filter to extract the signal in the γ frequency band;

[0019] s γ (t) = filter 30-100Hz (s(t));

[0020] Where s(t) is the EEG signal, s γ (t) is the filtered γ-band signal;

[0021] Perform Hilbert transform on the filtered signal to obtain the complex analysis signal, which is expressed as:

[0022] z(t)=s γ (t)+j·H{s γ (t)};

[0023] Where H{·} represents the Hilbert transform, j is the imaginary unit;

[0024] Extract phase information from the analysis signal, expression:

[0025]

[0026] Among them, the atan2 function returns the arc tangent of two parameters;

[0027] According to the phase information of the two signals in time, the phase synchronization index PSI is calculated, and the expression is:

[0028]

[0029] in, and are the phases of the two signals at time t, and N is the number of samples;

[0030] The obtained phase synchronization index was statistically tested, and the difference was judged based on the statistical significance level p value, with p < 0.05 as a significant difference. When the phase synchronization index was significantly different from random synchronization, the consistency of the whole brain phase difference at multiple time points was calculated, and the expression was:

[0031]

[0032] in, is the phase at time t, M is the number of delay steps considered;

[0033] By calculating the phase synchronization index PSI between multiple signals or regions in the whole brain, the mean PSI value is obtained;

[0034] The brain function state was evaluated according to the value range of the PSI mean; among them, the whole brain PSI mean = [0,0.5) inhibition state, the whole brain PSI mean = [0.5,0.8) intermediate state, and the whole brain PSI mean = [0.8,1] excitement state.

[0035] As a further description of the present invention, the relationship between the sample entropy of the electroencephalogram and the excitation and inhibition state of brain function includes the following:

[0036] The EEG signal is converted from the time domain to the frequency domain, and the spectral density of the EEG signal is calculated by fast Fourier transform (FFT), which is expressed as:

[0037] P(f)=|FFT(s(t))|2;

[0038] Where s(t) is the EEG signal, P(f) is the power spectrum in the frequency domain, and f is the frequency;

[0039] Perform non-periodic component analysis in the power spectrum of the EEG signal and identify beta waves, gamma waves, and high-frequency oscillations by observing specific frequency ranges in the power spectrum;

[0040] Sample entropy SampEn is calculated using the expression:

[0041]

[0042] Where m is the length of the template sequence, γ is the similarity threshold, and N is the total number of samples in the signal;

[0043] The brain function state is evaluated according to the value range of the sample entropy SampEn; among them, SampEn=[0,0.5) inhibition state; SampEn=[0.5,1.5) intermediate state; SampEn=[1.5,∞) excited state; the value of 0 indicates a completely periodic or deterministic signal, and the value of ∞ indicates that the signal has higher complexity and randomness.

[0044] As a further description of the present invention, the relationship between the exponent of the power spectrum density 1 / f curve of the electroencephalogram and the excitation and inhibition state of brain function includes the following:

[0045] Through logarithmic transformation, the attenuation rate of the power spectrum as the frequency changes is described as follows:

[0046] PSD(f)=kf·-β,

[0047] Log(PSD(f))=log(k)-β·log(f);

[0048] Where PSD(f) is the power spectral density at frequency f, k is a positive constant representing the scale factor of the power spectrum at the lowest frequency, and β is the power law exponent;

[0049] Use the least squares method to perform linear regression on Log(PSD(f)) and log(f) to find the best fit line, the expression is:

[0050] Log(PSD(f))=β+b·log(f);

[0051] Where b is the slope;

[0052] The power law exponent β is obtained from the slope b of the fitting line: β = -b;

[0053] The brain function state is evaluated according to the value range of the power law exponent β, where β = [0, 0.5) is the excited state; β = [0.5, 0.8) is the intermediate state; and β = [0.8, 2) is the inhibited state.

[0054] As a further description of the present invention, the relationship between the E / I index extracted from the detrended fluctuation analysis of the electroencephalogram and the excitation and inhibition state of brain function includes the following:

[0055] Remove linear or nonlinear trends from EEG signals and eliminate non-stationarity in the signal. The expression is:

[0056] y(t)=s(t)-trend(s(t));

[0057] Where s(t) is the EEG signal and y(t) is the detrended signal;

[0058] Calculate the volatility of the detrended signal and get the cumulative volatility F(k), which is expressed as:

[0059]

[0060] Where F(k) is the cumulative fluctuation and k is the time point;

[0061] Perform DFA analysis on the cumulative fluctuations and calculate the fluctuations at different time scales. The expression is:

[0062]

[0063] Among them, N k is the number of samples in the kth time window;

[0064] The E / I index is calculated by comparing the cumulative fluctuations in different frequency bands or under different conditions. The expression is:

[0065]

[0066] Among them, F E (k) and F I (k) The cumulative fluctuations in the excitatory frequency band and the inhibitory frequency band, respectively;

[0067] The brain function state is evaluated according to the value range of the E / I index; among them, E / I index = [0, 0.5) inhibition state; E / I index = [0.5, 0.8) intermediate state; E / I index = [0.8, 1] excitement state.

[0068] As a further description of the present invention, the relationship between the alpha power of the electroencephalogram and the excitation and inhibition state of brain function includes the following:

[0069] From the EEG signal, the signal in the alpha wave frequency band is extracted through a bandpass filter, and the expression is:

[0070] S α (t) = filter 8-13Hz(s(t));

[0071] Where s(t) is the EEG signal, S α (t) is the filtered α wave signal;

[0072] The power spectral density of the filtered α wave signal is calculated using Fourier transform, and the expression is:

[0073] PSD α (f) = |FFT(S α (t))|2;

[0074] Where FFT stands for Fast Fourier Transform, and f is the frequency;

[0075] Integrate the power spectrum density in the α wave frequency band to obtain the α wave power, which is expressed as:

[0076]

[0077] Among them, f1 and f2 are the lower and upper frequency limits of the α wave frequency band, respectively;

[0078] Compare the alpha wave power with the total power and standardize it. The expression is:

[0079]

[0080] Among them, P total is the total power;

[0081] The brain function state is evaluated according to the value range of α wave power; among them, α power = (0,10)μV2 is an excited state; α power = [10,50)μV2 is an intermediate state; α power = [50,∞)μV2 is an inhibited state.

[0082] As a further description of the present invention, the relationship between the fast wave / slow wave ratio of the electroencephalogram and the excitation and inhibition state of brain function includes the following:

[0083] From the EEG signal, the fast and slow wave frequency bands are extracted through a bandpass filter. The expression is:

[0084] S fast (t) = filter fast band (s(t));

[0085] S slow (t) = filter slow wave frequency band (s(t));

[0086] Where s(t) is the EEG signal, S fast (t) is the fast-wave frequency band signal, S slow (t) is the slow wave frequency band signal;

[0087] Calculate the power spectral density of the fast wave and slow wave frequency band signals after filtering respectively, the expression is:

[0088] PSD fast (f) = |FFT(S fast (t))I 2 ;

[0089] PSD slow (f)=|FFTS slow (t))|2;

[0090] The power spectrum density is integrated in the fast wave and slow wave frequency bands respectively to obtain the total power of the fast wave and slow wave, which is expressed as:

[0091]

[0092] Among them, P fast is the total fast wave power, P slow is the total slow wave power;

[0093] The ratio of the total power of fast waves to the total power of slow waves is expressed as:

[0094]

[0095] Among them, Ratio fast / slow is the power ratio;

[0096] Divide the total power of fast waves and the total power of slow waves by the total power to get the ratio of fast waves to slow waves. The expression is:

[0097]

[0098] Among them, Ratio fast and Ratio slow are the power ratios of fast and slow waves, P total is the total power;

[0099] The brain function state is evaluated according to the ratio of fast waves to slow waves; (0, 0.4) is the inhibitory state; (0.4, 0.6) is the intermediate state; and (0.6, 1) is the excited state.

[0100] As a further description of the present invention, the relationship between the characteristic parameters of the functional near-infrared spectroscopy imaging and the excitation and inhibition state of brain function includes the following:

[0101] The Beer-Lambert law and the modified equation are used to calculate the changes in the concentrations of oxyhemoglobin HbO and deoxyhemoglobin HbR. The expression is:

[0102] Δ HbO =ε HbR (λ2)·A(λ1)-ε HbR (λ1)·A(λ2);

[0103] Δ HbR =ε HbO (λ1)·A(λ2)-ε HbO (λ2)·A(λ1);

[0104] Among them, Δ HbO represents the change in oxygenated hemoglobin concentration, Δ HbR represents the change in deoxyhemoglobin concentration, εHbO (λ) and ε HbR (λ) are the absorption coefficients of oxyhemoglobin and deoxyhemoglobin at wavelength λ, respectively, and A(λ) is the absorbance change measured at wavelength λ;

[0105] According to the changes in the concentrations of oxygenated hemoglobin HbO and deoxygenated hemoglobin HbR, the index RCV of brain function excitement and inhibition is calculated, and the expression is:

[0106]

[0107] The brain function state is evaluated according to the value range of RCV; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state.

[0108] As a further description of the present invention, the relationship between the characteristic parameters of functional magnetic resonance imaging and the excitation and inhibition state of brain function includes the following:

[0109] Extract time series signals in the brain region of interest, and the time series of each brain region is recorded as X i (t), where (i = 1, 2, ..., n), n represents the number of brain regions of interest;

[0110] The Pearson correlation coefficient was used to calculate the functional connectivity between each pair of brain regions, expressed as:

[0111]

[0112] where T equals the number of time points, and are the time series means of the ith and jth brain regions, respectively;

[0113] The functional connectivity coefficients between all brain regions are combined into an n×n matrix R, where the functional connectivity matrix R ij =r ij ;

[0114] The correlation coefficients in the functional connectivity matrix R are thresholded to distinguish significant connections from non-significant connections:

[0115]

[0116] Among them, θ is the preset threshold;

[0117] Then, the excitation / inhibition index E of each brain region i and inhibition index I i , the expression is:

[0118]

[0119] The excitation / inhibition balance state of any brain region is defined as the difference between the excitation index and the inhibition index of the brain region: B i =E i -I i ;

[0120] According to B i The value range of is used to evaluate the brain function state; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state;

[0121] The overall balance state of the whole brain is defined as the average of the balance states of all brain regions:

[0122]

[0123] According to B total The value range of is used to evaluate the brain function state; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state.

[0124] As a further description of the present invention, according to the detection tool selected by the evaluator, the evaluation results of each characteristic parameter under the detection tool are integrated to obtain the current brain function excitation and inhibition state of the evaluator;

[0125] When more than 50% of the characteristic parameters of the detection tools are the same evaluation result, it is defined that the evaluator's brain is currently in the state of the evaluation result.

[0126] Compared with the prior art, the technical effects of the present invention are:

[0127] The present invention provides an evaluation method for the excitation and inhibition state of brain function, including the selection of detection tools, signal acquisition, preprocessing, characteristic parameter extraction and evaluation results. The detection tools include electroencephalogram, functional near-infrared spectroscopy imaging, and functional magnetic resonance imaging. The detection tools are selected according to the patient's cooperation, and the treatment strategy is customized in an individualized manner. The method has strong pertinence and can quickly evaluate the patient's brain function state. By extracting a number of characteristic parameters as the basis for evaluation, the dynamic properties of the brain are comprehensively considered to ensure the accuracy and comprehensiveness of the detection results. At the same time, based on the assignment of characteristic parameters, it is helpful to evaluate the dynamic changes of brain regions in real time when they interact, and the dynamic characteristics of the brain are deeply considered and integrated to ensure the accuracy and reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0128] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0129] Figure 2 In the example of the evaluation method of the present invention, EEG is selected as the power spectrum density diagram of the detection tool. DETAILED DESCRIPTION

[0130] The present invention is described in detail below in conjunction with the accompanying drawings:

[0131] In one embodiment of the present invention, a method for evaluating the excitation and inhibition state of brain function is disclosed, referring to Figure 1 As shown, the method includes the selection of detection tools, signal acquisition, preprocessing, feature parameter extraction and evaluation results.

[0132] Specifically, in this embodiment, the above evaluation method includes the following steps:

[0133] 1. Select the detection tools according to the cooperation of the assessor, and the detection tools include: electroencephalogram, functional near-infrared spectroscopy imaging, and functional magnetic resonance imaging.

[0134] Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indicators. When the brain is active, the postsynaptic potentials of a large number of neurons are summed up. It records the changes in electric waves during brain activity and is a general reflection of the electrophysiological activity of brain nerve cells on the cerebral cortex or scalp surface. EEG is an important neuroscience tool and is of great significance for understanding brain function and diagnosing neurological diseases.

[0135] Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive brain imaging technology that infers brain activity by measuring changes in blood oxygen concentration in the cerebral cortex.

[0136] Functional magnetic resonance imaging (fMRI) is a method used to measure and map brain activity based on the principle of blood oxygen level dependent (BOLD) contrast. By analyzing the signal changes between brain regions, researchers can infer the excitation or inhibition state of different functional areas.

[0137] It should be noted that, in this embodiment, the above-mentioned evaluators are usually patients, but are not limited to patients, and also include other experimenters used for research, etc.

[0138] The specific selection method is: fMRI is the first choice for those who are awake, have no metal devices in their bodies, have an IQ of more than 80 on the fourth edition of the Wechsler Intelligence Test, can lie still and tolerate noise of 130 decibels for 25 minutes; fMRI is the first choice for those who are awake and can sit still for 20 minutes with a range of motion less than 15 cm 2fNIRS can be selected for patients who are awake and can sit still for 10 minutes with a range of motion less than 10 cm 2 Patients can choose EEG.

[0139] 2. Collect signals from different detection tools separately.

[0140] The EEG signal acquisition is as follows: the electrical activity signals of the brain are recorded by placing electrodes on the surface of the assessor's scalp; specifically, the EEG recording is performed using a BrainAmp 32MRplus amplifier (BrainProducts GmbH, Germany) and 32 electrodes, with a sampling rate of 5kHz. The reference electrodes are for both ears, and the ground electrode is placed on the right arm, and the electrode impedance is kept below 10kΩ; in a shielded room, the subject lies comfortably and records the EEG data with eyes open for 5 minutes.

[0141] The signal acquisition of the functional near-infrared spectroscopy imaging is as follows: by placing multiple probes on the surface of the assessor's scalp, the concentration change signals of oxygenated hemoglobin and deoxygenated hemoglobin in different areas of the cerebral cortex are recorded; specifically, multiple probes (light source and detector) are placed in specific areas of the scalp using an fNIRS device, and the probes are fixed on the scalp to ensure good contact; the fNIRS device is started to start data acquisition; standardized tasks or stimuli are provided to induce brain activity, and the concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in different areas of the brain are recorded.

[0142] The operation mode of fNIRS equipment is controlled according to the selected model. Taking NIRScout as an example: ① Double-click NIRScout to open the main interface; ② Click configure hardware—hardware specification, select the number of detectors and light sources (according to your own experimental settings); ③ Select a template. The templates available include occipital lobe, frontal lobe, temporal lobe, and parietal lobe. The commonly used template is "prefrontal 8×8". After selection, click "OK"; ④ Select "CALIBRATE" for calibration, and then watch the System Data quality in Setup (white means signal loss, at this time you need to check whether the probe is in contact with the scalp or whether the probe is damaged; red means poor data quality (the probe does not fully contact the scalp); yellow means good data quality, which can be adjusted; green means the best data quality, generally speaking, the experiment can only be started when the data quality reaches all green, and the experiment can also be started if one or two positions are continuously yellow); ⑤ After debugging is completed, you can first "preview", click "preview" and enter the name, gender, and age of the subject; ⑥ When the subject's signal is relatively stable, press "record" to start recording; ⑦ After the experiment is over, it is necessary to record for about 10 seconds before stopping to prevent the loss of the last mark, and then click STOP; before removing the subject's hat, you can check whether the data has been saved.

[0143] The signal acquisition of functional magnetic resonance imaging is: based on the blood oxygen level dependence effect, monitoring the change signal of blood oxygen level during brain activity. Specifically, a nuclear magnetic resonance device is used to scan a preset part and record the scanning data.

[0144] The operation mode of fMRI equipment is controlled according to the selected model. Taking the German Siemens 3.0T nuclear magnetic resonance superconducting magnetic resonance imaging system for image acquisition as an example, the head sponge pad is fixed, and all subjects are given 10% chloral hydrate orally or enema before scanning after falling asleep. T2W1 scanning is used as anatomical positioning, and those with visible organic diseases of the nervous system are excluded. Among them, the T1W1 scanning parameters are as follows: echo time = 3.24ms, fault thickness = 1mm, repetition time = 8292ms, scanning field of view = 256×256mm2, number of layers = 156 layers, cumulative scanning time = 4min. The DTI scanning parameters are as follows: diffusion sensitivity coefficient b value = 1000s / mm2, diffusion sensitivity gradient direction = 25, scanning field of view = 220mm×220mm, slice thickness = 2mm, repetition time = 8000ms, echo time = 102ms, number of slices = 75 slices, scanning time = 7min; fMRI scanning parameters: TR = 2000ms, TE = 30ms, time point = 140, slices = 30, scanning time: 8 minutes and 7 seconds.

[0145] 3. Pre-process the signals collected by each detection tool.

[0146] The signal preprocessing of the electroencephalogram includes: filtering, processing noise, and removing artifacts; specifically, EEG data analysis is performed using custom codes in EEGLab, Brainstorm, and Matlab (The Mathworks, Version 2022b); first, a bandpass filter is applied to the EEG data to retain signals in the frequency range of 1-256Hz, and at the same time, a notch filter is applied to remove power supply interference of a specific frequency, and the data is processed to a sampling rate of 1024Hz. Then, the data is visually inspected to identify and remove fragments affected by artifacts; for channels affected by noise, spline interpolation is used for repair. Subsequently, the clean_rawdata toolbox is used to automatically identify and remove sudden and short-term artifacts in the EEG data, and ICA (runica algorithm) is applied to separate biological components and artifact components in the EEG signal. Using default parameters, the IClabel toolbox automatically identifies and rejects artifact IC components. Then, the processed EEG data is imported into the Brainstorm software. Source analysis was performed using a standard MRI template (ICBM 512) and standard EEG locations, and the minimum norm estimation method was used to reconstruct the sensor-level EEG data onto the cortical surface to estimate the electrical activity of the cerebral cortex. The noise covariance matrix was calculated based on the entire EEG signal to evaluate the noise level of the data; combined with the Desikan-Killiany atlas, the brain function excitation / inhibition (E / I) estimate was calculated in the source space.

[0147] The signal preprocessing of the functional near-infrared spectroscopy imaging includes: processing artifacts, filtering, segmentation, baseline comparison, and format conversion; specifically, 1. Format conversion: converting the original fNIRS data from a device-specific format to a universal format. 2. Processing artifacts: Systematic artifacts require the determination of the coefficient of variation to identify and delete channels with poor signal quality; external factors are manually deleted and automatically detected and deleted by the card threshold method to remove artifacts caused by external factors (such as movement, equipment failure, etc.). 3. Filtering: Apply a filter to remove signals with a frequency lower than 0.1 Hz and retain the frequency signal belonging to fNIRS, that is, remove unnecessary noise and interference while retaining useful signals related to brain activity. 4. Segmentation: Extract hemodynamic data of the same stimulation type by marking, and obtain the overall effect by superposition and averaging; usually, the 5-10 seconds before and 10-20 seconds after the stimulation are taken as nodes for segmentation to capture the hemodynamic changes before and after the stimulation. 5. Baseline comparison: compare the hemodynamic data during stimulation with the baseline period (i.e., a period of time before or after stimulation) to assess the impact of stimulation on brain activity. 6. Optical density conversion to blood oxygen concentration data: use the modified Lambert-Beer law to convert the optical density measured by the fNIRS device into the relative changes in oxygenated hemoglobin and deoxygenated hemoglobin concentrations.

[0148] The signal preprocessing of the functional magnetic resonance imaging includes: correction, standardization, filtering, regression, and smoothing; specifically, in this embodiment, the standard procedure for fMRI (functional magnetic resonance imaging) data processing based on the Matlab platform includes: 1. Removing the initial time point image: Since it takes a certain amount of time for both the machine and the subject to enter a stable state, the images of the first 5 time points at the beginning of the scan are removed, and the remaining 135 time points are retained for subsequent analysis, which helps to ensure that the data for subsequent analysis is stable and reliable. 2. Time layer correction: fMRI scanning is performed layer by layer, so there is a time delay between images of different layers. Time layer correction is intended to correct this delay to ensure that images of all layers are aligned in time. 3. Head movement correction: Remove subjects whose head translation exceeds 2mm or rotation exceeds 2°. Head movement will affect the accuracy and reliability of fMRI data. 4. Standardization: All functional images are aligned to the first image, and the images are converted to the Montreal Neurological Institute (MNI) space, and the voxel size is resampled to 3×3×3mm 3, to unify the spatial resolution of the data. 5. Time domain filtering: Filtering all images within the bandpass filter range of 0.01-0.08Hz helps reduce the impact of low-frequency drift (such as scanner instability) and high-frequency physiological noise (such as heartbeat and breathing) on ​​the data. 6. T1 image segmentation and regression removal: Segment the T1 image (structural image) into gray matter, white matter, and cerebrospinal fluid, use the Friston24 parameter model to remove the impact of head movement, and regress and remove white matter and cerebrospinal fluid signals to reduce the interference of these tissues on functional data analysis. 7. Data smoothing: To avoid the impact of noise signals on the data, smoothing is performed based on a Gaussian kernel function with a full width at half maximum (FWHM) of 6mm to improve the signal-to-noise ratio of the data and highlight signals related to brain activity.

[0149] Fourth, the characteristic parameters of each detection tool are extracted from the preprocessed signal, and the excitation and inhibition states of the evaluator's brain function under different detection tools are evaluated by the characteristic parameters. The evaluation results include inhibition state, intermediate state and excitation state.

[0150] The characteristic parameters of the electroencephalogram (EEG) include: EEG phase synchronization, sample entropy, index of power spectrum density 1 / f curve, E / I index extracted from detrended fluctuation analysis, alpha power, and fast wave / slow wave ratio.

[0151] 1. The relationship between EEG phase synchronization and brain function excitation and inhibition states, including the following:

[0152] EEG phase synchronization (phase consistency of the gamma frequency band): The gamma frequency band is related to higher brain functions such as cognitive function and attention;

[0153] (1) Bandpass filtering: The EEG signal is passed through a bandpass filter to extract the signal in the γ frequency band;

[0154] s γ (t) = filter 30-100Hz (s(t));

[0155] Where s(t) is the original EEG signal, s γ (t) is the filtered γ-band signal.

[0156] (2) Hilbert transform: Perform Hilbert transform on the filtered signal to obtain the complex analysis signal, which is expressed as:

[0157] z(t)=s γ (t)+j·H{s γ (t)};

[0158] Here, H{·} represents the Hilbert transform, and j is an imaginary unit.

[0159] (3) Calculate the phase: Extract the phase information from the analysis signal. The expression is:

[0160]

[0161] The atan2 function returns the arc tangent of its two arguments.

[0162] (4) Phase Synchronization Index (PSl): The phase synchronization index PSI is calculated based on the temporal phase information of the two signals. The expression is:

[0163]

[0164] in, and are the phases of the two signals at time t, and N is the number of samples.

[0165] (5) Statistical test: A statistical test (Wilcoxon rank sum test (Mann-Whitney U test)) was performed on the calculated phase synchronization index to determine whether it was significantly different.

[0166] Specifically, in this embodiment, a non-parametric test is performed on the obtained phase synchronization index. Based on the characteristics that the bit synchronization index and the random synchronization index are generally small samples and do not conform to the normal distribution, a Wilcoxon rank sum test (Mann-Whitney U test) is usually used. The specific formula is as follows:

[0167]

[0168] Among them, U1 and U2 are the rank sum statistics of the two samples, n1 and n2 are the sample sizes, and R1 and R2 are the rank sums of the two samples. Select the smallest of U1 and U2 as the final U statistic, and look up the Mann-Whitney U test statistical value table to find the corresponding statistical significance level value p, with p < 0.05 as a significant difference.

[0169] (6) Whole-brain phase consistency: When the phase synchronization index is significantly different from random synchronization, the consistency of the whole-brain phase difference at multiple time points is calculated. The expression is:

[0170]

[0171] in, is the phase at time t and M is the number of delay steps considered.

[0172] (7) The range of the mean value of PSI in the whole brain: The mean value of PSI was obtained by calculating the phase synchronization index PSI between multiple signals or regions in the whole brain;

[0173] The brain function state was evaluated according to the value range of the PSI mean; among them, the whole brain PSI mean = [0,0.5) inhibition state, the whole brain PSI mean = [0.5,0.8) intermediate state, and the whole brain PSI mean = [0.8,1] excitement state.

[0174] 2. The relationship between the sample entropy (non-periodic component and sample entropy) of the EEG and the excitation and inhibition state of brain function, including the following:

[0175] (1) Power spectrum calculation: The EEG signal is converted from the time domain to the frequency domain, and the spectrum density of the EEG signal is calculated by fast Fourier transform (FFT), which is expressed as:

[0176] P(f)=|FFT(s(t))|2;

[0177] Where s(t) is the original EEG signal, P(f) is the power spectrum in the frequency domain, and f is the frequency.

[0178] (2) Non-periodic component analysis: Non-periodic component analysis is performed in the power spectrum of the EEG signal, and beta waves, gamma waves, and high-frequency oscillations are identified by observing the specific frequency range in the power spectrum.

[0179] (3) Sample entropy SampEn calculation: A lower sample entropy means that the signal is more orderly and predictable, which may be related to the inhibitory state of the brain; while a higher sample entropy means that the signal is more disordered and complex, which may be related to the excitatory state of the brain. The expression is:

[0180]

[0181] Where m is the length of the template sequence, γ is the similarity threshold, and N is the total number of samples in the signal.

[0182] (4) Value range: Evaluate the brain function state according to the value range of sample entropy SampEn; where SampEn = [0, 0.5) inhibition state; SampEn = [0.5, 1.5) intermediate state; SampEn = [1.5, ∞) excited state; the value of 0 indicates a completely periodic or deterministic signal, and the value of ∞ indicates that the signal has higher complexity and randomness.

[0183] 3. The relationship between the exponent of the 1 / f curve of the power spectrum density of the electroencephalogram and the excitation and inhibition state of brain function, including the following:

[0184] (1) Logarithmic transformation: Logarithmic transformation is used to describe the attenuation rate of the power spectrum as the frequency changes. The expression is:

[0185] PSD(f)=kf·-β,

[0186] Log(PSD(f))=log(k)-β·log(f);

[0187] Where PSD(f) is the power spectrum density at frequency f, k is a positive constant representing the scale factor of the power spectrum at the lowest frequency, and β is the power law exponent describing the decay rate of the power spectrum with frequency.

[0188] (2) Linear regression: Use the least squares method to perform linear regression on Log(PSD(f)) and log(f) to find the best fit line, which is expressed as:

[0189] Log(PSD(f))=β+b·log(f);

[0190] Where b is the slope.

[0191] (3) Calculate the slope: The power law exponent β is obtained from the slope b of the fitting line: β = -b.

[0192] (4) Value range: Evaluate the brain function state according to the value range of the power law exponent β; where β = [0, 0.5) excitatory state; β = [0.5, 0.8) intermediate state; β = [0.8, 2) inhibitory state.

[0193] 4. The relationship between the E / I index extracted from the detrended fluctuation analysis of the electroencephalogram and the excitation and inhibition state of brain function includes the following:

[0194] (1) Detrending: Remove linear or nonlinear trends from EEG signals to eliminate non-stationarity in the signal. The expression is:

[0195] y(t)=s(t)-trend(s(t));

[0196] Here, s(t) is the EEG signal and y(t) is the detrended signal.

[0197] (2) Calculate the volatility: Calculate the volatility of the detrended signal to obtain the cumulative volatility F(k), which is expressed as:

[0198]

[0199] Where F(k) is the cumulative fluctuation and k is the time point.

[0200] (3) Detrended volatility analysis: DFA analysis is performed on the cumulative volatility to calculate the volatility at different time scales. The expression is:

[0201]

[0202] Among them, N k is the number of samples in the kth time window.

[0203] (4) Cumulative fluctuations of excitatory and inhibitory frequency bands F E (k) and F I The calculation formula of (k) is:

[0204]

[0205] (5) Calculation of E / I index: The E / I index is calculated by comparing the cumulative fluctuations in different frequency bands or under different conditions. The expression is:

[0206]

[0207] Among them, F E (k) and F I (k) are the accumulated fluctuations in the excitatory frequency band and the inhibitory frequency band, respectively.

[0208] (6) Value range: Evaluate the brain function state according to the value range of the E / I index; where E / I index = [0, 0.5) inhibition state; E / I index = [0.5, 0.8) intermediate state; E / I index = [0.8, 1] excitement state.

[0209] 5. The relationship between the alpha power of the EEG and the excitation and inhibition state of brain function includes the following:

[0210] (1) Bandpass filtering: From the EEG signal, the alpha wave frequency band signal (usually in the range of 8-13 Hz) is extracted through a bandpass filter. The expression is:

[0211] S α (t) = filter 8-13Hz(s(t));

[0212] Where s(t) is the EEG signal, S α (t) is the filtered α wave signal.

[0213] (2) Calculate the power spectral density (PSD): Use Fourier transform to calculate the power spectral density of the filtered α wave signal, and the expression is:

[0214] PSD α (f) = |FFT(S α (t))|2;

[0215] Here, FFT stands for Fast Fourier Transform and f is the frequency.

[0216] (3) Integration: Integrate the power spectrum density in the α wave frequency band to obtain the α wave power, which is expressed as:

[0217]

[0218] Among them, f1 and f are the lower and upper limit frequencies of the α wave frequency band respectively.

[0219] (4) Standardization: Compare the α wave power with the total power and perform standardization. The expression is:

[0220]

[0221] Among them, P total is the total power;

[0222] (5) Value range: The brain function state is evaluated based on the value range of α wave power; among them, α power = (0,10)μV2 is an excited state; α power = [10,50)μV2 is an intermediate state; α power = [50,∞)μV2 is an inhibited state.

[0223] 6. The relationship between the fast wave / slow wave ratio of the EEG and the excitation and inhibition state of brain function, including the following:

[0224] (1) Bandpass filtering: From the EEG signal, the fast and slow wave frequency bands are extracted through a bandpass filter. The expression is:

[0225] S fast (t) = filter fast band (s(t));

[0226] S slow (t) = filter slow wave frequency band (s(t));

[0227] Where s(t) is the EEG signal, S fast (t) is the fast-wave frequency band signal, S slow (t) is the slow wave frequency band signal.

[0228] (2) Calculate the power spectral density (PSD): Calculate the power spectral density of the fast wave and slow wave frequency band signals after filtering respectively. The expression is:

[0229] PSD fast (f) = |FFT(S fast (t))I 2 ;

[0230] PSD slow (f)=|FFTS slow (t))|2.

[0231] (3) Integration: Integrate the power spectrum density in the fast wave and slow wave frequency bands to obtain the total power of the fast wave and slow wave. The expression is:

[0232]

[0233] Among them, P fast is the total fast wave power, P slow is the total slow-wave power.

[0234] (4) Calculation ratio: the ratio of the total power of the fast wave to the total power of the slow wave, expressed as:

[0235]

[0236] Among them, Ratio fast / slow is the power ratio.

[0237] (5) Standardization: The power ratio is normalized relative to the total power to obtain the value of the ratio of fast waves to slow waves.

[0238] Specifically, the total power of fast waves and the total power of slow waves are divided by the total power to obtain the ratio of fast waves to slow waves, which is expressed as:

[0239]

[0240] Among them, Ratio fast and Ratio slow are the power ratios of fast and slow waves, P total is the total power;

[0241] (6) Value range: The brain function state is evaluated based on the ratio of fast waves to slow waves; (0, 0.4) is the inhibitory state; (0.4, 0.6) is the intermediate state; and (0.6, 1) is the excited state.

[0242] The characteristic parameters of the functional near-infrared spectroscopy (fNIRS) include changes in the concentrations of oxygenated hemoglobin HbO and deoxygenated hemoglobin HbR.

[0243] The relationship between the characteristic parameters of the functional near-infrared spectroscopy imaging and the excitation and inhibition states of brain functions includes the following:

[0244] 1. Calculation of blood oxygen concentration changes:

[0245] The Beer-Lambert law and the modified equation are used to calculate the changes in the concentrations of oxyhemoglobin HbO and deoxyhemoglobin HbR. The expression is:

[0246] Δ HbO =ε HbR (λ2)·A(λ1)-ε HbR (λ1)·A(λ2);

[0247] Δ HbR =ε HbO (λ1)·A(λ2)-ε HbO (λ2)·A(λ1);

[0248] Among them, Δ HbO represents the change in oxygenated hemoglobin concentration, Δ HbR represents the change in deoxyhemoglobin concentration, ε HbO (λ) and ε HbR (λ) are the absorption coefficients of oxyhemoglobin and deoxyhemoglobin at wavelength λ, respectively, and A(λ) is the absorbance change measured at wavelength λ.

[0249] 2. Calculation of excitation / inhibition balance index:

[0250] According to the changes in the concentrations of oxygenated hemoglobin HbO and deoxygenated hemoglobin HbR, the indexes of brain function excitation and inhibition are calculated. A commonly used index is the relative change value (RCV), which is expressed as:

[0251]

[0252] RCV value range:

[0253] The brain function state is evaluated according to the value range of RCV; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state.

[0254] The characteristic parameters of functional magnetic resonance imaging (fMRI) include: the amplitude of whole-brain hemodynamic activity and whole-brain functional connection characteristics.

[0255] The relationship between the characteristic parameters of functional magnetic resonance imaging and the excitation and inhibition state of brain function includes the following:

[0256] 1. Time series extraction:

[0257] Extract time series signals in the brain region of interest, and the time series of each brain region is recorded as X i (t), where (i = 1, 2, ..., n), n represents the number of brain regions of interest;

[0258] 2. Functional connectivity calculation:

[0259] The Pearson correlation coefficient was used to calculate the functional connectivity between each pair of brain regions, expressed as:

[0260]

[0261] where T equals the number of time points, and are the time series means of the ith and jth brain regions, respectively.

[0262] 3. Construct the functional connectivity matrix:

[0263] The functional connectivity coefficients between all brain regions are combined into an n×n matrix R, where the functional connectivity matrix R ij =r ij .

[0264] 4. Excitation / inhibition state determination:

[0265] In order to determine the excitatory or inhibitory state of each brain region, the following steps are included:

[0266] (1) Thresholding:

[0267] The correlation coefficients in the functional connectivity matrix R are thresholded to distinguish significant connections from non-significant connections:

[0268]

[0269] Among them, θ is the preset threshold.

[0270] (2) Calculation of excitation / inhibition index:

[0271] Define the excitation / inhibition index E for each brain region i and inhibition index I i , the expression is:

[0272]

[0273] (3) Equilibrium state calculation:

[0274] The excitation / inhibition balance state of any brain region is defined as the difference between the excitation index and the inhibition index of the brain region: B i =E i -I i ;

[0275] According to B i The value range of is used to evaluate the brain function state; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state;

[0276] (4) Whole brain balance state:

[0277] The overall balance state of the whole brain is defined as the average of the balance states of all brain regions:

[0278]

[0279] According to B total The value range of is used to evaluate the brain function state; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state.

[0280] 5. Comprehensively evaluate the characteristic parameters of the same testing tool to obtain the final excitation and inhibition state of the evaluator's brain function.

[0281] Specifically, in this embodiment, different detection tools are selected according to different evaluators, and the evaluation results of each characteristic parameter under the detection tool are integrated to obtain the current excited and inhibited state of the evaluator's brain function; wherein, when more than 50% of the characteristic parameters of each detection tool are the same evaluation result, the evaluator's brain is currently defined as the state of the evaluation result. Taking the 6 characteristic parameters of the electroencephalogram as an example, if more than 3 of the characteristic parameters are in an excited state, the evaluator's brain is currently defined as being in an excited state.

[0282] In summary, this embodiment uses a child's EEG as an example to illustrate the method: after the collected and processed EEG is input into MATLAB software, the frequency domain signal is obtained by Fourier transform and the power spectrum density in the α band is calculated. The average spectrum power in the α band of the Fz channel is 9.52μV2, that is, the excited state. Figure 2 As shown, it is a power spectrum density diagram of the EEG sample, the horizontal axis is the frequency band, and the vertical axis is the power spectrum density.

[0283] Through the above content, the technical solution of the present invention is disclosed. Compared with the prior art, the present invention has the following advantages:

[0284] 1. The evaluation method of the present invention can select detection tools according to the degree of cooperation of the evaluator, customize the treatment strategy in a personalized way, has strong pertinence, and can quickly obtain the brain function status of the evaluator;

[0285] 2. The evaluation method of the present invention can extract several characteristic parameters as the basis for evaluation, comprehensively consider the dynamic properties of the brain, and ensure the accuracy and comprehensiveness of the test results;

[0286] 3. The evaluation method of the present invention is based on the assignment of characteristic parameters, which helps to evaluate the dynamic changes of brain regions in real time when they interact with each other. By deeply considering and integrating the dynamic characteristics of the brain, the accuracy and reliability of the detection results can be ensured.

[0287] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the excitation and inhibition state of brain function, characterized in that: The method includes the selection of detection tools, signal acquisition, preprocessing, feature parameter extraction and evaluation results; The specific steps include: Selecting detection tools according to the degree of cooperation of the assessor, the detection tools include electroencephalogram, functional near infrared spectroscopy imaging, and functional magnetic resonance imaging; Collect signals from different detection tools separately; The signal acquisition of the electroencephalogram is: recording the electrical activity signals of the brain by placing conductive electrodes on the surface of the assessor's scalp; the signal acquisition of the functional near-infrared spectroscopy imaging is: recording the concentration change signals of oxyhemoglobin and deoxyhemoglobin in different areas of the cerebral cortex by placing multiple probes on the surface of the assessor's scalp; the signal acquisition of the functional magnetic resonance imaging is: based on the blood oxygen level dependence effect, monitoring the change signals of blood oxygen level during brain activity; Preprocess the signals collected by each detection tool; The signal preprocessing of the electroencephalogram includes: filtering, processing noise, and removing artifacts; the signal preprocessing of the functional near-infrared spectral imaging includes: processing artifacts, filtering, segmentation, baseline comparison, data conversion, and conversion of optical density into blood oxygen concentration data; the signal preprocessing of the functional magnetic resonance imaging includes: correction, standardization, filtering, regression, and smoothing; the characteristic parameters of each detection tool are extracted from the preprocessed signal, and the excitation and inhibition states of the evaluator's brain function under different detection tools are evaluated respectively by the characteristic parameters, and the evaluation results include inhibition state, intermediate state, and excitement state; the characteristic parameters of the electroencephalogram include: EEG phase synchronization, sample entropy, index of the power spectrum density 1 / f curve, E / I index extracted from detrended fluctuation analysis, α power, and fast wave / slow wave ratio; the characteristic parameters of the functional near-infrared spectral imaging include: changes in the concentrations of oxygenated hemoglobin and deoxygenated hemoglobin; the characteristic parameters of the functional magnetic resonance imaging include: the amplitude of whole-brain hemodynamic activity and whole-brain functional connectivity characteristics; By combining the evaluation results of various characteristic parameters of the same detection tool, the final brain function excitation and inhibition state of the evaluator can be obtained.

2. A method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the EEG phase synchronization and the excitation and inhibition state of brain function includes the following: The EEG signal is passed through a bandpass filter to extract the signal in the γ frequency band; s γ (t) = filter 30-100Hz (s(t)); Where s(t) is the EEG signal, s γ (t) is the filtered γ-band signal; Perform Hilbert transform on the filtered signal to obtain the complex analysis signal, which is expressed as: z(t)=s γ (t)+j·H{s γ (t)}; Where H{·} represents the Hilbert transform, j is the imaginary unit; Extract phase information from the analysis signal, expression: Among them, the atan2 function returns the arc tangent of two parameters; According to the phase information of the two signals in time, the phase synchronization index PSI is calculated, and the expression is: in, and are the phases of the two signals at time t, and N is the number of samples; The obtained phase synchronization index was statistically tested, and the difference was judged based on the statistical significance level p value, with p < 0.05 as a significant difference. When the phase synchronization index was significantly different from random synchronization, the consistency of the whole brain phase difference at multiple time points was calculated, and the expression was: in, is the phase at time t, M is the number of delay steps considered; By calculating the phase synchronization index PSI between multiple signals or regions in the whole brain, the PSI mean is obtained; The brain function state was evaluated according to the value range of the PSI mean; among them, the whole brain PSI mean = [0,0.5) inhibition state, the whole brain PSI mean = [0.5,0.8) intermediate state, and the whole brain PSI mean = [0.8,1] excitement state.

3. The method for evaluating the state of brain function excitation and inhibition according to claim 1, characterized in that: The relationship between the sample entropy of the electroencephalogram and the excitation and inhibition state of brain function includes the following: The EEG signal is converted from the time domain to the frequency domain, and the spectral density of the EEG signal is calculated by fast Fourier transform (FFT), which is expressed as: P(f)=|FFT(s(t))| 2 ; Where s(t) is the EEG signal, P(f) is the power spectrum in the frequency domain, and f is the frequency; Perform non-periodic component analysis in the power spectrum of the EEG signal and identify beta waves, gamma waves, and high-frequency oscillations by observing specific frequency ranges in the power spectrum; Sample entropy SampEn is calculated using the expression: Where m is the length of the template sequence, γ is the similarity threshold, and N is the total number of samples in the signal; The brain function state is evaluated according to the value range of the sample entropy SampEn; among them, SampEn=[0,0.5) inhibition state; SampEn=[0.5,1.5) intermediate state; SampEn=[1.5,∞) excited state; the value of 0 indicates a completely periodic or deterministic signal, and the value of ∞ indicates that the signal has higher complexity and randomness.

4. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the exponent of the power spectrum density 1 / f curve of the electroencephalogram and the excitation and inhibition state of brain function includes the following: Through logarithmic transformation, the attenuation rate of the power spectrum as the frequency changes is described as follows: PSD(f)=kf·-β, Log(PSD(f))=log(k)-β·log(f); Where PSD(f) is the power spectral density at frequency f, k is a positive constant representing the scale factor of the power spectrum at the lowest frequency, and β is the power law exponent; Use the least squares method to perform linear regression on Log(PSD(f)) and log(f) to find the best fit line, the expression is: Log(PSD(f))=β+b·log(f); Where b is the slope; The power law exponent β is obtained from the slope b of the fitting line: β = -b; The brain function state is evaluated according to the value range of the power law exponent β, where β = [0, 0.5) is the excited state; β = [0.5, 0.8) is the intermediate state; and β = [0.8, 2) is the inhibited state.

5. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the E / I index extracted from the detrended fluctuation analysis of the electroencephalogram and the excitation and inhibition state of brain function includes the following: Remove linear or nonlinear trends from EEG signals and eliminate non-stationarity in the signal. The expression is: y(t)=s(t)-trend(s(t)); Where s(t) is the EEG signal and y(t) is the detrended signal; Calculate the volatility of the detrended signal and get the cumulative volatility F(k), which is expressed as: Where F(k) is the cumulative fluctuation and k is the time point; Perform DFA analysis on the cumulative fluctuations and calculate the fluctuations at different time scales. The expression is: Among them, N k is the number of samples in the kth time window; The E / I index is calculated by comparing the cumulative fluctuations in different frequency bands or under different conditions. The expression is: Among them, F E (k) and F I (k) The cumulative fluctuations in the excitatory frequency band and the inhibitory frequency band, respectively; The brain function state is evaluated according to the value range of the E / I index; among them, E / I index = [0, 0.5) inhibition state; E / I index = [0.5, 0.8) intermediate state; E / I index = [0.8, 1] excitement state.

6. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the alpha power of the electroencephalogram and the excitation and inhibition state of brain function includes the following: From the EEG signal, the signal in the alpha wave frequency band is extracted through a bandpass filter, and the expression is: S α (t) = filter 8-13Hz(s(t)); Where s(t) is the EEG signal, S α (t) is the filtered α wave signal; The power spectral density of the filtered α wave signal is calculated using Fourier transform, and the expression is: PSD α (f)=|FFT(S α (t))| 2 ; Where FFT stands for Fast Fourier Transform, and f is the frequency; Integrate the power spectrum density in the α wave frequency band to obtain the α wave power, which is expressed as: Among them, f1 and f2 are the lower and upper frequency limits of the α wave frequency band, respectively; Compare the alpha wave power with the total power and standardize it. The expression is: Among them, P total is the total power; The brain function status is evaluated based on the range of α wave power; α power = (0,10) μV 2 Excited state; α power = [10,50) μV 2 It is the intermediate state; α power = [50,∞)μV 2 Inhibited state.

7. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the fast wave / slow wave ratio of the electroencephalogram and the excitation and inhibition state of brain function includes the following: From the EEG signal, the fast and slow wave frequency bands are extracted through a bandpass filter. The expression is: S fast (t) = filter fast band (s(t)); S slow (t) = filter slow wave frequency band (s(t)); Where s(t) is the EEG signal, S fast (t) is the fast-wave frequency band signal, S slow (t) is the slow wave frequency band signal; Calculate the power spectral density of the fast wave and slow wave frequency band signals after filtering respectively, the expression is: PSD fast (f)=|FFT(S fast (t))I 2 ; PSD slow (f)=|FFTS slow (t))| 2 ; The power spectrum density is integrated in the fast wave and slow wave frequency bands respectively to obtain the total power of the fast wave and slow wave, which is expressed as: Among them, P fast is the total fast wave power, P slow is the total slow wave power; The ratio of the total power of fast waves to the total power of slow waves is expressed as: Among them, Ratio fast / slow is the power ratio; Divide the total power of fast waves and the total power of slow waves by the total power to get the ratio of fast waves to slow waves. The expression is: Among them, Ratio fast and Ratio slow are the power ratios of fast and slow waves, P total is the total power; The brain function state is evaluated according to the ratio of fast waves to slow waves; (0, 0.4) is the inhibitory state; (0.4, 0.6) is the intermediate state; and (0.6, 1) is the excited state.

8. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the characteristic parameters of the functional near-infrared spectroscopy imaging and the excitation and inhibition states of brain functions includes the following: The Beer-Lambert law and the modified equation are used to calculate the changes in the concentrations of oxyhemoglobin HbO and deoxyhemoglobin HbR. The expression is: D HbO =e HbR (λ2)·A(λ1)-ε HbR (λ1)·A(λ2); D HbR =e HbO (λ1)·A(λ2)-ε HbO (λ2)·A(λ1); Among them, Δ HbO represents the change in oxygenated hemoglobin concentration, Δ HbR represents the change in deoxyhemoglobin concentration, ε HbO (λ) and ε HbR (λ) are the absorption coefficients of oxyhemoglobin and deoxyhemoglobin at wavelength λ, respectively, and A(λ) is the absorbance change measured at wavelength λ; According to the changes in the concentrations of oxygenated hemoglobin HbO and deoxygenated hemoglobin HbR, the index RCV of brain function excitement and inhibition is calculated, and the expression is: The brain function state is evaluated according to the value range of RCV; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state.

9. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: The relationship between the characteristic parameters of functional magnetic resonance imaging and the excitation and inhibition state of brain function includes the following: Extract time series signals in the brain region of interest, and the time series of each brain region is recorded as X i (t), where (i = 1, 2, ..., n), n represents the number of brain regions of interest; The Pearson correlation coefficient was used to calculate the functional connectivity between each pair of brain regions, expressed as: where T equals the number of time points, and are the time series means of the ith and jth brain regions, respectively; The functional connectivity coefficients between all brain regions are combined into an n×n matrix R, where the functional connectivity matrix R ij =r ij ; The correlation coefficients in the functional connectivity matrix R are thresholded to distinguish significant connections from non-significant connections: Among them, θ is the preset threshold; Then, the excitation / inhibition index E of each brain region i and inhibition index I i , the expression is: The excitation / inhibition balance state of any brain region is defined as the difference between the excitation index and the inhibition index of the brain region: B i =E i -I i According to B i The value range of is used to evaluate the brain function state; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state; The overall balance state of the whole brain is defined as the average of the balance states of all brain regions: According to B total The value range of is used to evaluate the brain function state; among them, (-0.4, 0.4) is the inhibitory state; (-0.6, -0.4] ∪ [0.4, 0.6) is the intermediate state; (-1, -0.6] ∪ [0.6, 1) is the excited state.

10. The method for evaluating the excitation and inhibition state of brain function according to claim 1, characterized in that: According to the detection tool selected by the evaluator, the evaluation results of each characteristic parameter under the detection tool are integrated to obtain the current brain function excitation and inhibition state of the evaluator; When more than 50% of the characteristic parameters of the detection tools are the same evaluation result, it is defined that the evaluator's brain is currently in the state of the evaluation result.

Images