Zebra fish electroencephalogram physiological signal acquisition and quantitative analysis method and device

Abstract

The invention relates to a zebra fish electroencephalogram physiological signal acquisition and quantitative analysis method and device, and belongs to the field of medical molecular biology. A zebra fish electroencephalogram physiological signal acquisition and quantitative analysis method comprises the following steps: S1, constructing a zebra fish electroencephalogram physiological acquisition system: filling a glass microelectrode with liquid in the electrode, fixing the glass microelectrode on a stereotaxic instrument, and submerging an electrode needle of the glass microelectrode below the liquid level of the liquid in the microelectrode; s2, fixing the zebrafish: soaking a plurality of adult zebrafish in an ice-water mixture at 0 DEG C until the zebrafish stops moving, and placing the plurality of zebrafish in cracks of a wet cotton pad; s3, collecting electroencephalogram physiological signals: positioning the microelectrode in a specific brain area, positioning the reference electrode at the tail part of the zebra fish, and carrying out uninterrupted electroencephalogram examination for 5-20 minutes; and S4, quantitative analysis: sampling electroencephalogram signals, performing signal acquisition on the electroencephalogram signals by taking 5 seconds as a basic time unit, and recording power spectrum values of four frequency bands.

Description

Technical Field

[0001] The invention relates to medical molecular biology, and in particular to a method and a device for collecting and quantitatively analyzing zebrafish brain electrophysiological signals. Background Art

[0002] Epilepsy is one of the most common serious brain diseases, and zebrafish has attracted attention for its applicability in the construction of central nervous system disease models. In the prior art, there are several problems with the EEG recording system of the adult zebrafish brain. First, copper core electrodes with too large diameters are used as recording electrodes. Since the adult zebrafish has a small head, such thick diameter electrodes may cause damage to its brain. Secondly, although a multi-channel EEG electrode array is designed to adapt to the size of adult zebrafish, this method requires the placement of a four-channel electrode array on the skin and above the telencephalon and midbrain, and the continuous oral infusion of anesthetics during the recording process, which may affect the electrophysiological activity of the zebrafish brain, and the preparation and implantation process of the microarray microelectrode is relatively complicated. In addition, the existing method of connecting the active electrode and the reference electrode to the signal acquisition system can record the EEG, but cannot perform quantitative analysis on the collected EEG signals. Summary of the invention

[0003] Based on this, it is necessary to provide a method and device for collecting and quantitatively analyzing zebrafish brain electrophysiological signals, so as to achieve effective collection and quantitative analysis of adult zebrafish brain electrophysiological signals.

[0004] A method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals comprises the following steps:

[0005] S1: Construct a zebrafish brain electrophysiological acquisition system: fill the glass microelectrode with electrode liquid and fix it on a stereotaxic instrument, with the electrode needle of the glass microelectrode submerged below the liquid level of the microelectrode liquid;

[0006] S2: Fixation of zebrafish: Multiple adult zebrafish were immersed in a mixture of ice water at 0°C until they stopped moving, and multiple zebrafish were placed in the cracks of moistened cotton pads;

[0007] S3: Collection of electroencephalographic signals: The microelectrode is positioned in a specific brain area, the reference electrode is positioned in the tail of the zebrafish, and an uninterrupted electroencephalogram examination is performed for 5-20 minutes;

[0008] S4: Quantitative analysis: The EEG signals were sampled with 5 seconds as a basic time unit, and the power spectrum values ​​of four frequency bands were recorded. The four frequency bands were alpha wave band, beta wave band, theta wave band and delta wave band.

[0009] The first aspect of the present application discloses a method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals. The stability and precise positioning of the electrode are ensured by filling the glass microelectrode with electrode liquid and fixing it on a stereotaxic instrument. The design of the electrode needle being immersed below the liquid surface avoids the tip of the electrode from being exposed to the air, reduces signal noise, and improves signal quality. Anesthesia is performed using a 0°C ice-water mixture, which is milder than chemical anesthesia and reduces interference with the physiological state of zebrafish, thereby ensuring that the collected EEG signals are closer to the natural state. Placing the zebrafish in the cracks of a moist cotton pad can effectively fix the zebrafish and reduce variables during the experiment. The microelectrode is positioned in a specific brain area, the reference electrode is positioned at the tail of the zebrafish, and an uninterrupted EEG examination is performed for 5-20 minutes. This method can capture the continuous changes in zebrafish brain activity and ensure the standardization of the acquisition process. The power spectrum values ​​of the four frequency bands of alpha, beta, theta, and delta waves are recorded. This method can provide an in-depth analysis of different EEG activity states and help understand the relationship between different EEG waves and specific brain function states. The EEG signal is collected with 5 seconds as a basic time unit. This method can provide precise time resolution and facilitate more detailed analysis of EEG activities.

[0010] In any of the above technical solutions, the glass microelectrode is prepared by the following method:

[0011] A glass microelectrode tip with a tip diameter of 0.2-0.4 μm is pulled by a needle puller;

[0012] Control the drawing parameters during the working process of the needle puller, and the drawing parameters include pressure, heating temperature, drawing force, drawing speed and delay time.

[0013] By controlling the specific parameters of the needle puller, glass microelectrodes with a tip diameter in the range of 0.2-0.4μm can be accurately produced, which is crucial for the acquisition of brain electrophysiological signals. Finer electrodes can reduce damage to brain tissue and improve the sensitivity and accuracy of signal acquisition. Setting specific pulling parameters can ensure that the glass microelectrodes produced each time are highly consistent and repeatable.

[0014] In any of the above technical solutions, the preparation method of the electrode internal solution is as follows:

[0015] Prepare the electrode internal solution according to 130mM potassium chloride, 15mM sodium chloride, 0.5mM calcium chloride, 1mM magnesium chloride, 1mM Mg-ATP, and 10mM EGTA;

[0016] Adjust the electrode solution to a pH of 7.4 with sodium bicarbonate;

[0017] Filter through a 0.22 μm microporous membrane.

[0018] By adjusting the concentrations of potassium chloride, sodium chloride, calcium chloride, and magnesium chloride, as well as adding Mg-ATP and EGTA, the ion concentration and environment of the intracellular fluid are simulated, thereby reducing the interference of the electrode fluid on the physiological state of the cells and ensuring that the collected EEG signals are closer to the real physiological state. The pH value of the electrode fluid is adjusted to 7.4 using sodium bicarbonate to ensure the physiological pH value of the electrode fluid, avoiding damage to cells or the impact of excessively high or low pH values ​​on electrode performance. Filtering with a 0.22μm microporous membrane can effectively remove bacteria, particles and other impurities in the electrode fluid, avoiding the impact of impurities on electrode performance and reducing potential contamination of brain tissue.

[0019] In any of the above technical solutions, the step of fixing the zebrafish: multiple adult zebrafish are immersed in a mixture of ice water at 0°C until they stop moving, and the specific steps of placing the multiple zebrafish in the cracks of the moist cotton pad are as follows:

[0020] S21: 3-month-old adult zebrafish were immersed in a mixture of ice and water at 0°C until they stopped moving;

[0021] S22: All zebrafish were placed on a cotton pad placed in a 120 mm diameter Petri dish with a 45 mm slit inside the pad;

[0022] S23: Use the cotton swab tip to lift the zebrafish's head.

[0023] By using a 0°C ice-water mixture to anesthetize the zebrafish, potential damage caused by the use of chemical anesthetics is avoided. The design of the cotton pad cracks fixes the zebrafish's head in a specific position, reduces motion artifacts, and improves the stability of the EEG signal. The moist environment of the cotton pad ensures the physiological state of the zebrafish and avoids abnormal EEG signals caused by dryness. The method of using the cotton swab head to lift the zebrafish's head avoids excessive squeezing of the zebrafish's body and minimizes damage to the zebrafish.

[0024] In any of the above technical solutions, the steps of collecting brain electrophysiological signals are as follows: positioning the microelectrode in a specific brain area, positioning the reference electrode in the tail of the zebrafish, and performing an uninterrupted electroencephalogram examination for 5-20 minutes:

[0025] S31: Using a precise X, Y, and Z axis manipulator, the microelectrodes were positioned in specific brain regions, the left forebrain, right forebrain, left midbrain, and right midbrain, with the reference electrode positioned in the caudal region;

[0026] S32: Perform an uninterrupted EEG examination for 5-20 minutes.

[0027] By using precise X, Y, and Z axis manipulators, the microelectrode can be quickly and accurately positioned to the target brain area, improving experimental efficiency. The brain areas where signals need to be collected (left forebrain, right forebrain, left midbrain, and right midbrain) are clearly identified, providing a basis for studying the functions and connections of different brain areas. Conducting 5-20 minutes of uninterrupted EEG examinations ensures the continuity and integrity of the collected EEG signals, can capture changes in EEG activity at different time scales, and provide sufficient data for subsequent signal analysis.

[0028] In any of the above technical solutions,

[0029] S311: The positioning of the glass microelectrode is represented by the following state space model:

[0030]

[0031] y(t)=Cx(t)+Du(t)

[0032] Among them, x(t) is the system state vector, which contains information such as position and speed, u(t) is the input control signal, that is, the driving force of the motor, and y(t) is the output signal. represents the actual position of the electrode, A, B, C, and D are the state matrix and output matrix of the system, describing the dynamic characteristics of the system;

[0033] S312: Error modeling and adaptive control are performed. The error model is expressed by the following formula:

[0034] e(t)=p target -p(t)

[0035] Among them, p target is the target position, p(t) is the current electrode position, and e(t) is the position error;

[0036] S313: In order to ensure that the glass microelectrode accurately reaches a specific brain area, the motion trajectory of the glass microelectrode is adjusted through precise path planning, and its control expression is as follows:

[0037]

[0038] Among them, K p , K d , K i are proportional, differential, and integral gains, which adjust the control performance, e(τ) is the error function, θ(t) is the adaptive adjustment term, and the control parameters are dynamically adjusted through the adaptive mechanism to compensate for the system uncertainty, and e(t) is the position error;

[0039] S314: In order to ensure that the glass microelectrode accurately reaches a specific brain area, the motion trajectory of the glass microelectrode is adjusted through precise path planning. The function of the electrode path is:

[0040]

[0041] in:

[0042] represents the energy of motion (the square of the velocity of the path);

[0043] ||e(t)|| 2 is the penalty term for position error;

[0044] λ is a tuning parameter that balances speed and error.

[0045] S315: adjusting the electrode position according to the real-time EEG signal to avoid signal quality degradation;

[0046] S316: Optimize signal quality through gain adjustment, the expression is:

[0047] u(t)=-K(y(t)-y target (t))+S(t)

[0048] in:

[0049] y(t) is the current EEG signal, y target (t) is the expected signal;

[0050] K is the gain matrix;

[0051] S(t) is the signal quality feedback, which can be obtained through power spectrum analysis.

[0052] By establishing a state-space model, the motion state of the microelectrode can be accurately described, including information such as position and speed, providing a theoretical basis for the subsequent control algorithm. Through error modeling, the error between the current electrode position and the target position can be calculated in real time to provide feedback information for the control algorithm. Adaptive control algorithms can be used to dynamically adjust control parameters according to the error, compensate for system uncertainty, and ensure that the microelectrode can accurately reach the target position. Path planning is used to optimize the motion trajectory of the microelectrode, thereby improving positioning efficiency and avoiding damage to brain tissue. Adjusting the electrode position according to the real-time EEG signal can avoid the degradation of signal quality caused by electrode position offset and ensure that the collected signal has a high signal-to-noise ratio. Gain adjustment can optimize signal quality, improve signal amplitude and clarity, and provide more reliable data for subsequent signal analysis.

[0053] In any of the above technical solutions, the quantitative analysis: sampling the EEG signal, the EEG signal is collected with 5 seconds as a basic time unit, and the power spectrum values ​​of four frequency bands: alpha wave, beta wave, theta wave and delta wave are recorded in the following specific steps:

[0054] S41: The glass microelectrode impedance was set to: 30 Hz high frequency, 1 Hz low frequency, 100 μV / mm sensitivity and 30 mm / s time resolution;

[0055] S42: EEG signals are collected with 5 seconds as a basic time unit;

[0056] S43: record the power spectrum values ​​of the alpha wave band, beta wave band, theta wave band and delta wave band, where the alpha wave is 8.0-12.9 Hz, the beta wave is 13-30 Hz, the theta wave is 4-7.9 Hz, and the delta wave is 1.5-3.9 Hz;

[0057] S43: Calculate the relative power ratio. The calculation formula of the relative power ratio is as follows:

[0058] [(theta+delta)] / [(alpha+beta)]ratio, DTABR)

[0059] Among them, alpha is the power spectrum value of the alpha wave frequency band, beta is the power spectrum value of the beta wave frequency band, theta is the power spectrum value of the theta wave frequency band, and delta is the power spectrum value of the delta wave frequency band;

[0060] S45: Calculate the DTABR of the left region and the right region respectively, where the left region is the left forebrain and left midbrain region, and the right region is the right forebrain and right midbrain region;

[0061] S46: Statistical alpha rhythm, apical waves, K-complex waves, sleep spindles, diffuse slow wave activity, periodic transitions, rapid eye movement periods, and the emergence of epileptic discharge waves.

[0062] By setting the impedance of the glass microelectrode, the stability and reliability of signal acquisition are ensured, noise interference is reduced, and signal quality is improved. The EEG signal is collected with 5 seconds as the basic time unit, which can not only ensure the collection of sufficient information, but also improve the collection efficiency and reduce the experimental time. Recording the power spectrum values ​​of four frequency bands, including alpha waves, beta waves, theta waves and delta waves, can more comprehensively reflect the state of zebrafish EEG activity. Calculating the relative power ratio (DTABR) can more accurately reflect the activity differences between different brain regions and provide a more reliable indicator for studying brain function. Calculating the DTABR of the left and right regions separately can more finely analyze the activity differences between the left and right brains, providing a more accurate basis for studying the left and right lateralization of brain function. Statistics of various EEG waveforms, including alpha rhythm, top wave, K-complex wave, sleep spindle wave, diffuse slow wave activity, periodic transition, rapid eye movement period, epileptic discharge wave, can more comprehensively analyze the brain function states of zebrafish such as sleep, wakefulness, learning, and memory.

[0063] In any of the above technical solutions, the specific method of recording the power spectrum values ​​of the alpha wave band, the beta wave band, theta wave band and the delta wave band is as follows:

[0064] S431: Use adaptive filters to dynamically remove noise sources,

[0065] S432: Use an adaptive filtering algorithm to achieve noise elimination by minimizing the error between the noise and the main signal, where the filter update formula is as follows:

[0066]

[0067] Where w(t) is the filter coefficient, d(t) is the noise estimate of the auxiliary channel, is the signal after denoising, and n(t) is the signal before denoising;

[0068] S433: After obtaining the denoised signal, perform frequency domain analysis and decompose the signal using continuous wavelet transform;

[0069] S434: Record the processed data to obtain power spectrum values ​​of the alpha wave band, the beta wave band, theta wave band, and the delta wave band, respectively.

[0070] By using adaptive filters to dynamically remove noise sources, such as environmental noise, electrode noise, etc., the signal-to-noise ratio of EEG signals is improved, making subsequent signal analysis more accurate and reliable. Based on minimizing the error between noise and the main signal, the use of adaptive filtering algorithms can effectively remove noise while retaining the useful information of the original signal, thereby improving the quality of the signal. By using continuous wavelet transform to decompose the signal, the information of the signal at different times and frequencies can be obtained. Compared with the traditional Fourier transform, the wavelet transform has better time-frequency resolution and can more accurately analyze the frequency components of the EEG signal. Selecting a suitable mother wavelet can better adapt to the characteristics of EEG signals and improve the accuracy of signal decomposition. By recording the signal after wavelet transform in frequency bands, the power spectrum values ​​of the alpha, beta, theta and delta bands can be obtained respectively, providing accurate data for subsequent quantitative analysis. Through adaptive filtering and continuous wavelet transform, high-quality EEG signals can be obtained, which reduces the experimental errors caused by poor signal quality and improves the reliability of experimental results.

[0071] In any of the above technical solutions,

[0072] S451: Divide the signal collection area into a left area and a right area, wherein: the left area includes the left forebrain and the left midbrain, and the right area includes the right forebrain and the right midbrain;

[0073] S452: standardize the signal so that the signal in each region has a zero mean and a unit standard deviation;

[0074] S453: Analyze the frequency spectrum of the signal by using wavelet transform or fast Fourier transform, and calculate the power spectrum value of each frequency band;

[0075] S454: To calculate the DTABR value of the left region, we sum the frequency band power spectrum values ​​of the left forebrain and left midbrain respectively and substitute them into the DTABR formula:

[0076]

[0077] Among them, P α，left The sum of the power spectrum values ​​of the alpha wave frequency band of the left forebrain and left midbrain, P β，left The sum of the power spectrum values ​​of the beta wave frequency bands in the left forebrain and left midbrain, P θ，left P is the sum of the power spectrum values ​​of the theta wave frequency band of the left forebrain and left midbrain, δ，left Sum the band power spectrum values ​​of the delta wave band of the left forebrain and left midbrain;

[0078] S455: Calculate the DTABR value of the right area, sum the frequency band power spectrum values ​​of the right forebrain and right midbrain respectively, and substitute them into the DTABR formula:

[0079]

[0080] Among them, P α，right The sum of the power spectrum values ​​of the alpha wave frequency band of the right forebrain and right midbrain, P β，right The sum of the power spectrum values ​​of the beta wave frequency bands in the right forebrain and right midbrain, P θ，right is the sum of the power spectrum values ​​of the theta wave frequency band in the right forebrain and right midbrain, P δ，right Sum the band power spectrum values ​​of the delta wave band of the right forebrain and right midbrain;

[0081] S456: Automatically adjust the processing parameters, filter cutoff frequency and signal sampling rate of each frequency band according to real-time data and signal characteristics.

[0082] By clearly dividing the signal acquisition area into the left area (left forebrain and left midbrain) and the right area (right forebrain and right midbrain), a clear regional definition is provided for the subsequent DTABR value calculation. Standardizing the signal to have a zero mean and unit standard deviation can eliminate the influence of the difference in signal amplitude in different regions and ensure the accuracy of DTABR value calculation. The power spectrum values ​​of the frequency bands of the left forebrain and left midbrain are summed up, and the power spectrum values ​​of the frequency bands of the right forebrain and right midbrain are summed up, and then substituted into the DTABR formula, which can more accurately reflect the overall EEG activity state of the left and right regions. Using wavelet transform or fast Fourier transform to analyze the spectrum of the signal, the appropriate analysis method can be selected according to different signal characteristics, which improves the flexibility of signal analysis. Automatically adjusting the processing parameters, filter cutoff frequency and signal sampling rate of each frequency band according to real-time data and signal characteristics can optimize the signal processing process and improve the adaptability of signal analysis.

[0083] A zebrafish brain electrophysiological signal acquisition and quantitative analysis device is used to execute the zebrafish brain electrophysiological signal acquisition and quantitative analysis method described above.

[0084] The second aspect of the present application discloses a zebrafish brain electrophysiological signal acquisition and quantitative analysis device, through which the various steps of the zebrafish brain electrophysiological signal acquisition and quantitative analysis method are integrated into one system, realizing the integration and automation of zebrafish brain electrophysiological signal acquisition and quantitative analysis, and improving the experimental efficiency and the reliability of the experimental results. The device can realize the functions of accurate electrode positioning, physiological electrode internal fluid, adaptive control, accurate signal processing, etc. involved in the zebrafish brain electrophysiological signal acquisition and quantitative analysis method, ensuring the high precision and high reliability of brain electrical signal acquisition and analysis. Accurate control and signal processing can obtain high-quality brain electrical signals, providing more reliable data for subsequent analysis. Integrating complex experimental processes into the device makes the operation simpler and easier, and is convenient for researchers to use. Standardized design and operating procedures make the device easy to promote and apply, and can provide technical support for more researchers. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 This is a schematic diagram of the process of zebrafish brain electrophysiological signal acquisition and quantitative analysis;

[0086] Figure 2 Schematic diagram of the distribution of electrodes and brain regions;

[0087] Figure 3 This is the electroencephalogram before and after the microelectrode needle reaches the surface of the zebrafish brain;

[0088] Figure 4 Electroencephalogram of zebrafish after intramuscular injection of PTZ;

[0089] Figure 5 Electroencephalogram of zebrafish after intramuscular injection of saline. DETAILED DESCRIPTION

[0090] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0091] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0092] The following describes the zebrafish brain electrophysiological signal acquisition and quantitative analysis methods and devices according to some embodiments of the present invention with reference to the accompanying drawings.

[0093] Example 1

[0094] like Figure 1As shown, this embodiment discloses a method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals, comprising the following steps:

[0095] S1: Construct a zebrafish brain electrophysiological acquisition system: fill the glass microelectrode with electrode liquid and fix it on a stereotaxic instrument, with the electrode needle of the glass microelectrode submerged below the liquid level of the microelectrode liquid;

[0096] S2: Fixation of zebrafish: Multiple adult zebrafish were immersed in a mixture of ice water at 0°C until they stopped moving, and multiple zebrafish were placed in the cracks of moistened cotton pads;

[0097] S3: Collection of electroencephalographic signals: The microelectrode is positioned in a specific brain area, the reference electrode is positioned in the tail of the zebrafish, and an uninterrupted electroencephalogram examination is performed for 5-20 minutes;

[0098] S4: Quantitative analysis: The EEG signals were sampled with 5 seconds as a basic time unit, and the power spectrum values ​​of four frequency bands were recorded. The four frequency bands were alpha wave band, beta wave band, theta wave band and delta wave band.

[0099] The first aspect of the present application discloses a method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals. The stability and precise positioning of the electrode are ensured by filling the glass microelectrode with electrode liquid and fixing it on a stereotaxic instrument. The design of the electrode needle being immersed below the liquid surface avoids the tip of the electrode from being exposed to the air, reduces signal noise, and improves signal quality. Anesthesia is performed using a 0°C ice-water mixture, which is milder than chemical anesthesia and reduces interference with the physiological state of zebrafish, thereby ensuring that the collected EEG signals are closer to the natural state. Placing the zebrafish in the cracks of a moist cotton pad can effectively fix the zebrafish and reduce variables during the experiment. The microelectrode is positioned in a specific brain area, the reference electrode is positioned at the tail of the zebrafish, and an uninterrupted EEG examination is performed for 5-20 minutes. This method can capture the continuous changes in zebrafish brain activity and ensure the standardization of the acquisition process. The power spectrum values ​​of the four frequency bands of alpha, beta, theta, and delta waves are recorded. This method can provide an in-depth analysis of different EEG activity states and help understand the relationship between different EEG waves and specific brain function states. The EEG signal is collected with 5 seconds as a basic time unit. This method can provide precise time resolution and facilitate more detailed analysis of EEG activities.

[0100] In any of the above technical solutions, the glass microelectrode is prepared by the following method:

[0101] A glass microelectrode tip with a tip diameter of 0.2-0.4 μm is pulled by a needle puller;

[0102] Control the drawing parameters during the working process of the needle puller, and the drawing parameters include pressure, heating temperature, drawing force, drawing speed and delay time.

[0103] By controlling the specific parameters of the needle puller, glass microelectrodes with a tip diameter in the range of 0.2-0.4μm can be accurately produced, which is crucial for the acquisition of brain electrophysiological signals. Finer electrodes can reduce damage to brain tissue and improve the sensitivity and accuracy of signal acquisition. Setting specific pulling parameters can ensure that the glass microelectrodes produced each time are highly consistent and repeatable.

[0104] In any of the above technical solutions, the preparation method of the electrode internal solution is as follows:

[0105] Prepare the electrode internal solution according to 130mM potassium chloride, 15mM sodium chloride, 0.5mM calcium chloride, 1mM magnesium chloride, 1mM Mg-ATP, and 10mM EGTA;

[0106] Adjust the electrode solution to a pH of 7.4 with sodium bicarbonate;

[0107] Filter through a 0.22 μm microporous membrane.

[0108] By adjusting the concentrations of potassium chloride, sodium chloride, calcium chloride, and magnesium chloride, as well as adding Mg-ATP and EGTA, the ion concentration and environment of the intracellular fluid are simulated, thereby reducing the interference of the electrode fluid on the physiological state of the cells and ensuring that the collected EEG signals are closer to the real physiological state. The pH value of the electrode fluid is adjusted to 7.4 using sodium bicarbonate to ensure the physiological pH value of the electrode fluid, avoiding damage to cells or the impact of excessively high or low pH values ​​on electrode performance. Filtering with a 0.22μm microporous membrane can effectively remove bacteria, particles and other impurities in the electrode fluid, avoiding the impact of impurities on electrode performance and reducing potential contamination of brain tissue.

[0109] In any of the above technical solutions, the steps of fixing the zebrafish are as follows: a plurality of adult zebrafish are immersed in a mixture of ice water at 0°C until they stop moving, and the specific steps of placing the plurality of zebrafish in the cracks of the moist cotton pad are as follows:

[0110] S21: 3-month-old adult zebrafish were immersed in a mixture of ice and water at 0°C until they stopped moving;

[0111] S22: All zebrafish were placed on a cotton pad placed in a 120 mm diameter Petri dish with a 45 mm slit inside the pad;

[0112] S23: Use the cotton swab tip to lift the zebrafish's head.

[0113] By using a 0°C ice-water mixture to anesthetize the zebrafish, potential damage caused by the use of chemical anesthetics is avoided. The design of the cotton pad cracks fixes the zebrafish's head in a specific position, reduces motion artifacts, and improves the stability of the EEG signal. The moist environment of the cotton pad ensures the physiological state of the zebrafish and avoids abnormal EEG signals caused by dryness. The method of using the cotton swab head to lift the zebrafish's head avoids excessive squeezing of the zebrafish's body and minimizes damage to the zebrafish.

[0114] In any of the above technical solutions, the steps of collecting brain electrophysiological signals are as follows: positioning the microelectrode in a specific brain area, positioning the reference electrode in the tail of the zebrafish, and performing an uninterrupted EEG examination for 5-20 minutes:

[0115] S31: Using a precise X, Y, and Z axis manipulator, the microelectrodes were positioned in specific brain regions, the left forebrain, right forebrain, left midbrain, and right midbrain, with the reference electrode positioned in the caudal region;

[0116] S32: Perform an uninterrupted EEG examination for 5-20 minutes.

[0117] By using precise X, Y, and Z axis manipulators, the microelectrode can be quickly and accurately positioned to the target brain area, improving experimental efficiency. The brain areas where signals need to be collected (left forebrain, right forebrain, left midbrain, and right midbrain) are clearly identified, providing a basis for studying the functions and connections of different brain areas. Conducting 5-20 minutes of uninterrupted EEG examinations ensures the continuity and integrity of the collected EEG signals, can capture changes in EEG activity at different time scales, and provide sufficient data for subsequent signal analysis.

[0118] In any of the above technical solutions,

[0119] S311: The positioning of the glass microelectrode is represented by the following state space model:

[0120]

[0121] y(t)=Cx(t)+Du(t)

[0122] Among them, x(t) is the system state vector, which contains information such as position and speed, u(t) is the input control signal, that is, the driving force of the motor, and y(t) is the output signal. represents the actual position of the electrode, A, B, C, and D are the state matrix and output matrix of the system, describing the dynamic characteristics of the system;

[0123] S312: Error modeling and adaptive control are performed. The error model is expressed by the following formula:

[0124] e(t)=p target -p(t)

[0125] Among them, p target is the target position, p(t) is the current electrode position, and e(t) is the position error;

[0126] S313: In order to ensure that the glass microelectrode accurately reaches a specific brain area, the motion trajectory of the glass microelectrode is adjusted through precise path planning, and its control expression is as follows:

[0127]

[0128] Among them, K p , K d , K i are proportional, differential, and integral gains, which adjust the control performance, e(τ) is the error function, θ(t) is the adaptive adjustment term, and the control parameters are dynamically adjusted through the adaptive mechanism to compensate for the system uncertainty, and e(t) is the position error;

[0129] S314: In order to ensure that the glass microelectrode accurately reaches a specific brain area, the motion trajectory of the glass microelectrode is adjusted through precise path planning. The function of the electrode path is:

[0130]

[0131] in:

[0132] represents the energy of motion (the square of the velocity of the path);

[0133] ||e(t)|| 2 is the penalty term for position error;

[0134] λ is a tuning parameter that balances speed and error.

[0135] S315: adjusting the electrode position according to the real-time EEG signal to avoid signal quality degradation;

[0136] S316: Optimize signal quality through gain adjustment, the expression is:

[0137] u(t)=-K(y(t)-y target (t))+S(t)

[0138] in:

[0139] y(t) is the current EEG signal, y target (t) is the expected signal;

[0140] K is the gain matrix;

[0141] S(t) is the signal quality feedback, which can be obtained through power spectrum analysis.

[0142] By establishing a state-space model, the motion state of the microelectrode can be accurately described, including information such as position and speed, providing a theoretical basis for the subsequent control algorithm. Through error modeling, the error between the current electrode position and the target position can be calculated in real time to provide feedback information for the control algorithm. Adaptive control algorithms can be used to dynamically adjust control parameters according to the error, compensate for system uncertainty, and ensure that the microelectrode can accurately reach the target position. Path planning is used to optimize the motion trajectory of the microelectrode, thereby improving positioning efficiency and avoiding damage to brain tissue. Adjusting the electrode position according to the real-time EEG signal can avoid the degradation of signal quality caused by electrode position offset and ensure that the collected signal has a high signal-to-noise ratio. Gain adjustment can optimize signal quality, improve signal amplitude and clarity, and provide more reliable data for subsequent signal analysis.

[0143] In any of the above technical solutions, the quantitative analysis is as follows: sampling of EEG signals is performed, the EEG signals are collected with 5 seconds as a basic time unit, and the power spectrum values ​​of four frequency bands are recorded: alpha wave, beta wave, theta wave and delta wave.

[0144] S41: The glass microelectrode impedance was set to: 30 Hz high frequency, 1 Hz low frequency, 100 μV / mm sensitivity and 30 mm / s time resolution;

[0145] S42: EEG signals are collected with 5 seconds as a basic time unit;

[0146] S43: record the power spectrum values ​​of the alpha wave band, beta wave band, theta wave band and delta wave band, where the alpha wave is 8.0-12.9 Hz, the beta wave is 13-30 Hz, the theta wave is 4-7.9 Hz, and the delta wave is 1.5-3.9 Hz;

[0147] S43: Calculate the relative power ratio. The calculation formula of the relative power ratio is as follows:

[0148] [(theta+delta)] / [(alpha+beta)]ratio, DTABR)

[0149] Among them, alpha is the power spectrum value of the alpha wave frequency band, beta is the power spectrum value of the beta wave frequency band, theta is the power spectrum value of the theta wave frequency band, and delta is the power spectrum value of the delta wave frequency band;

[0150] S45: Calculate the DTABR of the left region and the right region respectively, where the left region is the left forebrain and left midbrain region, and the right region is the right forebrain and right midbrain region;

[0151] S46: Statistical alpha rhythm, apical waves, K-complex waves, sleep spindles, diffuse slow wave activity, periodic transitions, rapid eye movement periods, and the emergence of epileptic discharge waves.

[0152] By setting the impedance of the glass microelectrode, the stability and reliability of signal acquisition are ensured, noise interference is reduced, and signal quality is improved. The EEG signal is collected with 5 seconds as the basic time unit, which can not only ensure the collection of sufficient information, but also improve the collection efficiency and reduce the experimental time. Recording the power spectrum values ​​of four frequency bands, including alpha waves, beta waves, theta waves and delta waves, can more comprehensively reflect the state of zebrafish EEG activity. Calculating the relative power ratio (DTABR) can more accurately reflect the activity differences between different brain regions and provide a more reliable indicator for studying brain function. Calculating the DTABR of the left and right regions separately can more finely analyze the activity differences between the left and right brains, providing a more accurate basis for studying the left and right lateralization of brain function. Statistics of various EEG waveforms, including alpha rhythm, top wave, K-complex wave, sleep spindle wave, diffuse slow wave activity, periodic transition, rapid eye movement period, epileptic discharge wave, can more comprehensively analyze the brain function states of zebrafish such as sleep, wakefulness, learning, and memory.

[0153] In any of the above technical solutions, the specific method of recording the power spectrum values ​​of the alpha wave band, the beta wave band, theta wave band and the delta wave band is as follows:

[0154] S431: Use adaptive filters to dynamically remove noise sources,

[0155] S432: Use an adaptive filtering algorithm to achieve noise elimination by minimizing the error between the noise and the main signal, where the filter update formula is as follows:

[0156]

[0157] Where w(t) is the filter coefficient, d(t) is the noise estimate of the auxiliary channel, is the signal after denoising, and n(t) is the signal before denoising;

[0158] S433: After obtaining the denoised signal, perform frequency domain analysis and decompose the signal using continuous wavelet transform;

[0159] S434: Record the processed data to obtain power spectrum values ​​of the alpha wave band, the beta wave band, theta wave band, and the delta wave band, respectively.

[0160] By using adaptive filters to dynamically remove noise sources, such as environmental noise, electrode noise, etc., the signal-to-noise ratio of EEG signals is improved, making subsequent signal analysis more accurate and reliable. Based on minimizing the error between noise and the main signal, the use of adaptive filtering algorithms can effectively remove noise while retaining the useful information of the original signal, thereby improving the quality of the signal. By using continuous wavelet transform to decompose the signal, the information of the signal at different times and frequencies can be obtained. Compared with the traditional Fourier transform, the wavelet transform has better time-frequency resolution and can more accurately analyze the frequency components of the EEG signal. Selecting a suitable mother wavelet can better adapt to the characteristics of EEG signals and improve the accuracy of signal decomposition. By recording the signal after wavelet transform in frequency bands, the power spectrum values ​​of the alpha, beta, theta and delta bands can be obtained respectively, providing accurate data for subsequent quantitative analysis. Through adaptive filtering and continuous wavelet transform, high-quality EEG signals can be obtained, which reduces the experimental errors caused by poor signal quality and improves the reliability of experimental results.

[0161] In any of the above technical solutions,

[0162] S451: Divide the signal collection area into a left area and a right area, wherein: the left area includes the left forebrain and the left midbrain, and the right area includes the right forebrain and the right midbrain;

[0163] S452: standardize the signal so that the signal in each region has a zero mean and a unit standard deviation;

[0164] S453: Analyze the frequency spectrum of the signal by using wavelet transform or fast Fourier transform, and calculate the power spectrum value of each frequency band;

[0165] S454: To calculate the DTABR value of the left region, we sum the frequency band power spectrum values ​​of the left forebrain and left midbrain respectively and substitute them into the DTABR formula:

[0166]

[0167] Among them, P α，left The sum of the power spectrum values ​​of the alpha wave frequency band of the left forebrain and left midbrain, P β，left The sum of the power spectrum values ​​of the beta wave frequency bands in the left forebrain and left midbrain, P θ，left P is the sum of the power spectrum values ​​of the theta wave frequency band of the left forebrain and left midbrain, δ，left Sum the band power spectrum values ​​of the delta wave band of the left forebrain and left midbrain;

[0168] S455: Calculate the DTABR value of the right area, sum the frequency band power spectrum values ​​of the right forebrain and right midbrain respectively, and substitute them into the DTABR formula:

[0169]

[0170] Among them, P α，right The sum of the power spectrum values ​​of the alpha wave frequency band of the right forebrain and right midbrain, P β，right The sum of the power spectrum values ​​of the beta wave frequency bands in the right forebrain and right midbrain, P θ，right is the sum of the power spectrum values ​​of the theta wave frequency band in the right forebrain and right midbrain, P δ，right Sum the band power spectrum values ​​of the delta wave band of the right forebrain and right midbrain;

[0171] S456: Automatically adjust the processing parameters, filter cutoff frequency and signal sampling rate of each frequency band according to real-time data and signal characteristics.

[0172] By clearly dividing the signal acquisition area into the left area (left forebrain and left midbrain) and the right area (right forebrain and right midbrain), a clear regional definition is provided for the subsequent DTABR value calculation. Standardizing the signal to have a zero mean and unit standard deviation can eliminate the influence of the difference in signal amplitude in different regions and ensure the accuracy of DTABR value calculation. The power spectrum values ​​of the frequency bands of the left forebrain and left midbrain are summed up, and the power spectrum values ​​of the frequency bands of the right forebrain and right midbrain are summed up, and then substituted into the DTABR formula, which can more accurately reflect the overall EEG activity state of the left and right regions. Using wavelet transform or fast Fourier transform to analyze the spectrum of the signal, the appropriate analysis method can be selected according to different signal characteristics, which improves the flexibility of signal analysis. Automatically adjusting the processing parameters, filter cutoff frequency and signal sampling rate of each frequency band according to real-time data and signal characteristics can optimize the signal processing process and improve the adaptability of signal analysis.

[0173] A zebrafish brain electrophysiological signal acquisition and quantitative analysis device is used to execute the above-mentioned zebrafish brain electrophysiological signal acquisition and quantitative analysis method.

[0174] The second aspect of the present application discloses a zebrafish brain electrophysiological signal acquisition and quantitative analysis device, through which the various steps of the zebrafish brain electrophysiological signal acquisition and quantitative analysis method are integrated into one system, realizing the integration and automation of zebrafish brain electrophysiological signal acquisition and quantitative analysis, and improving the experimental efficiency and the reliability of the experimental results. The device can realize the functions of accurate electrode positioning, physiological electrode internal fluid, adaptive control, accurate signal processing, etc. involved in the zebrafish brain electrophysiological signal acquisition and quantitative analysis method, ensuring the high precision and high reliability of brain electrical signal acquisition and analysis. Accurate control and signal processing can obtain high-quality brain electrical signals, providing more reliable data for subsequent analysis. Integrating complex experimental processes into the device makes the operation simpler and easier, and is convenient for researchers to use. Standardized design and operating procedures make the device easy to promote and apply, and can provide technical support for more researchers.

[0175] Example 2

[0176] The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals of the present invention comprises the following experimental steps:

[0177] 1. Animal selection: 3-month-old adult AB wild-type zebrafish weighing approximately 450 mg.

[0178] 2. Grouping: PTZ group and normal saline group.

[0179] 3. Experimental process:

[0180] 3.1 Construction of zebrafish brain electrophysiological acquisition system

[0181] The electrode solution is prepared and used immediately. The liquid formula is 130mM potassium chloride, 15mM sodium chloride, 0.5mM calcium chloride, 1mM magnesium chloride, 1mM Mg-ATP, and 10mM EGTA. The electrode solution is adjusted to pH 7.4 with sodium bicarbonate and filtered through a 0.22μm microporous membrane.

[0182] Glass microelectrodes are pulled from thin-walled borosilicate glass using a Sutter-P97 needle puller. The tip size of the glass microelectrode ranges from 0.2-0.4 μm with a taper of ~4 mm shank. The supplied glass microelectrode contains an electrode liquid. To avoid bubbles in the glass microelectrode, the electrode tip is immersed below the surface of the liquid during the filling process.

[0183] 3.2 Construction of zebrafish epilepsy model and acquisition of full-length electroencephalographic signals

[0184] Adult zebrafish aged 3 months were immersed in a mixture of ice and water at 0°C until they stopped moving. Each zebrafish was placed on a soft cotton pad placed in a 120 mm diameter Petri dish with a 45 mm slit. We used the tip of a cotton swab to lift the zebrafish head.

[0185] Microelectrodes are attached to the zebrafish head using a precise X, Y, Z axis manipulator and camera to record EEG data. The microelectrodes are positioned in specific brain regions. Figure 2 As shown in the figure, the positions of the electrodes in each brain region are: Fp1-left front, C3-left back, Fp2-right front, C4-right back, Re-reference electrode. Figure 3 As shown, Figure 3 This is the electroencephalogram before and after the microelectrode needle reaches the surface of the zebrafish brain.

[0186] The zebrafish were injected intramuscularly with PTZ (pentylenetetrazole, PTZ) solution, the injection dose was 350mg / kg, the volume was 10μL. The control group was injected intramuscularly with an equal amount of saline. Figure 4 As shown, Figure 4 This is the electroencephalogram of zebrafish after intramuscular injection of PTZ; Figure 5 As shown, Figure 5 Electroencephalogram of zebrafish after intramuscular injection of saline.

[0187] 4. Quantitative analysis: The electrode impedance was set to: 30 Hz high frequency, 1 Hz low frequency, 100 μV / mm sensitivity, 30 mm / s time resolution. The sampling time of the EEG signal was 5 seconds per unit. An uninterrupted EEG examination of 5-20 min was required. The power spectrum values ​​of four frequency bands were recorded: alpha (8.0-12.9 Hz), beta (13-30 Hz), theta (4-7.9 Hz) and delta (1.5-3.9 Hz). The relative power ratio ([theta+delta] / [alpha+beta], DTABR) was calculated. The DTABR of the left area (F1-F2) and the right area (C3-C4) were calculated respectively. The EEG quantitative analysis data are shown in Table 1.

[0188] Table 1 EEG quantitative analysis data

[0189]

[0190] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals, characterized in that: The steps include: S1: Construct a zebrafish brain electrophysiological acquisition system: fill the glass microelectrode with electrode liquid and fix it on a stereotaxic instrument, with the electrode needle of the glass microelectrode submerged below the liquid level of the microelectrode liquid; S2: Fixation of zebrafish: Multiple adult zebrafish were immersed in a mixture of ice and water at 0°C until they stopped moving, and multiple zebrafish were placed in the cracks of moistened cotton pads; S3: Collection of electroencephalographic signals: The microelectrode is positioned in a specific brain area, the reference electrode is positioned in the tail of the zebrafish, and an uninterrupted electroencephalogram examination is performed for 5-20 minutes; S4: Quantitative analysis: The EEG signals were sampled with 5 seconds as a basic time unit, and the power spectrum values ​​of four frequency bands were recorded. The four frequency bands were alpha wave band, beta wave band, theta wave band and delta wave band.

2. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 1, characterized in that: The glass microelectrode is prepared by the following method: A glass microelectrode tip with a tip diameter of 0.2-0.4 μm is pulled by a needle puller; Control the drawing parameters during the working process of the needle puller, and the drawing parameters include pressure, heating temperature, drawing force, drawing speed and delay time.

3. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 1, characterized in that: The preparation method of the electrode internal solution is as follows: Prepare the electrode internal solution according to 130mM potassium chloride, 15mM sodium chloride, 0.5mM calcium chloride, 1mM magnesium chloride, 1mM Mg-ATP, and 10mM EGTA; Adjust the electrode solution to a pH of 7.4 with sodium bicarbonate; Filter through a 0.22 μm microporous membrane.

4. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 1, characterized in that: The steps for fixing zebrafish are as follows: Soak multiple adult zebrafish in a mixture of ice water at 0°C until they stop moving. The specific steps for placing multiple zebrafish in the cracks of a moist cotton pad are as follows: S21: 3-month-old adult zebrafish were immersed in a mixture of ice and water at 0°C until they stopped moving; S22: All zebrafish were placed on a cotton pad placed in a 120 mm diameter Petri dish with a 45 mm slit inside the pad; S23: Use the cotton swab tip to lift the zebrafish's head.

5. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 1, characterized in that: The steps of collecting electroencephalographic physiological signals are as follows: positioning the microelectrode in a specific brain area, positioning the reference electrode in the tail of the zebrafish, and performing an uninterrupted electroencephalogram examination for 5-20 minutes: S31: Using a precise X, Y, and Z axis manipulator, the microelectrodes were positioned in specific brain regions, the left forebrain, right forebrain, left midbrain, and right midbrain, with the reference electrode positioned in the caudal region; S32: Perform an uninterrupted EEG examination for 5-20 minutes.

6. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 5, characterized in that: S311: The positioning of the glass microelectrode is represented by the following state space model: y(t)=Cx(t)+Du(t) Among them, x(t) is the system state vector, which contains information such as position and speed, u(t) is the input control signal, that is, the driving force of the motor, and y(t) is the output signal. represents the actual position of the electrode, A, B, C, and D are the state matrix and output matrix of the system, describing the dynamic characteristics of the system; S312: Error modeling and adaptive control are performed. The error model is expressed by the following formula: e(t)=p target -p(t) Among them, p target is the target position, p(t) is the current electrode position, and e(t) is the position error; S313: In order to ensure that the glass microelectrode accurately reaches a specific brain area, the motion trajectory of the glass microelectrode is adjusted through precise path planning, and its control expression is as follows: Among them, K p , K d , K i are proportional, differential, and integral gains, which adjust the control performance, e(τ) is the error function, θ(t) is the adaptive adjustment term, and the control parameters are dynamically adjusted through the adaptive mechanism to compensate for the system uncertainty, and e(t) is the position error; S314: In order to ensure that the glass microelectrode accurately reaches a specific brain area, the motion trajectory of the glass microelectrode is adjusted through precise path planning. The function of the electrode path is: in: represents the energy of motion (the square of the velocity of the path); ||e(t)|| 2 is the penalty term for position error; λ is a tuning parameter that balances speed and error. S315: adjusting the electrode position according to the real-time EEG signal to avoid signal quality degradation; S316: Optimize signal quality through gain adjustment, the expression is: u(t)=-K(y(t)-y target (t))+S(t) in: y(t) is the current EEG signal, y target (t) is the expected signal; K is the gain matrix; S(t) is the signal quality feedback, which can be obtained through power spectrum analysis.

7. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 5, characterized in that: The quantitative analysis: sampling of EEG signals, using 5 seconds as a basic time unit for signal acquisition, and recording power spectrum values ​​of four frequency bands: alpha wave, beta wave, theta wave and delta wave. The specific steps are as follows: S41: The glass microelectrode impedance was set to: 30 Hz high frequency, 1 Hz low frequency, 100 μV / mm sensitivity and 30 mm / s time resolution; S42: EEG signals are collected with 5 seconds as a basic time unit; S43: record the power spectrum values ​​of the alpha wave band, beta wave band, theta wave band and delta wave band, where the alpha wave is 8.0-12.9 Hz, the beta wave is 13-30 Hz, the theta wave is 4-7.9 Hz, and the delta wave is 1.5-3.9 Hz; S43: Calculate the relative power ratio. The calculation formula of the relative power ratio is as follows: [(theta+delta)] / [(alpha+beta)]ratio, DTABR) Among them, alpha is the power spectrum value of the alpha wave frequency band, beta is the power spectrum value of the beta wave frequency band, theta is the power spectrum value of the theta wave frequency band, and delta is the power spectrum value of the delta wave frequency band; S45: Calculate the DTABR of the left region and the right region respectively, where the left region is the left forebrain and left midbrain region, and the right region is the right forebrain and right midbrain region; S46: Statistical alpha rhythm, apical waves, K-complex waves, sleep spindles, diffuse slow wave activity, periodic transitions, rapid eye movement periods, and the emergence of epileptic discharge waves.

8. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 7, characterized in that: The specific method of recording the power spectrum values ​​of the alpha wave frequency band, the beta wave frequency band, theta wave frequency band and the delta wave frequency band is as follows: S431: Use adaptive filters to dynamically remove noise sources, S432: Use an adaptive filtering algorithm to achieve noise elimination by minimizing the error between the noise and the main signal, where the filter update formula is as follows: Where w(t) is the filter coefficient, d(t) is the noise estimate of the auxiliary channel, is the signal after denoising, and n(t) is the signal before denoising; S433: After obtaining the denoised signal, perform frequency domain analysis and decompose the signal using continuous wavelet transform; S434: Record the processed data to obtain power spectrum values ​​of the alpha wave band, the beta wave band, theta wave band, and the delta wave band, respectively.

9. The method for collecting and quantitatively analyzing zebrafish brain electrophysiological signals according to claim 7, characterized in that: S451: Divide the signal collection area into a left area and a right area, wherein: the left area includes the left forebrain and the left midbrain, and the right area includes the right forebrain and the right midbrain; S452: standardize the signal so that the signal in each region has a zero mean and a unit standard deviation; S453: Analyze the frequency spectrum of the signal by using wavelet transform or fast Fourier transform, and calculate the power spectrum value of each frequency band; S454: To calculate the DTABR value of the left region, we sum the frequency band power spectrum values ​​of the left forebrain and left midbrain respectively and substitute them into the DTABR formula: Among them, P α，left The sum of the power spectrum values ​​of the alpha wave frequency band of the left forebrain and left midbrain, P β，left The sum of the power spectrum values ​​of the beta wave frequency bands in the left forebrain and left midbrain, P θ，left P is the sum of the power spectrum values ​​of the theta wave frequency band of the left forebrain and left midbrain, δ，left Sum the band power spectrum values ​​of the delta wave band of the left forebrain and left midbrain; S455: Calculate the DTABR value of the right area, sum the frequency band power spectrum values ​​of the right forebrain and right midbrain respectively, and substitute them into the DTABR formula: Among them, P α，right The sum of the power spectrum values ​​of the alpha wave frequency band of the right forebrain and right midbrain, P β，right The sum of the power spectrum values ​​of the beta wave frequency bands in the right forebrain and right midbrain, P θ，right is the sum of the power spectrum values ​​of the theta wave frequency band in the right forebrain and right midbrain, P δ，right Sum the band power spectrum values ​​of the delta wave band of the right forebrain and right midbrain; S456: Automatically adjust the processing parameters, filter cutoff frequency and signal sampling rate of each frequency band according to real-time data and signal characteristics.

10. A device for collecting and quantitatively analyzing zebrafish brain electrophysiological signals, characterized in that: The zebrafish brain electrophysiological signal acquisition and quantitative analysis device is used to execute the zebrafish brain electrophysiological signal acquisition and quantitative analysis method according to any one of claims 1 to 9.

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