Implanted brain-computer interface animal experiment device and method in Mars gravity simulation cabin

Abstract

The invention discloses an implantable brain-computer interface animal experiment method and device in a Mars gravity simulation cabin. The device comprises an implantable brain-computer interface module, an animal behavior monitoring module, an environment measurement module and a data evaluation module. The implantable brain-computer interface module is used for collecting and transmitting a neural signal set of an experimental animal brain; the animal behavior monitoring module is used for collecting and analyzing behavior activities of experimental animals to obtain an animal behavior data set; the environment measurement module is used for measuring and controlling gravity, temperature, humidity and illumination in the Mars gravity simulation cabin to obtain an environment measurement information set; the data evaluation module is respectively connected with the implantable brain-computer interface module, the animal behavior monitoring module and the environment measurement module, and is used for carrying out comprehensive evaluation processing on the collected environment measurement information set, the neural signal set and the animal behavior data set to obtain an evaluation result information set.

Description

Technical Field

[0001] The present invention relates to the field of aerospace medicine and bioengineering technology, and in particular to an implantable brain-computer interface animal experiment device and method in a Mars gravity simulation cabin. Background Art

[0002] As human exploration of Mars continues to deepen, it is crucial to understand the impact of the Martian environment on organisms. The gravity on Mars is about 38% of that on Earth. This low-gravity environment may have a significant impact on the physiological and neural functions of organisms. As an emerging neuroscience research method, brain-computer interface technology provides a powerful tool for exploring the changes in neural adaptability and cognitive function of organisms in special environments. Mars presents unique environmental characteristics, which brings unique challenges to organisms. However, there are currently no animal experimental devices and methods specifically for implantable brain-computer interfaces in the Martian gravity simulation environment. Summary of the invention

[0003] The present invention mainly solves the problem of studying and realizing an implantable brain-computer interface animal experiment device and method specifically for a Mars gravity simulation environment. The present invention discloses an implantable brain-computer interface animal experiment device and method in a Mars gravity simulation cabin.

[0004] In a first aspect of an embodiment of the present invention, an implantable brain-computer interface animal experiment device in a Mars gravity simulation cabin is disclosed, comprising: an implantable brain-computer interface module, an animal behavior monitoring module, an environment measurement module, and a data evaluation module;

[0005] The implantable brain-computer interface module is used to collect and transmit a collection of neural signals from the brain of an experimental animal;

[0006] The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set;

[0007] The environmental measurement module is used to measure and control the gravity, temperature, humidity and light in the Mars gravity simulation cabin to obtain an environmental measurement information set;

[0008] The data evaluation module is connected to the implantable brain-computer interface module, the animal behavior monitoring module, and the environmental measurement module respectively, and is used to perform comprehensive evaluation and processing on the collected environmental measurement information set, neural signal set, and animal behavior data set to obtain an evaluation result information set.

[0009] The implantable brain-computer interface module includes an implantable EEG microsensor, a signal transmission line and an external receiving device; the implantable EEG microsensor is arranged inside the brain of an experimental animal and is used to collect a set of neural signals from the brain of the experimental animal; the signal transmission line is connected to the implantable EEG microsensor and the external receiving device respectively, and is used to send the neural signal set to the external receiving device.

[0010] The animal behavior monitoring module includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to acquire behavior images of experimental animals; the image analysis unit is used to extract key parts of the behavior images of the experimental animals to obtain a motion feature information set; the motion feature information set includes motion trajectory information of key points.

[0011] The environment measurement module includes a control unit, a gravity sensor, a humidity sensor, a temperature sensor, a illuminometer, a humidifier, an electric heater and a light-emitting unit;

[0012] The gravity sensor, humidity sensor, temperature sensor and illuminometer are used to measure the gravity sequence, humidity sequence, temperature sequence and light sequence in the Mars gravity simulation cabin respectively;

[0013] The control unit is connected to the humidifier, the electric heater and the light-emitting unit respectively, and is used to control the humidifier, the electric heater and the light-emitting unit respectively according to the received instructions.

[0014] The data evaluation module performs comprehensive evaluation processing on the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an evaluation result information set, including:

[0015] Preprocessing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain a preprocessed information set;

[0016] Performing neural assessment processing on the environmental measurement information set and the neural signal set in the preprocessing information set to obtain neural assessment result information;

[0017] Performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information;

[0018] An assessment result information set is constructed using the neural assessment result information and the behavioral assessment result information.

[0019] The performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information includes:

[0020] Obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0021] Subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0022] Subtracting the neural signal set from the EEG standard value to obtain a difference neural signal sequence;

[0023] The difference neural signal sequence and all environmental difference data sequences are subjected to fusion evaluation calculation processing to obtain neural evaluation result information.

[0024] In a second aspect of the embodiment of the present invention, an implantable brain-computer interface animal experiment method in a Mars gravity simulation cabin is disclosed, which is implemented using the implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin, and includes:

[0025] S1, using the implantable brain-computer interface module to collect and transmit a set of neural signals from the brain of the experimental animal;

[0026] S2, using the animal behavior monitoring module to collect and analyze the behavior activities of the experimental animals to obtain an animal behavior data set;

[0027] S3, using the environmental measurement module to measure and control the gravity, temperature, humidity and light in the Mars gravity simulation cabin to obtain an environmental measurement information set;

[0028] S4, using the data evaluation module, comprehensively evaluating and processing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an evaluation result information set.

[0029] The collected environmental measurement information set, neural signal set and animal behavior data set are subjected to comprehensive evaluation processing to obtain an evaluation result information set, including:

[0030] S41, preprocessing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain a preprocessed information set;

[0031] S42, performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information;

[0032] S43, performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information;

[0033] S44, constructing an assessment result information set using the neural assessment result information and the behavioral assessment result information.

[0034] The performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information includes:

[0035] S421, obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0036] S422, subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0037] S423, performing subtraction processing on the neural signal set and the EEG standard value to obtain a difference neural signal sequence;

[0038] S424, performing fusion evaluation calculation processing on the difference neural signal sequence and all environmental difference data sequences to obtain neural evaluation result information.

[0039] The performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information includes:

[0040] S431, obtaining a set of standard environmental measurement values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0041] S432, subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0042] S433, performing environmental assessment processing on all environmental difference data sequences to obtain an environmental assessment value set;

[0043] S434, performing time-frequency domain statistical processing on the animal behavior data set to obtain a time-frequency statistical quantity set;

[0044] S435, performing fusion behavior evaluation calculation processing on the time-frequency statistics set and the environmental evaluation value set to obtain behavior evaluation result information.

[0045] The beneficial effects of the present invention are:

[0046] This invention realizes for the first time the animal experiment of implantable brain-computer interface in a Martian gravity simulation environment, providing direct experimental evidence for studying the impact of the Martian environment on the neural function of organisms.

[0047] The device of the present invention integrates multiple technical means such as gravity simulation, brain-computer interface and behavioral monitoring, and can comprehensively and systematically evaluate the physiological and neural adaptive changes of animals in the Martian gravity environment.

[0048] After obtaining the environmental measurement information set, the neural signal set and the animal behavior data set, the present invention uses the above information to perform fusion evaluation processing to obtain an evaluation result information set. When performing fusion evaluation processing, the collected environmental measurement information set, neural signal set and animal behavior data set are first preprocessed to eliminate irrelevant data and data that do not match the data pattern; neural evaluation result information is obtained by performing neural evaluation processing on the environmental measurement information set and the neural signal set in the preprocessed information set; behavioral evaluation processing is performed on the environmental measurement information set and the animal behavior data set in the preprocessed information set to obtain behavioral evaluation result information. By using different evaluation models for different types of data, the accuracy and efficiency of the evaluation results are ensured.

[0049] The experimental method of the present invention is scientific and reasonable, easy to operate, and provides reliable data, which provides important technical support and theoretical basis for subsequent aerospace medical research and Mars exploration missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 1 is a flow chart for implementing the method of the present invention;

[0051] Figure 2 It is a schematic diagram of the composition of the device of the present invention. DETAILED DESCRIPTION

[0052] In order to better understand the content of the present invention, an embodiment is given here.

[0053] Figure 1 It is a flow chart for implementing the method of the present invention. Figure 2 It is a schematic diagram of the composition of the device of the present invention.

[0054] In a first aspect of an embodiment of the present invention, an implantable brain-computer interface animal experiment device in a Mars gravity simulation cabin is disclosed, comprising: an implantable brain-computer interface module, an animal behavior monitoring module, an environment measurement module, and a data evaluation module;

[0055] The implantable brain-computer interface module is used to collect and transmit a collection of neural signals from the brain of an experimental animal;

[0056] The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set;

[0057] The environmental measurement module is used to measure and control the gravity, temperature, humidity and light in the Mars gravity simulation cabin to obtain an environmental measurement information set;

[0058] The data evaluation module is connected to the implantable brain-computer interface module, the animal behavior monitoring module, and the environmental measurement module respectively, and is used to perform comprehensive evaluation and processing on the collected environmental measurement information set, neural signal set, and animal behavior data set to obtain an evaluation result information set.

[0059] The implantable brain-computer interface module includes an implantable EEG microsensor, a signal transmission line and an external receiving device; the implantable EEG microsensor is arranged inside the brain of an experimental animal and is used to collect a set of neural signals from the brain of the experimental animal; the signal transmission line is connected to the implantable EEG microsensor and the external receiving device respectively, and is used to send the neural signal set to the external receiving device.

[0060] The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set;

[0061] The animal behavior monitoring module includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to acquire behavior images of experimental animals; the image analysis unit is used to extract key parts of the behavior images of the experimental animals to obtain a motion feature information set; the motion feature information set includes motion trajectory information of key points.

[0062] The environment measurement module includes a control unit, a gravity sensor, a humidity sensor, a temperature sensor, a illuminometer, a humidifier, an electric heater and a light-emitting unit;

[0063] The gravity sensor, humidity sensor, temperature sensor and illuminometer are used to measure the gravity sequence, humidity sequence, temperature sequence and light sequence in the Mars gravity simulation cabin respectively;

[0064] The control unit is connected to the humidifier, the electric heater and the light-emitting unit respectively, and is used to control the humidifier, the electric heater and the light-emitting unit respectively according to the received instructions.

[0065] The data evaluation module performs comprehensive evaluation processing on the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an evaluation result information set, including:

[0066] Preprocessing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain a preprocessed information set;

[0067] Performing neural assessment processing on the environmental measurement information set and the neural signal set in the preprocessing information set to obtain neural assessment result information;

[0068] Performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information;

[0069] An assessment result information set is constructed using the neural assessment result information and the behavioral assessment result information.

[0070] The collected environmental measurement information set, neural signal set and animal behavior data set are preprocessed to obtain a preprocessed information set, including:

[0071] Performing data cleaning processing on various information sets to obtain a first information set;

[0072] Performing time registration processing on the first information set to obtain a second information set;

[0073] The second information set is subjected to pattern discrimination processing to obtain a preprocessed information set.

[0074] The time registration process is to unify different types of data to the same time reference; the time registration process can adopt the internal push / extrapolation method, the Lagrange three-point interpolation method, etc.;

[0075] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0076] The mode discrimination process comprises:

[0077] For each type of data attribute of the second information set, the data collection information of the data is used as a known independent variable, the data value of the data is used as a known dependent variable, and the curve to be approximated is constructed by using the known independent variable and the known dependent variable;

[0078] Performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial for the class data attribute;

[0079] Using the best consistent approximation polynomial, the known independent variable is calculated to obtain an approximate dependent variable;

[0080] Determine whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second information set; if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0081] Performing fusion processing on all the data after the execution mode discrimination of the second information set to obtain a preprocessing information set;

[0082] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0083] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0084] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0085] The performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information includes:

[0086] Obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0087] Subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0088] Subtracting the neural signal set from the EEG standard value to obtain a difference neural signal sequence;

[0089] The difference neural signal sequence and all environmental difference data sequences are subjected to fusion evaluation calculation processing to obtain neural evaluation result information.

[0090] The expression of the fusion evaluation calculation process is:

[0091]

[0092] Among them, A ijis the jth element of the environmental difference data sequence of the i-th category, z i For all elements A ij The i-th eigenvalue of the constructed matrix A is the element of the i-th row and j-th column of the matrix A. ij , the eigenvalue numbers of the matrix A are sorted from high to low, p is the neural assessment result information, is the mean of the i-th row of matrix A, A i ′ is the median value of the i-th row of matrix A, f k is the kth element of the difference neural signal sequence, m and n are the number of categories and the total number of elements of the environmental difference data sequence, respectively, and f max is the maximum element of the difference neural signal sequence, and r is the total number of elements in the difference neural signal sequence.

[0093] The performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information includes:

[0094] Obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0095] Subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0096] Perform environmental assessment processing on all environmental difference data sequences to obtain an environmental assessment value set;

[0097] Performing time-frequency domain statistical processing on the animal behavior data set to obtain a time-frequency statistical quantity set; the time-frequency statistical quantity set includes the mean, variance, harmonic mean and mobility value of the motion trajectory information sequence of each key point;

[0098] The harmonic mean is the mean of all harmonic frequencies of the FFT sequence of the motion trajectory information sequence;

[0099] The mobility value is the square root of the variance of the first-order derivative of the motion trajectory information sequence divided by the variance of the signal;

[0100] The time-frequency statistics set and the environmental assessment value set are subjected to fusion behavior assessment calculation processing to obtain behavior assessment result information.

[0101] The environmental evaluation process is performed on all environmental difference data sequences to obtain an environmental evaluation value set, including:

[0102] Perform similarity evaluation on all environmental difference data sequences to obtain environmental similarity evaluation values;

[0103] Perform environmental volatility assessment on all environmental difference data sequences to obtain environmental volatility assessment values;

[0104] An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

[0105] The environmental volatility assessment process is performed on all environmental difference data sequences to obtain an environmental volatility assessment value, including:

[0106] Using all the environmental difference data sequences, an environmental difference matrix is ​​constructed; the row vector of the environmental difference matrix is ​​the environmental difference data sequence;

[0107] Decomposing the environmental difference matrix to obtain a feature matrix;

[0108] The decomposition process is calculated as follows:

[0109] Y=UAV,

[0110] Among them, U is the left decomposition matrix, Y is the environmental difference matrix, A is the feature matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0111] Extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0112] Performing a first feature calculation process on the feature vector to obtain an environment similarity evaluation value;

[0113] The expression of the first feature calculation process is:

[0114]

[0115] Among them, t is the environmental similarity evaluation value, t i is the i-th item of the eigenvector, N is the number of elements contained in the eigenvector, is the mean of the eigenvector, t max is the maximum value of the eigenvector.

[0116] The calculation expression of the environmental volatility assessment process is:

[0117]

[0118] Among them, x ij is the jth element of the i-th type of environmental measurement information sequence in the environmental measurement information set in the preprocessing information set, is the mean of the i-th type of environmental measurement information sequence in the environmental measurement information set in the preprocessing information set, u is a preset calculation constant, which may be 0.5, b is an environmental volatility assessment value, M1 and M2 are the total number of categories of the environmental measurement information sequence and the number of elements contained in the environmental measurement information sequence, respectively.

[0119] The expression of the fusion behavior evaluation calculation process is:

[0120]

[0121] Among them, a ij is the jth element of the time-frequency statistics set of the motion trajectory information sequence of the i-th key point. The first to fourth elements of the time-frequency statistics set are the mean, variance, harmonic mean and mobility value, respectively. N is the number of elements in a time-frequency statistics set, N=4, α i is the behavior evaluation result of the i-th key point, β is the behavior evaluation result information, α max is the maximum value of the behavior evaluation result of the key points, and y is the number of key points.

[0122] The device of the present invention also includes a Mars gravity simulation cabin for simulating the gravity environment of Mars, including a cabin, a gravity adjustment system and an environmental monitoring system. The cabin is made of high-strength materials and has good sealing and heat preservation properties. The gravity adjustment system generates a low gravity field similar to the gravity of Mars by means of rotation or centrifugation.

[0123] Specifically, the control unit controls the humidity value of the humidifier, the heating value of the electric heater and the luminous value of the luminous unit according to the received instructions.

[0124] The signal transmission line also includes an antenna, which is used to radiate the neural signal set to an external receiving device.

[0125] The key points include head, hands, and body;

[0126] The key part extraction can be implemented by using the OpenPose algorithm in OpenCV to extract the motion trajectory information of the key points of the experimental animal, and construct a motion feature information set using the motion trajectory information of all the key points.

[0127] In a second aspect of the embodiment of the present invention, an implantable brain-computer interface animal experiment method in a Mars gravity simulation cabin is disclosed, which is implemented using the implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin, and includes:

[0128] S1, using the implantable brain-computer interface module to collect and transmit a set of neural signals from the brain of the experimental animal;

[0129] S2, using the animal behavior monitoring module to collect and analyze the behavior activities of the experimental animals to obtain an animal behavior data set;

[0130] S3, using the environmental measurement module to measure and control the gravity, temperature, humidity and light in the Mars gravity simulation cabin to obtain an environmental measurement information set;

[0131] S4, using the data evaluation module, comprehensively evaluating and processing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an evaluation result information set.

[0132] The collected environmental measurement information set, neural signal set and animal behavior data set are subjected to comprehensive evaluation processing to obtain an evaluation result information set, including:

[0133] Preprocessing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain a preprocessed information set;

[0134] Performing neural assessment processing on the environmental measurement information set and the neural signal set in the preprocessing information set to obtain neural assessment result information;

[0135] Performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information;

[0136] An assessment result information set is constructed using the neural assessment result information and the behavioral assessment result information.

[0137] The collected environmental measurement information set, neural signal set and animal behavior data set are preprocessed to obtain a preprocessed information set, including:

[0138] Performing data cleaning processing on various information sets to obtain a first information set;

[0139] Performing time registration processing on the first information set to obtain a second information set;

[0140] The second information set is subjected to pattern discrimination processing to obtain a preprocessed information set.

[0141] The time registration process is to unify different types of data to the same time reference; the time registration process can adopt the internal push / extrapolation method, the Lagrange three-point interpolation method, etc.;

[0142] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0143] The mode discrimination process comprises:

[0144] For each type of data attribute of the second information set, the data collection information of the data is used as a known independent variable, the data value of the data is used as a known dependent variable, and the curve to be approximated is constructed by using the known independent variable and the known dependent variable;

[0145] Performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial for the class data attribute;

[0146] Using the best consistent approximation polynomial, the known independent variable is calculated to obtain an approximate dependent variable;

[0147] Determine whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second information set; if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0148] Performing fusion processing on all the data after the execution mode discrimination of the second information set to obtain a preprocessing information set;

[0149] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0150] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0151] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0152] The performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information includes:

[0153] Obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0154] Subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0155] Subtracting the neural signal set from the EEG standard value to obtain a difference neural signal sequence;

[0156] The difference neural signal sequence and all environmental difference data sequences are subjected to fusion evaluation calculation processing to obtain neural evaluation result information.

[0157] The expression of the fusion evaluation calculation process is:

[0158]

[0159] Among them, A ij is the jth element of the environmental difference data sequence of the i-th category, z i For all elements A ij The i-th eigenvalue of the constructed matrix A is the element of the i-th row and j-th column of the matrix A. ij , the eigenvalue numbers of the matrix A are sorted from high to low, p is the neural assessment result information, is the mean of the i-th row of matrix A, A i ′ is the median value of the i-th row of matrix A, f k is the kth element of the difference neural signal sequence, m and n are the number of categories and the total number of elements of the environmental difference data sequence, respectively, and f max is the maximum element of the difference neural signal sequence, and r is the total number of elements in the difference neural signal sequence.

[0160] The performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information includes:

[0161] Obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category;

[0162] Subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence;

[0163] Perform environmental assessment processing on all environmental difference data sequences to obtain an environmental assessment value set;

[0164] Performing time-frequency domain statistical processing on the animal behavior data set to obtain a time-frequency statistical quantity set; the time-frequency statistical quantity set includes the mean, variance, harmonic mean and mobility value of the motion trajectory information sequence of each key point;

[0165] The harmonic mean is the mean of all harmonic frequencies of the FFT sequence of the motion trajectory information sequence;

[0166] The mobility value is the square root of the variance of the first-order derivative of the motion trajectory information sequence divided by the variance of the signal;

[0167] The time-frequency statistics set and the environmental assessment value set are subjected to fusion behavior assessment calculation processing to obtain behavior assessment result information.

[0168] The environmental evaluation process is performed on all environmental difference data sequences to obtain an environmental evaluation value set, including:

[0169] Perform similarity evaluation on all environmental difference data sequences to obtain environmental similarity evaluation values;

[0170] Perform environmental volatility assessment on all environmental difference data sequences to obtain environmental volatility assessment values;

[0171] An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

[0172] The environmental volatility assessment process is performed on all environmental difference data sequences to obtain an environmental volatility assessment value, including:

[0173] Using all the environmental difference data sequences, an environmental difference matrix is ​​constructed; the row vector of the environmental difference matrix is ​​the environmental difference data sequence;

[0174] Decomposing the environmental difference matrix to obtain a feature matrix;

[0175] The decomposition process is calculated as follows:

[0176] Y=UAV,

[0177] Among them, U is the left decomposition matrix, Y is the environmental difference matrix, A is the feature matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0178] Extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0179] Performing a first feature calculation process on the feature vector to obtain an environment similarity evaluation value;

[0180] The expression of the first feature calculation process is:

[0181]

[0182] Among them, t is the environmental similarity evaluation value, t i is the i-th item of the eigenvector, N is the number of elements contained in the eigenvector, is the mean of the eigenvector, t max is the maximum value of the eigenvector.

[0183] The calculation expression of the environmental volatility assessment process is:

[0184]

[0185] Among them, x ij is the jth element of the i-th type of environmental measurement information sequence in the environmental measurement information set in the preprocessing information set, is the mean of the i-th type of environmental measurement information sequence in the environmental measurement information set in the preprocessing information set, u is a preset calculation constant, which may be 0.5, b is an environmental volatility assessment value, M1 and M2 are the total number of categories of the environmental measurement information sequence and the number of elements contained in the environmental measurement information sequence, respectively.

[0186] The expression of the fusion behavior evaluation calculation process is:

[0187]

[0188] Among them, a ij is the jth element of the time-frequency statistics set of the motion trajectory information sequence of the i-th key point. The first to fourth elements of the time-frequency statistics set are the mean, variance, harmonic mean and mobility value, respectively. N is the number of elements in a time-frequency statistics set, N=4, α i is the behavior evaluation result of the i-th key point, β is the behavior evaluation result information, α max is the maximum value of the behavior evaluation result of the key points, and y is the number of key points.

[0189] The third aspect of the present invention discloses an animal testing method using the device of the present invention, comprising:

[0190] 1. Selection and pretreatment of experimental animals:

[0191] Select healthy experimental animals, such as mice or rats. Adapt the animals to ensure that their physiological and psychological states are stable in the experimental environment. Before the experiment, the animals are anesthetized and the electrodes of the implantable brain-computer interface system are implanted into specific areas of the brain through minimally invasive surgery.

[0192] 2. Setting of Mars gravity simulation environment:

[0193] According to the experimental requirements, the gravity adjustment system is used to set the appropriate Martian gravity level. The parameters of the environmental monitoring system are adjusted to make the temperature, humidity, pressure and gas composition in the cabin meet the characteristics of the Martian environment.

[0194] 3. Monitoring and operation during the experiment:

[0195] After the animals enter the Mars gravity simulation cabin, the implanted brain-computer interface system and animal behavior monitoring system are activated to collect EEG signals and behavior data in real time. The collected data is monitored and analyzed in real time through the experimental control and data processing system. The experimental parameters and operating conditions are adjusted in a timely manner according to the progress of the experiment and the results of data analysis.

[0196] 4. Processing and analysis of experimental data:

[0197] After the experiment, the collected EEG and behavioral data were analyzed in depth; statistical methods and neuroscience theories were used to reveal the mechanism by which the Martian gravity environment affects the neural activity and behavioral performance of animal brains.

[0198] Specifically, this test method includes:

[0199] 1. Experimental Animal Preparation

[0200] Twenty healthy adult mice were selected and randomly divided into a control group and an experimental group, with 10 mice in each group.

[0201] The mice in the control group were raised under normal gravity, while the mice in the experimental group underwent implantable brain-computer interface electrode implantation surgery and recovered for one week after the surgery.

[0202] 2. Mars Gravity Simulation Cabin Setup

[0203] The mice in the experimental group were placed in a Mars gravity simulation chamber with the gravity level set to 38% of Earth's gravity.

[0204] Adjust the cabin environmental parameters, control the temperature at 22-25℃, maintain the humidity at 40%-60%, and simulate the Martian atmosphere with the gas composition.

[0205] 3. Experimental Procedure

[0206] After the mice entered the cabin, the implanted brain-computer interface system and animal behavior monitoring system were activated to continuously record EEG signals and behavior data for 24 hours. During this period, the mice's diet, drinking water and activities were observed to ensure that their vital signs were normal.

[0207] 4. Experimental Data Processing and Analysis

[0208] The collected EEG data were subjected to spectrum analysis, time domain analysis and coherence analysis to compare the differences in EEG activities between the control group and the experimental group of mice in different frequency bands.

[0209] Using behavioral analysis software, we conducted quantitative analysis on behavioral data such as the mice's movement trajectory, activity time, and spatial distribution, and evaluated the impact of the Martian gravity environment on the mice's behavioral patterns.

[0210] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. An implantable brain-computer interface animal experiment device in a Mars gravity simulation cabin, characterized in that: include: Implantable brain-computer interface module, animal behavior monitoring module, environmental measurement module, data evaluation module; The implantable brain-computer interface module is used to collect and transmit a collection of neural signals from the brain of an experimental animal; The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set; The environmental measurement module is used to measure and control the gravity, temperature, humidity and light in the Mars gravity simulation cabin to obtain an environmental measurement information set; The data evaluation module is connected to the implantable brain-computer interface module, the animal behavior monitoring module, and the environmental measurement module respectively, and is used to perform comprehensive evaluation and processing on the collected environmental measurement information set, neural signal set, and animal behavior data set to obtain an evaluation result information set.

2. The implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin as claimed in claim 1, characterized in that: The implantable brain-computer interface module includes an implantable EEG microsensor, a signal transmission line and an external receiving device; the implantable EEG microsensor is arranged inside the brain of an experimental animal and is used to collect a set of neural signals from the brain of the experimental animal; the signal transmission line is connected to the implantable EEG microsensor and the external receiving device respectively, and is used to send the neural signal set to the external receiving device.

3. The implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin as claimed in claim 1, characterized in that: The animal behavior monitoring module includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to acquire behavior images of experimental animals; the image analysis unit is used to extract key parts of the behavior images of the experimental animals to obtain a motion feature information set; the motion feature information set includes motion trajectory information of key points.

4. The implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin as claimed in claim 1, characterized in that: The environment measurement module includes a control unit, a gravity sensor, a humidity sensor, a temperature sensor, a illuminometer, a humidifier, an electric heater and a light-emitting unit; The gravity sensor, humidity sensor, temperature sensor and illuminometer are used to measure the gravity sequence, humidity sequence, temperature sequence and light sequence in the Mars gravity simulation cabin respectively; The control unit is connected to the humidifier, the electric heater and the light-emitting unit respectively, and is used to control the humidifier, the electric heater and the light-emitting unit respectively according to the received instructions.

5. The implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin as claimed in claim 1, characterized in that: The data evaluation module performs comprehensive evaluation processing on the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an evaluation result information set, including: Preprocessing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain a preprocessed information set; Performing neural assessment processing on the environmental measurement information set and the neural signal set in the preprocessing information set to obtain neural assessment result information; Performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information; An assessment result information set is constructed using the neural assessment result information and the behavioral assessment result information.

6. The implantable brain-computer interface animal experiment device in the Mars gravity simulation cabin as claimed in claim 1, characterized in that: The performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information includes: Obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category; Subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence; Subtracting the neural signal set from the EEG standard value to obtain a difference neural signal sequence; The difference neural signal sequence and all environmental difference data sequences are subjected to fusion evaluation calculation processing to obtain neural evaluation result information.

7. An implantable brain-computer interface animal experiment method in a Mars gravity simulation cabin, implemented using the implantable brain-computer interface animal experiment device in a Mars gravity simulation cabin as claimed in any one of claims 1 to 6, characterized in that: include: S1, using the implantable brain-computer interface module to collect and transmit a set of neural signals from the brain of the experimental animal; S2, using the animal behavior monitoring module to collect and analyze the behavior activities of the experimental animals to obtain an animal behavior data set; S3, using the environmental measurement module to measure and control the gravity, temperature, humidity and light in the Mars gravity simulation cabin to obtain an environmental measurement information set; S4, using the data evaluation module, comprehensively evaluating and processing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an evaluation result information set.

8. The implantable brain-computer interface animal experiment method in a Mars gravity simulation cabin as claimed in claim 7, characterized in that: The collected environmental measurement information set, neural signal set and animal behavior data set are subjected to comprehensive evaluation processing to obtain an evaluation result information set, including: S41, preprocessing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain a preprocessed information set; S42, performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information; S43, performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information; S44, constructing an assessment result information set using the neural assessment result information and the behavioral assessment result information.

9. The implantable brain-computer interface animal experiment method in a Mars gravity simulation cabin as claimed in claim 8, characterized in that: The performing neural assessment processing on the environmental measurement information set and the neural signal set in the pre-processing information set to obtain neural assessment result information includes: S421, obtaining a set of standard environmental measurement values ​​and EEG standard values; the set of standard environmental measurement values ​​includes standard environmental data of each category; S422, subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence; S423, performing subtraction processing on the neural signal set and the EEG standard value to obtain a difference neural signal sequence; S424, performing fusion evaluation calculation processing on the difference neural signal sequence and all environmental difference data sequences to obtain neural evaluation result information.

10. The implantable brain-computer interface animal experiment method in a Mars gravity simulation cabin as claimed in claim 8, characterized in that: The performing behavior evaluation processing on the environmental measurement information set and the animal behavior data set in the pre-processing information set to obtain behavior evaluation result information includes: S431, obtaining a set of standard environmental measurement values; the set of standard environmental measurement values ​​includes standard environmental data of each category; S432, subtracting each type of environmental measurement data sequence in the environmental measurement information set from the corresponding standard environmental data to obtain a corresponding environmental difference data sequence; S433, performing environmental assessment processing on all environmental difference data sequences to obtain an environmental assessment value set; S434, performing time-frequency domain statistical processing on the animal behavior data set to obtain a time-frequency statistical quantity set; S435, performing fusion behavior evaluation calculation processing on the time-frequency statistics set and the environmental evaluation value set to obtain behavior evaluation result information.

Images