Implanted animal brain-computer interface device and method for gravity simulation cabin of lunar base

Abstract

The invention discloses an implantable animal brain-computer interface device and method for a gravity simulation cabin of a lunar base. The device comprises an implantable brain-computer interface module, an animal behavior monitoring module, an environment control module and a data processing and analyzing 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 control module is used for measuring and controlling gravity, temperature, humidity and illumination in the moon gravity simulation cabin to obtain an environment measurement information set; and the data processing and analysis module is used for processing and analyzing the collected environment measurement information set, the neural signal set and the animal behavior data set to obtain an analysis 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 animal brain-computer interface device and method for a gravity simulation cabin of a lunar base. Background Art

[0002] As human exploration of the moon continues to deepen, establishing a lunar base has become an important goal for future space development. However, the lunar gravity environment is very different from that of the Earth, which poses many challenges to biological modules that survive on the moon for a long time. In order to better understand and respond to these challenges, it is necessary to conduct simulation experiments on biological survival on the ground to explore the relevant physiological indicators of organisms in a low-gravity environment.

[0003] As an emerging neuroscience research method, brain-computer interface technology has great application potential. By directly connecting brain neural signals with external devices, real-time monitoring and regulation of biological behavior and physiological state can be achieved. However, there is currently no relevant device and method for conducting animal experiments on implantable brain-computer interfaces in a lunar gravity simulation environment. Summary of the invention

[0004] The present invention mainly solves the problem of designing related devices for conducting implantable brain-computer interface animal experiments in a lunar gravity simulation environment. The present invention discloses an implantable animal brain-computer interface device and method for a lunar base gravity simulation cabin.

[0005] In a first aspect of an embodiment of the present invention, an implantable animal brain-computer interface device for a lunar base gravity simulation cabin is disclosed, characterized in that it comprises: an implantable brain-computer interface module, an animal behavior monitoring module, an environment control module, and a data processing and analysis module;

[0006] The implantable brain-computer interface module is used to collect and transmit a collection of neural signals from the brain of an experimental animal;

[0007] The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set;

[0008] The environmental control module is used to measure and control the gravity, temperature, humidity and light in the lunar gravity simulation cabin to obtain an environmental measurement information set;

[0009] The data processing and analysis module is connected to the implantable brain-computer interface module, the animal behavior monitoring module, and the environmental control module respectively, and is used to process and analyze the collected environmental measurement information set, neural signal set, and animal behavior data set to obtain an analysis result information set.

[0010] 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.

[0011] 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.

[0012] The environmental control 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;

[0013] 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 lunar gravity simulation cabin respectively;

[0014] 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.

[0015] The performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set includes:

[0016] Get the standard environment value set;

[0017] Performing similarity evaluation processing on the standard environmental value set and the environmental measurement information set in the preprocessing information set to obtain an environmental similarity evaluation value;

[0018] Performing environmental volatility assessment processing on the environmental measurement information set in the pre-processed information set to obtain an environmental volatility assessment value;

[0019] An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

[0020] In a second aspect of the embodiment of the present invention, an implantable animal brain-computer interface method for a lunar base gravity simulation cabin is disclosed, which is implemented using the implantable animal brain-computer interface device for the lunar base gravity simulation cabin, and includes:

[0021] S1, using the implantable brain-computer interface module to collect and transmit a set of neural signals from the brain of the experimental animal;

[0022] 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;

[0023] S3, using the environmental control module to measure and control the gravity, temperature, humidity and light in the lunar gravity simulation cabin to obtain an environmental measurement information set;

[0024] S4, using the data processing and analysis module, processing and analyzing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an analysis result information set.

[0025] The collected environmental measurement information set, neural signal set and animal behavior data set are processed and analyzed to obtain an analysis result information set, including:

[0026] Preprocessing various types of information sets collected to obtain preprocessed information sets;

[0027] Performing environmental assessment processing on the environmental measurement information set in the preprocessing information set to obtain an environmental assessment value set;

[0028] Performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the preprocessing information set to obtain a behavior evaluation value;

[0029] Performing neural activity evaluation processing on the behavior evaluation value and the neural signal set in the preprocessing information set to obtain an electroencephalogram evaluation value;

[0030] The environmental evaluation value set, the behavior evaluation value and the EEG evaluation value are used to construct an analysis result information set.

[0031] The preprocessing of the various types of information sets collected to obtain a preprocessed information set includes:

[0032] Performing data cleaning processing on various information sets to obtain a first information set;

[0033] Performing category discrimination processing on the first information set to obtain a second information set;

[0034] Performing pattern discrimination processing on the second information set to obtain a preprocessing information set.

[0035] The performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set includes:

[0036] Get the standard environment value set;

[0037] Performing similarity evaluation processing on the standard environmental value set and the environmental measurement information set in the preprocessing information set to obtain an environmental similarity evaluation value;

[0038] Performing environmental volatility assessment processing on the environmental measurement information set in the pre-processed information set to obtain an environmental volatility assessment value;

[0039] An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

[0040] The step of performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the pre-processing information set to obtain a behavior evaluation value includes:

[0041] Performing statistical analysis on the animal behavior data set in the preprocessing information set to obtain a statistical value set; the statistical value set includes a mean, an equation, a median value and a mode value of the motion trajectory information of each type of key point;

[0042] Performing behavior evaluation calculation processing on the statistical value set and the environmental evaluation value set to obtain a behavior evaluation value;

[0043] The expression of the behavior evaluation calculation process is:

[0044]

[0045] Among them, κ i is the median value of the motion trajectory information of the i-th key point, λ i is the mode value of the motion trajectory information of the i-th key point, α i is the variance value of the motion trajectory information of the i-th key point, β i is the mean value of the motion trajectory information of the i-th key point, N is the number of the key points, and P1 is the behavior evaluation value.

[0046] The beneficial effects of the present invention are:

[0047] The present invention can simulate the lunar gravity environment on the ground, providing an effective experimental means for studying the effect of lunar gravity on biological neural modules. The implantable brain-computer interface module can collect brain neural signals of experimental animals in real time and accurately, providing important data for in-depth understanding of brain functions and neural mechanisms. Through the analysis of experimental data, it can provide theoretical basis and technical support for conducting biological experiments on the moon and protecting the health of astronauts in the future.

[0048] When the present invention performs EEG evaluation of animals at the lunar base, firstly, various types of information sets collected are preprocessed to obtain a preprocessed information set. Through the preprocessing process, the influence of bad signals and clutter is suppressed; the environmental measurement information set in the preprocessed information set is subjected to environmental evaluation processing to obtain an environmental evaluation value set, and a comprehensive environmental evaluation quantitative value is obtained by performing volatility evaluation and standardization evaluation on the environmental collection information; the environmental evaluation value set and the animal behavior data set in the preprocessed information set are subjected to behavioral evaluation processing to obtain a behavioral evaluation value. In the behavioral evaluation process, the environmental evaluation result and the animal behavior data are combined to improve the comprehensiveness and accuracy of the evaluation result; the behavioral evaluation value and the neural signal set in the preprocessed information set are subjected to neural activity evaluation processing to obtain an EEG evaluation value, and the accuracy of the neural activity evaluation is ensured by fusing the behavioral evaluation value and the neural signal set. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the composition of the device of the present invention;

[0050] Figure 2 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0051] In order to better understand the content of the present invention, an embodiment is given here.

[0052] Figure 1 It is a schematic diagram of the composition of the device of the present invention; Figure 2 It is a flow chart for implementing the method of the present invention.

[0053] In a first aspect of an embodiment of the present invention, an implantable animal brain-computer interface device for a lunar base gravity simulation cabin is disclosed, comprising: an implantable brain-computer interface module, an animal behavior monitoring module, an environment control module, and a data processing and analysis module;

[0054] The implantable brain-computer interface module is used to collect and transmit a collection of neural signals from the brain of an experimental animal;

[0055] The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set;

[0056] The environmental control module is used to measure and control the gravity, temperature, humidity and light in the lunar gravity simulation cabin to obtain an environmental measurement information set;

[0057] The data processing and analysis module is connected to the implantable brain-computer interface module, the animal behavior monitoring module, and the environmental control module respectively, and is used to process and analyze the collected environmental measurement information set, neural signal set, and animal behavior data set to obtain an analysis result information set.

[0058] 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.

[0059] The animal behavior monitoring module is used to collect and analyze the behavior activities of experimental animals to obtain an animal behavior data set;

[0060] 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.

[0061] The environmental control 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;

[0062] 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 lunar gravity simulation cabin respectively;

[0063] 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.

[0064] The data processing and analysis module processes and analyzes the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an analysis result information set, including:

[0065] Preprocessing various types of information sets collected to obtain preprocessed information sets;

[0066] Performing environmental assessment processing on the environmental measurement information set in the preprocessing information set to obtain an environmental assessment value set;

[0067] Performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the preprocessing information set to obtain a behavior evaluation value;

[0068] Performing neural activity evaluation processing on the behavior evaluation value and the neural signal set in the preprocessing information set to obtain an electroencephalogram evaluation value;

[0069] The environmental evaluation value set, the behavior evaluation value and the EEG evaluation value are used to construct an analysis result information set.

[0070] The preprocessing of the various types of information sets collected to obtain a preprocessed information set includes:

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

[0072] Performing category discrimination processing on the first information set to obtain a second information set;

[0073] Performing pattern discrimination processing on the second information set to obtain a preprocessing information set.

[0074] The category determination process is to determine whether each type of data in the first information set is consistent with the corresponding data category, and delete the inconsistent data from the first information set.

[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] Performing mode discrimination processing on the second information set to obtain a preprocessing information set includes:

[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 the best consistent approximation polynomial f(Ix) of the class data attribute;

[0079] Using the best consistent approximation polynomial f(Ix), 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 processing of the second information set to obtain a data set after boundary check;

[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 environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set includes:

[0086] Get the standard environment value set;

[0087] Performing similarity evaluation processing on the standard environmental value set and the environmental measurement information set in the preprocessing information set to obtain an environmental similarity evaluation value;

[0088] Performing environmental volatility assessment processing on the environmental measurement information set in the pre-processed information set to obtain an environmental volatility assessment value;

[0089] An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

[0090] The performing similarity evaluation processing on the standard environment value set and the environment measurement information set in the preprocessing information set to obtain an environment similarity evaluation value includes:

[0091] Subtracting each type of environmental measurement information sequence in the environmental measurement information set in the preprocessing information set from the standard environmental value in the corresponding standard environmental value set to obtain a corresponding environmental difference value sequence;

[0092] Using all the environmental difference value sequences, an environmental difference matrix is ​​constructed; the row vector of the environmental difference matrix is ​​the environmental difference value sequence;

[0093] Decomposing the environmental difference matrix to obtain a feature matrix;

[0094] The decomposition process is calculated as follows:

[0095] Y=UAV,

[0096] 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;

[0097] Extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0098] Performing a first feature calculation process on the feature vector to obtain an environment similarity evaluation value;

[0099] The expression of the first feature calculation process is:

[0100]

[0101] 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.

[0102] The calculation expression of the environmental volatility assessment process is:

[0103]

[0104] 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.

[0105] The step of performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the pre-processing information set to obtain a behavior evaluation value includes:

[0106] Performing statistical analysis on the animal behavior data set in the preprocessing information set to obtain a statistical value set; the statistical value set includes a mean, an equation, a median value and a mode value of the motion trajectory information of each type of key point;

[0107] Performing behavior evaluation calculation processing on the statistical value set and the environmental evaluation value set to obtain a behavior evaluation value;

[0108] The expression of the behavior evaluation calculation process is:

[0109]

[0110] Among them, κ i is the median value of the motion trajectory information of the i-th key point, λ i is the mode value of the motion trajectory information of the i-th key point, α i is the variance value of the motion trajectory information of the i-th key point, β i is the mean value of the motion trajectory information of the i-th key point, N is the number of the key points, and P1 is the behavior evaluation value.

[0111] The performing neural activity evaluation processing on the behavior evaluation value and the neural signal set in the preprocessing information set to obtain an electroencephalogram evaluation value includes:

[0112]

[0113] Among them, T i () represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i represents the i-th element of the neural signal set, L1 is the total number of data contained in the neural signal set, and v is the EEG evaluation value;

[0114] 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.

[0115] The signal transmission line also includes an antenna, which is used to radiate the neural signal set to an external receiving device.

[0116] The user feature extraction can be implemented by using SURF feature point detection or corner point detection algorithm.

[0117] The key points include head, hands, and body;

[0118] 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.

[0119] The device of the present invention also includes a lunar gravity simulation cabin for simulating lunar gravity environments of different degrees, including 1 / 6 of the earth's gravity and 1 / 3 of the earth's gravity.

[0120] In a second aspect of the embodiment of the present invention, an implantable animal brain-computer interface method for a lunar base gravity simulation cabin is disclosed, which is implemented using the implantable animal brain-computer interface device for the lunar base gravity simulation cabin, and includes:

[0121] S1, using the implantable brain-computer interface module to collect and transmit a set of neural signals from the brain of the experimental animal;

[0122] 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;

[0123] S3, using the environmental control module to measure and control the gravity, temperature, humidity and light in the lunar gravity simulation cabin to obtain an environmental measurement information set;

[0124] S4, using the data processing and analysis module, processing and analyzing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an analysis result information set.

[0125] The collected environmental measurement information set, neural signal set and animal behavior data set are processed and analyzed to obtain an analysis result information set, including:

[0126] S41, preprocessing the various types of information sets collected to obtain a preprocessed information set;

[0127] S42, performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set;

[0128] S43, performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the pre-processing information set to obtain a behavior evaluation value;

[0129] S44, performing neural activity evaluation processing on the behavior evaluation value and the neural signal set in the preprocessing information set to obtain an electroencephalogram evaluation value;

[0130] S45 constructs an analysis result information set using the environment evaluation value set, behavior evaluation value and EEG evaluation value.

[0131] The preprocessing of the various types of information sets collected to obtain a preprocessed information set includes:

[0132] Performing data cleaning processing on various information sets to obtain a first information set;

[0133] Performing category discrimination processing on the first information set to obtain a second information set;

[0134] Performing pattern discrimination processing on the second information set to obtain a preprocessing information set.

[0135] The category determination process is to determine whether each type of data in the first information set is consistent with the corresponding data category, and delete the inconsistent data from the first information set.

[0136] 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.

[0137] Performing mode discrimination processing on the second information set to obtain a preprocessing information set includes:

[0138] 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;

[0139] Performing curve fitting on the curve to be approximated by using a function approximation method to obtain the best consistent approximation polynomial f(Ix) of the class data attribute;

[0140] Using the best consistent approximation polynomial f(Ix), the known independent variable is calculated to obtain an approximate dependent variable;

[0141] 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;

[0142] Performing fusion processing on all the data after the execution mode discrimination processing of the second information set to obtain a data set after boundary check;

[0143] 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:

[0144] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0145] 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);

[0146] The performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set includes:

[0147] Get the standard environment value set;

[0148] Performing similarity evaluation processing on the standard environmental value set and the environmental measurement information set in the preprocessing information set to obtain an environmental similarity evaluation value;

[0149] Performing environmental volatility assessment processing on the environmental measurement information set in the pre-processed information set to obtain an environmental volatility assessment value;

[0150] An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

[0151] The performing similarity evaluation processing on the standard environment value set and the environment measurement information set in the preprocessing information set to obtain an environment similarity evaluation value includes:

[0152] Subtracting each type of environmental measurement information sequence in the environmental measurement information set in the preprocessing information set from the standard environmental value in the corresponding standard environmental value set to obtain a corresponding environmental difference value sequence;

[0153] Using all the environmental difference value sequences, an environmental difference matrix is ​​constructed; the row vector of the environmental difference matrix is ​​the environmental difference value sequence;

[0154] Decomposing the environmental difference matrix to obtain a feature matrix;

[0155] The decomposition process is calculated as follows:

[0156] Y=UAV,

[0157] 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;

[0158] Extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0159] Performing a first feature calculation process on the feature vector to obtain an environment similarity evaluation value;

[0160] The expression of the first feature calculation process is:

[0161]

[0162] 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.

[0163] The calculation expression of the environmental volatility assessment process is:

[0164]

[0165] 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.

[0166] The step of performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the pre-processing information set to obtain a behavior evaluation value includes:

[0167] Performing statistical analysis on the animal behavior data set in the preprocessing information set to obtain a statistical value set; the statistical value set includes a mean, an equation, a median value and a mode value of the motion trajectory information of each type of key point;

[0168] Performing behavior evaluation calculation processing on the statistical value set and the environmental evaluation value set to obtain a behavior evaluation value;

[0169] The expression of the behavior evaluation calculation process is:

[0170]

[0171] Among them, κ i is the median value of the motion trajectory information of the i-th key point, λ i is the mode value of the motion trajectory information of the i-th key point, αi is the variance value of the motion trajectory information of the i-th key point, β i is the mean value of the motion trajectory information of the i-th key point, N is the number of the key points, and P1 is the behavior evaluation value.

[0172] The performing neural activity evaluation processing on the behavior evaluation value and the neural signal set in the preprocessing information set to obtain an electroencephalogram evaluation value includes:

[0173]

[0174] Among them, T i () represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i represents the i-th element of the neural signal set, L1 is the total number of data contained in the neural signal set, and v is the EEG evaluation value;

[0175] In the third aspect of the embodiment of the present invention, an animal experiment method based on the device of the present invention is disclosed. Healthy mice are selected as experimental animals, and micro sensors are implanted in specific areas of their brains. The mice are placed in a lunar gravity simulation cabin, and the gravity is set to 1 / 6 of the earth's gravity. After adapting for 24 hours, sound stimulation is given, and brain-computer interface signals and behavioral data are collected at the same time. After the experiment, the data is processed and analyzed to observe the changes in the mouse brain neural activity and behavioral responses.

[0176] In a fourth aspect of the embodiments of the present invention, an animal experiment method based on the device of the present invention is disclosed. Rats are selected as experimental animals to perform the same implantable brain-computer interface surgery. The gravity in the lunar gravity simulation cabin is set to 1 / 3 of the earth's gravity. After 48 hours of adaptation, a light stimulation experiment is performed and relevant data is recorded. By analyzing the data, the effects of gravity changes on the rat nervous system and behavior are studied.

[0177] 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 animal brain-computer interface device for a lunar base gravity simulation cabin, characterized in that: include: Implantable brain-computer interface module, animal behavior monitoring module, environmental control module, data processing and analysis 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 control module is used to measure and control the gravity, temperature, humidity and light in the lunar gravity simulation cabin to obtain an environmental measurement information set; The data processing and analysis module is connected to the implantable brain-computer interface module, the animal behavior monitoring module, and the environmental control module respectively, and is used to process and analyze the collected environmental measurement information set, neural signal set, and animal behavior data set to obtain an analysis result information set.

2. The implantable animal brain-computer interface device for the lunar base 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 animal brain-computer interface device for the lunar base 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 animal brain-computer interface device for the lunar base gravity simulation cabin as claimed in claim 1, characterized in that: The environmental control 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 lunar 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 animal brain-computer interface device for the lunar base gravity simulation cabin as claimed in claim 1, characterized in that: The performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set includes: Get the standard environment value set; Performing similarity evaluation processing on the standard environmental value set and the environmental measurement information set in the preprocessing information set to obtain an environmental similarity evaluation value; Performing environmental volatility assessment processing on the environmental measurement information set in the pre-processed information set to obtain an environmental volatility assessment value; An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

6. An implantable animal brain-computer interface method for a lunar base gravity simulation cabin, implemented using the implantable animal brain-computer interface device for a lunar base gravity simulation cabin according to any one of claims 1 to 5, comprising: 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 control module to measure and control the gravity, temperature, humidity and light in the lunar gravity simulation cabin to obtain an environmental measurement information set; S4, using the data processing and analysis module, processing and analyzing the collected environmental measurement information set, neural signal set and animal behavior data set to obtain an analysis result information set.

7. The implantable animal brain-computer interface method of the lunar base gravity simulation cabin as claimed in claim 6, characterized in that: The collected environmental measurement information set, neural signal set and animal behavior data set are processed and analyzed to obtain an analysis result information set, including: Preprocessing various types of information sets collected to obtain preprocessed information sets; Performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set; Performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the preprocessing information set to obtain a behavior evaluation value; Performing neural activity evaluation processing on the behavior evaluation value and the neural signal set in the preprocessing information set to obtain an electroencephalogram evaluation value; The environmental evaluation value set, the behavior evaluation value and the EEG evaluation value are used to construct an analysis result information set.

8. The implantable animal brain-computer interface method of the lunar base gravity simulation cabin as claimed in claim 7, characterized in that: The preprocessing of the various types of information sets collected to obtain a preprocessed information set includes: Performing data cleaning processing on various information sets to obtain a first information set; Performing category discrimination processing on the first information set to obtain a second information set; Performing pattern discrimination processing on the second information set to obtain a preprocessing information set.

9. The implantable animal brain-computer interface method of the lunar base gravity simulation cabin as claimed in claim 7, characterized in that: The performing environmental assessment processing on the environmental measurement information set in the pre-processing information set to obtain an environmental assessment value set includes: Get the standard environment value set; Performing similarity evaluation processing on the standard environmental value set and the environmental measurement information set in the preprocessing information set to obtain an environmental similarity evaluation value; Performing environmental volatility assessment processing on the environmental measurement information set in the pre-processed information set to obtain an environmental volatility assessment value; An environmental assessment value set is constructed using the environmental similarity assessment value and the environmental volatility assessment value.

10. The implantable animal brain-computer interface method of the lunar base gravity simulation cabin as claimed in claim 9, characterized in that: The step of performing behavior evaluation processing on the environmental evaluation value set and the animal behavior data set in the pre-processing information set to obtain a behavior evaluation value includes: Performing statistical analysis on the animal behavior data set in the preprocessing information set to obtain a statistical value set; the statistical value set includes a mean, an equation, a median value and a mode value of the motion trajectory information of each type of key point; Performing behavior evaluation calculation processing on the statistical value set and the environmental evaluation value set to obtain a behavior evaluation value; The expression of the behavior evaluation calculation process is: Among them, κ i is the median value of the motion trajectory information of the i-th key point, λ i is the mode value of the motion trajectory information of the i-th key point, α i is the variance value of the motion trajectory information of the i-th key point, β i is the mean value of the motion trajectory information of the i-th key point, N is the number of the key points, and P1 is the behavior evaluation value.

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