Implanted rodent wireless brain-computer interface system and method in variable gravity environment

Abstract

The invention discloses an implantable rodent wireless brain-computer interface system and method in a variable gravity environment. The system comprises an implantable nerve sensor, a signal processing module, a wireless transmission module, an energy supply module and a control module, the implantable nerve sensor is used for acquiring cranial nerve electric signals of an implanted rodent; the signal processing module is used for amplifying, filtering and digitizing the acquired cranial nerve electric signals to obtain acquired signals; the wireless transmission module is connected with the signal processing module and is used for sending the acquired signal to external receiving equipment; the energy supply module is respectively connected with the implantable nerve sensor, the signal processing module and the wireless transmission module and is used for acquiring a biopotential signal; and the control module is used for extracting the user instruction information from the wireless transmission module and controlling the opening and closing of the energy supply module according to the user instruction information.

Description

Technical Field

[0001] The present invention relates to the fields of biomedical engineering and aerospace technology, and in particular to an implantable rodent wireless brain-computer interface system and method in a variable gravity environment. Background Art

[0002] With the continuous development of aerospace technology, human beings have been increasingly studying the physiological and behavioral changes of biological organisms in the space environment. Rodents, as commonly used experimental animal models, play an important role in space biology research. However, in the variable gravity space environment, real-time and accurate monitoring of rodent brain neural activity has always been a technical challenge. Existing brain-computer interface technology has achieved certain results in the ground environment, but its application in the space environment faces many challenges, such as: the impact of gravity changes on device performance, the stability of signal transmission, the reliability of energy supply, and the miniaturization and implantable design of equipment. Summary of the invention

[0003] The present invention mainly solves the problem of real-time and accurate monitoring of rodent brain neural activity in a variable gravity space environment. The present invention discloses an implantable rodent wireless brain-computer interface system and method in a variable gravity environment.

[0004] In a first aspect of an embodiment of the present invention, an implantable rodent wireless brain-computer interface system in a variable gravity environment is disclosed, comprising: an implantable neural sensor, a signal processing module, a wireless transmission module, an energy supply module and a control module;

[0005] The implantable neural sensor is used to collect brain neural electrical signals of the implanted rodent;

[0006] The signal processing module is connected to the implantable neural sensor and is used to amplify, filter and digitize the collected brain nerve electrical signals to obtain collected signals;

[0007] The wireless transmission module is connected to the signal processing module and is used to send the collected signal to an external receiving device;

[0008] The energy supply module is connected to the implantable neural sensor, the signal processing module, and the wireless transmission module, respectively, and is used to collect biopotential signals, store the biopotential signals, and output them to the implantable neural sensor, the signal processing module, and the wireless transmission module;

[0009] The control module is connected to the implantable neural sensor, signal processing module, wireless transmission module and energy supply module respectively, and is used to extract user command information from the wireless transmission module and control the opening and closing of the energy supply module according to the user command information.

[0010] The implantable neural sensor comprises a microelectrode array, a switch matrix, a stimulation generation submodule and a signal acquisition submodule;

[0011] The microelectrode array is distributed in different or the same brain regions that need to be collected and stimulated, and is connected to the switch matrix to measure and obtain brain nerve electrical signals in the brain regions;

[0012] The switch matrix is ​​connected to the stimulus generation submodule and the signal acquisition submodule respectively;

[0013] The stimulation generation submodule is used to generate an analog electrical stimulation signal, and apply the analog electrical stimulation signal to the microelectrode array through the switch matrix;

[0014] The signal acquisition submodule is used to acquire the brain nerve electrical signals measured by the microelectrode array.

[0015] The signal processing module amplifies, filters and digitizes the collected brain nerve electrical signals to obtain collected signals, including:

[0016] The signal processing module performs quantization amplification processing on the collected signal to obtain an amplified signal;

[0017] Performing pattern filtering on the amplified signal to obtain a filtered signal;

[0018] The filtered signal is digitally sampled to obtain a sampled signal.

[0019] In a second aspect of an embodiment of the present invention, a method for implanting a rodent wireless brain-computer interface in a variable gravity environment is disclosed, which is implemented using the implanted rodent wireless brain-computer interface system in a variable gravity environment, including:

[0020] S1, using the energy supply module to collect biopotential signals, store the biopotential signals and output them to the implantable neural sensor, signal processing module and wireless transmission module;

[0021] S2, using the implantable neural sensor to collect brain nerve electrical signals of the implanted rodent;

[0022] S3, using the signal processing module to amplify, filter and digitize the collected brain nerve electrical signals to obtain collected signals;

[0023] S4, using the wireless transmission module to send the collected signal to an external receiving device.

[0024] The step of amplifying, filtering and digitizing the collected brain nerve electrical signals to obtain collected signals includes:

[0025] S31, performing quantization amplification processing on the collected signal to obtain an amplified signal;

[0026] S32, performing pattern filtering on the amplified signal to obtain a filtered signal;

[0027] S33, performing digital sampling processing on the filtered signal to obtain a sampled signal.

[0028] The step of performing quantization amplification processing on the collected signal to obtain an amplified signal includes:

[0029] S311, performing statistical distribution processing on the collected signal to obtain a probability density function;

[0030] S312, constructing a signal amplification model based on the probability density function;

[0031] S313: using the signal amplification model, amplify the collected signal to obtain an amplified signal.

[0032] The step of sending the collected signal to an external receiving device comprises:

[0033] S41, performing feature statistical processing on the collected signal to obtain a feature statistical value set;

[0034] S42, performing joint discrimination processing on the characteristic statistical value set to obtain a signal type value;

[0035] S43, determining a corresponding modulation and coding model according to the signal type value;

[0036] S44, using the determined modulation and coding model, performing coding and modulation processing on the collected signal to obtain a signal to be sent;

[0037] S45, sending the signal to be sent to an external receiving device through the antenna included in the wireless transmission module.

[0038] The performing feature statistical processing on the collected signal to obtain a feature statistical value set includes:

[0039] S411, performing time domain statistical processing on the collected signal to obtain a time domain statistical information set; the time domain statistical information set includes a mean, a variance, and an average rise time;

[0040] S412, performing frequency domain statistical processing on the collected signal to obtain a frequency domain statistical information set; the frequency domain statistical information set includes a harmonic mean, a mobility value, and an average derivative;

[0041] S413: construct a characteristic statistical value set by using the time domain statistical information set and the frequency domain statistical information set.

[0042] The performing joint discrimination processing on the statistical value set to obtain a signal type value includes:

[0043] Performing time domain feature calculation processing on the time domain statistical information set to obtain a time domain feature value;

[0044] Performing frequency domain feature calculation processing on the frequency domain statistical information set to obtain frequency domain feature values;

[0045] Determine the classification intervals of the time domain eigenvalues ​​and the frequency domain eigenvalues ​​by using a preset eigenvalue classification interval set;

[0046] Determine the type value corresponding to the classification interval as the obtained signal type value.

[0047] The expression for the time domain feature calculation process is:

[0048]

[0049] Among them, α is the mean, β is the variance, λ is the average rise time, and L1 is the time domain eigenvalue.

[0050] The expression for the frequency domain feature calculation process is:

[0051]

[0052] Among them, μ is the harmonic mean, T2() is the second-order Legendre polynomial, η is the mobility value, δ is the average derivative, and L2 is the frequency domain eigenvalue.

[0053] The beneficial effects of the present invention are:

[0054] The present invention provides an implantable rodent wireless brain-computer interface microsystem in a variable gravity space environment, which solves the problem of real-time and accurate monitoring of rodent brain neural activity in a variable gravity space environment, and can realize long-term, stable, real-time monitoring and regulation of rodent brain neural activity in a complex space environment.

[0055] The present invention is optimized for variable gravity environments: through special structure and material selection, the impact of gravity changes on system performance is reduced to ensure stable operation under different gravity conditions; through implantable design: trauma and interference to rodents are reduced, while the accuracy and reliability of signal acquisition are improved.

[0056] The wireless transmission technology of the present invention effectively improves the energy utilization rate of information transmission and realizes remote real-time acquisition of data by statistically analyzing the time domain characteristics and frequency domain characteristics of data and selecting a corresponding modulation coding model according to the statistical characteristics.

[0057] The invention provides a new technical means for space biology research, which helps to gain a deeper understanding of the impact of gravity changes on the biological nervous system. It can monitor the brain neural activity of rodents in a variable gravity space environment in real time and accurately, providing important data support for aerospace medicine and space life science research. The implantable design and wireless transmission technology reduce interference and restrictions during the experiment, and improve the feasibility and reliability of the experiment. The characteristics of low power consumption and miniaturization enable the system to operate stably for a long time in a space environment, reducing the cost and risk of the experiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 1 is a flow chart for implementing the method of the present invention;

[0059] Figure 2 It is a schematic diagram of the composition of the device of the present invention. DETAILED DESCRIPTION

[0060] In order to better understand the content of the present invention, an embodiment is given here.

[0061] 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.

[0062] In a first aspect of an embodiment of the present invention, an implantable rodent wireless brain-computer interface system in a variable gravity environment is disclosed, comprising: an implantable neural sensor, a signal processing module, a wireless transmission module, an energy supply module and a control module;

[0063] The implantable neural sensor is used to collect brain neural electrical signals of the implanted rodent;

[0064] The signal processing module is connected to the implantable neural sensor and is used to amplify, filter and digitize the collected brain nerve electrical signals to obtain collected signals;

[0065] The wireless transmission module is connected to the signal processing module and is used to send the collected signal to an external receiving device;

[0066] The energy supply module is connected to the implantable neural sensor, the signal processing module, and the wireless transmission module, respectively, and is used to collect biopotential signals, store the biopotential signals, and output them to the implantable neural sensor, the signal processing module, and the wireless transmission module;

[0067] The control module is connected to the implantable neural sensor, the signal processing module, the wireless transmission module and the energy supply module respectively, and is used to extract user instruction information from the wireless transmission module and control the opening and closing of the energy supply module according to the user instruction information;

[0068] The energy supply module receives energy through wireless energy supply, and then charges the battery in the energy supply unit through the energy supply unit containing impedance matching circuit, energy recovery circuit and post-stage circuit. The shell unit of the entire energy supply module adopts packaging materials with good heat dissipation performance, and adopts "ceramic-metal" heterogeneous material welding packaging. The top cover of the energy supply module adopts ceramic material to facilitate the transmission of wireless energy.

[0069] The wireless transmission module extracts user instruction information, the user instruction information includes identification header information, the wireless transmission module extracts the identification header information from the received information, and extracts the user instruction information from the received information based on the identification header information.

[0070] The implantable neural sensor comprises a microelectrode array, a switch matrix, a stimulation generation submodule and a signal acquisition submodule;

[0071] The microelectrode arrays are distributed in different or the same brain regions that need to be sampled and stimulated, and are connected to a switch matrix;

[0072] The switch matrix is ​​connected to the stimulus generation submodule and the signal acquisition submodule respectively;

[0073] The stimulation generation submodule is used to generate an analog electrical stimulation signal, and apply the analog electrical stimulation signal to the microelectrode array through the switch matrix;

[0074] The signal acquisition submodule is used to acquire the brain nerve electrical signals measured by the microelectrode array.

[0075] The signal processing module amplifies, filters and digitizes the collected brain nerve electrical signals to obtain collected signals, including:

[0076] The signal processing module performs quantization amplification processing on the collected signal to obtain an amplified signal;

[0077] Performing pattern filtering on the amplified signal to obtain a filtered signal;

[0078] Performing digital sampling processing on the filtered signal to obtain a sampled signal;

[0079] The step of performing quantization amplification processing on the collected signal to obtain an amplified signal includes:

[0080] Performing statistical distribution processing on the collected signal to obtain a probability density function;

[0081] Based on the probability density function, a signal amplification model is constructed;

[0082] The signal amplification model is used to amplify the collected signal to obtain an amplified signal.

[0083] The wireless transmission module sends the collected signal to an external receiving device, including:

[0084] The wireless transmission module performs feature statistical processing on the collected signal to obtain a feature statistical value set;

[0085] Performing joint discrimination processing on the statistical value set to obtain a signal type value;

[0086] Determine the corresponding modulation and coding model according to the signal type value;

[0087] Using the determined modulation and coding model, the collected signal is coded and modulated to obtain a signal to be sent;

[0088] The signal to be sent is sent to an external receiving device through the antenna included in the wireless transmission module.

[0089] The performing feature statistical processing on the collected signal to obtain a feature statistical value set includes:

[0090] Performing time-domain statistical processing on the collected signal to obtain a time-domain statistical information set;

[0091] Performing frequency domain statistical processing on the collected signal to obtain a frequency domain statistical information set;

[0092] Using the time domain statistical information set and the frequency domain statistical information set, construct a characteristic statistical value set;

[0093] The time domain statistical information set includes mean, variance, and average rise time;

[0094] The frequency domain statistical information set includes harmonic mean, mobility value, and average derivative;

[0095] The performing joint discrimination processing on the statistical value set to obtain a signal type value includes:

[0096] Performing time domain feature calculation processing on the time domain statistical information set to obtain a time domain feature value;

[0097] Performing frequency domain feature calculation processing on the frequency domain statistical information set to obtain frequency domain feature values;

[0098] Determine the classification intervals of the time domain eigenvalues ​​and the frequency domain eigenvalues ​​by using a preset eigenvalue classification interval set;

[0099] Determine the type value corresponding to the classification interval as the obtained signal type value;

[0100] The expression for the time domain feature calculation process is:

[0101]

[0102] Among them, α is the mean, β is the variance, λ is the average rise time, and L1 is the time domain eigenvalue.

[0103] The expression for the frequency domain feature calculation process is:

[0104]

[0105] Among them, μ is the harmonic mean, T2() is the second-order Legendre polynomial, η is the mobility value, δ is the average derivative, and L2 is the frequency domain eigenvalue.

[0106] The feature value classification interval set includes a plurality of classification intervals and corresponding type values;

[0107] The classification interval includes a time domain eigenvalue classification interval and a frequency domain eigenvalue classification interval; specifically, the classification interval includes a first classification interval and a second classification interval, the first classification interval is 0≤L1≤0.8, 0≤L2≤1.2, and the corresponding signal type value is a first type value, the second classification interval is 0.8<L1≤5.7, 1.2<L2≤6.8, and the corresponding signal type value is a second type value, 5.7 and 6.8 are upper limit values ​​of L1 and L2 respectively;

[0108] The determining, according to the signal type value, a corresponding modulation and coding model includes:

[0109] The signal type value is a first type value and a second type value;

[0110] When the value of the signal type value is a first type value, determining that the corresponding modulation and coding model is a first modulation and coding model;

[0111] When the value of the signal type value is a second type value, determining that the corresponding modulation and coding model is a second modulation and coding model;

[0112] The expression of the first modulation and coding model is:

[0113]

[0114] Wherein, y1(z,x) is the element of the z-th row and x-th column of the transformation data matrix obtained by the first modulation coding model, g1() is the first transformation function, s(k) is the k-th item of the acquired signal, g1(kT) is the value of g1() at kT, T and F are the time domain transformation length and the frequency domain transformation length respectively, NS is the number of information segments, h is the angle average value, and k is the time domain sequence number value; the transformation data matrix is ​​spliced ​​according to the row vector to obtain the encoded signal sequence;

[0115] The expression of the second modulation and coding model is:

[0116]

[0117] Among them, y2(z,x) is the element of the z-th row and x-th column of the transformed data matrix obtained by the second modulation coding model, g2() is the second transformation function, g2(xT) is the value of g2() at xT, and k1 is the first angle average value.

[0118] The first transformation function may be a Gaussian function, and the second transformation function may be a Gabor function.

[0119] The average rise time is the average rise time of all amplitudes of the collected signal;

[0120] The harmonic mean is the mean of all harmonic frequencies of the FFT sequence of the acquired signal;

[0121] The mobility value is the square root of the variance of the first-order derivative of the FFT sequence of the collected signal divided by the variance of the signal;

[0122] The average derivative is the average value of the derivative of each point of the FFT sequence of the acquisition signal;

[0123] The performing pattern filtering on the amplified signal to obtain a filtered signal includes:

[0124] For each type of data attribute of the amplified signal, the data acquisition information of the data is used as the independent variable, and the data value of the data is used as the dependent variable, and autoregression-sliding average modeling is performed to obtain the regression model of the data attribute of the type;

[0125] Using the regression model, the independent variable is calculated and processed to obtain a regression data value; judging whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, the data is deleted from the amplified signal; if it is less than or equal to the first regression discrimination threshold, the data is not processed;

[0126] Performing fusion processing on the data of all execution mode filtering processes of the amplified signal to obtain a filtered signal;

[0127] The signal amplification model is constructed based on the probability density function, including:

[0128] The value interval of the collected signal is classified to obtain several sub-intervals κ i , κ i represents the i-th subinterval; according to the probability density function, determine the probability value of each subinterval;

[0129] The probability value of each sub-interval is calculated and processed to obtain the corresponding amplitude amplification factor, AF i represents the amplitude amplification factor of the ith subinterval, and the expression of the calculation process is AF i =gr i , r i is the probability value of the ith subinterval, and g is the preset proportional factor;

[0130] The signal amplification model is constructed by using the amplitude amplification factors of all sub-intervals; the expression of the signal amplification model is:

[0131] y=AF i x,x∈κ i ,

[0132] Wherein, x represents the value of the collected signal, and y represents the value of the amplified collected signal;

[0133] Determining the probability value of each sub-interval according to the probability density function is to accumulate and sum the probability density values ​​of each sub-interval to obtain the probability value of each sub-interval.

[0134] For the preparation of implantable neural sensors, materials with good biocompatibility and flexibility, such as polyimide and graphene, are selected, and micro-electrode arrays are manufactured through micro-machining technology. The size and spacing of the electrodes are carefully designed to adapt to the structure and neural activity characteristics of the rodent brain.

[0135] For the realization of the signal processing module, high-performance analog amplifiers and digital filter chips are used to amplify and filter the collected neural electrical signals. Microcontrollers are used for digital conversion and preliminary data processing to reduce signal noise and improve the signal-to-noise ratio.

[0136] For the construction of wireless transmission modules, choose wireless communication technologies suitable for space environments, such as Bluetooth Low Energy (BLE) or Zigbee. Design efficient antenna structures to ensure stable signal transmission in complex environments. Use encryption technology to ensure the security and confidentiality of data transmission.

[0137] For the design of the energy supply module, miniaturized lithium batteries or energy collection technologies based on vibration, light energy, etc. are used to provide energy for the system. At the same time, it is equipped with a power management chip to achieve efficient use and reasonable distribution of energy, ensuring stable power supply of the system under different working conditions.

[0138] For the development of control modules, embedded software is used to write control programs to realize functions such as parameter setting, working mode switching and fault diagnosis of the entire system. Through the communication interface with external devices, remote control and data interaction of the system can be realized.

[0139] The energy supply module is configured to convert the potential signal measured by the energy transmission electrode into a DC voltage and output it, mainly including energy transmission electrode measurement, matching circuit, N-level rectifier, and DC-DC converter (DC-DC converter). After the energy transmission electrode attached to the cranial nerve receives energy, it is converted into DC energy through the matching circuit in the energy supply module and then through the rectifier circuit, and finally converted through the DC-DC converter to power the entire implantable brain-computer interface system circuit. The input end of the matching network is the information and energy transmission electrode, and the output end is the N-level rectifier. The design of the matching circuit can be verified in simulation software such as ADS, and then a test circuit is built according to the simulation results and secondary adjustments are made according to the actual test results. The matching circuit is followed by an N-level rectifier, which adopts a voltage doubler rectifier structure composed of diodes, capacitors, etc., to achieve high voltage output. The rectifier not only needs to achieve high energy collection efficiency within a large input amplitude range, but also must have a low working threshold so that it can collect energy from weaker signals. Therefore, the present invention will adopt a multi-stage low-threshold rectification architecture to improve the energy collection sensitivity, and at the same time introduce multi-stage control technology to select the optimal rectification structure according to the input signal amplitude to improve the comprehensive energy transmission efficiency under a large input range.

[0140] In a second aspect of an embodiment of the present invention, a method for implanting a rodent wireless brain-computer interface in a variable gravity environment is disclosed, which is implemented using the implanted rodent wireless brain-computer interface system in a variable gravity environment, including:

[0141] S1, using the energy supply module to collect biopotential signals, store the biopotential signals and output them to the implantable neural sensor, signal processing module and wireless transmission module;

[0142] S2, using the implantable neural sensor to collect brain nerve electrical signals of the implanted rodent;

[0143] S3, using the signal processing module to amplify, filter and digitize the collected brain nerve electrical signals to obtain collected signals;

[0144] S4, using the wireless transmission module to send the collected signal to an external receiving device.

[0145] The step of amplifying, filtering and digitizing the collected brain nerve electrical signals to obtain collected signals includes:

[0146] Performing quantization and amplification processing on the collected signal to obtain an amplified signal;

[0147] Performing pattern filtering on the amplified signal to obtain a filtered signal;

[0148] Performing digital sampling processing on the filtered signal to obtain a sampled signal;

[0149] The step of performing quantization amplification processing on the collected signal to obtain an amplified signal includes:

[0150] Performing statistical distribution processing on the collected signal to obtain a probability density function;

[0151] Based on the probability density function, a signal amplification model is constructed;

[0152] The signal amplification model is used to amplify the collected signal to obtain an amplified signal.

[0153] The step of sending the collected signal to an external receiving device comprises:

[0154] Performing feature statistical processing on the collected signal to obtain a feature statistical value set;

[0155] Performing joint discrimination processing on the statistical value set to obtain a signal type value;

[0156] Determine the corresponding modulation and coding model according to the signal type value;

[0157] Using the determined modulation and coding model, the collected signal is coded and modulated to obtain a signal to be sent;

[0158] The signal to be sent is sent to an external receiving device through the antenna included in the wireless transmission module.

[0159] The performing feature statistical processing on the collected signal to obtain a feature statistical value set includes:

[0160] Performing time-domain statistical processing on the collected signal to obtain a time-domain statistical information set;

[0161] Performing frequency domain statistical processing on the collected signal to obtain a frequency domain statistical information set;

[0162] Using the time domain statistical information set and the frequency domain statistical information set, construct a characteristic statistical value set;

[0163] The time domain statistical information set includes mean, variance, and average rise time;

[0164] The frequency domain statistical information set includes harmonic mean, mobility value, and average derivative;

[0165] The performing joint discrimination processing on the statistical value set to obtain a signal type value includes:

[0166] Performing time domain feature calculation processing on the time domain statistical information set to obtain a time domain feature value;

[0167] Performing frequency domain feature calculation processing on the frequency domain statistical information set to obtain frequency domain feature values;

[0168] Determine the classification intervals of the time domain eigenvalues ​​and the frequency domain eigenvalues ​​by using a preset eigenvalue classification interval set;

[0169] Determine the type value corresponding to the classification interval as the obtained signal type value;

[0170] The expression for the time domain feature calculation process is:

[0171]

[0172] Among them, α is the mean, β is the variance, λ is the average rise time, and L1 is the time domain eigenvalue.

[0173] The expression for the frequency domain feature calculation process is:

[0174]

[0175] Among them, μ is the harmonic mean, T2() is the second-order Legendre polynomial, η is the mobility value, δ is the average derivative, and L2 is the frequency domain eigenvalue.

[0176] The feature value classification interval set includes a plurality of classification intervals and corresponding type values;

[0177] The classification interval includes a time domain eigenvalue classification interval and a frequency domain eigenvalue classification interval; specifically, the classification interval includes a first classification interval and a second classification interval, the first classification interval is 0≤L1≤0.8, 0≤L2≤1.2, and the corresponding signal type value is a first type value, the second classification interval is 0.8<L1≤5.7, 1.2<L2≤6.8, and the corresponding signal type value is a second type value, 5.7 and 6.8 are upper limit values ​​of L1 and L2 respectively;

[0178] The determining, according to the signal type value, a corresponding modulation and coding model includes:

[0179] The signal type value is a first type value and a second type value;

[0180] When the value of the signal type value is a first type value, determining that the corresponding modulation and coding model is a first modulation and coding model;

[0181] When the value of the signal type value is a second type value, determining that the corresponding modulation and coding model is a second modulation and coding model;

[0182] The expression of the first modulation and coding model is:

[0183]

[0184] Wherein, y1(z,x) is the element of the z-th row and x-th column of the transformation data matrix obtained by the first modulation coding model, g1() is the first transformation function, s(k) is the k-th item of the acquired signal, g1(kT) is the value of g1() at kT, T and F are the time domain transformation length and the frequency domain transformation length respectively, NS is the number of information segments, h is the angle average value, and k is the time domain sequence number value; the transformation data matrix is ​​spliced ​​according to the row vector to obtain the encoded signal sequence;

[0185] The expression of the second modulation and coding model is:

[0186]

[0187] Among them, y2(z,x) is the element of the z-th row and x-th column of the transformed data matrix obtained by the second modulation coding model, g2() is the second transformation function, g2(xT) is the value of g2() at xT, and k1 is the first angle average value.

[0188] 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 rodent wireless brain-computer interface system in a variable gravity environment, characterized in that: include: Implantable neural sensor, signal processing module, wireless transmission module, energy supply module and control module; The implantable neural sensor is used to collect brain neural electrical signals of the implanted rodent; The signal processing module is connected to the implantable neural sensor and is used to amplify, filter and digitize the collected brain nerve electrical signals to obtain collected signals; The wireless transmission module is connected to the signal processing module and is used to send the collected signal to an external receiving device; The energy supply module is connected to the implantable neural sensor, the signal processing module, and the wireless transmission module, respectively, and is used to collect biopotential signals, store the biopotential signals, and output them to the implantable neural sensor, the signal processing module, and the wireless transmission module; The control module is connected to the implantable neural sensor, signal processing module, wireless transmission module and energy supply module respectively, and is used to extract user command information from the wireless transmission module and control the opening and closing of the energy supply module according to the user command information.

2. The implantable rodent wireless brain-computer interface system in a variable gravity environment as claimed in claim 1, characterized in that: The implantable neural sensor comprises a microelectrode array, a switch matrix, a stimulation generation submodule and a signal acquisition submodule; The microelectrode array is distributed in different or the same brain regions that need to be collected and stimulated, and is connected to the switch matrix to measure and obtain brain nerve electrical signals in the brain regions; The switch matrix is ​​connected to the stimulus generation submodule and the signal acquisition submodule respectively; The stimulation generation submodule is used to generate an analog electrical stimulation signal, and apply the analog electrical stimulation signal to the microelectrode array through the switch matrix; The signal acquisition submodule is used to acquire the brain nerve electrical signals measured by the microelectrode array.

3. The implantable rodent wireless brain-computer interface system in a variable gravity environment as claimed in claim 2, characterized in that: The signal processing module amplifies, filters and digitizes the collected brain nerve electrical signals to obtain collected signals, including: The signal processing module performs quantization amplification processing on the collected signal to obtain an amplified signal; Performing pattern filtering on the amplified signal to obtain a filtered signal; The filtered signal is digitally sampled to obtain a sampled signal.

4. A method for implanting a rodent wireless brain-computer interface in a variable gravity environment, characterized in that: The method is implemented by using an implantable rodent wireless brain-computer interface system in a variable gravity environment according to any one of claims 1 to 3, comprising: S1, using the energy supply module to collect biopotential signals, store the biopotential signals and output them to the implantable neural sensor, signal processing module and wireless transmission module; S2, using the implantable neural sensor to collect brain nerve electrical signals of the implanted rodent; S3, using the signal processing module to amplify, filter and digitize the collected brain nerve electrical signals to obtain collected signals; S4, using the wireless transmission module to send the collected signal to an external receiving device.

5. The method for implanting a rodent wireless brain-computer interface in a variable gravity environment as claimed in claim 4, characterized in that: The step of amplifying, filtering and digitizing the collected brain nerve electrical signals to obtain collected signals includes: S31, performing quantization amplification processing on the collected signal to obtain an amplified signal; S32, performing pattern filtering on the amplified signal to obtain a filtered signal; S33, performing digital sampling processing on the filtered signal to obtain a sampled signal.

6. The method for implanting a rodent wireless brain-computer interface in a variable gravity environment as claimed in claim 5, characterized in that: The step of performing quantization amplification processing on the collected signal to obtain an amplified signal includes: S311, performing statistical distribution processing on the collected signal to obtain a probability density function; S312, constructing a signal amplification model based on the probability density function; S313: using the signal amplification model, amplify the collected signal to obtain an amplified signal.

7. The method for implanting a rodent wireless brain-computer interface in a variable gravity environment as claimed in claim 6, characterized in that: The step of sending the collected signal to an external receiving device comprises: S41, performing feature statistical processing on the collected signal to obtain a feature statistical value set; S42, performing joint discrimination processing on the characteristic statistical value set to obtain a signal type value; S43, determining a corresponding modulation and coding model according to the signal type value; S44, using the determined modulation and coding model, performing coding and modulation processing on the collected signal to obtain a signal to be sent; S45, sending the signal to be sent to an external receiving device through the antenna included in the wireless transmission module.

8. The method for implanting a rodent wireless brain-computer interface in a variable gravity environment as claimed in claim 7, characterized in that: The performing feature statistical processing on the collected signal to obtain a feature statistical value set includes: S411, performing time domain statistical processing on the collected signal to obtain a time domain statistical information set; the time domain statistical information set includes a mean, a variance, and an average rise time; S412, performing frequency domain statistical processing on the collected signal to obtain a frequency domain statistical information set; the frequency domain statistical information set includes a harmonic mean, a mobility value, and an average derivative; S413: construct a characteristic statistical value set by using the time domain statistical information set and the frequency domain statistical information set.

9. The method for implanting a rodent wireless brain-computer interface in a variable gravity environment as claimed in claim 7, characterized in that: The performing joint discrimination processing on the statistical value set to obtain a signal type value includes: Performing time domain feature calculation processing on the time domain statistical information set to obtain a time domain feature value; Performing frequency domain feature calculation processing on the frequency domain statistical information set to obtain frequency domain feature values; Using a preset eigenvalue classification interval set, determining the classification intervals of the time domain eigenvalues ​​and the frequency domain eigenvalues; Determine the type value corresponding to the classification interval as the obtained signal type value.

10. The method for implanting a rodent wireless brain-computer interface in a variable gravity environment as claimed in claim 9, characterized in that: The expression for the time domain feature calculation process is: Among them, α is the mean, β is the variance, λ is the average rise time, and L1 is the time domain eigenvalue. The expression for the frequency domain feature calculation process is: Among them, μ is the harmonic mean, T2() is the second-order Legendre polynomial, η is the mobility value, δ is the average derivative, and L2 is the frequency domain eigenvalue.

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