Data acquisition and circulation method and device for space capsule animal implantable brain-computer interface

Abstract

The invention discloses a data acquisition and circulation method and device for a space capsule animal implantable brain-computer interface. The device comprises a brain-computer interface module, a data acquisition module, an intelligent processing module and a data circulation module. The brain-computer interface module is used for acquiring electroencephalogram signals of animals; the data acquisition module is connected with the brain-computer interface module and is used for preprocessing the acquired electroencephalogram signal to obtain an acquired signal; the intelligent processing module is connected with the data acquisition module and is used for detecting and extracting the acquired signals to obtain a detection information set; and the data circulation module is connected with the intelligent processing module and is used for carrying out transmission processing on the detection information set and sending the detection information set to a ground receiving terminal from the space capsule.

Description

Technical Field

[0001] The present invention relates to the technical field of brain-computer interfaces, and in particular to a method and device for collecting and transferring data of an implantable brain-computer interface for animals in a space capsule. Background Art

[0002] In space exploration, the study of the physiological and behavioral states of animals in the space capsule environment is of great significance. Brain-computer interface technology makes it possible to obtain neural signals from the animal brain, but in the special environment of the space capsule, the application of brain-computer interface faces many challenges, such as the accuracy and stability of data acquisition, as well as the efficiency and intelligence of data processing and circulation. The existing brain-computer interface technology has the following shortcomings in space applications:

[0003] 1. Data collection is easily affected by factors such as electromagnetic interference and radiation in the space capsule, resulting in reduced data quality.

[0004] 2. Traditional data processing methods are inefficient and difficult to analyze and process in real time when faced with a large number of complex EEG signals.

[0005] 3. The lack of effective security mechanisms during data transfer can easily lead to data leakage or loss. Summary of the invention

[0006] The present invention mainly solves the problems existing in the existing brain-computer interface technology in space applications. The present invention discloses a data acquisition and circulation method and device for an implantable brain-computer interface for animals in a space capsule.

[0007] In a first aspect of an embodiment of the present invention, a data acquisition and circulation device for an implantable brain-computer interface for an animal in a space capsule is disclosed, comprising: a brain-computer interface module, a data acquisition module, an intelligent processing module, and a data circulation module;

[0008] The brain-computer interface module is used to collect the brain electrical signals of the animal;

[0009] The data acquisition module is connected to the brain-computer interface module and is used to pre-process the collected EEG signals to obtain collected signals;

[0010] The intelligent processing module is connected to the data acquisition module and is used to perform detection and extraction processing on the collected signals to obtain a detection information set;

[0011] The data transfer module is connected to the intelligent processing module, and is used to transmit and process the detection information set, and send the detection information set from the space capsule to the ground receiving terminal.

[0012] The data acquisition module pre-processes the acquired EEG signal to obtain the acquired signal, including:

[0013] The data acquisition module performs data cleaning processing on the collected EEG signal to obtain a first signal;

[0014] Performing time alignment processing on the first signal to obtain a second signal;

[0015] performing normalization processing on the second signal to obtain a third signal;

[0016] The third signal is subjected to data paradigm discrimination processing to obtain an acquisition signal.

[0017] The intelligent processing module performs detection and extraction processing on the collected signal to obtain a detection information set, including:

[0018] The intelligent processing module performs pattern discrimination processing on the collected signal to obtain EEG pattern type information;

[0019] Performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information;

[0020] The detection information set is constructed using the EEG pattern type information and abnormal feature information.

[0021] In a second aspect of the embodiment of the present invention, a method for data collection and circulation of a space capsule animal implantable brain-computer interface is disclosed, which is implemented by using the data collection and circulation device of the space capsule animal implantable brain-computer interface, and includes:

[0022] S1, using the brain-computer interface module to collect the brain electrical signal of the animal;

[0023] S2, using the data acquisition module to pre-process the acquired EEG signal to obtain an acquired signal;

[0024] S3, using the intelligent processing module to perform detection and extraction processing on the collected signal to obtain a detection information set;

[0025] S4, utilizing the data transfer module to perform transmission processing on the detection information set, and sending the detection information set from the space capsule to a ground receiving terminal.

[0026] The step of preprocessing the collected EEG signal to obtain the collected signal includes:

[0027] S21, performing data cleaning processing on the collected electroencephalogram signal to obtain a first signal;

[0028] S22, performing time alignment processing on the first signal to obtain a second signal;

[0029] S23, normalizing the second signal to obtain a third signal;

[0030] S24, performing data paradigm discrimination processing on the third signal to obtain a collection signal.

[0031] The performing data paradigm discrimination processing on the third signal to obtain a collection signal comprises:

[0032] S241, performing cross-correlation calculation processing on all signal sequences included in the third signal to obtain a cross-correlation matrix;

[0033] S242, performing eigenvector calculation processing on the cross-correlation matrix to obtain an eigenvector;

[0034] S243, performing fitting calculation processing on the elements and element sequence numbers of the feature vector to obtain a fitting polynomial;

[0035] S244, using the fitting polynomial, performing deviation discrimination processing on each signal sequence included in the third signal to obtain a discriminated signal sequence;

[0036] S245, constructing a collection signal using all the identified signal sequences.

[0037] The detecting and extracting process is performed on the collected signal to obtain a detection information set, including:

[0038] S31, performing pattern discrimination processing on the collected signal to obtain EEG pattern type information;

[0039] S32, performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information;

[0040] S33, constructing a detection information set using the EEG pattern type information and abnormal feature information.

[0041] The performing pattern discrimination processing on the collected signal to obtain EEG pattern type information includes:

[0042] S311, constructing a collection signal matrix using all signal sequences contained in the collection signal; a row vector of the collection signal matrix is ​​a signal sequence;

[0043] S312, performing mode value calculation processing on the collected signal matrix to obtain a mode value;

[0044] S313, using a preset mode value distribution interval set, performing a value interval discrimination process on the mode value to obtain a value interval corresponding to the mode value; the mode value distribution interval set includes a plurality of value intervals and corresponding mode type information;

[0045] S314, determining that the pattern type information corresponding to the value interval corresponding to the pattern value is the EEG pattern type information;

[0046] The mode value calculation process includes:

[0047]

[0048] Wherein, M and N are the row dimension and column dimension of the acquisition signal matrix respectively, z ij is the element of the i-th row and j-th column of the acquisition signal matrix, is the i-th element of the first signal vector, is the i-th element of the second signal vector, p i is the i-th element of the third signal vector, and H1 is the mode value.

[0049] The performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information includes:

[0050] S321, performing statistical processing on the collected signal to obtain a statistical value set; the statistical value set includes: mean, variance, average derivative, median value, and mode value;

[0051] S322, performing abnormal amount calculation processing on the statistical value set to obtain abnormal feature information; the abnormal feature information is used to characterize the abnormal degree value of the collected EEG signal;

[0052] The expression for the abnormal amount calculation process is:

[0053]

[0054] Where P represents the order value, N is the column dimension of the acquisition signal matrix, Indicates rounding up, L P () represents the Pth Laguerre polynomial, u, δ, κ and λ represent the mean, variance, average derivative, median value and mode value respectively, and τ is the abnormal feature information.

[0055] The transmitting and processing the detection information set, and sending the detection information set from the space capsule to a ground receiving terminal, includes:

[0056] S41, modeling the channel transmission link to obtain channel model information;

[0057] S42, constructing a channel coding model based on the channel model information;

[0058] S43, using the channel coding model, encoding the detection information set to obtain a coded information set;

[0059] S44, performing modulation processing on the coded information set to obtain a modulated signal;

[0060] S45, sending the modulated signal from the space capsule to a ground receiving terminal.

[0061] The beneficial effects of the present invention are:

[0062] The present invention discloses a data collection and circulation method and device for a space capsule animal implantable brain-computer interface, which solves the problems existing in the existing brain-computer interface technology in space applications.

[0063] During the data collection process, the present invention uses the data collection module to pre-process the collected EEG signals, establishes a data paradigm discrimination processing model, realizes the filtering of irrelevant data and non-same mode data, and ensures the accuracy of subsequent detection and extraction.

[0064] The present invention utilizes the intelligent processing module to perform detection and extraction processing on the collected signals, obtains EEG pattern type information by performing pattern discrimination processing on the collected signals; performs abnormal feature extraction processing on the collected signals to obtain abnormal feature information; realizes feature extraction in two dimensions, pattern and abnormality, and ensures the comprehensiveness and objectivity of EEG monitoring.

[0065] In the process of information transmission, the present invention constructs a channel coding matrix based on channel detection information, which can realize fast coding of transmission information and improve the efficiency and reliability of air-to-ground transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 1 is a flow chart for implementing the method of the present invention;

[0067] Figure 2 It is a composition diagram of the device of the present invention. DETAILED DESCRIPTION

[0068] In order to better understand the content of the present invention, an embodiment is given here.

[0069] Figure 1 is a flow chart for implementing the method of the present invention, Figure 2 It is a composition diagram of the device of the present invention.

[0070] In a first aspect of an embodiment of the present invention, a data acquisition and circulation device for an implantable brain-computer interface for an animal in a space capsule is disclosed, comprising: a brain-computer interface module, a data acquisition module, an intelligent processing module, and a data circulation module;

[0071] The brain-computer interface module is used to collect the brain electrical signals of the animal;

[0072] The data acquisition module is connected to the brain-computer interface module and is used to pre-process the collected EEG signals to obtain collected signals;

[0073] The intelligent processing module is connected to the data acquisition module and is used to perform detection and extraction processing on the collected signals to obtain a detection information set;

[0074] The data transfer module is connected to the intelligent processing module, and is used to transmit and process the detection information set, and send the detection information set from the space capsule to the ground receiving terminal.

[0075] The data acquisition module pre-processes the acquired EEG signal to obtain the acquired signal, including:

[0076] The data acquisition module performs data cleaning processing on the collected EEG signal to obtain a first signal;

[0077] Performing time alignment processing on the first signal to obtain a second signal;

[0078] performing normalization processing on the second signal to obtain a third signal;

[0079] Performing data paradigm discrimination processing on the third signal to obtain a collection signal;

[0080] The time alignment process is to unify different types of data to the same time base; the time alignment process can adopt the internal push / extrapolation method, the Lagrange three-point interpolation method, etc.;

[0081] The performing data paradigm discrimination processing on the third signal to obtain a collection signal comprises:

[0082] Performing cross-correlation calculation processing on all signal sequences included in the third signal to obtain a cross-correlation matrix;

[0083] Performing eigenvector calculation processing on the cross-correlation matrix to obtain an eigenvector;

[0084] Performing fitting calculation processing on the elements and element sequence numbers of the characteristic vector to obtain a fitting polynomial;

[0085] Using the fitting polynomial, performing deviation discrimination processing on each signal sequence included in the third signal to obtain a discriminated signal sequence;

[0086] Using all the identified signal sequences, a collection signal is constructed;

[0087] The intelligent processing module performs detection and extraction processing on the collected signal to obtain a detection information set, including:

[0088] The intelligent processing module performs pattern discrimination processing on the collected signal to obtain EEG pattern type information;

[0089] Performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information;

[0090] The detection information set is constructed using the EEG pattern type information and abnormal feature information.

[0091] The performing pattern discrimination processing on the collected signal to obtain EEG pattern type information includes:

[0092] Using all signal sequences contained in the acquisition signal, an acquisition signal matrix is ​​constructed; a row vector of the acquisition signal matrix is ​​a signal sequence;

[0093] Performing mode value calculation processing on the acquisition signal matrix to obtain a mode value;

[0094] Using a preset mode value distribution interval set, the mode value is subjected to value interval discrimination processing to obtain a value interval corresponding to the mode value; the mode value distribution interval set includes a plurality of value intervals and corresponding mode type information;

[0095] Determine the pattern type information corresponding to the value interval corresponding to the pattern value, which is the EEG pattern type information;

[0096] The mode value calculation process includes:

[0097]

[0098] Wherein, M and N are the row dimension and column dimension of the acquisition signal matrix respectively, z ij is the element of the i-th row and j-th column of the acquisition signal matrix, is the i-th element of the first signal vector, is the i-th element of the second signal vector, p i is the i-th element of the third signal vector, and H1 is the mode value.

[0099] The performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information includes:

[0100] Performing statistical processing on the collected signal to obtain a statistical value set; the statistical value set includes: mean, variance, average derivative, median value, and mode value;

[0101] Performing abnormal amount calculation processing on the statistical value set to obtain abnormal feature information; the abnormal feature information is used to characterize the abnormal degree value of the collected EEG signal;

[0102] The expression for the abnormal amount calculation process is:

[0103]

[0104] Where P represents the order value, N is the column dimension of the acquisition signal matrix, Indicates rounding up, L P () represents the Pth Laguerre polynomial, u, δ, κ and λ represent the mean, variance, average derivative, median value and mode value respectively, and τ is the abnormal feature information;

[0105] The data transfer module performs transmission processing on the detection information set and sends the detection information set from the space capsule to the ground receiving terminal, including:

[0106] The data flow module performs modeling processing on the channel transmission link to obtain channel model information;

[0107] Based on the channel model information, construct a channel coding model;

[0108] Using the channel coding model, encoding the detection information set to obtain a coded information set;

[0109] Performing modulation processing on the coded information set to obtain a modulated signal;

[0110] Transmitting the modulated signal from the space capsule to a ground receiving terminal;

[0111] The modeling process of the channel transmission link to obtain channel model information includes:

[0112] generating a channel sounding sequence;

[0113] The channel detection sequence is sent from the space capsule to the ground receiving terminal through the channel transmission link, and the ground receiving terminal receives the received sequence;

[0114] Performing channel modeling processing on the channel detection sequence and the receiving sequence to obtain channel model information; the channel model information is a channel model matrix;

[0115] The performing channel modeling processing on the channel sounding sequence and the receiving sequence to obtain channel model information includes:

[0116] Segmenting the channel detection sequence and the receiving sequence respectively to obtain a plurality of subsequences;

[0117] Using all subsequences of the channel sounding sequence and receiving sequence respectively, construct a sounding matrix and a receiving matrix;

[0118] Calculating and processing the detection matrix and the receiving matrix to obtain a channel model matrix;

[0119] The calculation expression of the channel model matrix is:

[0120]

[0121] Where H is the channel model matrix, cov(,) represents the calculated covariance matrix, D(X) represents the divergence matrix of the detection matrix X, D(Y) represents the divergence matrix of the reception matrix Y, a1 and b1 are the eigenvectors corresponding to the maximum eigenvalues ​​of the detection matrix and the reception matrix respectively;

[0122] The channel coding model is obtained by multiplying the channel model matrix H with the matrix corresponding to the detection information set to obtain a coding information matrix; and all row vectors of the coding information matrix are used to construct the coding information set.

[0123] The eigenvector calculation process may be implemented by using a matrix eigenvector calculation method, such as a matrix eigenvalue decomposition method;

[0124] The step of performing eigenvector calculation processing on the cross-correlation matrix to obtain the eigenvector comprises:

[0125] Performing Gabor transformation on each signal sequence included in the third signal to obtain a corresponding transformation sequence;

[0126] Using all transformation sequences, a transformation matrix is ​​constructed;

[0127] Performing a first transformation process on the mutual correlation matrix and the transformation matrix to obtain a transformation feature matrix;

[0128] The expression of the first transformation process is:

[0129] C=A 1 / 2 RA -1 / 2 ,

[0130] Among them, A is the transformation matrix, R is the cross-correlation matrix, and C is the transformation feature matrix;

[0131] Performing eigendecomposition processing on the transformation feature matrix to obtain a eigenvector.

[0132] The eigenvalue decomposition process can be implemented by using an eigenvalue decomposition algorithm of a matrix.

[0133] The fitting calculation process is performed on the elements and element sequence values ​​of the feature vector to obtain a fitting polynomial, including:

[0134] The elements of the characteristic vector are used as dependent variables, the element sequence values ​​of the characteristic vector are used as independent variables, and polynomial fitting processing is performed on the dependent variable and the independent variable to obtain a fitting polynomial.

[0135] The step of using the fitting polynomial to perform deviation discrimination processing on each signal sequence included in the third signal to obtain a discriminated signal sequence includes:

[0136] The element sequence value in each signal sequence included in the third signal is calculated and processed by using the fitting polynomial to obtain a corresponding discriminant value;

[0137] It is determined whether the difference between the discrimination value and the element corresponding to the element sequence number value is greater than a set discrimination threshold, and the elements greater than the discrimination threshold are deleted from the signal sequence to obtain a discriminated signal sequence.

[0138] The average derivative is the average value of the derivative at each data point of the collected signal.

[0139] The brain-computer interface module can be implemented using an implantable brain-computer interface.

[0140] In a second aspect of an embodiment of the present invention, a data collection and circulation method for an implantable brain-computer interface for an animal in a space capsule is disclosed, comprising:

[0141] S1, using the brain-computer interface module to collect the electroencephalogram (EEG) signal of the animal;

[0142] S2, using the data acquisition module to pre-process the acquired EEG to obtain an acquired signal;

[0143] S3, using the intelligent processing module to perform detection and extraction processing on the collected signal to obtain a detection information set;

[0144] S4, utilizing the data transfer module to perform transmission processing on the detection information set, and sending the detection information set from the space capsule to a ground receiving terminal.

[0145] The step of preprocessing the collected EEG signal to obtain the collected signal includes:

[0146] S21, performing data cleaning processing on the collected electroencephalogram signal to obtain a first signal;

[0147] S22, performing time alignment processing on the first signal to obtain a second signal;

[0148] S23, normalizing the second signal to obtain a third signal;

[0149] S24, performing data paradigm discrimination processing on the third signal to obtain a collection signal;

[0150] The performing data paradigm discrimination processing on the third signal to obtain a collection signal comprises:

[0151] S241, performing cross-correlation calculation processing on all signal sequences included in the third signal to obtain a cross-correlation matrix;

[0152] S242, performing eigenvector calculation processing on the cross-correlation matrix to obtain an eigenvector;

[0153] S243, performing fitting calculation processing on the elements and element sequence numbers of the feature vector to obtain a fitting polynomial;

[0154] S244, using the fitting polynomial, performing deviation discrimination processing on each signal sequence included in the third signal to obtain a discriminated signal sequence;

[0155] S245, constructing a collection signal using all the identified signal sequences;

[0156] The detecting and extracting process is performed on the collected signal to obtain a detection information set, including:

[0157] S31, performing pattern discrimination processing on the collected signal to obtain EEG pattern type information;

[0158] S32, performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information;

[0159] S33, constructing a detection information set using the EEG pattern type information and abnormal feature information.

[0160] The performing pattern discrimination processing on the collected signal to obtain EEG pattern type information includes:

[0161] S311, constructing a collection signal matrix using all signal sequences contained in the collection signal; a row vector of the collection signal matrix is ​​a signal sequence;

[0162] S312, performing mode value calculation processing on the collected signal matrix to obtain a mode value;

[0163] S313, using a preset mode value distribution interval set, performing a value interval discrimination process on the mode value to obtain a value interval corresponding to the mode value; the mode value distribution interval set includes a plurality of value intervals and corresponding mode type information;

[0164] S314, determining that the pattern type information corresponding to the value interval corresponding to the pattern value is the EEG pattern type information;

[0165] The mode value calculation process includes:

[0166]

[0167] Wherein, M and N are the row dimension and column dimension of the acquisition signal matrix respectively, z ij is the element of the i-th row and j-th column of the acquisition signal matrix, is the i-th element of the first signal vector, is the i-th element of the second signal vector, p i is the i-th element of the third signal vector, and H1 is the mode value.

[0168] The performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information includes:

[0169] S321, performing statistical processing on the collected signal to obtain a statistical value set; the statistical value set includes: mean, variance, average derivative, median value, and mode value;

[0170] S322, performing abnormal amount calculation processing on the statistical value set to obtain abnormal feature information; the abnormal feature information is used to characterize the abnormal degree value of the collected EEG signal;

[0171] The expression for the abnormal amount calculation process is:

[0172]

[0173] Where P represents the order value, N is the column dimension of the acquisition signal matrix, Indicates rounding up, L P () represents the Pth Laguerre polynomial, u, δ, κ and λ represent the mean, variance, mean derivative, median value, and mode value, respectively;

[0174] The transmitting and processing the detection information set, and sending the detection information set from the space capsule to a ground receiving terminal, includes:

[0175] S41, modeling the channel transmission link to obtain channel model information;

[0176] S42, constructing a channel coding model based on the channel model information;

[0177] S43, using the channel coding model, encoding the detection information set to obtain a coded information set;

[0178] S44, performing modulation processing on the coded information set to obtain a modulated signal;

[0179] S45, sending the modulated signal from the space capsule to a ground receiving terminal;

[0180] The sending of the modulated signal from the space capsule to the ground receiving terminal can be achieved by using a satellite-to-ground satellite communication device.

[0181] The modeling process of the channel transmission link to obtain channel model information includes:

[0182] generating a channel sounding sequence;

[0183] The channel detection sequence is sent from the space capsule to the ground receiving terminal through the channel transmission link, and the ground receiving terminal receives the received sequence;

[0184] Performing channel modeling processing on the channel detection sequence and the receiving sequence to obtain channel model information; the channel model information is a channel model matrix;

[0185] The performing channel modeling processing on the channel sounding sequence and the receiving sequence to obtain channel model information includes:

[0186] Segmenting the channel detection sequence and the receiving sequence respectively to obtain a plurality of subsequences;

[0187] Using all subsequences of the channel sounding sequence and receiving sequence respectively, construct a sounding matrix and a receiving matrix;

[0188] Calculating and processing the detection matrix and the receiving matrix to obtain a channel model matrix;

[0189] The calculation expression of the channel model matrix is:

[0190]

[0191] Where H is the channel model matrix, cov(,) represents the calculated covariance matrix, D(X) represents the divergence matrix of the detection matrix X, D(Y) represents the divergence matrix of the reception matrix Y, a1 and b1 are the eigenvectors corresponding to the maximum eigenvalues ​​of the detection matrix and the reception matrix respectively;

[0192] The channel coding model is obtained by multiplying the channel model matrix H with the matrix corresponding to the detection information set to obtain a coding information matrix; and all row vectors of the coding information matrix are used to construct the coding information set.

[0193] The eigenvector calculation process may be implemented by using a matrix eigenvector calculation method, such as a matrix eigenvalue decomposition method;

[0194] The step of performing eigenvector calculation processing on the cross-correlation matrix to obtain the eigenvector comprises:

[0195] Performing Gabor transformation on each signal sequence included in the third signal to obtain a corresponding transformation sequence;

[0196] Using all transformation sequences, a transformation matrix is ​​constructed;

[0197] Performing a first transformation process on the mutual correlation matrix and the transformation matrix to obtain a transformation feature matrix;

[0198] The expression of the first transformation process is:

[0199] C=A 1 / 2 RA -1 / 2 ,

[0200] Among them, A is the transformation matrix, R is the cross-correlation matrix, and C is the transformation feature matrix;

[0201] Performing eigendecomposition processing on the transformation feature matrix to obtain a eigenvector.

[0202] The eigenvalue decomposition process can be implemented by using an eigenvalue decomposition algorithm of a matrix.

[0203] The fitting calculation process is performed on the elements and element sequence values ​​of the feature vector to obtain a fitting polynomial, including:

[0204] The elements of the characteristic vector are used as dependent variables, the element sequence values ​​of the characteristic vector are used as independent variables, and polynomial fitting processing is performed on the dependent variable and the independent variable to obtain a fitting polynomial.

[0205] The step of using the fitting polynomial to perform deviation discrimination processing on each signal sequence included in the third signal to obtain a discriminated signal sequence includes:

[0206] The element sequence value in each signal sequence included in the third signal is calculated and processed by using the fitting polynomial to obtain a corresponding discriminant value;

[0207] It is determined whether the difference between the discrimination value and the element corresponding to the element sequence number value is greater than a set discrimination threshold, and the elements greater than the discrimination threshold are deleted from the signal sequence to obtain a discriminated signal sequence.

[0208] 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. A data acquisition and circulation device for an implantable brain-computer interface for space capsule animals, characterized in that: include: Brain-computer interface module, data acquisition module, intelligent processing module, data flow module; The brain-computer interface module is used to collect the brain electrical signals of the animal; The data acquisition module is connected to the brain-computer interface module and is used to pre-process the collected EEG signals to obtain collected signals; The intelligent processing module is connected to the data acquisition module and is used to perform detection and extraction processing on the collected signals to obtain a detection information set; The data transfer module is connected to the intelligent processing module, and is used to transmit and process the detection information set, and send the detection information set from the space capsule to the ground receiving terminal.

2. The data acquisition and circulation device for the space capsule animal implantable brain-computer interface as claimed in claim 1, characterized in that: The data acquisition module pre-processes the acquired EEG signal to obtain the acquired signal, including: The data acquisition module performs data cleaning processing on the collected EEG signal to obtain a first signal; Performing time alignment processing on the first signal to obtain a second signal; performing normalization processing on the second signal to obtain a third signal; The third signal is subjected to data paradigm discrimination processing to obtain an acquisition signal.

3. The data acquisition and circulation device for the space capsule animal implantable brain-computer interface as claimed in claim 1, characterized in that: The intelligent processing module performs detection and extraction processing on the collected signal to obtain a detection information set, including: The intelligent processing module performs pattern discrimination processing on the collected signal to obtain EEG pattern type information; Performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information; The detection information set is constructed using the EEG pattern type information and abnormal feature information.

4. A data collection and circulation method for a space capsule animal implantable brain-computer interface, characterized in that: The data acquisition and circulation device of the space capsule animal implantable brain-computer interface according to any one of claims 1 to 3 is used to realize the invention, comprising: S1, using the brain-computer interface module to collect the brain electrical signal of the animal; S2, using the data acquisition module to pre-process the acquired EEG signal to obtain an acquired signal; S3, using the intelligent processing module to perform detection and extraction processing on the collected signal to obtain a detection information set; S4, utilizing the data transfer module to perform transmission processing on the detection information set, and sending the detection information set from the space capsule to a ground receiving terminal.

5. The data collection and circulation method of the space capsule animal implantable brain-computer interface as claimed in claim 4, characterized in that: The step of preprocessing the collected EEG signal to obtain the collected signal includes: S21, performing data cleaning processing on the collected EEG signal to obtain a first signal; S22, performing time alignment processing on the first signal to obtain a second signal; S23, normalizing the second signal to obtain a third signal; S24, performing data paradigm discrimination processing on the third signal to obtain a collection signal.

6. The data collection and circulation method of the space capsule animal implantable brain-computer interface as claimed in claim 5, characterized in that: The performing data paradigm discrimination processing on the third signal to obtain a collection signal comprises: S241, performing cross-correlation calculation processing on all signal sequences included in the third signal to obtain a cross-correlation matrix; S242, performing eigenvector calculation processing on the cross-correlation matrix to obtain an eigenvector; S243, performing fitting calculation processing on the elements and element sequence numbers of the feature vector to obtain a fitting polynomial; S244, using the fitting polynomial, performing deviation discrimination processing on each signal sequence included in the third signal to obtain a discriminated signal sequence; S245, constructing a collection signal using all the identified signal sequences.

7. The data collection and circulation method of the space capsule animal implantable brain-computer interface as claimed in claim 5, characterized in that: The detecting and extracting process is performed on the collected signal to obtain a detection information set, including: S31, performing pattern discrimination processing on the collected signal to obtain EEG pattern type information; S32, performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information; S33, constructing a detection information set using the EEG pattern type information and abnormal feature information.

8. The data collection and circulation method of the space capsule animal implantable brain-computer interface as claimed in claim 7, characterized in that: The performing pattern discrimination processing on the collected signal to obtain EEG pattern type information includes: S311, constructing a collection signal matrix using all signal sequences contained in the collection signal; a row vector of the collection signal matrix is ​​a signal sequence; S312, performing mode value calculation processing on the collected signal matrix to obtain a mode value; S313, using a preset mode value distribution interval set, performing a value interval discrimination process on the mode value to obtain a value interval corresponding to the mode value; the mode value distribution interval set includes a plurality of value intervals and corresponding mode type information; S314, determining that the pattern type information corresponding to the value interval corresponding to the pattern value is the EEG pattern type information; The mode value calculation process includes: Wherein, M and N are the row dimension and column dimension of the acquisition signal matrix respectively, z ij is the element of the i-th row and j-th column of the acquisition signal matrix, is the i-th element of the first signal vector, is the i-th element of the second signal vector, p i is the i-th element of the third signal vector, and H1 is the mode value.

9. The data collection and circulation method of the space capsule animal implantable brain-computer interface as claimed in claim 7, characterized in that: The performing abnormal feature extraction processing on the collected signal to obtain abnormal feature information includes: S321, performing statistical processing on the collected signal to obtain a statistical value set; the statistical value set includes: mean, variance, average derivative, median value, and mode value; S322, performing abnormal amount calculation processing on the statistical value set to obtain abnormal feature information; the abnormal feature information is used to characterize the abnormal degree value of the collected EEG signal; The expression for the abnormal amount calculation process is: Where P represents the order value, N is the column dimension of the acquisition signal matrix, Indicates rounding up, L P () represents the Pth Laguerre polynomial, u, δ, κ and λ represent the mean, variance, average derivative, median value and mode value respectively, and τ is the abnormal feature information.

10. The data collection and circulation method of the space capsule animal implantable brain-computer interface as claimed in claim 5, characterized in that: The transmitting and processing the detection information set, and sending the detection information set from the space capsule to a ground receiving terminal, includes: S41, modeling the channel transmission link to obtain channel model information; S42, constructing a channel coding model based on the channel model information; S43, using the channel coding model, encoding the detection information set to obtain a coded information set; S44, performing modulation processing on the coded information set to obtain a modulated signal; S45, sending the modulated signal from the space capsule to a ground receiving terminal.

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