Method and device for testing brain-computer interface equipment in complex electromagnetic environment

Abstract

The application discloses a test method and device for a brain-computer interface device in a complex electromagnetic environment, and the method comprises the following steps: acquiring a signal parameter information set; the signal parameter information set comprises signal parameter information; the signal parameter information comprises frequency, amplitude and phase; a standard electroencephalogram signal sequence is collected by using a brain-computer interface device to be tested; a complex electromagnetic signal is generated according to the signal parameter information set, and a set of electroencephalogram signal sequences in a complex electromagnetic signal environment is measured; and the signal parameter information set, the standard electroencephalogram signal sequence and the set of electroencephalogram signal sequences are evaluated and processed to obtain evaluation result information of the brain-computer interface device in the complex electromagnetic environment.

Description

Technical Field

[0001] This invention relates to the field of brain-computer interface technology, and specifically to a testing method and apparatus for brain-computer interface devices in complex electromagnetic environments. Background Technology

[0002] With the rapid development of brain-computer interface (BCI) technology, its applications in medical, rehabilitation, and entertainment fields are becoming increasingly widespread. However, in practical applications, BCIs are often affected by interference from complex electromagnetic environments, impacting their performance and reliability. Currently, there is a relative lack of testing methods and devices for BCIs in complex electromagnetic environments, making it impossible to effectively evaluate their operation in real-world electromagnetic environments. Summary of the Invention

[0003] This invention primarily addresses the problem of evaluating the operational status of brain-computer interfaces in real electromagnetic environments. It discloses a testing method and apparatus for brain-computer interface devices in complex electromagnetic environments.

[0004] In a first aspect, this invention discloses a testing method for a brain-computer interface device under complex electromagnetic environments, comprising:

[0005] S1, acquire a set of signal parameter information; the set of signal parameter information includes signal parameter information; the signal parameter information includes frequency, amplitude and phase;

[0006] S2, using the brain-computer interface device to be tested, acquires a standard EEG signal sequence;

[0007] S3, Based on the set of signal parameter information, generate complex electromagnetic signals and measure the set of EEG signal sequences under complex electromagnetic signal environment;

[0008] S4, evaluate and process the signal parameter information set, standard EEG signal sequence, and EEG signal sequence set to obtain the evaluation result information of the brain-computer interface device in a complex electromagnetic environment.

[0009] The process of generating complex electromagnetic signals based on the set of signal parameter information and measuring a set of EEG signal sequences under complex electromagnetic signal conditions includes:

[0010] S31, generate a corresponding complex electromagnetic signal based on each signal parameter information in the signal parameter information set;

[0011] S32, The brain-computer interface device is placed in the environment of the complex electromagnetic signal to acquire the brain signal sequence;

[0012] S33, for each signal parameter information in the signal parameter information set, execute S31 to S32 to construct a set of brainwave signal sequences using all the acquired brainwave signal sequences.

[0013] The evaluation and processing of the signal parameter information set, standard EEG signal sequence, and EEG signal sequence set to obtain evaluation result information of brain-computer interface devices in complex electromagnetic environments includes:

[0014] S41, The signal parameter information set, standard EEG signal sequence and EEG signal sequence set are modeled and processed to obtain the EEG signal integrity prediction model;

[0015] S42, Perform feature extraction processing on the signal parameter information set to obtain feature signal parameters;

[0016] S43, using the EEG signal integrity prediction model, the feature signal parameters are calculated and processed to obtain integrity assessment information;

[0017] S44, Perform volatility assessment processing on the standard EEG signal sequence and the set of EEG signal sequences to obtain volatility assessment information;

[0018] S45, using the integrity assessment information and volatility assessment information, construct the assessment result information of the brain-computer interface device under complex electromagnetic environment.

[0019] The process of modeling the signal parameter information set, standard EEG signal sequence, and EEG signal sequence set to obtain an EEG signal integrity prediction model includes:

[0020] S411, For each EEG signal sequence in the set of EEG signal sequences, subtract the standard EEG signal sequence to obtain the corresponding difference sequence;

[0021] S412, using all the difference sequences, construct the difference matrix; the row vectors of the difference matrix are the difference sequences;

[0022] S413, using the signal parameter information set, a signal parameter matrix is ​​constructed; the row vectors of the signal parameter matrix are the signal parameter information.

[0023] S414, using the difference matrix and signal parameter matrix, an integrity optimization model is constructed;

[0024] S415, Solve the integrity optimization model to obtain the calculation result of the evaluation matrix;

[0025] S416. Using the calculation results of the evaluation matrix, a prediction model for the integrity of EEG signals is constructed.

[0026] The process of performing volatility assessment on the standard EEG signal sequence and the set of EEG signal sequences to obtain volatility assessment information includes:

[0027] S441, For each EEG signal sequence in the set of EEG signal sequences, subtract the standard EEG signal sequence to obtain the corresponding difference sequence;

[0028] S442, using all the difference sequences, construct the difference matrix;

[0029] S443, Perform deviation calculation on the difference matrix to obtain the deviation vector;

[0030] S444, Perform correlation calculation on the difference matrix to obtain the correlation matrix; the element in the i-th row and j-th column of the correlation matrix is ​​obtained by multiplying the vector in the i-th row and the vector in the j-th row of the difference matrix;

[0031] S445, Perform eigenvalue decomposition on the correlation matrix to obtain an eigenvalue vector; the eigenvalue vector is a vector obtained by arranging all the eigenvalues ​​of the correlation matrix in descending order of their values.

[0032] S446, Perform a vector dot product on the deviation vector and the eigenvalue vector to obtain volatility assessment information.

[0033] The deviation calculation process is expressed as follows:

[0034] t=(P T V+aI) -1 P T y,

[0035] V = QR -1 ,

[0036] Where t is the calculated deviation vector, Q and R are the Q matrix and R matrix obtained by QR decomposition of the difference matrix P, respectively, V is the intermediate matrix, a is the largest eigenvalue of the difference matrix P, and y is the eigenvector of the difference matrix P.

[0037] A second aspect of this invention discloses a testing apparatus for brain-computer interface devices in complex electromagnetic environments, used to implement the testing method for brain-computer interface devices in complex electromagnetic environments, comprising:

[0038] Complex signal generation module and evaluation module;

[0039] The complex signal generation module is connected to the evaluation model and is used to acquire a set of signal parameter information and generate complex electromagnetic signals based on the set of signal parameter information.

[0040] The evaluation module is used to collect a set of EEG signal sequences, evaluate and process the set of signal parameter information, the standard EEG signal sequence, and the set of EEG signal sequences, and obtain the evaluation result information of the brain-computer interface device in a complex electromagnetic environment.

[0041] According to a third aspect of this invention, a testing device for brain-computer interface devices in complex electromagnetic environments is disclosed, the device comprising:

[0042] Memory containing executable program code;

[0043] A processor coupled to the memory;

[0044] The processor calls the executable program code stored in the memory to execute the test method for brain-computer interface devices in complex electromagnetic environments.

[0045] In a fourth aspect, the present invention discloses a computer-storable medium storing computer instructions, wherein the computer instructions, when invoked by a computer, are used to execute the testing method for a brain-computer interface device in a complex electromagnetic environment.

[0046] In a fifth aspect, the present invention discloses an information data processing terminal, which is used to implement the testing method for brain-computer interface devices in complex electromagnetic environments.

[0047] The beneficial effects of this invention are as follows:

[0048] This invention can realistically simulate complex electromagnetic environments, enabling comprehensive and accurate performance testing of brain-computer interfaces (BCIs), and providing reliable technical support for their research and application. This invention helps identify potential problems with BCIs under electromagnetic interference, allowing for proactive optimization and improvement, thereby enhancing the BCI's anti-interference capabilities and reliability. This invention can provide data support for the development of relevant standards, promoting the standardized development of BCI technology.

[0049] When evaluating brain-computer interface devices in complex electromagnetic environments, this invention proposes to evaluate brain-computer interface devices from two aspects: integrity evaluation information and volatility evaluation information, based on the characteristics of electroencephalogram (EEG) signals. A corresponding evaluation algorithm has been specifically established, which can be used to achieve efficient and accurate evaluation of the above two indicators. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0051] To better understand the content of this invention, an embodiment is provided here.

[0052] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0053] In a first aspect, this invention discloses a testing method for a brain-computer interface device under complex electromagnetic environments, comprising:

[0054] S1, acquire a set of signal parameter information; the set of signal parameter information includes signal parameter information; the signal parameter information includes frequency, amplitude and phase;

[0055] S2, using the brain-computer interface device to be tested, acquires a standard EEG signal sequence;

[0056] S3, Based on the set of signal parameter information, generate complex electromagnetic signals and measure the set of EEG signal sequences under complex electromagnetic signal environment;

[0057] S4, evaluate and process the signal parameter information set, standard EEG signal sequence and EEG signal sequence set to obtain the evaluation result information of brain-computer interface device in complex electromagnetic environment;

[0058] The process of generating complex electromagnetic signals based on the set of signal parameter information and measuring a set of EEG signal sequences under complex electromagnetic signal conditions includes:

[0059] S31, generate a corresponding complex electromagnetic signal based on each signal parameter information in the signal parameter information set;

[0060] S32, The brain-computer interface device is placed in the environment of the complex electromagnetic signal to acquire the brain signal sequence;

[0061] S33, for each signal parameter information in the signal parameter information set, execute S31 to S32, and construct a set of brainwave signal sequences using all the acquired brainwave signal sequences;

[0062] The evaluation and processing of the signal parameter information set, standard EEG signal sequence, and EEG signal sequence set to obtain evaluation result information of brain-computer interface devices in complex electromagnetic environments includes:

[0063] S41, The signal parameter information set, standard EEG signal sequence and EEG signal sequence set are modeled and processed to obtain the EEG signal integrity prediction model;

[0064] S42, Perform feature extraction processing on the signal parameter information set to obtain feature signal parameters;

[0065] S43, using the EEG signal integrity prediction model, the feature signal parameters are calculated and processed to obtain integrity assessment information;

[0066] S44, Perform volatility assessment processing on the standard EEG signal sequence and the set of EEG signal sequences to obtain volatility assessment information;

[0067] S45, using the integrity assessment information and volatility assessment information, construct the assessment result information of the brain-computer interface device under complex electromagnetic environment.

[0068] The process of modeling the signal parameter information set, standard EEG signal sequence, and EEG signal sequence set to obtain an EEG signal integrity prediction model includes:

[0069] For each EEG signal sequence in the set of EEG signal sequences, subtract the standard EEG signal sequence to obtain the corresponding difference sequence;

[0070] Using all the difference sequences, a difference matrix is ​​constructed; the row vectors of the difference matrix are the difference sequences.

[0071] Using the aforementioned set of signal parameter information, a signal parameter matrix is ​​constructed; the row vectors of the signal parameter matrix represent the signal parameter information.

[0072] Using the difference matrix and signal parameter matrix, an integrity optimization model is constructed.

[0073] Solve the integrity optimization model to obtain the calculation result A of the evaluation matrix;

[0074] Using the calculation results of the evaluation matrix, a prediction model for the integrity of EEG signals is constructed.

[0075] The process of performing volatility assessment on the standard EEG signal sequence and the set of EEG signal sequences to obtain volatility assessment information includes:

[0076] S441, For each EEG signal sequence in the set of EEG signal sequences, subtract the standard EEG signal sequence to obtain the corresponding difference sequence;

[0077] S442, using all the difference sequences, construct the difference matrix;

[0078] S443, Perform deviation calculation on the difference matrix to obtain the deviation vector;

[0079] S444, Perform correlation calculation on the difference matrix to obtain the correlation matrix; the element in the i-th row and j-th column of the correlation matrix is ​​obtained by multiplying the vector in the i-th row and the vector in the j-th row of the difference matrix;

[0080] S445, Perform eigenvalue decomposition on the correlation matrix to obtain an eigenvalue vector; the eigenvalue vector is a vector obtained by arranging all the eigenvalues ​​of the correlation matrix in descending order of their values.

[0081] S446, Perform a vector dot product on the deviation vector and the eigenvalue vector to obtain volatility assessment information;

[0082] The deviation calculation process is expressed as follows:

[0083] g=(P T V+aI) -1 P T z,

[0084] V = QR -1 ,

[0085] Where g is the calculated deviation vector, Q and R are the Q matrix and R matrix obtained by QR decomposition of the difference matrix P, respectively, V is the intermediate matrix, a is the largest eigenvalue of the difference matrix P, and z is the eigenvector of the difference matrix P.

[0086] The eigenvalue decomposition process can be implemented using a matrix eigenvalue decomposition algorithm.

[0087] The integrity optimization model is expressed as follows:

[0088]

[0089] subject to AA T =I A ,

[0090] Among them, I A Let E be the identity matrix with the row dimensions of matrix A, and let E be the integrity difference matrix. ij Let E represent the element in the i-th row and j-th column of the integrity difference matrix. The expression for E is:

[0091] E = W(KA - P),

[0092] Where W is the weighted transformation matrix and K is the signal parameter matrix. Considering the influence on each item in the sequence corresponding to the signal parameter information set, it can be taken as a two-dimensional angular discrete matrix with dimensions Hr×Hs. The element in the i-th row and j-th column is represented as W. ij =cos(2πi / Hr+θ1)sin(2πj / Hs+θ2), where θ1 and θ2 are the starting angles of the weighted transformation matrix;

[0093] The integrity optimization model is solved to obtain the calculation result A of the evaluation matrix, including:

[0094] S101, using the initial evaluation matrix A0 as the initial solution, determine the increment matrix ΔA;

[0095] S102, the objective function Let f(A) be a function of matrix A;

[0096] S103, using the elements of matrix A as independent variables, obtain the first-order partial derivative matrix of f(A) with respect to the independent variables at the value A0.

[0097] S104, Construct the first solution equation for the iterative increment matrix ΔA0:

[0098]

[0099] S105, Solve the first equation to obtain the value of the iteration increment matrix ΔA0; Determine whether |ΔA0| is less than a set discrimination threshold. If it is less than the set discrimination threshold, determine A0+ΔA0 as the calculation result A; otherwise, replace A0 with A0+ΔA0 and execute S103.

[0100] The step of performing feature extraction processing on the signal parameter information set to obtain feature signal parameters includes:

[0101] Using the aforementioned set of signal parameter information, a signal parameter matrix is ​​constructed; the row vectors of the signal parameter matrix represent the signal parameter information.

[0102] The signal parameter matrix is ​​decomposed to obtain the feature matrix;

[0103] Extract the diagonal elements of the feature matrix to obtain the feature vector;

[0104] The feature vector is confirmed to be a feature signal parameter;

[0105] The vector corresponding to the feature signal parameters is the feature vector.

[0106] The decomposition process is calculated using the following expression:

[0107] Y = UAV,

[0108] Where U is the left decomposition matrix, Y is the signal parameter matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0109] The EEG signal integrity prediction model includes:

[0110] Using the calculation result A of the evaluation matrix, a complete deviation vector estimation sub-model is constructed; the expression of the complete deviation vector estimation sub-model is:

[0111] χ=ρA,

[0112] Where ρ is the vector corresponding to the feature signal parameters, and χ is the fusion deviation vector;

[0113] The fusion deviation vector is compared with the standard EEG signal sequence by deviation fusion calculation to obtain integrity assessment information;

[0114] The expression for the deviation fusion calculation process is:

[0115]

[0116] Where qa represents integrity assessment information, and T i () denotes the i-th order polynomial of the first kind of Chebyshev polynomial, N1 represents the length of the standard EEG signal sequence, and ρ i μ represents the i-th element of the vector corresponding to the characteristic signal parameters. i This represents the i-th element of a standard EEG signal sequence.

[0117] According to a second aspect of the present invention, a testing apparatus for a brain-computer interface device under complex electromagnetic environments is disclosed, the apparatus comprising:

[0118] Memory containing executable program code;

[0119] A processor coupled to the memory;

[0120] The processor calls the executable program code stored in the memory to execute the test method for brain-computer interface devices in complex electromagnetic environments.

[0121] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the testing method for a brain-computer interface device in a complex electromagnetic environment.

[0122] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the testing method for brain-computer interface devices in complex electromagnetic environments.

[0123] A fifth aspect of this invention discloses a testing apparatus for brain-computer interface devices in complex electromagnetic environments, used to implement the testing method for brain-computer interface devices in complex electromagnetic environments, comprising:

[0124] Complex signal generation module and evaluation module;

[0125] The complex signal generation module is connected to the evaluation model and is used to acquire a set of signal parameter information and generate complex electromagnetic signals based on the set of signal parameter information.

[0126] The evaluation module is used to collect a set of EEG signal sequences, evaluate and process the set of signal parameter information, the standard EEG signal sequence, and the set of EEG signal sequences, and obtain the evaluation result information of the brain-computer interface device in a complex electromagnetic environment.

[0127] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for testing a brain-computer interface device in a complex electromagnetic environment, characterized in that, The method comprises the following steps: S1, acquiring a signal parameter information set; the signal parameter information set comprises signal parameter information; The signal parameter information comprises frequency, amplitude and phase; S2, collecting a standard electroencephalogram signal sequence by using a brain-computer interface device to be tested; S3, generating a complex electromagnetic signal according to the signal parameter information set, and measuring an electroencephalogram signal sequence set in a complex electromagnetic signal environment; S4, evaluating the signal parameter information set, the standard electroencephalogram signal sequence and the electroencephalogram signal sequence set to obtain evaluation result information of the brain-computer interface device in the complex electromagnetic environment.

2. The method of testing a brain-computer interface device in a complex electromagnetic environment of claim 1, wherein, The step of generating a complex electromagnetic signal according to the signal parameter information set, and measuring an electroencephalogram signal sequence set in a complex electromagnetic signal environment comprises the following steps: S31, generating a corresponding complex electromagnetic signal according to each signal parameter information in the signal parameter information set; S32, setting the brain-computer interface device in the complex electromagnetic signal environment to collect an electroencephalogram signal sequence; S33, performing S31 to S32 for each signal parameter information in the signal parameter information set, and constructing an electroencephalogram signal sequence set by using all the collected electroencephalogram signal sequences.

3. The method of testing a brain-computer interface device in a complex electromagnetic environment of claim 1, wherein, The step of evaluating the signal parameter information set, the standard electroencephalogram signal sequence and the electroencephalogram signal sequence set to obtain evaluation result information of the brain-computer interface device in the complex electromagnetic environment comprises the following steps: S41, modeling the signal parameter information set, the standard electroencephalogram signal sequence and the electroencephalogram signal sequence set to obtain an electroencephalogram signal integrity prediction model; S42, extracting feature signal parameters from the signal parameter information set; S43, calculating the feature signal parameters by using the electroencephalogram signal integrity prediction model to obtain integrity evaluation information; S44, performing fluctuation evaluation processing on the standard electroencephalogram signal sequence and the electroencephalogram signal sequence set to obtain fluctuation evaluation information; S45, constructing the evaluation result information of the brain-computer interface device in the complex electromagnetic environment by using the integrity evaluation information and the fluctuation evaluation information.

4. The method of testing a brain-computer interface device in a complex electromagnetic environment of claim 3, wherein, The step of modeling the signal parameter information set, the standard electroencephalogram signal sequence and the electroencephalogram signal sequence set to obtain an electroencephalogram signal integrity prediction model comprises the following steps: S411, subtracting each electroencephalogram signal sequence in the electroencephalogram signal sequence set from the standard electroencephalogram signal sequence to obtain a corresponding difference sequence; S412, constructing a difference matrix by using all the difference sequences; a row vector of the difference matrix is a difference sequence; S413, constructing a signal parameter matrix by using the signal parameter information set; a row vector of the signal parameter matrix is signal parameter information; S414, constructing an integrity optimization model by using the difference matrix and the signal parameter matrix; S415, solving the integrity optimization model to obtain a calculation result of an evaluation matrix; S416, constructing the electroencephalogram signal integrity prediction model by using the calculation result of the evaluation matrix.

5. The method of testing a brain-computer interface device in a complex electromagnetic environment of claim 3, wherein, The fluctuation evaluation processing on the standard electroencephalogram sequence and the electroencephalogram sequence set obtains fluctuation evaluation information, and comprises the following steps: S441, subtracting each electroencephalogram sequence in the electroencephalogram sequence set from the standard electroencephalogram sequence to obtain a corresponding difference sequence; S442, constructing a difference matrix by using all the difference sequences; S443, performing a deviation calculation processing on the difference matrix to obtain a deviation vector; S444, performing a correlation calculation processing on the difference matrix to obtain a correlation matrix; the element in the ith row and jth column of the correlation matrix is obtained by multiplying the ith row vector and the jth row vector in the difference matrix; S445, performing a characteristic decomposition processing on the correlation matrix to obtain an eigenvalue vector; the eigenvalue vector is a vector obtained by arranging all eigenvalues of the correlation matrix in descending order according to the value; S446, performing a vector dot product on the deviation vector and the eigenvalue vector to obtain the fluctuation evaluation information.

6. The method of testing a brain-computer interface device in a complex electromagnetic environment of claim 5, wherein, The deviation calculation processing has an expression as follows: t = (P T V + aI) -1 P T y, V = QR -1 , Where t is the calculated deviation vector, Q and R are a Q matrix and an R matrix obtained by QR decomposition on the difference matrix P, V is an intermediate matrix, a is the largest eigenvalue of the difference matrix P, and y is an eigenvector of the difference matrix P.

7. A testing device for a brain-computer interface device in a complex electromagnetic environment, characterized in that The test method for the brain-computer interface device in a complex electromagnetic environment according to any one of claims 1 to 6 comprises: a complex signal generation module and an evaluation module; The complex signal generation module is connected with the evaluation module, and is configured to obtain a signal parameter information set, and generate a complex electromagnetic signal according to the signal parameter information set; The evaluation module is configured to collect an electroencephalogram sequence set, and perform an evaluation processing on the signal parameter information set, a standard electroencephalogram sequence and the electroencephalogram sequence set to obtain evaluation result information of the brain-computer interface device in the complex electromagnetic environment.

8. A testing device for a brain-computer interface device in a complex electromagnetic environment, characterized in that The device comprises: a memory storing executable program codes; a processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute the test method for the brain-computer interface device in the complex electromagnetic environment according to any one of claims 1 to 6.

9. A computer storable medium, characterized by The computer storage medium stores computer instructions, which are invoked by a computer to execute the test method for the brain-computer interface device in the complex electromagnetic environment according to any one of claims 1 to 6.

10. An information data processing terminal, characterized by The information data processing terminal is configured to implement the test method for the brain-computer interface device in the complex electromagnetic environment according to any one of claims 1 to 6.

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