ECoG electrode performance monitoring and hardware parameter optimization method, device and equipment

Abstract

The present invention discloses a method, device, and equipment for monitoring ECoG electrode performance and optimizing hardware parameters, relating to the field of medical technology assistance. The method includes performing impedance and parameter testing on ECoG electrodes, collecting signals from each channel of the ECoG electrodes for signal analysis; evaluating the performance status of the target ECoG electrode based on the signal analysis and test results; and determining hardware issues with the target ECoG electrode based on the signal analysis results and optimizing the parameters. This application can evaluate electrode status and trace issues, and can also calculate the theoretically optimal electrode parameter combination.

Description

Technical Field

[0001] The present application relates to the field of medical technology assistance, and specifically to a method, device and equipment for ECoG electrode performance monitoring and hardware parameter optimization. Background Art

[0002] Currently, ECoG (Electrocorticography) electrodes are designed to be implanted long-term in the skull, transmitting the signals collected by the electrodes to an external host computer for algorithm decoding and targeted application. Currently, ECoG electrode hardware testing generally involves electrochemical testing, impedance testing, and quality control inspections before implantation. If the current version of the electrode does not meet requirements and requires improvement, the corresponding target size parameters must be determined through experiments using a large number of electrodes with different specifications.

[0003] After ECoG electrodes are implanted in the brain, electrode performance is generally judged based on factors such as the signal-to-noise ratio and impedance. This method of judgment lacks comprehensiveness, and technologies that reverse-engineer the current performance of electrodes based on signal decoding results in combination with actual application scenarios are rare. Strictly speaking, optimizing and improving hardware based on issues exposed by the electrode's operating status is more of a qualitative improvement. Parameters such as contact size, contact spacing, and number of contacts are not directly calculated based on accumulated experimental data from previous experiments. Instead, a large number of experiments are required to select the best-performing electrode from all electrodes used in the next experiment as the revised electrode. However, the best electrode selected is only the relatively best among the current batch of electrodes manufactured, and is not the theoretically optimal electrode. Summary of the Invention

[0004] The present application provides an ECoG electrode performance monitoring and hardware parameter optimization method, device and equipment, which can evaluate the electrode status and trace problems, and can also calculate the theoretical optimal electrode parameter combination.

[0005] In a first aspect, an embodiment of the present application provides an ECoG electrode performance monitoring and hardware parameter optimization method, the ECoG electrode performance monitoring and hardware parameter optimization method comprising:

[0006] Conduct impedance and parameter tests on ECoG electrodes, and collect signals from each channel of the ECoG electrodes for signal analysis;

[0007] Evaluate the performance status of the target ECoG electrode based on the signal analysis and test results;

[0008] Based on the signal analysis results, hardware issues of the target ECoG electrode are identified and parameter optimization is achieved.

[0009] In conjunction with the first aspect, in one embodiment,

[0010] The impedance test is to perform impedance testing on each channel of the ECoG electrode to obtain the impedance test results:

[0011]

[0012] Among them, R represents the impedance test result of the ECoG electrode, R mn R represents the impedance of the contact of the mth row and nth column of the ECoG electrode, where each contact of the ECoG electrode is a channel, m is the total number of rows of ECoG electrode contacts, n is the total number of columns of ECoG electrode contacts, and R ij represents the impedance of the contact at row i and column j of the ECoG electrode;

[0013] The parameter test is a size parameter test of the ECoG electrode, including contact size C1, contact row spacing C2, contact column spacing C3, and contact distribution area C4. The size parameter test result is C=[C1, C2, C3, C4].

[0014] In conjunction with the first aspect, in one embodiment,

[0015] The signal analysis includes bad track screening, impedance analysis, signal-to-noise ratio analysis, and decoding;

[0016] The bad track screening is used to evaluate whether the ECoG electrode channel is abnormal, so as to evaluate the ECoG electrode channel status. When any judgment condition is met, it is determined that the current channel is abnormal during the current signal acquisition time period.

[0017] The judgment conditions include: all the data collected at each moment are 0 values, random outlier interference at random moments and the number of occurrences is less than a preset number, the data is clearly segmented and there is random outlier interference at the segmentation moment, the data baseline is lower than the set baseline value and the amplitude is less than the preset amplitude, the data is interfered with by a specific outlier at random moments and the number of occurrences is greater than a set number, the data baseline shows a decreasing trend and there is interference with a specific outlier at random moments and the number of occurrences is greater than a set threshold;

[0018] The evaluation of the ECoG electrode channel status specifically includes:

[0019] Within the total time range of ECoG electrode signal acquisition, the proportion of time each channel has abnormalities is counted. Based on the proportion of time the channel has abnormalities, channels with high abnormality among ECoG electrodes are eliminated. Specifically:

[0020]

[0021] Among them, τ ij It represents the time proportion of abnormality in the channel of row i and column j in the ECoG electrode. ijWhen ≥α, the current channel is judged to be a channel with a high degree of abnormality. tT represents the total time range length of ECoG electrode signal acquisition, Δt ij represents the total time that the channel in row i and column j of the ECoG electrode has abnormalities within the total time range of signal acquisition, and α represents the threshold;

[0022] For the channels retained in the ECoG electrodes, create a channel set U1 and mark the time intervals where abnormalities occur in the retained channels: tA ij =[[t t1 , t t2 ],[t t3 , t t4 ]…[t tp , t tp+1 ]],tA ij Indicates the time interval when the channel in row i and column j of the ECoG electrode is abnormal, t tp Indicates the start time of the pth abnormality, t tp+1 Indicates the end time when the pth exception occurs;

[0023] Eliminate the impedance of the channel with abnormally high impedance in R and obtain R1.

[0024] In conjunction with the first aspect, in one embodiment, impedance analysis specifically includes:

[0025] The dynamic change of impedance of each channel in U1 is stored to obtain the set R U1 ;

[0026] According to the marking results of the abnormal time interval of each channel in U1, R U1 The corresponding impedance value fragments of each channel in are removed to obtain R U1t ;

[0027] Calculate R U1t The ratio of the impedance variance of each channel to the average impedance is used to obtain the impedance variation coefficient matrix

[0028] R U1t The impedance values ​​of each channel are fitted by the least square method to obtain the fitting approximate straight line l for each channel. R1t , calculate l R1t The slope gets the impedance slope matrix k R1t ;

[0029] Calculate the impedance value of each channel in R1 to l R1t The impedance value offset distance matrix d is obtained R1t .

[0030] In conjunction with the first aspect, in one embodiment,

[0031] For signal-to-noise ratio analysis, specifically including:

[0032] Perform sliding window cropping on the signals of each channel in U1 and calculate the signal-to-noise ratio of each window segment to form the signal-to-noise ratio matrix SNR1. Each channel in SNR1 contains multiple signal-to-noise ratio values.

[0033] Calculate the ratio of the signal-to-noise ratio variance of each channel in SNR1 to the average signal-to-noise ratio to obtain the signal-to-noise ratio variation coefficient matrix

[0034] Calculate the signal-to-noise ratio of each channel in SNR1 and perform least squares fitting to obtain the slope value of the fitting approximate straight line of each channel, and obtain the signal-to-noise ratio slope matrix k SNR1 ;

[0035] For decoding, specifically including:

[0036] Based on the sliding window segments of each channel in U1, the decoding model is trained to obtain the decoding accuracy matrix S of each sliding window of each channel. U1 ;

[0037] Calculate the mean of the decoding accuracy of all window segments of each channel to obtain the decoding accuracy matrix S U1-mean .

[0038] In conjunction with the first aspect, in one embodiment, the performance status evaluation of the target ECoG electrode based on the signal analysis results and the test results specifically includes:

[0039] R U1t The signals of each channel in the sliding window are clipped to obtain multiple segments, and the mean of each segment is calculated to obtain the set R U1t-mean ;

[0040] R U1t-mean 、SNR1、S U1 The values ​​of each channel in R are transposed and stored in columns, and the transposed R U1t-mean 、SNR1、S U1 Perform concat splicing to obtain a fusion matrix, where each row in the fusion matrix represents a time segment, and each time segment corresponds to the performance state of an electrode;

[0041] Based on the experimental data, the performance status of ECoG electrodes at different times is collected and made into status labels. U1 ;

[0042] Each row of the fusion matrix is ​​used as training data, and label is used as U1 For labels, build an electrode performance status prediction model;

[0043] The signal data generated by the target ECoG electrode in the actual application state is input into the electrode performance state prediction model to obtain the performance state evaluation result of the target ECoG electrode at the current moment.

[0044] In conjunction with the first aspect, in one embodiment, determining the hardware problem of the target ECoG electrode based on the signal analysis result specifically includes:

[0045] Construct the channel position matrix U of U1 1xy , U 1xy Where x and y represent the row and column of each channel in U1 in U0, where U0 is the set of all channels of the ECoG electrodes;

[0046] based on k R1t d R1t 、 k SNR1 、S U1-mean 、U 1xy Construct 7-channel input data;

[0047] Build a convolutional neural network model U1 , the model U1 The input data shape of the first convolutional layer is (m1,n1,7), where m1 represents the total number of rows of U1 and n1 represents the total number of columns of U1;

[0048] Obtain the signal data generated by the actual application state of the ECoG electrode and the corresponding problem type, and train the convolutional neural network model with the problem type as the label U1 ;

[0049] Based on the trained convolutional neural network model U1 Enables prediction of hardware issues for target ECoG electrodes;

[0050] Among them, the types of problems include the falling off of modifying materials, adhesion of electrodes to brain tissue, failure of sealing causing corrosion of electrodes by body fluids, delamination of the insulating layer of the electrode itself, and poor contact of electrode connections.

[0051] In conjunction with the first aspect, in one embodiment, the implementing parameter optimization specifically includes:

[0052] Obtain ECoG electrodes of various specifications and perform signal analysis. For each specification of ECoG electrode, obtain C, S U1-mean 、U 1xy ;

[0053] The regression model constructed by training the size parameters of each specification of ECoG electrode as labels;

[0054] S U1-mean and U 1xy Multiply the corresponding positions to get the matrix SU U1 , SU U1 Reshape it into one dimension and concatenate it with the size parameters of the current specification ECoG electrode. The result is input into the regression model to obtain the parameter recommendation model. C ;

[0055] Get the target ECoG electrode and perform signal analysis to get the SU corresponding to the target ECoG electrode U1 , SU U1 Input to model C , and obtain the optimized size parameters of the target ECoG electrode.

[0056] In a second aspect, an embodiment of the present application provides an ECoG electrode performance monitoring and hardware parameter optimization device, the ECoG electrode performance monitoring and hardware parameter optimization device comprising:

[0057] Signal analysis module, which is used to perform impedance testing and parameter testing on ECoG electrodes and collect signals from each channel of the ECoG electrodes to perform signal analysis;

[0058] A status evaluation module, which is used to evaluate the performance status of the target ECoG electrode based on the signal analysis results and the test results;

[0059] The hardware problem analysis and parameter optimization module is used to determine the hardware problems of the target ECoG electrode based on the signal analysis results and implement parameter optimization.

[0060] In a third aspect, an embodiment of the present application provides an ECoG electrode performance monitoring and hardware parameter optimization device, which includes a processor, a memory, and an ECoG electrode performance monitoring and hardware parameter optimization program stored in the memory and executable by the processor, wherein when the ECoG electrode performance monitoring and hardware parameter optimization program is executed by the processor, the steps of the above-mentioned ECoG electrode performance monitoring and hardware parameter optimization method are implemented.

[0061] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0062] Based on artificial intelligence technology, the performance of ECoG electrodes that have been in actual application for a long time can be monitored in real time, and the electrode status can be evaluated and problems can be traced according to signal quality, impedance, and decoding accuracy, as well as parameter optimization. Specifically, based on the characteristic matrix corresponding to impedance, signal-to-noise ratio, and channel decoding accuracy, the current performance status of the ECoG electrode can be predicted in real time, and the problems of the ECoG electrode in the application state can be located according to the impedance changes, dynamic changes in signal-to-noise ratio, and decoding accuracy. Based on the decoding accuracy and the parameter configuration of the previous generation of electrodes, the next generation of iterative optimization parameters of the electrodes can be predicted with the help of artificial intelligence technology to determine the theoretically optimal electrode parameter combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 Schematic diagram of the process of ECoG electrode performance monitoring and hardware parameter optimization method of this application;

[0064] Figure 2 Schematic diagram of the functional modules of the ECoG electrode performance monitoring and hardware parameter optimization device of this application;

[0065] Figure 3 Schematic diagram of the hardware structure of the ECoG electrode performance monitoring and hardware parameter optimization equipment for this application. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0067] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0068] First, the embodiments of the present application provide an ECoG electrode performance monitoring and hardware parameter optimization method. Based on artificial intelligence technology, the performance of ECoG electrodes that are in actual application for a long time can be monitored in real time, and the electrode status can be evaluated and problems can be traced based on signal quality, impedance, and decoding accuracy. The theoretically optimal parameter combination can also be calculated through artificial intelligence technology based on the electrode decoding effect.

[0069] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the ECoG electrode performance monitoring and hardware parameter optimization method of this application. Figure 1As shown, the ECoG electrode performance monitoring and hardware parameter optimization method includes:

[0070] S1: Perform impedance and parameter tests on the ECoG electrodes, and collect signals from each channel of the ECoG electrodes for signal analysis;

[0071] S2: Based on the signal analysis results and test results, the performance status of the target ECoG electrode is evaluated;

[0072] S3: Determine the hardware issues of the target ECoG electrode based on the signal analysis results and optimize the parameters.

[0073] It should be noted that the above step S1 is a signal analysis for a single ECoG electrode, and steps S2 and S3 are based on the signal analysis results and test results of multiple ECoG electrodes, thereby realizing the performance status evaluation of the target ECoG electrode (i.e., the ECoG electrode to be subjected to performance status evaluation, hardware problem prediction, and parameter optimization), as well as hardware problem prediction and parameter optimization.

[0074] Furthermore, in one embodiment, the impedance test is performed on each channel of the ECoG electrode to obtain the impedance test results:

[0075]

[0076] Among them, R represents the impedance test result of the ECoG electrode, R mn R represents the impedance of the contact of the mth row and nth column of the ECoG electrode, where each contact of the ECoG electrode is a channel, m is the total number of rows of ECoG electrode contacts, n is the total number of columns of ECoG electrode contacts, and R ij represents the impedance of the contact at row i and column j of the ECoG electrode;

[0077] The parameter test is a size parameter test of the ECoG electrode, including contact size C1, contact row spacing C2, contact column spacing C3, and contact distribution area C4. The size parameter test result is C = [C1, C2, C3, C4]. The size parameter test result obtained by the test is stored.

[0078] It should be noted that the impedance test and parameter test are performed on the ECoG electrodes before leaving the factory.

[0079] In this application, when performing signal analysis on each channel signal of the ECoG electrode, it mainly includes bad track screening, impedance analysis, signal-to-noise ratio analysis, and decoding.

[0080] Since ECoG electrodes may show obvious abnormalities during a certain period of time during signal acquisition, but then return to normal in the next period, the subsequent analysis of this application is based on the assumption that the channel is normal, so it is necessary to evaluate whether each channel is abnormal. This application analyzes the signals collected by each channel from different angles to evaluate whether each channel is damaged.

[0081] Specifically, the bad track screening is used to evaluate whether the ECoG electrode channel is abnormal, so as to evaluate the ECoG electrode channel status. When any judgment condition is met, it is determined that the current channel is abnormal during the current signal acquisition time period.

[0082] The judgment conditions include that the data collected at each moment are all 0 values, random outlier interference at random moments and the number of occurrences is less than the preset number (i.e., a small amount of random outlier interference at random moments), the data are obviously segmented and there is random outlier interference at the segmented moments (i.e., the data are obviously segmented and there is random outlier interference at the segmented moments), the data baseline is lower than the set baseline value and the amplitude is less than the preset amplitude (i.e., the data baseline is low and the amplitude is small), specific outlier interference at random moments and the number of occurrences is greater than the set number (i.e., multiple random outlier interferences), and the data baseline shows a downward trend while specific outlier interference at random moments and the number of occurrences is greater than the set threshold (i.e., the baseline is reduced and there are multiple random outlier interferences).

[0083] Among them, the assessment of ECoG electrode channel status includes:

[0084] S101: Within the total time range of ECoG electrode signal acquisition, calculate the time proportion of each channel in which an abnormality occurs. Based on the time proportion of the channel in which an abnormality occurs, remove the ECoG electrode channels with a high degree of abnormality. Specifically:

[0085]

[0086] Among them, τ ij It represents the time proportion of abnormality in the channel of row i and column j in the ECoG electrode. ij When ≥α, the current channel is judged to be a channel with a high degree of abnormality. tT represents the total time range length of ECoG electrode signal acquisition, Δt ij represents the total time during which abnormalities occur in the channel of row i and column j of the ECoG electrode within the total time range of signal acquisition. α represents a threshold value, which can be determined based on experiments or experience, such as 0.1. This application does not impose any specific restrictions thereon.

[0087] S102: For the channels retained in the ECoG electrodes, create a channel set U1 and mark the time intervals where abnormalities occur in the retained channels: tA ij =[[t t1 , tt2 ],[t t3 , t t4 ]…[t tp , t tp+1 ]]tA ij Indicates the time interval when the channel in row i and column j of the ECoG electrode is abnormal, t tp Indicates the start time of the pth abnormality, t tp+1 Indicates the end time when the pth exception occurs;

[0088] S103: Eliminate the impedance of the channel with a high degree of abnormality in R to obtain R1.

[0089] In this application, impedance analysis specifically includes:

[0090] S111: Store the dynamic change of impedance of each channel in U1 and obtain the set R U1 , set R U1 Each subset in represents the impedance change of a channel in U1 over the test time;

[0091] Specifically, the dynamic change of impedance of each channel in U1 is stored, for example:

[0092]

[0093] Among them, R mntT It represents the impedance of the channel in row m and column n at time tT. “ / ” represents the eliminated channel. The number of channels without “ / ” is equal to the number of channels in U1, that is, R U1 .

[0094] S112: According to the marking result of the abnormal time interval of each channel in U1, R U1 The corresponding impedance value fragments of each channel in are removed to obtain R U1t ;

[0095] S113: Calculate R U1t The ratio of the impedance variance of each channel to the average impedance is used to obtain the impedance variation coefficient matrix

[0096] S114: R U1t The impedance values ​​of each channel are fitted by the least square method to obtain the fitting approximate straight line l for each channel. R1t , calculate l R1t The slope gets the impedance slope matrix k R1t ;

[0097] S115: Calculate the impedance value of each channel in R1 to l R1t The impedance value offset distance matrix d is obtained R1t.

[0098] In this application, the signal-to-noise ratio analysis specifically includes:

[0099] S121: Perform sliding window cropping on the signals of each channel in U1 and calculate the signal-to-noise ratio of each window segment to form a signal-to-noise ratio matrix SNR1. Each channel in SNR1 contains multiple signal-to-noise ratio values.

[0100] S122: Calculate the ratio of the signal-to-noise ratio variance of each channel in SNR1 to the average signal-to-noise ratio to obtain the signal-to-noise ratio variation coefficient matrix

[0101] S123: Calculate the signal-to-noise ratio of each channel in SNR1 and perform least squares fitting to obtain the slope value of the fitting approximate straight line of each channel and obtain the signal-to-noise ratio slope matrix k SNR1 .

[0102] In this application, decoding specifically includes:

[0103] S131: Based on the sliding window segments of each channel in U1, the decoding model is trained to obtain the decoding accuracy matrix S of each sliding window of each channel. U1 ;

[0104] S132: Calculate the mean of the decoding accuracy of all window segments of each channel to obtain the decoding accuracy matrix S U1-mean .

[0105] Furthermore, in one embodiment, based on the signal analysis results and the test results, a performance status evaluation of the target ECoG electrode is performed, specifically including:

[0106] S201: R U1t The signals of each channel in the sliding window are clipped to obtain multiple segments, and the mean of each segment is calculated to obtain the set R U1t-mean ; The step size of the sliding window at this step is the same as that in step S121;

[0107] S202: R U1t-mean 、SNR1、S U1 The values ​​of each channel in R are transposed and stored in columns, and the transposed R U1t-mean 、SNR1、S U1 Perform concat splicing to obtain a fusion matrix, where each row in the fusion matrix represents a time segment, and each time segment corresponds to the performance state of an electrode;

[0108] concat, that is, the concat function, is used to concatenate multiple strings into a new string;

[0109] S203: Based on the experimental data, the performance status of the ECoG electrodes at different times is collected and made into status labels U1 Specifically, a large number of experiments can be conducted to collect the performance status of ECoG electrodes at different times, thereby producing a status label. U1 ;

[0110] S204: Each line of the fusion matrix is ​​used as training data, and label is used as U1 For labels, build an electrode performance status prediction model;

[0111] S205: Input the signal data generated by the target ECoG electrode in actual use into the electrode performance state prediction model to obtain the performance state evaluation result of the target ECoG electrode at the current moment. If the electrode performance state prediction model predicts that the target ECoG electrode is in a poor state, the target ECoG electrode needs to be discontinued.

[0112] Based on the signal quality, signal decoding accuracy, and impedance changes collected by the ECoG electrode, this application can reversely calculate possible problems with the target ECoG electrode itself, such as detachment of contact modification material, adhesion of clicks to brain tissue, corrosion of the electrode by body fluids due to seal failure, delamination of the electrode's own insulation layer, and poor contact of the electrode connection.

[0113] Furthermore, in one embodiment, determining a hardware problem of a target ECoG electrode based on the signal analysis results specifically includes:

[0114] S301: Construct the channel position matrix U of U1 1xy , U 1xy Where x and y represent the row and column of each channel in U1 in U0, where U0 is the set of all channels of the ECoG electrodes;

[0115] S302: Based on k R1t d R1t 、 k SNR1 、S U1-mean 、U 1xy Construct 7-channel input data;

[0116] S303: Building a convolutional neural network model U1 , the model U1 The input data shape of the first convolutional layer is (m1,n1,7), where m1 represents the total number of rows of U1 and n1 represents the total number of columns of U1; shape, that is, the shape function, is used to obtain the dimension information of the array;

[0117] The algorithm model can be a traditional machine learning model or a deep learning model. This application uses a convolutional neural network model as an example.

[0118] S304: Obtain the signal data generated by the actual application state of the ECoG electrode and the corresponding problem type, and train the convolutional neural network model using the problem type as a label U1 ;

[0119] Specifically, we obtain the output signals of a large number of ECoG electrodes in actual application and the corresponding problem types, and then train the convolutional neural network model with the problem types as labels. U1 ;

[0120] S305: Based on the trained convolutional neural network model U1 Predict hardware problems for the target ECoG electrode; among these, the types of problems include detachment of the modifying material, adhesion of the electrode to brain tissue, corrosion of the electrode by body fluids due to seal failure, delamination of the electrode's insulation layer, and poor contact of the electrode connection.

[0121] Furthermore, in one embodiment, parameter optimization is implemented, specifically including:

[0122] S311: Obtain ECoG electrodes of various specifications and perform signal analysis. For each specification of ECoG electrode, obtain C, S U1-mean 、U 1xy ;

[0123] Specifically, ECoG electrodes of various specifications were obtained for testing, and each specification of ECoG electrode had the above-mentioned C, S U1-mean 、U 1xy ;

[0124] S312: A regression model is constructed by training the size parameters of each specification of ECoG electrodes as labels;

[0125] Specifically, the regression model is trained using the optimized and upgraded size parameters of each ECoG electrode specification as labels. In practical applications, the regression model can be a MultiOutputRegressor, DecisionTreeRegressor, RandomForestRegressor, or RNN network model in machine learning algorithms.

[0126] S313: S U1-mean and U 1xy Multiply the corresponding positions to get the matrix SU U1 , SU U1Reshape it into one dimension and concatenate it with the size parameters of the current specification ECoG electrode. The result is input into the regression model to obtain the parameter recommendation model. C ;

[0127] reshape, that is, the reshape function, is used to transform the specified matrix into a matrix of a specific dimension.

[0128] S314: Obtain the target ECoG electrode and perform signal analysis to obtain the SU corresponding to the target ECoG electrode U1 , SU U1 Input to model C , and obtain the optimized size parameters of the target ECoG electrode.

[0129] Specifically, for the target ECoG electrode in actual application state, the corresponding SU is obtained according to the output signal of the electrode. U1 , SU U1 Input to model C , you can get the parameter recommendation model model C Recommended next generation optimized size parameters.

[0130] The ECoG electrode performance monitoring and hardware parameter optimization method of the embodiment of the present application is based on artificial intelligence technology, and can monitor the performance of ECoG electrodes that are in actual application for a long time in real time, and evaluate the electrode status and trace problems according to signal quality, impedance, and decoding accuracy, as well as optimize parameters. Specifically, based on the characteristic matrix corresponding to impedance, signal-to-noise ratio, and channel decoding accuracy, the current performance status of the ECoG electrode can be predicted in real time, and problems existing in the ECoG electrode in the application state can be located according to the impedance changes, dynamic changes in the signal-to-noise ratio, and decoding accuracy. Based on the decoding accuracy and the parameter configuration of the previous generation of electrodes, the next generation iterative optimization parameters of the electrodes are predicted with the help of artificial intelligence technology to determine the theoretically optimal electrode parameter combination.

[0131] In a second aspect, an embodiment of the present application also provides an ECoG electrode performance monitoring and hardware parameter optimization device.

[0132] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of the ECoG electrode performance monitoring and hardware parameter optimization device of this application. Figure 2 As shown, the ECoG electrode performance monitoring and hardware parameter optimization device includes: a signal analysis module, a state evaluation module, and a hardware problem analysis and parameter optimization module.

[0133] The signal analysis module is used to perform impedance and parameter tests on the ECoG electrodes and collect signals from each channel of the ECoG electrodes to perform signal analysis. The status evaluation module is used to evaluate the performance status of the target ECoG electrodes based on the signal analysis and test results. The hardware problem analysis and parameter optimization module is used to determine the hardware problems of the target ECoG electrodes based on the signal analysis results and to perform parameter optimization.

[0134] In a third aspect, an embodiment of the present application provides an ECoG electrode performance monitoring and hardware parameter optimization device, which can be a personal computer (PC), laptop, server, or other device with data processing capabilities.

[0135] Reference Figure 3 , Figure 3 Schematic diagram of the hardware structure of the ECoG electrode performance monitoring and hardware parameter optimization device involved in the embodiment of the present application. In the embodiment of the present application, the ECoG electrode performance monitoring and hardware parameter optimization device may include a processor, a memory, a communication interface, and a communication bus.

[0136] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0137] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the ECoG electrode performance monitoring and hardware parameter optimization device, as well as interfaces that connect the device to other devices (e.g., other computing devices or user devices). Physical interfaces can include Ethernet, fiber, or ATM interfaces; user devices can include displays and keyboards.

[0138] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0139] The processor may be a general-purpose processor that can call the ECoG electrode performance monitoring and hardware parameter optimization program stored in the memory and execute the ECoG electrode performance monitoring and hardware parameter optimization method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the ECoG electrode performance monitoring and hardware parameter optimization program is called can refer to the various embodiments of the ECoG electrode performance monitoring and hardware parameter optimization method of the present application, and will not be repeated here.

[0140] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0141] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0142] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0143] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0144] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0146] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for ECoG electrode performance monitoring and hardware parameter optimization, characterized in that: The ECoG electrode performance monitoring and hardware parameter optimization method includes: Conduct impedance and parameter tests on ECoG electrodes, and collect signals from each channel of the ECoG electrodes for signal analysis; Evaluate the performance status of the target ECoG electrode based on the signal analysis and test results; Identify hardware issues with the target ECoG electrode based on signal analysis results and optimize parameters. The impedance test is to perform impedance testing on each channel of the ECoG electrode to obtain the impedance test results: in, Indicates the impedance test results of the ECoG electrode, Indicates the ECoG electrode Rank The impedance of the contacts in the series, and each contact of the ECoG electrode is a channel, and is the total number of rows of ECoG electrode contacts, is the total number of columns of ECoG electrode contacts, Indicates the ECoG electrode Rank Impedance of column contacts; The parameter test is the size parameter test of the ECoG electrode, including the contact size , contact row spacing , contact row spacing , contact distribution area , the size parameter test results are .

2. The ECoG electrode performance monitoring and hardware parameter optimization method according to claim 1, wherein: The signal analysis includes bad track screening, impedance analysis, signal-to-noise ratio analysis, and decoding; The bad track screening is used to evaluate whether the ECoG electrode channel is abnormal, so as to evaluate the ECoG electrode channel status. When any judgment condition is met, it is determined that the current channel is abnormal during the current signal acquisition time period. The judgment conditions include: all the data collected at each moment are 0 values, random outlier interference at random moments and the number of occurrences is less than a preset number, the data is clearly segmented and there is random outlier interference at the segmentation moment, the data baseline is lower than the set baseline value and the amplitude is less than the preset amplitude, the data is interfered with by a specific outlier at random moments and the number of occurrences is greater than a set number, the data baseline shows a decreasing trend and there is interference with a specific outlier at random moments and the number of occurrences is greater than a set threshold; The evaluation of the ECoG electrode channel status specifically includes: Within the total time range of ECoG electrode signal acquisition, the proportion of time each channel has abnormalities is counted. Based on the proportion of time the channel has abnormalities, channels with high abnormality among ECoG electrodes are eliminated. Specifically: in, Indicates the first Rank The proportion of time when the channel is abnormal. When , the current channel is judged to be a channel with high abnormality. Indicates the total time range length of ECoG electrode signal acquisition, Indicates the first Rank The total time that the column channel has abnormalities within the total time range of signal acquisition. Indicates the threshold value; For the channels retained in the ECoG electrodes, create a channel set , and mark the time intervals where abnormalities occur in the reserved channels: , Indicates the first Rank The time interval during which the column channel appears abnormal. Indicates the The start time of the exception. Indicates the End time when the exception occurs; culling The impedance of the channel with high abnormality is obtained .

3. The ECoG electrode performance monitoring and hardware parameter optimization method according to claim 2, wherein: For impedance analysis, specifically including: right The dynamic impedance change of each channel is stored and the collection is obtained. ; According to The abnormal time interval of each channel is marked. The corresponding impedance value fragments of each channel in are eliminated, and we get ; calculate The ratio of the impedance variance of each channel to the average impedance is used to obtain the impedance variation coefficient matrix ; right The impedance values ​​of each channel are fitted by the least square method to obtain the fitting approximate straight line of each channel ,calculate Slope to get the impedance slope matrix ; calculate The impedance value of each channel in The impedance value offset distance matrix is ​​obtained .

4. The method for ECoG electrode performance monitoring and hardware parameter optimization according to claim 3, wherein: For signal-to-noise ratio analysis, specifically including: right The signal of each channel in the sliding window is clipped, and the signal-to-noise ratio of each window segment is calculated to form a signal-to-noise ratio matrix , Each channel in contains multiple signal-to-noise ratio values; calculate The ratio of the signal-to-noise ratio variance of each channel to the average signal-to-noise ratio is used to obtain the signal-to-noise ratio coefficient of variation matrix ; calculate The signal-to-noise ratio of each channel is calculated and the least squares fitting is performed to obtain the slope value of the fitting approximate straight line of each channel and the signal-to-noise ratio slope matrix ; For decoding, specifically including: based on The decoding model is trained on the sliding window segments of each channel in the , and the decoding accuracy matrix of each sliding window of each channel is obtained. ; Calculate the mean decoding accuracy of all window segments of each channel to obtain the decoding accuracy matrix .

5. The ECoG electrode performance monitoring and hardware parameter optimization method according to claim 4, wherein: The performance status evaluation of the target ECoG electrode based on the signal analysis results and the test results specifically includes: right The signals of each channel in the sliding window are clipped to obtain multiple fragments, and the mean of each fragment is calculated to obtain the set ; Will 、 、 The values ​​of each channel in the array are transposed and stored in columns, and the transposed values ​​are 、 、 Perform concat splicing to obtain a fusion matrix, where each row in the fusion matrix represents a time segment, and each time segment corresponds to the performance state of an electrode; Based on the experimental data, the performance status of ECoG electrodes at different times is collected and made into status labels. ; Each row of the fusion matrix is ​​used as training data, For labels, build an electrode performance status prediction model; The signal data generated by the target ECoG electrode in the actual application state is input into the electrode performance state prediction model to obtain the performance state evaluation result of the target ECoG electrode at the current moment.

6. The ECoG electrode performance monitoring and hardware parameter optimization method according to claim 4, wherein: Determining the hardware problem of the target ECoG electrode based on the signal analysis results specifically includes: Build The channel position matrix , in 、 express Each channel in The rows and columns in It is the collection of all channels of ECoG electrodes; based on 、 、 、 、 、 、 Construct 7-channel input data; Building a convolutional neural network model , The input data shape of the first convolutional layer is ,in, express The total number of rows, express The total number of columns; Obtain the signal data generated by the actual application state of the ECoG electrode, as well as the corresponding problem type, and train the convolutional neural network model with the problem type as the label ; Based on the trained convolutional neural network model Enables prediction of hardware issues for target ECoG electrodes; Among them, the types of problems include the falling off of modifying materials, adhesion of electrodes to brain tissue, failure of sealing causing corrosion of electrodes by body fluids, delamination of the insulating layer of the electrode itself, and poor contact of electrode connections.

7. The method for ECoG electrode performance monitoring and hardware parameter optimization according to claim 6, wherein: The parameter optimization is implemented, specifically including: Obtain ECoG electrodes of various specifications and perform signal analysis. 、 、 ; The regression model constructed by training the size parameters of each specification of ECoG electrode as labels; Will and Multiply the corresponding positions to get the matrix ,Will Reshape it into one dimension and concatenate it with the size parameters of the current specification ECoG electrode. The result is input into the regression model to obtain the parameter recommendation model. ; Get the target ECoG electrode and perform signal analysis to get the corresponding ,Will Input to , and obtain the optimized size parameters of the target ECoG electrode.

8. An ECoG electrode performance monitoring and hardware parameter optimization device, characterized in that: The ECoG electrode performance monitoring and hardware parameter optimization device includes: Signal analysis module, which is used to perform impedance testing and parameter testing on ECoG electrodes and collect signals from each channel of the ECoG electrodes to perform signal analysis; A status evaluation module, which is used to evaluate the performance status of the target ECoG electrode based on the signal analysis results and the test results; Hardware problem analysis and parameter optimization module, which is used to identify hardware problems of the target ECoG electrode based on signal analysis results and implement parameter optimization; The impedance test is to perform impedance testing on each channel of the ECoG electrode to obtain the impedance test results: in, Indicates the impedance test results of the ECoG electrode, Indicates the ECoG electrode Rank The impedance of the contacts in the series, and each contact of the ECoG electrode is a channel, and is the total number of rows of ECoG electrode contacts, is the total number of columns of ECoG electrode contacts, Indicates the ECoG electrode Rank Impedance of column contacts; The parameter test is the size parameter test of the ECoG electrode, including the contact size , contact row spacing , contact row spacing , contact distribution area , the size parameter test results are .

9. An ECoG electrode performance monitoring and hardware parameter optimization device, characterized in that: The ECoG electrode performance monitoring and hardware parameter optimization device includes a processor, a memory, and an ECoG electrode performance monitoring and hardware parameter optimization program stored in the memory and executable by the processor, wherein when the ECoG electrode performance monitoring and hardware parameter optimization program is executed by the processor, the steps of the ECoG electrode performance monitoring and hardware parameter optimization method according to any one of claims 1 to 7 are implemented.

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