Multi-channel bio-electricity signal modulation method, device, equipment and storage medium

Abstract

The invention belongs to the technical field of signal processing, and provides a multi-channel signal strength control method, device and equipment and a storage medium, the multi-channel signal strength control method comprises the following steps: receiving a multi-channel bio-electricity signal, and carrying out pre-noise reduction processing on the bio-electricity signal; generating a bio-electricity signal model based on the bio-electricity signal after the pre-noise-reduction processing; according to the bio-electricity signal model, calculating a signal-to-noise ratio mean value of the bio-electricity signals in the current period; and if the mean value of the signal-to-noise ratio is greater than a preset threshold value, performing interference signal elimination on the multi-channel bio-electricity signal. The signal-to-noise ratio mean value of the bio-electricity signals in the corresponding period is determined by generating the bio-electricity signal model, then interference signals are eliminated after the signal-to-noise ratio mean value is larger than the preset threshold value, interference between signals can be effectively isolated and reduced, and the accuracy of multi-channel bio-electricity signal transmission is improved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a multi-channel bioelectric signal modulation method, device, equipment and storage medium. Background Art

[0002] Bioelectric signals, the most typical of which are EEG signals, ECG signals, EMG signals and EO signals. These signals are related to many diseases of the human body. With the continuous development of science and technology, people have been able to simulate or collect bioelectric signals to study and treat various diseases.

[0003] Since bioelectric signals (such as electromyographic signals, electrocardiographic signals, etc.) are highly nonlinear and random, this means that the signal fluctuation amplitude, frequency component, morphology and other characteristics may vary greatly between different individuals, at different times and under different conditions, and bioelectric signals are usually of low amplitude and easily interfered by external noise. When transmitting multi-channel bioelectric signals, not only a control system with higher sensitivity and accuracy is required to process these signals, but also the signal phase difference caused by signal asynchrony must be considered, which in turn causes signal interference or distortion. When multiple signal channels are in adjacent or shared physical space, mutual interference or cross-coupling may occur between the signals, thus affecting the overall performance research. Therefore, how to effectively isolate and reduce interference between signals is a major challenge in multi-channel bioelectric signal transmission. Summary of the invention

[0004] The embodiments of the present application provide a multi-channel bioelectric signal modulation method, device, equipment and storage medium, aiming to.

[0005] In a first aspect, an embodiment of the present application provides a multi-channel bioelectric signal modulation method, comprising:

[0006] Receive multi-channel bioelectric signals and perform pre-noise reduction on the bioelectric signals; generate a bioelectric signal model based on the bioelectric signals after pre-noise reduction; calculate the mean signal-to-noise ratio of the bioelectric signals in the current cycle according to the bioelectric signal model; if the mean signal-to-noise ratio is greater than a preset threshold, eliminate interference signals from the multi-channel bioelectric signals.

[0007] In one embodiment, a bioelectric signal model is generated based on the bioelectric signal after pre-noise reduction processing, including: obtaining the bioelectric signal vector after pre-noise reduction processing and the bioelectric signal transfer matrix; decomposing the bioelectric signal after pre-noise reduction processing to obtain the real component and noise vector of the bioelectric signal; generating the bioelectric signal model according to the bioelectric signal vector, the bioelectric signal transfer matrix, the real component and the noise vector of the bioelectric signal.

[0008] In one embodiment, the bioelectric signal model is expressed as:

[0009] y(t)=A·s(t)+n(t)

[0010] Among them, y(t) is the acquired bioelectric signal vector, A is the bioelectric signal transfer matrix, s(t) is the true component of the bioelectric signal, and n(t) is the noise vector.

[0011] In one embodiment, based on the bioelectric signal model, the mean signal-to-noise ratio of the bioelectric signal in the current cycle is calculated, including: calculating the average power of the bioelectric signal in the current cycle based on the real component of the bioelectric signal; calculating the noise power of the bioelectric signal in the current cycle based on the noise vector; calculating the wallpaper of the average power and the noise power to obtain the mean signal-to-noise ratio of the bioelectric signal.

[0012] In one embodiment, if the mean signal-to-noise ratio is greater than a preset threshold, interference signals are eliminated from multi-channel bioelectric signals, including: for each channel, separating the interference signal from the bioelectric signal; extracting characteristic parameters from the separated interference signal, matching the extracted characteristic parameters with a preset feature library of the interference signal, and detecting the interference signal; inputting the interference signal into a preset adaptive model for parameter adjustment, and reconstructing the interference signal; and collaboratively processing the reconstructed interference signal on multiple channels, and using phase cancellation or amplitude cancellation to eliminate the interference signal in the bioelectric signal.

[0013] In one embodiment, for each channel, the interference signal is separated from the bioelectric signal, including: for each channel, separating the interference signal from the bioelectric signal based on a blind source separation algorithm.

[0014] In one embodiment, the interference signal is input into a preset adaptive model for parameter adjustment, and the interference signal is reconstructed, including: inputting the interference signal into a preset adaptive model, dynamically adjusting the parameters through iterative calculation in the adaptive model, matching the characteristics of the interference signal, and completing the reconstruction of the interference signal based on the characteristics of the interference signal.

[0015] In a second aspect, an embodiment of the present application provides a multi-channel bioelectric signal modulation device, including:

[0016] A processing module, used for receiving multi-channel bioelectric signals and performing pre-noise reduction processing on the bioelectric signals;

[0017] A generation module, used for generating a bioelectric signal model based on the bioelectric signal after pre-noise reduction processing;

[0018] A calculation module, used to calculate the mean signal-to-noise ratio of the bioelectric signal in the current cycle according to the bioelectric signal model;

[0019] The elimination module is used to eliminate interference signals from multi-channel bioelectric signals if the mean signal-to-noise ratio is greater than a preset threshold.

[0020] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0021] Memory and processing modules;

[0022] The memory is used to store computer programs;

[0023] The processing module is used to execute the computer program and implement the steps of the multi-channel bioelectric signal modulation method as described in the first aspect above when executing the computer program.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program;

[0025] When the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the multi-channel bioelectric signal modulation method as described in the first aspect above.

[0026] The embodiments of the present application provide a multi-channel bioelectric signal modulation method, apparatus, device and storage medium, wherein the multi-channel bioelectric signal modulation method includes: receiving multi-channel bioelectric signals, and performing pre-noise reduction processing on the bioelectric signals; generating a bioelectric signal model based on the bioelectric signals after the pre-noise reduction processing; calculating the mean signal-to-noise ratio of the bioelectric signals in the current cycle according to the bioelectric signal model; if the mean signal-to-noise ratio is greater than a preset threshold, eliminating interference signals from the multi-channel bioelectric signals. The bioelectric signal model is generated by the bioelectric signals after the pre-noise reduction processing to determine the mean signal-to-noise ratio of the bioelectric signals in the corresponding cycle, and then after the mean signal-to-noise ratio is greater than the preset threshold, the interference signals are eliminated, which can effectively isolate and reduce interference between signals and improve the accuracy of multi-channel bioelectric signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0028] Figure 1 A schematic diagram of the implementation process of the multi-channel bioelectric signal modulation method provided in the embodiment of the present application;

[0029] Figure 2 yes Figure 1 Schematic diagram of the specific implementation process of S102;

[0030] Figure 3 A schematic diagram of the structure of a multi-channel bioelectric signal modulation device provided in an embodiment of the present application;

[0031] Figure 4 A schematic block diagram of an electronic device provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 the present invention.

[0033] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0034] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0035] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0036] The technical solution provided by this application will be described in detail below with reference to the accompanying drawings.

[0037] See also Figure 1 , Figure 1 A schematic diagram of the implementation flow of a multi-channel bioelectric signal modulation method provided in an embodiment of the present application. The multi-channel bioelectric signal modulation method is implemented by a signal modulation device, and the software and / or hardware included in the signal modulation device can execute corresponding steps to realize the modulation of multi-channel bioelectric signals, so that each channel outputs a more accurate bioelectric signal. The signal modulation device includes but is not limited to a server or various terminal devices.

[0038] Depend on Figure 1 It can be seen that the multi-frequency pulse signal generation method includes steps S101 to S104. The details are as follows:

[0039] S101: receiving multi-channel bioelectric signals, and performing pre-noise reduction processing on the multi-channel bioelectric signals.

[0040] Bioelectric signals refer to electrical signals generated by electrical activities in organisms, which are manifestations of the functional activities of various systems or organs of organisms, including but not limited to electrocardiogram signals, electroencephalogram signals, electromyography signals, electrooculogram signals, electroskin signals, gastric electrogram signals or neural electrogram signals.

[0041] The process of pre-noise reduction of multi-channel bioelectric signals aims to remove or reduce noise interference in bioelectric signals and retain useful information. Pre-noise reduction can ensure the authenticity and accuracy of the signal. Specifically, in multi-channel bioelectric signals, common-mode noise will appear on all channels at the same time. By averaging the signals of all channels at each time point, a signal representing the common-mode noise is obtained; the calculated common-mode noise (average signal) is subtracted from the original signal of each channel to remove the common-mode noise. After removing the common-mode noise, the independent component analysis algorithm is used to decompose the signal into multiple independent components, identify useful bioelectric signals and noise, and selectively remove the noise component to obtain a denoised multi-channel bioelectric signal.

[0042] S102: Generate a bioelectric signal model based on the bioelectric signal after pre-noise reduction processing.

[0043] The bioelectric signal after pre-noise reduction processing helps to highlight the important biological features in the bioelectric signal. After the noise reduction processing, the bioelectric signal model is generated, which not only improves the quality of the signal, but also simplifies the processing process with the help of the bioelectric signal model, and enhances the reliability of bioelectric signal interpretation.

[0044] For example, Figure 2 As shown, Figure 2 yes Figure 1 The specific implementation flow diagram of S102 is shown in FIG. Figure 2 It can be seen that S102 includes:

[0045] S1021: Obtain the bioelectric signal vector and bioelectric signal transfer matrix after pre-noise reduction processing.

[0046] The bioelectric signal after pre-noise reduction is a multi-channel signal obtained by the sensor. Assuming there are N channels, the bioelectric signal after pre-noise reduction can be expressed as an N×1 vector y(t), called a bioelectric signal vector, where each element corresponds to the bioelectric signal of one channel.

[0047] The bioelectric signal transfer matrix describes the characteristics of the bioelectric signal during the transmission process, reflecting the attenuation, reflection and other transmission characteristics of the bioelectric signal from the source to the entire transmission path. In practical applications, the bioelectric signal transfer matrix can be estimated by system calibration or model fitting based on historical data. Specifically, the specific method for obtaining the bioelectric signal transfer matrix is ​​not limited here.

[0048] S1022: Decompose the bioelectric signal after the pre-noise reduction process to obtain the real component and noise vector of the bioelectric signal.

[0049] The real component of the bioelectric signal refers to the meaningful biological information contained in the bioelectric signal. The noise vector is the undesirable component in the bioelectric signal, including environmental noise, electromagnetic interference, etc. The real component and the noise vector of the bioelectric signal can be directly separated by the signal separation algorithm. Specifically, the signal separation algorithm includes: centering and whitening the bioelectric signal after pre-noise reduction processing, and decomposing it into several independent components; analyzing each independent component to identify the real component and noise vector of the bioelectric signal.

[0050] Among them, the bioelectric signal after pre-noise reduction is centered and whitened, and then decomposed into several independent components, including: calculating the mean of the bioelectric signal of each channel after pre-noise reduction, and subtracting the corresponding mean from the bioelectric signal of each channel to obtain a centralized signal matrix; calculating the covariance matrix of the centralized signal matrix, and performing eigenvalue decomposition on the covariance matrix to obtain an eigenvalue matrix and an eigenvector matrix; using the eigenvalue matrix and the eigenvector matrix, whitening the centralized signal matrix to obtain a whitened signal matrix; using a linear transformation matrix, performing signal separation on the whitened signal matrix to identify the true components and noise vectors of the bioelectric signal.

[0051] Exemplarily, the linear transformation matrix includes a set of weight vectors, which are obtained by iteratively calculating the initialization weight vector based on a nonlinear function and its derivatives. For example, assume that a weight vector W is randomly initialized, and its length is usually 1. A nonlinear function g(y) and its derivative g`(y) are selected, and the process of iteratively calculating the weight vector set based on the nonlinear function and its derivatives includes:

[0052] ω new =E[X whitened g(w T X whitened )]-E[g`(w T X whitened )]w

[0053] Among them, E[.] represents the expected value, X whitenedrepresents the signal matrix after whitening, w T Represents the transposed matrix of the weight vector W.

[0054] In this embodiment, in order to ensure that the weight vector ω obtained in each iteration new Is orthogonal to all previous weight vectors W. During the iteration process, the weight vector ω new Specifically, the weight vector ω new Orthogonalization is performed, including:

[0055]

[0056] Among them, w i is the i-th weight vector obtained previously.

[0057] Further new Perform normalization:

[0058]

[0059] to w new After normalization, check w new Whether it converges with W, that is, w new Is the inner product with W close to 1? If so, a corresponding independent component is obtained; if not, repeat the above steps until it is close to 1. By repeating the above steps, all independent components in the whitened signal matrix can be found.

[0060] S1023: Generate a bioelectric signal model based on the bioelectric signal vector, the bioelectric signal transfer matrix, the real component of the bioelectric signal and the noise vector.

[0061] Specifically, the bioelectric signal model can be expressed as:

[0062] y(t)=A·s(t)+n(t)

[0063] Among them, y(t) is the acquired bioelectric signal vector, A is the bioelectric signal transfer matrix, s(t) is the true component of the bioelectric signal, and n(t) is the noise vector.

[0064] By establishing a bioelectric signal model, we can systematically understand and process complex bioelectric signals, thereby bringing more reliable and effective solutions to the study of bioelectric signals.

[0065] S103: Calculate the mean signal-to-noise ratio of the bioelectric signal in the current cycle according to the bioelectric signal model.

[0066] According to the bioelectric signal model, the mean signal-to-noise ratio of the bioelectric signal in the current cycle is calculated, including: calculating the average power of the bioelectric signal in the current cycle according to the real component of the bioelectric signal; calculating the noise power of the bioelectric signal in the current cycle according to the noise vector; calculating the wallpaper of the average power and the noise power to obtain the mean signal-to-noise ratio of the bioelectric signal.

[0067] S104: If the mean signal-to-noise ratio of the bioelectric signal in the current cycle is greater than a preset threshold, interference signals are eliminated from the multi-channel bioelectric signal.

[0068] If the mean signal-to-noise ratio is greater than a preset threshold, interference signals are eliminated from multi-channel bioelectric signals, including: for each channel, the interference signal is separated from the bioelectric signal; characteristic parameters are extracted from the separated interference signal, and the extracted characteristic parameters are matched with a preset characteristic library of the interference signal to detect the interference signal; the interference signal is input into a preset adaptive model for parameter adjustment, and the interference signal is reconstructed; the reconstructed interference signal is collaboratively processed on multiple channels, and the interference signal in the bioelectric signal is eliminated using a phase cancellation method or an amplitude cancellation method.

[0069] For each channel, the interference signal is separated from the bioelectric signal, including: for each channel, the interference signal is separated from the bioelectric signal based on a blind source separation algorithm.

[0070] The interference signal is input into a preset adaptive model for parameter adjustment, and the interference signal is reconstructed, including: inputting the interference signal into a preset adaptive model, dynamically adjusting the parameters through iterative calculation in the adaptive model, matching the characteristics of the interference signal, and completing the reconstruction of the interference signal based on the characteristics of the interference signal.

[0071] Through the above analysis, it can be known that the multi-channel bioelectric signal modulation method provided in the embodiment of the present application includes: receiving multi-channel bioelectric signals and performing pre-noise reduction processing on the bioelectric signals; generating a bioelectric signal model based on the bioelectric signals after the pre-noise reduction processing; calculating the mean signal-to-noise ratio of the bioelectric signals in the current cycle according to the bioelectric signal model; if the mean signal-to-noise ratio is greater than a preset threshold, eliminating interference signals from the multi-channel bioelectric signals. By generating a bioelectric signal model based on the bioelectric signals after the pre-noise reduction processing, the mean signal-to-noise ratio of the bioelectric signals in the corresponding cycle is determined, and then after the mean signal-to-noise ratio is greater than the preset threshold, interference signals are eliminated, which can effectively isolate and reduce interference between signals and improve the accuracy of multi-channel bioelectric signal transmission.

[0072] See also Figure 3 As shown, Figure 3 A schematic diagram of the structure of a multi-channel bioelectric signal modulation device provided in an embodiment of the present application.

[0073] Depend on Figure 3 It can be seen that the multi-channel bioelectric signal modulation device 30 provided in the embodiment of the present application includes:

[0074] The processing module 301 is used to receive multi-channel bioelectric signals and perform pre-noise reduction processing on the bioelectric signals;

[0075] A generating module 302, for generating a bioelectric signal model based on the bioelectric signal after the pre-noise reduction process;

[0076] The calculation module 303 is used to calculate the mean signal-to-noise ratio of the bioelectric signal in the current cycle according to the bioelectric signal model;

[0077] The elimination module 304 is used to eliminate interference signals from the multi-channel bioelectric signals if the mean value of the signal-to-noise ratio is greater than a preset threshold.

[0078] In one embodiment, the generating module 302 includes:

[0079] An acquisition unit, used to acquire the bioelectric signal vector and the bioelectric signal transfer matrix after pre-noise reduction processing;

[0080] A decomposition unit, used for decomposing the bioelectric signal after the pre-noise reduction process to obtain the real component and noise vector of the bioelectric signal;

[0081] The generating unit is used to generate a bioelectric signal model according to the bioelectric signal vector, the bioelectric signal transfer matrix, the real component of the bioelectric signal and the noise vector.

[0082] In one embodiment, the bioelectric signal model is expressed as:

[0083] y(t)=A·s(t)+n(t)

[0084] Among them, y(t) is the acquired bioelectric signal vector, A is the bioelectric signal transfer matrix, s(t) is the true component of the bioelectric signal, and n(t) is the noise vector.

[0085] In one embodiment, the calculation module 303 includes:

[0086] A first calculation unit, used for calculating the average power of the bioelectric signal in a current cycle according to the real component of the bioelectric signal;

[0087] A second calculation unit, used for calculating the noise power of the bioelectric signal in the current cycle according to the noise vector;

[0088] The third calculation unit is used to calculate the wallpaper of the average power and the noise power to obtain the mean signal-to-noise ratio of the bioelectric signal.

[0089] In one embodiment, the elimination module 304 includes:

[0090] A separation unit, used for separating interference signals from bioelectric signals for each channel;

[0091] An extraction unit, used to extract characteristic parameters from the separated interference signal, match the extracted characteristic parameters with a preset characteristic library of the interference signal, and detect the interference signal;

[0092] An adjustment unit, used for inputting the interference signal into a preset adaptive model to adjust parameters and reconstruct the interference signal;

[0093] The elimination unit is used to collaboratively process the reconstructed interference signal on multiple channels, and eliminate the interference signal in the bioelectric signal by using a phase cancellation method or an amplitude cancellation method.

[0094] In one embodiment, the separation unit is specifically used to: for each channel, separate the interference signal from the bioelectric signal based on a blind source separation algorithm.

[0095] In one embodiment, the adjustment unit is specifically used to: input the interference signal into a preset adaptive model, dynamically adjust parameters in the adaptive model through iterative calculation, match the characteristics of the interference signal, and complete the reconstruction of the interference signal based on the characteristics of the interference signal.

[0096] It should be noted that the specific implementation process of each of the above modules is the same as the specific implementation process of the corresponding steps in the method embodiment, and will not be repeated here.

[0097] See also Figure 4 As shown, Figure 4 A schematic block diagram of an electronic device provided in accordance with an embodiment of the present application.

[0098] Exemplarily, the electronic device 40 includes a processing module 401 and a memory 402 .

[0099] Exemplarily, the processing module 401 and the memory 402 are connected via a bus 403 , and the bus 403 is, for example, an I2C (Inter-integrated Circuit) bus.

[0100] Specifically, the processing module 401 may be a micro-controller unit (MCU), a central processing unit (CPU) or a digital signal processing module (DSP).

[0101] Specifically, the memory 402 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0102] The processing module 401 is used to run the computer program stored in the memory 402 and implement the steps of the above-mentioned multi-channel biological signal modulation method when executing the computer program.

[0103] Exemplarily, the processing module 401 is used to run the computer program stored in the memory 402, and implement the following steps when executing the computer program:

[0104] Receive multi-channel bioelectric signals and perform pre-noise reduction on the bioelectric signals; generate a bioelectric signal model based on the bioelectric signals after pre-noise reduction; calculate the mean signal-to-noise ratio of the bioelectric signals in the current cycle according to the bioelectric signal model; if the mean signal-to-noise ratio is greater than a preset threshold, eliminate interference signals from the multi-channel bioelectric signals.

[0105] In one embodiment, a bioelectric signal model is generated based on the bioelectric signal after pre-noise reduction processing, including: obtaining the bioelectric signal vector after pre-noise reduction processing and the bioelectric signal transfer matrix; decomposing the bioelectric signal after pre-noise reduction processing to obtain the real component and noise vector of the bioelectric signal; generating the bioelectric signal model according to the bioelectric signal vector, the bioelectric signal transfer matrix, the real component and the noise vector of the bioelectric signal.

[0106] In one embodiment, the bioelectric signal model is expressed as:

[0107] y(t)=A·s(t)+n(t)

[0108] Among them, y(t) is the acquired bioelectric signal vector, A is the bioelectric signal transfer matrix, s(t) is the true component of the bioelectric signal, and n(t) is the noise vector.

[0109] In one embodiment, based on the bioelectric signal model, the mean signal-to-noise ratio of the bioelectric signal in the current cycle is calculated, including: calculating the average power of the bioelectric signal in the current cycle based on the real component of the bioelectric signal; calculating the noise power of the bioelectric signal in the current cycle based on the noise vector; calculating the wallpaper of the average power and the noise power to obtain the mean signal-to-noise ratio of the bioelectric signal.

[0110] In one embodiment, if the mean signal-to-noise ratio is greater than a preset threshold, interference signals are eliminated from multi-channel bioelectric signals, including: for each channel, separating the interference signal from the bioelectric signal; extracting characteristic parameters from the separated interference signal, matching the extracted characteristic parameters with a preset feature library of the interference signal, and detecting the interference signal; inputting the interference signal into a preset adaptive model for parameter adjustment, and reconstructing the interference signal; and collaboratively processing the reconstructed interference signal on multiple channels, and using phase cancellation or amplitude cancellation to eliminate the interference signal in the bioelectric signal.

[0111] In one embodiment, for each channel, the interference signal is separated from the bioelectric signal, including: for each channel, separating the interference signal from the bioelectric signal based on a blind source separation algorithm.

[0112] In one embodiment, the interference signal is input into a preset adaptive model for parameter adjustment, and the interference signal is reconstructed, including: inputting the interference signal into a preset adaptive model, dynamically adjusting the parameters through iterative calculation in the adaptive model, matching the characteristics of the interference signal, and completing the reconstruction of the interference signal based on the characteristics of the interference signal.

[0113] The specific principles and implementation methods of the electronic device provided in the embodiments of the present application are similar to those of the multi-channel bioelectric signal modulation method in the aforementioned embodiments, and will not be repeated here.

[0114] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by the processing module, the processing module implements the following steps:

[0115] Receive multi-channel bioelectric signals and perform pre-noise reduction on the bioelectric signals; generate a bioelectric signal model based on the bioelectric signals after pre-noise reduction; calculate the mean signal-to-noise ratio of the bioelectric signals in the current cycle according to the bioelectric signal model; if the mean signal-to-noise ratio is greater than a preset threshold, eliminate interference signals from the multi-channel bioelectric signals.

[0116] In one embodiment, a bioelectric signal model is generated based on the bioelectric signal after pre-noise reduction processing, including: obtaining the bioelectric signal vector after pre-noise reduction processing and the bioelectric signal transfer matrix; decomposing the bioelectric signal after pre-noise reduction processing to obtain the real component and noise vector of the bioelectric signal; generating the bioelectric signal model according to the bioelectric signal vector, the bioelectric signal transfer matrix, the real component and the noise vector of the bioelectric signal.

[0117] In one embodiment, the bioelectric signal model is expressed as:

[0118] y(t)=A·s(t)+n(t)

[0119] Among them, y(t) is the acquired bioelectric signal vector, A is the bioelectric signal transfer matrix, s(t) is the true component of the bioelectric signal, and n(t) is the noise vector.

[0120] In one embodiment, based on the bioelectric signal model, the mean signal-to-noise ratio of the bioelectric signal in the current cycle is calculated, including: calculating the average power of the bioelectric signal in the current cycle based on the real component of the bioelectric signal; calculating the noise power of the bioelectric signal in the current cycle based on the noise vector; calculating the wallpaper of the average power and the noise power to obtain the mean signal-to-noise ratio of the bioelectric signal.

[0121] In one embodiment, if the mean signal-to-noise ratio is greater than a preset threshold, interference signals are eliminated from multi-channel bioelectric signals, including: for each channel, separating the interference signal from the bioelectric signal; extracting characteristic parameters from the separated interference signal, matching the extracted characteristic parameters with a preset feature library of the interference signal, and detecting the interference signal; inputting the interference signal into a preset adaptive model for parameter adjustment, and reconstructing the interference signal; and collaboratively processing the reconstructed interference signal on multiple channels, and using phase cancellation or amplitude cancellation to eliminate the interference signal in the bioelectric signal.

[0122] In one embodiment, for each channel, the interference signal is separated from the bioelectric signal, including: for each channel, separating the interference signal from the bioelectric signal based on a blind source separation algorithm.

[0123] In one embodiment, the interference signal is input into a preset adaptive model for parameter adjustment, and the interference signal is reconstructed, including: inputting the interference signal into a preset adaptive model, dynamically adjusting the parameters through iterative calculation in the adaptive model, matching the characteristics of the interference signal, and completing the reconstruction of the interference signal based on the characteristics of the interference signal.

[0124] The computer-readable storage medium may be an internal storage unit of the electronic device in the aforementioned embodiment, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device.

[0125] It should be understood that the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0126] It will also be understood that the term "and / or" as used in this application and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0127] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A multi-channel bioelectric signal modulation method, characterized in that: include: Receiving multi-channel bioelectric signals and performing pre-noise reduction processing on the bioelectric signals; Generate a bioelectric signal model based on the bioelectric signal after the pre-noise reduction process; Calculating the mean signal-to-noise ratio of the bioelectric signal in the current cycle according to the bioelectric signal model; If the signal-to-noise ratio mean is greater than a preset threshold, interference signals are eliminated from the multi-channel bioelectric signals.

2. The multi-channel bioelectric signal modulation method according to claim 1, characterized in that: The step of generating a bioelectric signal model based on the bioelectric signal after the pre-noise reduction process comprises: Obtaining the bioelectric signal vector and the bioelectric signal transfer matrix after the pre-noise reduction processing; Decomposing the bioelectric signal after the pre-noise reduction process to obtain a real component and a noise vector of the bioelectric signal; The bioelectric signal model is generated according to the bioelectric signal vector, the bioelectric signal transfer matrix, the real component of the bioelectric signal and the noise vector.

3. The multi-channel bioelectric signal modulation method according to claim 1 or 2, characterized in that: The bioelectric signal model is expressed as: y(t)=A·s(t)+n(t) Among them, y(t) is the acquired bioelectric signal vector, A is the bioelectric signal transfer matrix, s(t) is the true component of the bioelectric signal, and n(t) is the noise vector.

4. The multi-channel bioelectric signal modulation method according to claim 3, characterized in that: Calculating the mean signal-to-noise ratio of the bioelectric signal in the current cycle according to the bioelectric signal model includes: Calculating the average power of the bioelectric signal in a current cycle according to the real component of the bioelectric signal; Calculating the noise power of the bioelectric signal in the current cycle according to the noise vector; The wallpaper of the average power and the noise power is calculated to obtain the mean signal-to-noise ratio of the bioelectric signal.

5. The multi-channel bioelectric signal modulation method according to claim 3, characterized in that: If the mean value of the signal-to-noise ratio is greater than a preset threshold, eliminating interference signals from the multi-channel bioelectric signals, including: For each channel, separating the interference signal from the bioelectric signal; Extracting characteristic parameters from the separated interference signal, matching the extracted characteristic parameters with a preset interference signal characteristic library, and detecting the interference signal; Inputting the interference signal into a preset adaptive model to adjust parameters and reconstruct the interference signal; The reconstructed interference signal is processed collaboratively on multiple channels, and the interference signal in the bioelectric signal is eliminated by using a phase cancellation method or an amplitude cancellation method.

6. The multi-channel bioelectric signal modulation method according to claim 5, characterized in that: For each channel, separating the interference signal from the bioelectric signal includes: For each channel, the interference signal is separated from the bioelectric signal based on a blind source separation algorithm.

7. The multi-channel bioelectric signal modulation method according to claim 5, characterized in that: The step of inputting the interference signal into a preset adaptive model for parameter adjustment and reconstructing the interference signal includes: The interference signal is input into a preset adaptive model, and parameters are dynamically adjusted in the adaptive model through iterative calculation to match the characteristics of the interference signal, and the reconstruction of the interference signal is completed based on the characteristics of the interference signal.

8. A multi-channel bioelectric signal modulation device, characterized in that: The device comprises: A processing module, used for receiving multi-channel bioelectric signals and performing pre-noise reduction processing on the bioelectric signals; A generating module, used for generating a bioelectric signal model based on the bioelectric signal after the noise reduction process; A calculation module, used for calculating the mean signal-to-noise ratio of the bioelectric signal in the current cycle according to the bioelectric signal model; The elimination module is used to eliminate interference signals from the multi-channel bioelectric signals if the signal-to-noise ratio mean is greater than a preset threshold.

9. An electronic device, characterized in that: include: Memory and processing modules; The memory is used to store computer programs; The processing module is used to execute the computer program and implement the steps of the multi-channel bioelectric signal modulation method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program; When the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the multi-channel bioelectric signal modulation method according to any one of claims 1 to 7.

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