Bio-potential signal acquisition method and related equipment thereof

Abstract

The invention provides a biopotential signal acquisition method and related equipment thereof, which are applied to biopotential acquisition equipment, AD channels of an AD chip of the equipment are connected with all electrodes through a multi-path analog switch, and the method comprises the following steps: acquiring a task demand, and determining a sampling mode based on the task demand; collecting EEG data according to the sampling mode and a preset initial sampling weight; performing effective information comparison on the EEG data and the task demand to obtain a comparison result; when the comparison result is that the demand is met, the EEG data and the task demand are input into a prediction model, a prediction result is obtained, and the prediction result comprises a prediction area and a prediction weight; and respectively scanning the prediction area and other areas except the prediction area to acquire target data. According to the method and the device, the signal data meeting the task requirements are searched for in a large range by one part of sampling channels, and the prediction area is scanned by the other part of sampling channels according to the prediction weight, so that the signal acquisition accuracy and acquisition efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biopotential acquisition, and in particular to a biopotential signal acquisition method and related equipment. Background Art

[0002] As research on the human body deepens, it's been discovered that when the brain is active, nerve cells undergo corresponding electrophysiological activity, which is reflected in the cerebral cortex or scalp surface. By collecting and analyzing this electrophysiological activity, the results can be applied in a variety of fields, including the diagnosis of brain lesions and mental health issues.

[0003] Currently, EEG is a commonly used acquisition method. Using electrophysiological indicators to record brain activity, EEG offers high speed, but suffers from low spatial resolution and susceptibility to electrical noise. Traditional EEG equipment typically requires a large number of physical ADC channels to acquire multi-lead signals, resulting in high cost and bulk.

[0004] To address these issues, existing technologies have attempted to improve acquisition efficiency and reduce costs by optimizing signal processing algorithms or improving hardware design. However, in practical applications, significantly reducing equipment costs while ensuring data quality remains a challenging task. Especially for applications requiring multi-lead acquisition, traditional methods often struggle to strike an ideal balance between cost control and performance requirements.

[0005] That is, how to provide a biopotential signal acquisition method to achieve more efficient and economical biopotential signal acquisition is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The embodiments of the present invention provide a biopotential signal acquisition method and related equipment to solve at least one of the above technical problems.

[0007] In a first aspect, the present application provides a biopotential signal acquisition method, which is applied to a biopotential acquisition device, wherein the biopotential acquisition device includes a plurality of electrodes and an AD chip connected to the plurality of electrodes via a multi-way analog switch, so as to form a plurality of sampling channels between the electrodes and the AD chip, the channels of which are controlled and switched by the multi-way analog switch. Each AD channel of the AD chip is connected to the plurality of electrodes via the multi-way analog switch. The method comprises:

[0008] Obtaining task requirements, and determining a sampling mode based on the task requirements, wherein the sampling mode includes a limited precision sampling mode and an unlimited precision sampling mode;

[0009] Controlling the multi-channel analog switch to poll each sampling channel to acquire EEG data according to the sampling mode and the preset initial sampling weight;

[0010] Comparing the EEG data with the task requirements for effective information to obtain a comparison result, wherein the comparison result is used to indicate whether the EEG data meets the task requirements, and the comparison result includes whether the EEG data meets the requirements or does not meet the requirements;

[0011] When the comparison result is in compliance with the requirement, the EEG data and the task requirement are input into a pre-trained prediction model to obtain a prediction result, the prediction result including a prediction area and a prediction weight corresponding to the prediction area, the prediction area being composed of electrodes whose collected EEG data meets the task requirement;

[0012] The multi-way analog switch is controlled to select the sampling channel according to the prediction weight to respectively scan the prediction area and other areas except the prediction area to acquire target data.

[0013] Preferably, the method further comprises:

[0014] The target data and the task requirements are input into the prediction model again to obtain a target prediction area and a target prediction weight based on the target data. Then, the sampling channel is selected according to the target prediction weight to scan the target prediction area and other areas except the target prediction area separately to collect iterative target data.

[0015] Preferably, performing effective information comparison between the EEG data and the task requirements to obtain a comparison result includes:

[0016] Inputting the EEG data and the task requirements into an effective information function to obtain a goodness of fit matrix;

[0017] Each element in the consistency matrix is weighted and added together to obtain a comprehensive matching degree, wherein the comprehensive matching degree is used to represent the similarity between the EEG data and the task requirements;

[0018] The comprehensive matching degree is compared with the matching degree threshold to obtain a comparison result. When the comprehensive matching degree is greater than the matching degree threshold, the comparison result meets the requirements; when the comprehensive matching degree is less than the matching degree threshold, the comparison result does not meet the requirements.

[0019] Preferably, the method further comprises:

[0020] The weight parameters of the prediction model are adjusted based on the neural network online learning technology in combination with the fit matrix and the EEG data.

[0021] Preferably, the method further comprises:

[0022] When the comparison result does not meet the requirements, a full-coverage scanning method is used to collect signals. The full-coverage scanning method is a scanning method that controls the multi-channel analog switch to traverse all sampling channels for signal collection on the basis that the sampling rate meets the sampling condition. The sampling condition is that the sampling rate is higher than twice the highest frequency component in the analog signal spectrum.

[0023] Preferably, selecting the sampling channel according to the prediction weight to scan the prediction area and other areas except the prediction area separately to acquire target data includes:

[0024] determining an equivalent sampling rate based on the prediction weight and the sampling pattern;

[0025] selecting a portion of sampling channels according to the equivalent sampling rate for scanning the predicted area to obtain concentrated scanning data based on the predicted area;

[0026] Using the remaining sampling channels to scan areas other than the predicted area to obtain diffusion scanning data;

[0027] The concentrated scan data and the diffuse scan data are integrated to form target data.

[0028] Preferably, when the sampling mode is an unlimited precision sampling mode, determining the equivalent sampling rate based on the prediction weight and the sampling mode includes:

[0029] The unlimited sampling rate is calculated based on the predicted weight and the actual sampling rate of the AD chip;

[0030] Determining whether the unlimited sampling rate is greater than a required sampling rate determined according to the sampling condition for obtaining the task requirement;

[0031] If not, adjusting the unlimited sampling rate until the sampling condition is met;

[0032] If so, it is determined that the unlimited sampling rate is an equivalent sampling rate.

[0033] Preferably, when the sampling mode is a limited precision sampling mode, determining the equivalent sampling rate based on the prediction weight and the sampling mode includes:

[0034] Determining a required sampling rate and required sampling accuracy for obtaining the task requirements based on the sampling conditions;

[0035] Calibrate the correspondence between sampling rate and sampling accuracy;

[0036] A limited sampling rate and a sampling channel switching rate are determined according to the corresponding relationship, the prediction weight, the required sampling accuracy, and the required sampling rate. The limited sampling rate and the sampling channel switching rate constitute the equivalent sampling rate.

[0037] In a second aspect, the present application also provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor is used to implement a biopotential signal acquisition method as described in any one of claims 1 to 8 when executing the computer program stored in the memory.

[0038] In a third aspect, the present application further provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a biopotential signal acquisition method as described in any one of claims 1 to 8.

[0039] The biopotential signal acquisition method provided by the present invention is applied to a biopotential acquisition device. The biopotential acquisition device includes multiple electrodes and an AD chip connected to the multiple electrodes through a multi-way analog switch, so as to form a plurality of sampling channels between the electrodes and the AD chip, which are switched by the multi-way analog switch. Each AD channel of the AD chip is connected to all electrodes through the multi-way analog switch. The method includes: obtaining task requirements, determining a sampling mode based on the task requirements, the sampling mode including a limited precision sampling mode and an unlimited precision sampling mode; polling each sampling channel according to the sampling mode and a preset initial sampling weight to acquire EEG data; comparing the EEG data with the task requirements for effective information to obtain a comparison result, the comparison result being used to characterize whether the EEG data meets the task requirements, the comparison result including whether it meets the requirements and whether it does not meet the requirements; when the comparison result is that it meets the requirements, inputting the EEG data and the task requirements into a pre-trained prediction model to obtain a prediction result, the prediction result including a prediction area and a prediction weight corresponding to the prediction area, the prediction area being composed of electrodes whose collected EEG data meets the task requirements; and scanning the prediction area and other areas other than the prediction area to acquire target data. This application first uses an artificial intelligence model to predict the area where the task requirements may appear using the collected initial data to obtain a predicted area. Then, considering that the signal data corresponding to the task requirements will always be jumping in different brain areas, the sampling channel is divided into two parts, and one part of the sampling channel is used to scan other electrodes outside the predicted area to search for signal data that meets the task requirements in a larger range. At the same time, since the signal transmission in the brain is diffuse, that is, local, the signal data that meets the task requirements is likely to appear near the last signal. Therefore, a part of the sampling channel is used to scan the electrodes contained in the predicted area based on the output of the prediction model according to the prediction weight. The above-mentioned divided scanning method improves the accuracy and efficiency of signal acquisition. At the same time, each AD channel of the AD chip is connected to each electrode through a multi-way analog switch, which reduces the number of AD chips used and replaces the traditional method of configuring a large number of physical ADC channels to achieve multi-lead signal acquisition, saving costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A schematic flow chart of a biopotential signal acquisition method provided in this application;

[0042] Figure 2 A schematic diagram of the structure of an electronic device provided in this application;

[0043] Figure 3 A schematic diagram of the structure of the computer-readable storage medium provided in this application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0045] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0046] The division of modules in this application is a logical division. In actual application, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. Moreover, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed into multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application.

[0047] Next, see Figure 1 , Figure 1 The present invention provides a flow chart of a biopotential signal acquisition method according to an embodiment of the present invention. The method is applied to a biopotential acquisition device, which includes multiple electrodes and an AD chip connected to the multiple electrodes via a multi-way analog switch. The device forms multiple sampling channels between the electrodes and the AD chip, which are controlled and switched by the multi-way analog switch. Each AD channel of the AD chip is connected to all electrodes via the multi-way analog switch. The method specifically includes the following steps S110 to S150:

[0048] Step S110: Obtain task requirements, and determine a sampling mode based on the task requirements. The sampling mode includes a limited precision sampling mode and an unlimited precision sampling mode.

[0049] The limited-precision sampling mode limits both the sampling accuracy and the sampling rate, while the unrestricted-precision sampling mode limits only the sampling rate. EEG data can be acquired in both sampling modes. In actual operation, either the unrestricted-precision sampling mode or the limited-precision sampling mode can be selected based on actual needs.

[0050] Step S120: Control the multi-channel analog switch to poll each sampling channel to acquire EEG data according to the sampling mode and the preset initial sampling weight;

[0051] It should be emphasized here that the preset initial sampling weight is based on the corresponding sampling weight set for each sampling channel. When the sampling weight is lower than the minimum weight value set in advance, the sampling channel is skipped and no sampling is performed. The minimum weight value is greater than or equal to zero.

[0052] After the EEG data is collected in step S120 and before step S130, the method involved in the present application also includes filtering the EEG data according to the human brain wave frequency (1-200HZ), and using the filtered data to execute subsequent steps.

[0053] It can be understood that when there are n electrodes and the AD chip has m AD channels, then each electrode corresponds to m sampling channels, and n electrodes have m*n sampling channels. Therefore, the EEG data obtained at this time consists of m*n elements, and each element is a data sequence.

[0054] Step S130: performing effective information comparison between the EEG data and the task requirements to obtain a comparison result, which is used to indicate whether the EEG data meets the task requirements. The comparison result includes whether the EEG data meets the requirements or does not meet the requirements.

[0055] As an achievable method, the above step S130 involves performing effective information comparison between the EEG data and the task requirements to obtain a comparison result, which specifically includes the following sub-steps:

[0056] Input EEG data and task requirements into the effective information function to obtain the goodness of fit matrix;

[0057] The elements in the fit matrix are weighted and added together to obtain the comprehensive fit, which is used to characterize the similarity between EEG data and task requirements.

[0058] The comprehensive matching degree is compared with the matching degree threshold to obtain a comparison result. When the comprehensive matching degree is greater than the matching degree threshold, the comparison result meets the requirements; when the comprehensive matching degree is less than the matching degree threshold, the comparison result does not meet the requirements.

[0059] Specifically, the effective information function is used to compare the EEG data with the task requirements to determine whether the EEG data meets the task requirements based on the comparison results. This is because it is possible that the preset initial sampling weights cannot sample signal data that meets the task requirements.

[0060] Step S140: When the comparison result is consistent with the requirements, the EEG data and the task requirements are input into a pre-trained prediction model to obtain a prediction result, which includes a prediction region and a prediction weight corresponding to the prediction region. The prediction region is composed of electrodes whose collected EEG data meets the task requirements;

[0061] Among them, the prediction model is a neural network model, and the neural network model can use an LSTM model. The prediction model is obtained by training an initial model with sample EEG data and reference demand data that are pre-labeled with prediction results.

[0062] Furthermore, as another achievable manner, the method may further include the following step S170:

[0063] When the comparison result does not meet the requirements, a full coverage scanning method is used for signal acquisition. The full coverage scanning method is a scanning method that controls multi-channel analog switches to traverse all sampling channels for signal acquisition on the basis that the sampling rate meets the sampling conditions. The sampling condition is that the sampling rate is higher than twice the highest frequency component in the analog signal spectrum.

[0064] It should be noted that the full-coverage scanning mode is set up based on task requirements. Generally, all sampling channels are traversed. However, under certain task conditions, some sampling channels do not need to be traversed in the entire task process. In this case, the sampling channels that do not need to be traversed and collected can be closed before signal acquisition.

[0065] It can be understood that when the comparison result does not meet the requirements, the sampling weight corresponding to the sampling rate at this time should be adjusted, and it should not be the same as the preset initial sampling weight. If the signal sampling continues according to the preset initial sampling weight, it will fall into an infinite comparison loop.

[0066] Here, traversing all electrodes means traversing the sampling channels associated with all electrodes.

[0067] Step S150 : controlling the multi-way analog switch to select a sampling channel according to the prediction weight to scan the prediction area and other areas except the prediction area respectively to acquire target data.

[0068] As an implementable manner, the above-mentioned step S150 involves controlling the multi-way analog switch to select the sampling channel according to the prediction weight to scan the prediction area and other areas except the prediction area respectively to collect target data, which specifically includes the following sub-steps S151 to S154:

[0069] Sub-step S151, determining an equivalent sampling rate based on the prediction weight and the sampling mode;

[0070] As an implementable manner, when the sampling mode is the unlimited precision sampling mode, the determination of the equivalent sampling rate based on the prediction weight and the sampling mode involved in the above sub-step S151 specifically includes the following:

[0071] The unlimited sampling rate is calculated based on the predicted weight and the actual sampling rate of the AD chip;

[0072] Determining whether the unrestricted sampling rate is greater than the required sampling rate for obtaining task requirements determined based on the sampling conditions;

[0073] If not, the unlimited sampling rate is adjusted until the sampling conditions are met;

[0074] If so, the unlimited sampling rate is determined to be the equivalent sampling rate.

[0075] As another achievable manner, when the sampling mode is the limited precision sampling mode, the determination of the equivalent sampling rate based on the prediction weight and the sampling mode involved in the above sub-step S151 specifically includes the following:

[0076] Determine the required sampling rate and required sampling accuracy for obtaining task requirements based on sampling conditions;

[0077] Calibrate the correspondence between sampling rate and sampling accuracy;

[0078] Determine the limited sampling rate and the channel switching rate based on the corresponding relationship, the prediction weight, the required sampling accuracy and the required sampling rate, and the limited sampling rate and the channel switching rate constitute an equivalent sampling rate;

[0079] Sub-step S152: selecting a portion of sampling channels for scanning the predicted area by controlling a multi-way analog switch according to an equivalent sampling rate, so as to acquire concentrated scanning data based on the predicted area;

[0080] Sub-step S153: using the remaining sampling channels to scan areas other than the predicted area to acquire diffusion scanning data;

[0081] Sub-step S154 : integrating the concentrated scan data and the diffuse scan data to form target data.

[0082] As an implementable manner, after the above step S150, that is, after the target data is acquired, the method may further include the following step S160:

[0083] The target data and task requirements are input into the prediction model again to obtain the target prediction area and target prediction weight based on the target data. Then, according to the target prediction weight, the multi-way analog switch is controlled to select the sampling channel to scan the target prediction area and other areas except the target prediction area separately to collect iterative target data.

[0084] It is understood that the signal accuracy of the iterative target data is greater than that of the target data. The collected iterative target data can be used as a basis for subsequent signals with higher accuracy.

[0085] As an achievable approach, the method further includes adjusting the weight parameters of the prediction model based on neural network online learning technology in combination with the fit matrix and EEG data to further optimize the prediction model. This process of adjusting the weight parameters of the prediction model is related to the neural network model and is prior art, so it will not be further described here.

[0086] Specifically, an embodiment of the present invention provides a biopotential signal acquisition method, which is applied to a biopotential acquisition device. The biopotential acquisition device includes multiple electrodes and an AD chip connected to the multiple electrodes via a multi-way analog switch, so as to form a plurality of sampling channels between the electrodes and the AD chip, which are switched by the multi-way analog switch. Each AD channel of the AD chip is connected to all electrodes via the multi-way analog switch. The method includes: obtaining task requirements, determining a sampling mode based on the task requirements, wherein the sampling mode includes a limited precision sampling mode and an unlimited precision sampling mode; polling each sampling channel to acquire EEG data according to the sampling mode and a preset initial sampling weight; comparing the EEG data with the task requirements for effective information to obtain a comparison result, which is used to indicate whether the EEG data meets the task requirements, and the comparison result includes whether it meets the requirements and whether it does not meet the requirements; when the comparison result is that the EEG data meets the requirements, inputting the EEG data and the task requirements into a pre-trained prediction model to obtain a prediction result, which includes a prediction area and a prediction weight corresponding to the prediction area, wherein the prediction area is composed of electrodes whose collected EEG data meets the task requirements; and scanning the prediction area and other areas outside the prediction area to acquire target data. In the embodiment of the present application, the collected initial data is first used to predict the area where the task requirements may appear through an artificial intelligence model to obtain a predicted area. Then, considering that the signal data corresponding to the task requirements will always be jumping in different brain areas, the sampling channel is divided into two parts, and one part of the sampling channel is used to scan other electrodes outside the predicted area to search for signal data that meets the task requirements in a larger range; at the same time, since the signal transmission in the brain is diffuse, that is, local, the signal data that meets the task requirements is likely to appear near the last signal that appeared. Therefore, a part of the sampling channel is used to scan the electrodes contained in the predicted area based on the output of the prediction model according to the prediction weight. The above-mentioned divided scanning method improves the accuracy and efficiency of signal acquisition. At the same time, each AD channel of the AD chip is connected to each electrode through a multi-way analog switch, which reduces the number of AD chips used and replaces the traditional method of configuring a large number of physical ADC channels to achieve multi-lead signal acquisition, saving costs.

[0087] An electronic device is also provided in the embodiment of the present invention. Figure 2 , Figure 2 The following is a schematic diagram of an electronic device according to an embodiment of the present invention, including:

[0088] A memory 201, a processor 202, and a computer program 203 stored in the memory and executable on the processor, wherein the processor implements the above-mentioned biopotential signal acquisition method when executing the computer program 203 stored in the memory.

[0089] For ease of explanation, only the portions relevant to the embodiments of the present invention are shown. For specific technical details not disclosed, please refer to the section on the biopotential signal acquisition method in Embodiment 1 of the present invention. Memory 201 can be used to store computer program 203, which includes software programs, modules, and data. Processor 202 executes computer program 203 stored in memory 201 to perform various functional applications and data processing of the electronic device.

[0090] The present invention also provides a computer-readable storage medium. Figure 3 , Figure 3 Schematic diagram of an embodiment of a computer-readable storage medium in an embodiment of the present invention, wherein the computer-readable storage medium stores a computer program, which, when executed, includes some or all of the steps of a biopotential signal acquisition method described in the above method embodiment.

[0091] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described device, electronic device, and computer-readable storage medium can refer to the corresponding processes of the biopotential signal acquisition method in the aforementioned method embodiment, and will not be repeated here.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0093] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of a biopotential signal acquisition method in each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0096] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biopotential signal acquisition method, characterized in that: The method is applied to a biopotential acquisition device, the biopotential acquisition device comprising a plurality of electrodes and an AD chip connected to the plurality of electrodes via a multi-way analog switch, so as to form a plurality of sampling channels between the electrodes and the AD chip, the channels of which are controlled and switched by the multi-way analog switch. Each AD channel of the AD chip is connected to the plurality of electrodes via the multi-way analog switch. The method comprises: Obtaining task requirements, and determining a sampling mode based on the task requirements, wherein the sampling mode includes a limited precision sampling mode and an unlimited precision sampling mode; Controlling the multi-channel analog switch to poll each sampling channel to acquire EEG data according to the sampling mode and the preset initial sampling weight; Comparing the EEG data with the task requirements for effective information to obtain a comparison result, wherein the comparison result is used to indicate whether the EEG data meets the task requirements, and the comparison result includes whether the EEG data meets the requirements or does not meet the requirements; When the comparison result is in compliance with the requirement, the EEG data and the task requirement are input into a pre-trained prediction model to obtain a prediction result, the prediction result including a prediction area and a prediction weight corresponding to the prediction area, the prediction area being composed of electrodes whose collected EEG data meets the task requirement; The multi-way analog switch is controlled to select the sampling channel according to the prediction weight to respectively scan the prediction area and other areas except the prediction area to acquire target data.

2. The biopotential signal acquisition method according to claim 1, characterized in that: After acquiring the target data, the method further includes: The target data and the task requirements are input into the prediction model again to obtain a target prediction area and a target prediction weight based on the target data. Then, the sampling channel is selected according to the target prediction weight to scan the target prediction area and other areas except the target prediction area separately to collect iterative target data.

3. The biopotential signal acquisition method according to claim 1, characterized in that: Comparing the EEG data with the task requirements for effective information to obtain a comparison result includes: Inputting the EEG data and the task requirements into an effective information function to obtain a goodness of fit matrix; Each element in the consistency matrix is weighted and added together to obtain a comprehensive matching degree, wherein the comprehensive matching degree is used to represent the similarity between the EEG data and the task requirements; The comprehensive matching degree is compared with the matching degree threshold to obtain a comparison result. When the comprehensive matching degree is greater than the matching degree threshold, the comparison result meets the requirements; when the comprehensive matching degree is less than the matching degree threshold, the comparison result does not meet the requirements.

4. The biopotential signal acquisition method according to claim 3, characterized in that: The method further comprises: The weight parameters of the prediction model are adjusted based on the neural network online learning technology in combination with the fit matrix and the EEG data.

5. The biopotential signal acquisition method according to claim 1, characterized in that: The method further comprises: When the comparison result does not meet the requirements, a full-coverage scanning method is used to collect signals. The full-coverage scanning method is a scanning method that controls the multi-channel analog switch to traverse all sampling channels for signal collection on the basis that the sampling rate meets the sampling condition. The sampling condition is that the sampling rate is higher than twice the highest frequency component in the analog signal spectrum.

6. The biopotential signal acquisition method according to claim 1, characterized in that: The selecting the sampling channel according to the prediction weight to scan the prediction area and other areas except the prediction area respectively to acquire target data includes: determining an equivalent sampling rate based on the prediction weight and the sampling pattern; selecting a portion of sampling channels according to the equivalent sampling rate for scanning the predicted area to obtain concentrated scanning data based on the predicted area; Using the remaining sampling channels to scan areas other than the predicted area to obtain diffusion scanning data; The concentrated scan data and the diffuse scan data are integrated to form target data.

7. The biopotential signal acquisition method according to claim 6, characterized in that: When the sampling mode is an unlimited precision sampling mode, determining the equivalent sampling rate based on the prediction weight and the sampling mode includes: The unlimited sampling rate is calculated based on the predicted weight and the actual sampling rate of the AD chip; Determining whether the unlimited sampling rate is greater than a required sampling rate determined according to the sampling condition for obtaining the task requirement; If not, adjusting the unlimited sampling rate until the sampling condition is met; If so, it is determined that the unlimited sampling rate is an equivalent sampling rate.

8. The biopotential signal acquisition method according to claim 6, characterized in that: When the sampling mode is a limited precision sampling mode, determining the equivalent sampling rate based on the prediction weight and the sampling mode includes: Determining a required sampling rate and required sampling accuracy for obtaining the task requirements based on the sampling conditions; Calibrate the correspondence between sampling rate and sampling accuracy; A limited sampling rate and a sampling channel switching rate are determined according to the corresponding relationship, the prediction weight, the required sampling accuracy, and the required sampling rate. The limited sampling rate and the sampling channel switching rate constitute the equivalent sampling rate.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement a biopotential signal acquisition method according to any one of claims 1 to 8 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a biopotential signal acquisition method according to any one of claims 1 to 8 is implemented.

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