CN115186718A

CN115186718A - Generation method and system for analog acquisition of electroencephalogram signals

Info

Publication number: CN115186718A
Application number: CN202210910733.3A
Authority: CN
Inventors: 张家伟; 陈超
Original assignee: Sichuan Neosource Biotektronics Ltd
Current assignee: Sichuan Neosource Biotektronics Ltd
Priority date: 2022-06-13
Filing date: 2022-06-13
Publication date: 2022-10-14
Also published as: CN115238745A; CN114742116B; CN115034266A; CN114742116A

Abstract

The embodiment of the specification provides a generation method and a system for simulating an acquired electroencephalogram signal, wherein the method comprises the following steps: obtaining a noise-free simulated electroencephalogram signal based on the basic signal and the first parameter group; determining a first sub-noise signal based on the region of interest transfer matrix and the simulated brain noise signal; determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal; obtaining a target noise signal based on the first sub-noise signal and the second sub-noise signal, and obtaining a first weight of the simulated electroencephalogram signal and a second weight of the target noise signal; and obtaining the target analog acquisition electroencephalogram signal based on the analog electroencephalogram signal, the target noise signal, the first weight and the second weight. The simulated brain electrical signal, the target noise signal and the target simulated collected brain electrical signal can be used as the noiseless brain electrical signal in the human body, the noise signal in the collected brain electrical signal and the collected brain electrical signal to participate in the analysis and research of the brain electrical signal.

Description

Generation method and system for analog acquisition of electroencephalogram signals

Description of the cases

The application is a divisional application of Chinese patent application 202210659471.8 entitled "a method and system for generating analog-collected electroencephalogram signals" filed on 13.06.2022.

Technical Field

The present disclosure relates to the field of analog electroencephalogram signals, and in particular, to a method and a system for generating analog acquisition electroencephalogram signals.

Background

With the progress of science and the rapid development of biomedical technology, biological signals are applied to research in various fields. For example, electroencephalogram signals can be used to aid in the diagnosis of sleep classification, epilepsy, and depression. However, the amplitude of the electroencephalogram signal is small, generally, the electroencephalogram signal is only dozens to hundreds of microvolts, and the electroencephalogram signal is interfered by various noise signals in the acquisition process, and most commonly, some noise signals such as ocular artifacts and myoelectric artifacts are generated. Meanwhile, when electroencephalogram signals of a human body are collected by the electroencephalogram collecting equipment, the electroencephalogram signals and noise signals can be influenced by various factors such as a brain functional area and electrode points in the propagation process when being propagated to the electrode points in the electroencephalogram collecting equipment from the production positions of the electroencephalogram signals and the noise signals, the electroencephalogram signals and the noise signals in the collected electroencephalogram signals finally collected by the electrode points are different from the signals sent by the production positions of the electrode points, and therefore the electroencephalogram signals and the noise signals in the electroencephalogram signals are difficult to distinguish.

At present, a machine learning model can be used for denoising acquired electroencephalogram signals, but the acquired electroencephalogram signals and noise signals after propagation in the electroencephalogram signals are difficult to determine, so that a large amount of training data required by model training is difficult to acquire.

Therefore, it is necessary to provide a method for generating an analog-collected electroencephalogram signal, which can generate a propagated analog electroencephalogram signal, a propagated target noise signal, and an analog-collected electroencephalogram signal, so as to obtain a large amount of training data required by model training, thereby enabling a trained denoising model to have a better generalization effect, and also reducing the cost required for collecting an electroencephalogram signal of a human body when researching the electroencephalogram signal.

Disclosure of Invention

One or more embodiments of the present specification provide a method for generating a simulated acquired brain electrical signal, the method comprising: obtaining a noiseless simulated electroencephalogram signal based on the basic signal and the first parameter group, wherein the simulated electroencephalogram signal is used as the noiseless electroencephalogram signal in the human body to participate in the analysis and research of the electroencephalogram signal; obtaining a target noise signal based on an initial noise signal and a second parameter set, wherein the target noise signal is used as a noise signal in an acquired electroencephalogram signal of a human body to participate in analysis and research of the electroencephalogram signal, the second parameter set comprises an interested region transfer matrix and an electrode point transfer matrix, the initial noise signal comprises a simulated brain noise signal and an electrode point noise signal, and the obtaining the target noise signal based on the initial noise signal and the second parameter set comprises: determining a first sub-noise signal based on the region of interest transfer matrix and the simulated brain noise signal, wherein elements in the region of interest transfer matrix represent transfer relationships of signals when the signals are propagated between different regions of interest; determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal, wherein elements in the electrode point transfer matrix represent a transfer relationship of signals when the signals are transmitted between different electrode points; obtaining the target noise signal based on the first sub-noise signal and the second sub-noise signal; acquiring a first weight of the analog electroencephalogram signal and a second weight of the target noise signal; and obtaining a target simulation collection electroencephalogram signal based on the simulation electroencephalogram signal, the target noise signal, the first weight and the second weight, wherein the target simulation collection electroencephalogram signal is used as the collection electroencephalogram signal to participate in analysis and research of the electroencephalogram signal and reduce collection of a real electroencephalogram signal.

One or more embodiments of the present specification provide a generation system for simulating an acquired brain electrical signal, the system comprising: the first acquisition module is used for acquiring a noiseless simulated electroencephalogram signal based on the basic signal and the first parameter group, and the simulated electroencephalogram signal is used as a noiseless electroencephalogram signal in a human body to participate in analysis and research of the electroencephalogram signal; a second obtaining module, configured to obtain a target noise signal based on an initial noise signal and a second parameter set, where the target noise signal is used as a noise signal in an acquired electroencephalogram signal of a human body to participate in analysis and research of the electroencephalogram signal, the second parameter set includes an interested region transfer matrix and an electrode point transfer matrix, the initial noise signal includes a simulated brain noise signal and an electrode point noise signal, and the second obtaining module is further configured to: determining a first sub-noise signal based on the region of interest transfer matrix and the simulated brain noise signal, wherein elements in the region of interest transfer matrix represent transfer relationships of signals when the signals are propagated between different regions of interest; determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal, wherein elements in the electrode point transfer matrix represent a transfer relationship of signals when the signals are transmitted between different electrode points; obtaining the target noise signal based on the first sub-noise signal and the second sub-noise signal; the third acquisition module is used for acquiring the first weight of the analog electroencephalogram signal and the second weight of the target noise signal; and the fourth acquisition module is used for acquiring a target simulation acquisition electroencephalogram signal based on the simulation electroencephalogram signal, the target noise signal, the first weight and the second weight, wherein the target simulation acquisition electroencephalogram signal is used as the acquisition electroencephalogram signal to participate in analysis and research of the electroencephalogram signal and reduce acquisition of a real electroencephalogram signal.

One or more embodiments of the present specification provide a generation device for simulating acquired brain electrical signals, which includes a processor, and the processor is used for executing the generation device to realize the generation method for simulating acquired brain electrical signals according to any one of the above embodiments.

One or more embodiments of the present specification provide a computer-readable storage medium, which stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the computer to implement the generation method for simulating the acquired brain electrical signals according to any one of the above embodiments.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a generation system for simulating acquired brain electrical signals according to some embodiments of the present description;

FIG. 2 is an exemplary block diagram of a generation system for simulating acquired brain electrical signals, shown in accordance with some embodiments of the present description;

FIG. 3 is a schematic diagram illustrating the generation of analog acquired brain electrical signals according to some embodiments of the present description;

FIG. 4 is an exemplary flow chart illustrating obtaining a noise-free simulated brain electrical signal according to some embodiments of the present description;

FIG. 5 is an exemplary flow chart illustrating obtaining a target noise signal according to some embodiments of the present description;

FIG. 6 is an exemplary flow diagram illustrating obtaining a first set of parameters according to some embodiments of the present description;

FIG. 7 is an exemplary flow diagram of a method for de-noising acquired electroencephalogram signals, according to some embodiments of the present description;

FIG. 8 is an exemplary flow diagram illustrating obtaining signal transfer relationships according to some embodiments of the present description;

FIG. 9 is a schematic diagram of a signal transfer model according to some embodiments of the present description;

FIG. 10 is a schematic diagram of yet another signal transfer model according to some embodiments herein;

FIG. 11 is an exemplary flow chart illustrating the determination of a target noise signal according to some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

FIG. 1 is a schematic diagram of an application scenario of a generation system for simulating acquired brain electrical signals according to some embodiments of the present disclosure.

As shown in FIG. 1, an application scenario 100 simulating a generation system for acquiring brain electrical signals may include a server 110, a base signal 120, an initial noise signal 130, a network 140, a storage device 150, and a terminal device 160. The application scenario of the generation system for analog-acquired electroencephalogram signals can be implemented by implementing the method and/or process disclosed in the present specification to acquire analog-acquired electroencephalogram signals.

The server 110 may communicate with the base signal 120, the initial noise signal 130, the network 140, the storage device 150, and the terminal device 160 to implement various functions of the generation system for analog acquisition of brain electrical signals. In some embodiments, server 110 may receive base signal 120 and initial noise signal 130 via, for example, network 140, and may process them. In some embodiments, server 110 may output relevant data to storage device 150 and terminal device 160 via, for example, network 140. In some embodiments, the server 110 may be a single server or a group of servers. In some embodiments, server 110 may be connected locally to network 140 or remotely from network 140. In some embodiments, the server 110 may be implemented on a cloud platform.

The base signal 120 may be a simple signal. For example, the base signal 120 may be a sine wave. The base signal 120 may be generated by any one or more of a waveform generator, an analog signal generator, a waveform generator, and the like. In some embodiments, the base signal 120 may be transmitted to the server 110, the storage device 150, and the terminal device 160 via the network 140.

The initial noise signal 130 may be a noise signal that has not propagated while the brain electrical signal was acquired. The initial noise signal 130 may be generated by any one or more of a noise generator, an analog signal generator, a noise generator, and the like. In some embodiments, the incipient noise signal 130 may be transmitted to the server 110, the storage device 150, and the terminal device 160 via the network 140.

Network 140 may be used for the transmission of information and/or data. In some embodiments, one or more components (e.g., server 110 and/or storage 150, etc.) in the application scenario 100 may send information and/or data to another component in the application scenario 100 via the network 140.

Storage device 150 may store data and/or instructions. The data may include data related to the server 110, the terminal device 160, the base signal 120, the initial noise signal 130, and the like. For example, the storage device 150 may store the base signal 120 and the initial noise signal 130. In some embodiments, storage device 150 may store data and/or instructions that server 110 uses to perform or use to perform the exemplary methods described in this specification. In some embodiments, a storage device 150 may be connected to the network 140 to communicate with one or more components of the application scenario 100 (e.g., the server 110 and/or the terminal device 160). In some embodiments, the storage device 150 may be part of the server 110. In some embodiments, storage device 150 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 may be implemented on a cloud platform.

Terminal device 160 may refer to one or more terminals or software used by a user. Wherein the user may refer to a researcher. The user can view the relevant data of the respective components in the application scenario 100 through the terminal device 160. In some embodiments, the terminal device 160 may include a mobile device 160-1, a tablet computer 160-2, a laptop computer 160-3, or the like, or any combination thereof. In some embodiments, terminal device 160 may be fixed and/or mobile. For example, the terminal device 160 may be directly installed on the server 110, becoming a part of the server 110. As another example, the terminal device 160 may be a mobile device, and a user may carry the terminal device 160 at a remote location with respect to the server 110, the base signal 120, and the initial noise signal 130, and the terminal device 160 may be connected to and/or communicate with the server 110, the storage device 150 via the network 140.

It should be noted that the application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the application scenario 100 may also include a database. However, such changes and modifications do not depart from the scope of the present specification.

FIG. 2 is an exemplary block diagram of a generation system for simulating acquired brain electrical signals, shown in accordance with some embodiments of the present description.

As shown in FIG. 2, the generation system 200 for simulating the acquisition of electroencephalogram signals may include a first acquisition module 210, a second acquisition module 220, a third acquisition module 230, and a fourth acquisition module 240.

The first obtaining module 210 may be configured to obtain a noiseless simulated electroencephalogram signal based on the basic signal and the first parameter group, where the simulated electroencephalogram signal is used as a noiseless electroencephalogram signal in a human body to participate in analysis and research of the electroencephalogram signal. For more on the basis signal, the first parameter set and the simulated brain electrical signal, refer to fig. 3 and its associated description. In some embodiments, the first obtaining module 210 may be further configured to obtain an initial first parameter set; obtaining an intermediate simulated electroencephalogram signal based on the initial first parameter group and the basic signal; acquiring a reference electroencephalogram signal; constructing a loss function based on the intermediate simulated electroencephalogram signal and the reference electroencephalogram signal; and iteratively updating the parameters of the initial first parameter group based on the loss function until a preset condition is met, and obtaining the first parameter group. For more on the initial first parameter set, the intermediate simulated brain electrical signal, and the reference brain electrical signal, see FIG. 6 and its associated description. In some embodiments, the first parameter set includes a region-of-interest transfer matrix and a signal source transfer matrix, where elements in the region-of-interest transfer matrix represent transfer relationships when signals are transmitted between different regions of interest, and elements in the signal source transfer matrix represent transfer relationships when signals are transmitted between a signal source and an electrode point in the same region of interest, where different regions of interest represent different brain functional regions in a human brain, and the first obtaining module 210 may be further configured to determine, for each signal source in the virtual brain model, a target basic signal of the signal source based on the region-of-interest transfer matrix and a basic signal emitted by the signal source; aiming at each region of interest in the virtual brain model, determining an initial sub-simulation brain electric signal corresponding to each region of interest based on a target basic signal and a signal source transfer matrix of each signal source in the region of interest; and simulating the brain electrical signals based on the initial sub-model corresponding to each region of interest to obtain simulated brain electrical signals. For more on the region of interest transfer matrix, the signal source transfer matrix, the target basis signal and the initial sub-simulated brain electrical signal, see fig. 4 and its associated description.

The second obtaining module 220 may be configured to obtain a target noise signal based on the initial noise signal and the second parameter group, where the target noise signal is used as a noise signal in the acquired electroencephalogram signal of the human body to participate in analysis and research of the electroencephalogram signal. For more on the initial noise signal, the second parameter set, and the target noise signal, reference is made to fig. 3 and its associated description. In some embodiments, the initial noise signal includes a simulated brain noise signal and an electrode noise signal, the second parameter set includes an interest region transfer matrix and an electrode transfer matrix, a transfer relationship between an element characterizing signal in the interest region transfer matrix when propagating between different regions of interest, a transfer relationship between an element characterizing signal in the electrode transfer matrix when propagating between different electrode points, and the second obtaining module 220 is further configured to determine the first sub-noise signal based on the interest region transfer matrix and the simulated brain noise signal; determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal; and obtaining a target noise signal based on the first sub-noise signal and the second sub-noise signal. For more on the simulated brain noise signal, the electrode point transfer matrix, the first sub-noise signal and the second sub-noise signal, refer to fig. 5 and its related description.

In some embodiments, the second obtaining module 220 may include sub-modules such as a first determining module, a second determining module, and a signal processing module. The first determining module is used for determining the signal transfer relation of the measured object. The signal transfer relationship represents the signal change relationship of the initial noise signal when the initial noise signal propagates among all positions of the measured object. For more on the initial noise signal, the measured object and the signal transfer relationship, refer to fig. 7 and its related description. In some embodiments, the first determination module may be further configured to input an initial test signal from each location of the subject, wherein the initial test signal is greater in magnitude than the brain electrical signal of the subject; collecting signals from other positions of the object to be tested to obtain collected test signals; based on the initial test signal and the collected test signal, a signal transfer relationship is determined. For more on the initial test signal and the collected test signal, reference may be made to fig. 8 and its associated description. The second determination module is configured to determine a target noise signal based on the initial noise signal and the signal transfer relationship. See fig. 7 and its associated description for more on the target noise signal. In some embodiments, the signal transfer relationship comprises a plurality of simple signal transfer relationships, the simple signal transfer relationships characterizing a signal variation relationship of a first simple noise signal as it propagates between the locations of the measurand, wherein the first simple noise signal is obtained by transforming an initial noise signal, and the second determining module is further configured to transform the initial noise signal into the first simple noise signals of a plurality of different frequencies and intensities; for each first simple noise signal, processing the first simple noise signal based on a simple signal transfer relationship corresponding to the first simple noise signal, and determining a second simple noise signal corresponding to the first simple noise signal; and synthesizing each second simple noise signal to obtain a target noise signal. For more on the simple signal transfer relationship, the first simple noise signal, and the second simple noise signal, reference may be made to fig. 10 and its associated description. The signal processing module is used for removing a target noise signal from the acquired electroencephalogram signal acquired from the object to be tested to acquire a noiseless target electroencephalogram signal. For more details regarding the acquisition of the brain electrical signal and the target brain electrical signal, reference may be made to fig. 7 and its associated description.

The third obtaining module 230 may be configured to obtain a first weight of the simulated brain electrical signal and a second weight of the target noise signal. See fig. 3 and its associated description for more on the first weight and the second weight.

The fourth obtaining module 240 may be configured to obtain a target-simulated-acquisition electroencephalogram signal based on the simulated electroencephalogram signal, the target noise signal, the first weight, and the second weight, where the target-simulated-acquisition electroencephalogram signal is used as an acquisition electroencephalogram signal to participate in analysis and research of the electroencephalogram signal and reduce acquisition of a real electroencephalogram signal. For more on the target analog acquisition of the brain electrical signal, see fig. 3 and its associated description.

It should be noted that the above description of the analog acquisition electroencephalogram signal generating system 200 and the modules thereof is only for convenience of description, and the present specification is not limited to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the system, any combination of modules or sub-system may be configured to interface with other modules without departing from such teachings. In some embodiments, the first acquiring module 210, the second acquiring module 220, the third acquiring module 230 and the fourth acquiring module 240 disclosed in fig. 2 may be different modules in one system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.

FIG. 3 is a schematic diagram illustrating generation of an analog acquired brain electrical signal according to some embodiments of the present description. In some embodiments, flow 300 may be performed by server 110. As shown in fig. 3, the process 300 may include the following steps:

and step 310, acquiring a noise-free simulated electroencephalogram signal based on the basic signal and the first parameter group. In some embodiments, step 310 may be performed by the first acquisition module 210.

The base signal may refer to a simple signal of a signal change law. The simulated electroencephalogram signal can be used as a noise-free electroencephalogram signal in a human body to participate in the analysis and research of the electroencephalogram signal. The base signal may include, but is not limited to, a sine signal, a cosine signal, a square wave signal, and the like. The first acquisition module 210 may input the characteristics of the base signal into an analog signal generator to acquire the base signal. The characteristics of the basic signal may include, but are not limited to, the frequency, amplitude, etc. of the basic signal, and the size of the basic signal may be preset, so that the simulated brain electrical signal obtained based on the basic information conforms to the basic characteristics of the real brain electrical signal. For example, the frequency of the basic signal can be preset to be less than 100Hz, and the amplitude can be set to range from-200 uV to +200uV.

The first parameter set may refer to a parameter set that characterizes an influence of factors such as a region of interest in the virtual brain model, a signal source, and an electrode point installed on the virtual brain model on generation of the base signal when the base signal propagates. In some embodiments, the base signal may be processed by a plurality of different first parameter sets to obtain a plurality of different simulated brain electrical signals. Wherein different regions of interest in the virtual brain model may represent respective functional brain regions in the human brain, respectively. For example, a certain region of interest in the virtual brain model may represent the frontal lobe of the human brain's front that is responsible for related tasks such as concentration, short-term memory tasks, and the like.

It should be understood that when signals are transmitted in the brain of a human body, brain structures of different regions of interest, different signal sources and electrode points for acquiring electroencephalogram signals all affect the signals, so that the signals are changed. In some embodiments of the present description, the influence of the region of interest, the signal source, the electrode point, and the like on the signal may be determined through the first parameter set, so that the obtained simulated electroencephalogram signal is closer to a real electroencephalogram signal without noise.

In some embodiments, the first set of parameters may comprise a varying relationship of the signals as they propagate between different regions of interest of the virtual brain model. The first set of parameters may include a region of interest transfer matrix, which may represent the changing relationship of the underlying signals as they propagate between different regions of interest of the virtual brain model. See fig. 4 and its associated description for more on the region of interest transfer matrix.

In some embodiments, the first parameter set may further include a variation relationship of signals emitted from a signal source in a region of interest in the virtual brain model as they propagate between electrode points in the region of interest. The first parameter set may further include a signal source transfer matrix, which may represent a variation relationship of a fundamental signal emitted by a signal source in a certain region of interest in the virtual brain model when propagating between electrode points in the region of interest. See fig. 4 and its associated description for further details regarding the signal source transition matrix.

In some embodiments, the elements in the first parameter set may be definite values or interval range values. When the first parameter group is an interval range value, when the basic signal is processed based on the first parameter group, a specific value can be randomly selected from the interval range value to process the basic signal, so as to obtain the simulated electroencephalogram signal. The first parameter sets corresponding to different human bodies or different virtual brain models are different.

In some embodiments, the first set of parameters may be obtained in a variety of ways. For example, the first parameter set may be acquired through a network. For another example, the first parameter group may also be obtained by random initialization.

In some embodiments, the first parameter set is obtained based on a first preset parameter interval. The first parameter group may be obtained by setting a first preset parameter interval of the first parameter group and iteratively updating a range of the first preset parameter interval. The first preset parameter interval may refer to a preset interval range of each element in the first parameter set. The first preset parameter interval may be obtained in various ways, for example, may be determined by randomly selecting an initial range. For another example, the extension determination may be made based on a range of an existing first parameter set. The first preset parameter interval corresponding parameter set may be an initial first parameter set. In some embodiments, an intermediate simulated brain electrical signal may be obtained based on the initial first set of parameters and the base signal; constructing a loss function based on the intermediate simulated electroencephalogram signal and the reference electroencephalogram signal; and iteratively updating the initial first parameter group based on the loss function until a preset condition is met, and obtaining the first parameter group. For more on the above embodiment of obtaining the first parameter set, refer to fig. 6 and its related description.

In some embodiments of the present disclosure, an approximate range of the first parameter set may be determined by setting a first preset parameter interval of the first parameter set, so as to reduce the amount of calculation for determining the first parameter set and to determine the first parameter set more quickly.

The simulated brain electrical signal can refer to the simulated brain electrical signal of human brain. In some embodiments, the basic signal may be processed based on the first parameter group, and the influence of the region of interest in the virtual brain model, the signal source, the electrode points installed on the virtual brain model, and the like on the basic signal when the basic signal is acquired is determined, so as to obtain the simulated electroencephalogram signal. And acquiring a noise-free analog electroencephalogram signal. In some embodiments, for each region of interest in the virtual brain model, determining an initial sub-simulated brain electrical signal corresponding to the region of interest based on the signal source transfer matrix and the base signal; and simulating the electroencephalogram signal based on the region-of-interest transfer matrix and the initial sub-simulation electroencephalogram signals corresponding to the regions of interest to obtain the simulated electroencephalogram signal. See figure 4 and its associated description for more on the above embodiments.

In some embodiments, the acquired simulated electroencephalogram signal can have the basic characteristics of the electroencephalogram signal by emitting basic signals from a plurality of signal sources of the virtual brain model and overlapping the basic signals. The basic features of the electroencephalogram signal may include, but are not limited to, amplitude features (such as an amplitude range), frequency features (such as a frequency range) of the electroencephalogram signal, and the like. For example, the fundamental features of the brain electrical signal may include an amplitude ranging from-200 uV to +200uV, and a frequency less than 100Hz. 3 signal sources in the virtual brain model can send out 3 sinusoidal signals with amplitudes of 10uV, 30uV and 100uV and frequencies of 5Hz, 20Hz and 40Hz respectively, the 3 sinusoidal signals are superposed to obtain a more complex simulated electroencephalogram signal, the amplitude of the simulated electroencephalogram signal is less than 100uV, the frequency of the simulated electroencephalogram signal has 3 components and is less than 100Hz, and therefore the simulated electroencephalogram signal can have the basic characteristics of the electroencephalogram signal.

It should be understood that the analog electroencephalogram signal is directly obtained by processing the basic signal through the first parameter set, and therefore, the analog electroencephalogram signal does not contain noise signals caused by activities of acquisition equipment or other parts of a human body when the human electroencephalogram signal is acquired.

In step 320, a target noise signal is obtained based on the initial noise signal and the second parameter set. In some embodiments, step 320 may be performed by the second acquisition module 220.

The initial noise signal may refer to a noise signal that has not been propagated while the electroencephalogram signal is being acquired. The target noise signal can be used as a noise signal in the collected electroencephalogram signal of a human body to participate in the analysis and research of the electroencephalogram signal.

When the electroencephalogram signals are collected, various factors influence the collection, so that the collected electroencephalogram signals contain various noise signals. Accordingly, the initial noise signal may include a variety of noise signals. In some embodiments, the initial noise signal may be determined by acquisition or simulation. See fig. 5 and its associated description for more details regarding the determination of the initial noise signal.

The second parameter set may refer to a parameter set characterizing an influence of factors such as a region of interest in the virtual brain model, a signal source, and an electrode point installed on the virtual brain model on the generation of the initial noise signal when the initial noise signal propagates.

Similar to the first parameter set, the second parameter set may also comprise a region of interest transfer matrix. In some embodiments, the second parameter set may further include an electrode point transfer matrix that may represent a changing relationship of signals as they propagate between electrode points in the virtual brain model. See figure 4 and its associated description for more on the electrode point transfer matrix.

In some embodiments, the second parameter set may be obtained in a variety of ways. For example, the region of interest transfer matrix may be acquired through a network.

In some embodiments, the second parameter set is obtained based on a second preset parameter interval. The second parameter group may be obtained by setting a second preset parameter interval of the second parameter group and iteratively updating an interval range of the second preset parameter interval. The second preset parameter interval may refer to a preset interval range of an element in the second parameter value, and the second preset parameter interval may be obtained in a variety of ways, for example, may be determined by randomly selecting an initial range. The second preset parameter interval corresponding parameter set may be an initial second parameter set. In some embodiments, the second parameter set may be determined based on the initial second parameter set. See fig. 6 and its associated description for further details regarding determining the second parameter set based on the initial second parameter set.

In some embodiments, an initial test signal can also be input from each electrode point of the measured object, wherein the magnitude of the initial test signal is larger than that of the brain electrical signal of the measured object; collecting signals from other electrode points of the object to be tested to obtain collected test signals; determining a signal transfer relationship between the electrode point and other electrode points based on the initial test signal and the collected test signal; and determining an electrode point transfer matrix based on the signal transfer relationship between each electrode point and other electrode points. For more on the above embodiment of determining the electrode point transfer matrix, reference is made to fig. 8 and its associated description.

The target noise signal may refer to a noise signal in the analog acquired electroencephalogram signal. In some embodiments, the initial noise signal may be processed based on the second parameter set, and the influence of the region of interest in the virtual brain model, the signal source, the electrode points installed on the virtual brain model, and the like on the initial noise signal when the initial noise signal is acquired may be determined to obtain the target noise signal. In some embodiments, a first sub-noise signal is determined based on the region of interest transfer matrix and the simulated brain noise signal; determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal; and obtaining a target noise signal based on the first sub-noise signal and the second sub-noise signal. See figure 5 and its associated description for more on the above embodiments.

Step 330, a first weight of the simulated brain electrical signal and a second weight of the target noise signal are obtained. In some embodiments, step 330 may be performed by the third acquisition module 230.

The first weight may refer to a specific gravity of the simulated brain electrical signal in the target simulated acquired brain electrical signal. The second weight may refer to a specific gravity of the target noise signal in the target analog acquired brain electrical signal. The first weight and the second weight can be predetermined by analyzing the ratio of the electroencephalogram signal to the noise signal in the actually acquired electroencephalogram signal.

And 340, acquiring a target analog acquisition electroencephalogram signal based on the analog electroencephalogram signal, the target noise signal, the first weight and the second weight.

The target analog acquisition of the electroencephalogram signal may refer to an analog acquisition of the electroencephalogram signal. The target simulation collection of the electroencephalogram signals can be used for collecting the electroencephalogram signals to participate in analysis and research of the electroencephalogram signals and reduce the collection of real electroencephalogram signals. The collected electroencephalogram signals comprise electroencephalogram signals and noise signals, and correspondingly, the target simulation collected electroencephalogram signals can also comprise simulation electroencephalogram signals and target noise signals.

In some embodiments, the target simulated acquired brain electrical signal may be determined by equation (1):

x(t)＝αx _s (t)+βx _n (t) (1)

wherein x (t) represents the analog acquisition of electroencephalogram signals of a target, and x _s (t) represents an analog brain electrical signal, x _n (t) represents a target noise signal, and α and β represent a first weight and a second weight, respectively.

Some embodiments of the present description may further obtain a more real target simulated acquired electroencephalogram signal by setting the first parameter group and the second parameter group, so that influences of factors such as an area of interest in the virtual brain model, a signal source, and an electrode point installed on the virtual brain model on the signal may be determined. Some embodiments of the present description may use the acquired analog electroencephalogram signal, the target noise signal, and the target acquired electroencephalogram signal for analyzing and researching the electroencephalogram signal, thereby avoiding the problem of lack of research data during analyzing and researching the electroencephalogram signal. Meanwhile, the target acquisition of the electroencephalogram signals can be used for reducing the acquisition of real electroencephalogram signals, and the acquisition cost of the real electroencephalogram signals in the analysis and research of the electroencephalogram signals is reduced.

FIG. 4 is an exemplary flow chart illustrating obtaining a noise-free simulated brain electrical signal according to some embodiments of the present description. In some embodiments, the flow 400 may be performed by the first obtaining module 210. As shown in fig. 4, the process 400 may include the following steps:

step 410, for each signal source in the virtual brain model, determining a target basic signal of the signal source based on the region of interest transfer matrix and the basic signal emitted by the signal source.

The target basic signal can be a signal of which the basic signal emitted by a certain signal source is influenced by other interested areas.

In some embodiments, the first set of parameters may include a region of interest transfer matrix. The elements in the region of interest transfer matrix may characterize the transfer relationship of the signal as it propagates between different regions of interest. The region of interest transfer matrix may be a matrix of n x n, where n represents the number of regions of interest. In some embodiments, the elements in the region of interest transfer matrix may be real numbers, which are adjustment parameters between corresponding regions of interest, and may be used to determine a transfer relationship when a signal propagates between corresponding regions of interest. For example, a certain element in the region of interest transfer matrix may be a real number α, and the target basis signal may be obtained based on the following formula:

wherein the content of the first and second substances,

a target base signal representing the ith signal source in the jth region of interest,

representing the basic signal emitted by the signal source, alpha is the adjusting parameter of the signal from the l interested area to the j interested area,

representing the base signal emitted by the ith signal source in the ith region of interest.

In some embodiments, the elements in the region of interest transfer matrix may also be transfer functions that characterize the effect of the corresponding region of interest on the signal. The transfer function may be a linear function or a nonlinear function. Reference may be made to fig. 6 and its associated description regarding the manner of obtaining the region of interest transfer matrix.

In some embodiments, the target base signal may be obtained based on the following formula:

wherein, the first and the second end of the pipe are connected with each other,

representing the underlying signal emitted by the signal source,

representing the influence of other interested regions on the signal source, T representing interested region transfer matrix, T _l Representing the l-th row in the region of interest transfer matrix,

can be the average value of the signals emitted by the m signal sources of the ith region of interest,

representing the base signal from the ith signal source in the ith region of interest.

Step 420, for each region of interest in the virtual brain model, determining an initial sub-simulated brain electrical signal corresponding to the region of interest based on the target basic signal of each signal source in the region of interest and the signal source transfer matrix.

The initial sub-simulated brain electrical signal may be a signal emitted from a region of interest in the virtual brain model. The initial sub-analog electroencephalogram signal can be a signal formed by superposing target basic signals of a plurality of signal sources in the region of interest under the influence of the electrode points.

In some embodiments, the first set of parameters may further include a signal source transition matrix. The elements in the signal source transfer matrix can represent the transfer relation between the signal source and the electrode points in the same region of interest during transmission. The signal source transfer matrix may be a matrix of m x k, where m represents the number of signal sources in the region of interest and k represents the number of electrode points in the virtual brain model. Similar to the region-of-interest transfer matrix, the elements in the signal source transfer matrix may be real numbers, which are adjustment parameters between the corresponding signal source and the electrode points, and may be used to determine a transfer relationship when the signal propagates between the corresponding signal source and the electrode points. The elements in the signal source transfer matrix may also be transfer functions that may characterize the effect of the corresponding electrode point on the target basis signal of the corresponding signal source. For the acquisition manner of the signal source transition matrix, refer to fig. 6 and its related description.

In some embodiments, the initial sub-simulated brain electrical signals may be obtained based on the following formula:

wherein the content of the first and second substances,

an initial sub-simulated brain electrical signal representing the jth region of interest,

representing the target base signal of the ith signal source in the jth region of interest, a representing the signal source transfer matrix,

representing the ith row in the signal source transfer matrix and m representing the number of signal sources in the region of interest.

And 430, simulating the electroencephalogram signals based on the initial sub-simulation electroencephalogram signals corresponding to the regions of interest to obtain simulated electroencephalogram signals.

In some embodiments, the simulated brain electrical signals may be obtained by performing superposition processing based on the initial sub-simulated brain electrical signals corresponding to the respective regions of interest. The simulated brain electrical signal may be obtained based on the following formula:

wherein, X _s (t) represents the simulated brain electrical signal,

the initial sub-simulated brain electrical signals representing the jth region of interest, n representing the number of regions of interest.

Fig. 5 is an exemplary flow chart illustrating obtaining a target noise signal according to some embodiments of the present description. In some embodiments, the flow 500 may be performed by the second obtaining module 220. As shown in fig. 5, the process 500 may include the following steps:

step 510, determining a first sub-noise signal based on the region of interest transfer matrix and the simulated brain noise signal.

The initial noise signal may include a simulated brain noise signal and an electrode point noise signal. The analog brain noise signal can refer to a noise signal in a human brain in the analog collected human brain electrical signals, and the electrode point noise signal can refer to a noise signal brought by an electrode point in the collected human brain electrical signals. The simulated brain noise signal may include a background noise signal and a pink noise signal. The background noise signal and the pink noise signal may be obtained from historical data or from a network.

In some embodiments, the second set of parameters may also include a region of interest transfer matrix.

The first sub-noise signal may be a noise signal determined based on the background noise signal and the pink noise signal. The first sub-noise signal may be determined based on the following equation:

wherein the content of the first and second substances,

representing the noise signal due to the background noise signal in the first sub-noise signal, a representing the signal source transfer matrix,

representing the ith row in the signal source transfer matrix, m representing the number of signal sources in the region of interest, n representing the number of regions of interest in the virtual brain model,

representing the background noise signal emitted by the ith signal source in the jth region of interest; x is the number of _1/f (t) represents a noise signal due to a pink noise signal in the first sub-noise signal, f represents a frequency of the pink noise signal,

representing a pink noise signal emitted by an ith signal source in a jth region of interest; x is the number of ₁ (t) represents a first sub-noise signal, θ ₀ 、θ ₁ The weighting parameters respectively representing the background noise signal and the pink noise signal in the first sub-noise signal can be determined in advanceAnd (5) setting and determining.

And step 520, determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal.

In some embodiments, the second set of parameters may include an electrode point transfer matrix. The elements in the electrode point transfer matrix can represent the transfer relation of signals when the signals are transmitted between different electrode points. The electrode point transfer matrix may be a matrix of k x k, where k is the number of electrode points in the virtual brain model. Similar to the region of interest transfer matrix, the elements in the electrode point transfer matrix may be real numbers, which are adjustment parameters between corresponding electrode points, and may be used to determine a transfer relationship when a signal propagates between corresponding electrode points. The elements in the electrode point transfer matrix may also be transfer functions that characterize the effect of the corresponding electrode point on the electrode point noise signal.

Each element of the matrix may be a transfer function that characterizes the effect of the corresponding motor point on the electrode point noise signal generated by the electrode point. It should be understood that the element located at the diagonal position of the electrode point transfer matrix may be 1, indicating that the electrode point has no effect on itself. For more on the acquisition of the electrode point transfer matrix, reference may be made to fig. 8 and its associated description.

The second sub-noise signal may be a noise signal determined based on the electrode point noise signal. In some embodiments, the second sub-noise signal may be determined according to the following equation:

x ₂ (t)＝θ ₂ ε (t) (11) represents the noise signal due to the electrode point noise signal in the second sub-noise signal, σ _p (t) represents the noise signal of the electrode point of the p-th electrode point, H represents the electrode point transfer matrix, H _p Represents the p-th row in the electrode point transfer matrix, and k represents the number of electrode points; x is a radical of a fluorine atom ₂ (t) represents a second sub-noise signal, θ ₂ Is shown asThe weighting parameter of the noise signal caused by the electrode point noise signal in the second sub-noise signal of the two sub-noise signals can be determined by presetting.

In step 530, a target noise signal is obtained based on the first sub-noise signal and the second sub-noise signal.

In some embodiments, the target noise signal may be determined according to the following equation:

x _n (t)＝x ₁ (t)+x ₂ (t) (12)

wherein x is _n (t) represents a target noise signal, x ₁ (t) denotes a first sub-noise signal, x ₂ (t) represents the second sub-noise signal.

Some embodiments of the present description can determine the influence of the signal source, the region of interest, the electrode point, and other factors on the signal by setting the region of interest transfer matrix, the signal source transfer matrix, and the electrode point transfer matrix, so that the acquired simulated electroencephalogram signal and the target noise signal are more real and more conform to the actually acquired electroencephalogram signal.

It is worth to be noted that, in the modern technology, the collected electroencephalogram signals can be subjected to denoising processing based on a machine learning model, namely a denoising model, so that noiseless electroencephalogram signals are obtained. At this time, when a large amount of training data is needed to train the denoising model, the training data includes the acquired electroencephalogram signal and the acquired electroencephalogram signal after denoising. However, the existing data for acquiring the electroencephalogram signals are few, the acquisition cost is high, and because the electroencephalogram signals originally existing in the human body are unknown, the noise signals in the acquired electroencephalogram signals are also unknown, so that the acquired electroencephalogram signals cannot be subjected to denoising processing, and therefore, the training data are difficult to acquire in practice. Some embodiments of this description can generate a large amount of target simulation collection EEG signals and noiseless simulation EEG signals in order to simulate collection EEG signals and collection EEG signals after removing noise respectively to can train the model of removing noise as training data, make the model of removing noise after training have better generalization effect, the output of the model of removing noise is more accurate.

Fig. 6 is an exemplary flow diagram illustrating obtaining a first parameter set according to some embodiments of the present description. In some embodiments, the flow 600 may be performed by the server 110 or the first obtaining module 210. As shown in fig. 6, the process 600 may include the following steps:

at step 610, an initial first parameter set is obtained.

The initial first parameter set may refer to a parameter set corresponding to the first preset parameter interval. When the first preset parameter interval is determined, the corresponding initial first parameter set may be determined. In some embodiments, the first parameter set may include a region of interest transfer matrix and a signal source transfer matrix, and correspondingly, the initial first parameter set may include an initial region of interest transfer matrix and an initial signal source transfer matrix.

Step 620, obtaining an intermediate simulated electroencephalogram signal based on the initial first parameter set and the basic signal.

The intermediate electroencephalogram signal can be a simulated human electroencephalogram signal obtained by processing the basic signal through the initial first parameter set. In some embodiments, the base signal may be processed based on the initial first parameter set to obtain an intermediate brain electrical signal. The method for processing the basic signal based on the initial first parameter group may refer to a part of the description where the basic signal is processed based on the first parameter group to obtain the simulated electroencephalogram signal. For example, referring to formulas (2) to (5) in fig. 4, the method for processing the basic signal based on the initial first parameter group may replace the region of interest transfer matrix in the formula with the initial region of interest transfer matrix, replace the signal source transfer matrix with the initial signal source transfer matrix, and obtain the final signal which may be the intermediate simulated electroencephalogram signal.

Step 630, obtain the reference brain electrical signal.

The reference brain electrical signal may refer to a noiseless brain electrical signal in a human body. The reference brain electrical signal can be acquired through a human body brain electrical signal collected historically or through a network.

And step 640, constructing a loss function based on the intermediate simulated brain electrical signal and the reference brain electrical signal.

And step 650, iteratively updating the parameters of the initial first parameter set based on the loss function until a preset condition is met, and obtaining the first parameter set.

In some embodiments, the parameters of the initial first set of parameters may be iteratively updated based on a loss function. And when the initial first parameter group and the loss function of the reference electroencephalogram signal meet the preset condition, obtaining the first parameter group. The preset condition may be that the difference between the intermediate analog signal and the reference electroencephalogram signal is smaller than a threshold. The preset condition may also be other conditions, such as a loss function convergence, a number of iterations reaching a threshold, etc. The method of updating the parameters of the initial first set of parameters may be a gradient descent method. In some embodiments, a regularization term may also be added to the loss function to improve iteration efficiency and speed up the convergence of the loss function.

In some embodiments, an intermediate simulated electroencephalogram signal can be obtained based on an initial region-of-interest transfer matrix, an initial signal source transfer matrix and a basic signal, a loss function is constructed based on the intermediate simulated electroencephalogram signal and a reference electroencephalogram signal, parameters of the initial region-of-interest transfer matrix and the initial signal source transfer matrix are updated iteratively based on the loss function, and when the loss function meets a preset condition, specific parameters in the region-of-interest transfer matrix and the signal source transfer matrix can be determined.

In some embodiments, the loss function may include a first loss term and a second loss term. The first loss item can reflect the relationship between the initial region-of-interest transition matrix and the basic signals emitted by the signal sources, and the second loss item can reflect the relationship between the initial signal source transition matrix and the target basic signals of each signal source in the region-of-interest. The first loss term and the second loss term may be combined in various ways. For example, in weighted summation, different weights can be set for the first loss term and the second loss term to reflect different influences of different factors on the electroencephalogram signal.

Some embodiments of the present description may preset a larger parameter interval range, that is, a first preset parameter interval, for the initial parameter set, so as to avoid more calculation amounts, and perform iterative update based on the parameter interval, so as to obtain an iterative calculation result more quickly, thereby obtaining the first parameter set.

Similar to the first parameter set obtained based on the first preset parameter interval, the second parameter set may be obtained based on the second preset parameter interval. That is, an initial second parameter group corresponding to a second preset parameter interval may be acquired, and an intermediate noise signal may be acquired based on the initial second parameter group and the initial noise signal; acquiring a reference noise signal; constructing a loss function based on the intermediate noise signal participating in the reference noise signal; and iteratively updating the initial second parameter group based on the loss function until a preset condition is met, and obtaining a second parameter group. The intermediate noise signal may refer to a noise signal in the acquired simulated acquired electroencephalogram signal obtained by processing the initial noise signal through the initial second parameter set, and the reference noise signal may refer to a noise signal in the actual acquired electroencephalogram signal. Further details regarding the above embodiments may be found in relation to fig. 6.

The signal transfer relationship can be used to represent the transfer relationship of a signal as it propagates between different electrode points. In some embodiments, the signal transfer relationship may include an electrode point transfer matrix, also referred to as a signal transfer matrix. The signal transfer relationship may also include a signal transfer model. In some embodiments, the acquired electroencephalogram signals may be denoised based on the signal transfer relationship.

FIG. 7 is an exemplary flow chart of a method for de-noising acquired electroencephalogram signals according to some embodiments presented herein. In some embodiments, flow 700 may be performed by server 110. As shown in fig. 7, the process 700 may include the following steps:

and step 710, determining a signal transfer relationship of the measured object, wherein the signal transfer relationship represents a signal change relationship of the initial noise signal when the initial noise signal propagates among the positions of the measured object. In some embodiments, step 710 may be performed by a first determining module.

The measured object may refer to an object for which a signal transfer relationship needs to be determined. The object to be measured may be a living body, for example, a head region of a human body. The subject may also be a non-biological object, such as a virtual head model.

The initial noise signal may refer to a signal before a noise signal in the acquired electroencephalogram signal is propagated. The collected electroencephalogram signals can refer to electroencephalogram signals of a measured object collected through the electrode points.

It should be understood that, when the electroencephalogram signal of the object to be tested is collected, there are various factors (for example, the operation of the electroencephalogram collecting device or the physiological activities of the object to be tested) that affect the collection process, so that the collected electroencephalogram signal contains various noise signals. The noise signals commonly found in the collected electroencephalogram signals can include non-biological artifact signals and biological artifact signals. The non-biological artifact signal may be a noise signal brought by an external environment, a device, and the like, and may include but is not limited to a noise signal brought by an electroencephalogram acquisition device, a noise signal brought by mains interference, and the like. The abiotic artifact signals can be obtained by querying the relevant material. For example, the noise signal brought by the electroencephalogram acquisition device of the model can be determined through a network. The biological artifact signal may be a noise signal caused by the physiological activity of the object to be measured, and may include but is not limited to an electrocardiographic signal caused by the heartbeat, an electromyographic signal caused by the muscle activity, an electrooculogram signal caused by the eyeball rotation, and the like. The biological artifact signal can be acquired by the related acquisition equipment. Due to the difference of biological individuals, the biological artifact signals corresponding to different detected objects are different.

The signal transfer relationship may be used to determine the variation of the initial noise signal as it propagates between the various locations of the object under test. In some embodiments, the change of the signal of one position of the measured object when the signal propagates to another position can be determined based on the signal transfer relation. For example, based on the signal transfer relationship, the position O from the object to be measured can be determined _i A certain signal S is transmitted _i Position O of propagation to the object to be measured _j After that, change to the signal S _j . In some embodiments, the physiological condition of the tested object is different, and the structural shape of the brain is different, so that the tested object has different physiological conditions and different structural shapesThe corresponding signal transfer relations of the tested objects are different. For example, the signal transfer relationships between elderly people and children are different. For another example, a 26 year old female may have a different signal transfer relationship with a 26 year old male.

In some embodiments, the signal transfer relationship may be characterized as a signal transfer matrix of N × N, where N is the number of electrodes of the measurand, each electrode may represent a different location of the measurand. Each element in the signal transfer matrix may be a transfer function between two corresponding locations of the measurand. It should be understood that the transfer function between a location in the signal transfer matrix and the location itself may be 1 to indicate that the signal itself does not change when it is not propagating. In some embodiments, reference may be made to fig. 4 and its associated description for more information on obtaining a signal transfer matrix.

In some embodiments, the signal transfer relationship may also be characterized as a signal transfer model. The initial noise signal may be processed based on a signal transfer model to obtain a target noise signal. For more on the signal transition model, see fig. 5 and its associated description.

In some embodiments, when the measured object is a virtual head model, the attenuation relationship of each position in the virtual head model can be determined based on the material and shape of the virtual head model, so as to determine the signal transfer relationship of the virtual head model.

In some embodiments, an initial test signal may also be input from a first location of the subject, wherein the initial test signal is of a greater order of magnitude than the brain electrical signal of the subject; acquiring a signal from a second position of the object to be tested to obtain an acquisition test signal; based on the initial test signal and the collected test signal, a signal transfer relationship is determined. For more on the above embodiments, reference is made to fig. 4 and its associated description.

In step 720, a target noise signal is determined based on the initial noise signal and the signal transfer relationship. In some embodiments, step 720 may be performed by the second determination module.

The target noise signal may refer to a noise signal in the acquired electroencephalogram signal. In some embodiments, the target noise signal may be a noise set of noise signals acquired at various locations of the object under test.

In some embodiments, after the initial noise signals are determined, the corresponding underlying features of each initial noise signal may be determined. The corresponding basic features of the initial noise signal may include, but are not limited to, the amplitude, variance, etc. of the signal. For each electrode point of the plurality of electrode points, when electroencephalogram signals are collected, signals collected by the electrode points can be dynamically evaluated, and whether initial noise signals exist in the signals collected by the electrode points or not is judged by comparing the basic characteristics of the collected signals with the basic characteristics corresponding to the initial noise signals. When the initial noise signal exists in the signals acquired by the electrode points, the initial noise signal can be determined as a target noise signal in the electroencephalogram signals acquired by the electrode points correspondingly. Meanwhile, the propagated initial noise signals collected by other electrode points can be determined through a signal transfer relation, and the propagated initial noise signals are determined as target noise signals in the collected electroencephalogram signals collected by other electrode points.

Step 730, removing the target noise signal from the acquired electroencephalogram signal acquired from the object to be tested, and acquiring a noise-free target electroencephalogram signal. In some embodiments, step 730 may be performed by a signal processing module.

The target EEG signal can be the noiseless EEG signal of the object to be tested. The target brain electrical signal can be used for analyzing and diagnosing the brain diseases of the tested object.

In some embodiments, when the target noise signal collected by an electrode point is an initial noise signal that is not propagated at the electrode point, the initial noise signal may be directly removed from the collected electroencephalogram signal collected by the electrode point, so as to obtain the target electroencephalogram signal of the electrode point.

In some embodiments, when the target noise signal collected by a certain electrode point is the propagated initial noise signal, the target electroencephalogram signal of the electrode point can be determined based on the following formula:

wherein S is _clean Target EEG signal for electrode point with position j, S _obs For the acquisition of the EEG signal of the electrode point, h is a signal transfer matrix, h _ij Representing the transfer function, S, of a signal propagating from position i to position j in a signal transfer matrix _i Is the initial noise signal originating from the electrode point with position i, and k is the number of initial noise signals.

In some embodiments, an initial noise signal at a certain position may be processed based on a signal transfer model to obtain a target noise signal in acquired electroencephalogram signals at other positions. And then removing target noise signals in the acquired electroencephalogram signals at other positions to obtain target electroencephalogram signals at corresponding positions.

In some embodiments of the present description, a signal transfer relationship when a signal is transmitted between different positions is determined, so that a target noise signal after transmission can be accurately and quickly determined based on an initial noise signal and the signal transfer relationship, and thus the target noise signal in an acquired electroencephalogram signal can be removed to obtain a noise-free target electroencephalogram signal. Compared with the existing method for denoising the acquired electroencephalogram signals, the method has the advantage that the cost is greatly reduced. Meanwhile, the corresponding signal transfer relationship is determined based on each measured object, so that the determined signal transfer relationship is more accurate and accords with the actual condition of the measured object, and the target electroencephalogram signal cannot be removed on the basis of accurately and completely removing the target noise signal in the acquired electroencephalogram signal.

Fig. 8 is an exemplary flow diagram illustrating obtaining a signal transfer relationship in accordance with some embodiments of the present description. In some embodiments, the flow 800 may be performed by a first determination module. As shown in fig. 8, the process 800 may include the following steps:

step 810, aiming at each position of the tested object, inputting an initial test signal from the position, wherein the magnitude of the initial test signal is larger than that of the electroencephalogram signal of the tested object.

The initial test signal may refer to a test signal input to the object to be tested, and the initial test signal may be used for a change relationship when the test signal propagates between different electrode points. In some embodiments, the initial test signal is an order of magnitude greater than the brain electrical signal of the human body. For example, the magnitude of the electroencephalogram signal of the human body is μ V, the magnitude of the initial test signal may be 3 magnitudes larger than the electroencephalogram signal of the human body, that is, the magnitude of the selected initial test signal may be mV. In some embodiments, the initial test signal may comprise a plurality of different frequency, different strength signals. In some embodiments, the initial test signal may be determined by a preset. For example, the initial test signal may be a preset pulse signal having an order of magnitude larger than that of the brain electrical signal of the object to be tested. Each position of the object to be measured may refer to each electrode point of the object to be measured, and an initial test signal may be input at one electrode point of the object to be measured.

It should be understood that, because the magnitude of the initial test signal is greater than that of the electroencephalogram signal of the tested object, when the signal is collected, the collected signal is less affected by the electroencephalogram signal, and the accuracy of the test result can be ensured. Meanwhile, the initial test signal should be not higher than the order of magnitude of the signal that the human body can bear to ensure the safety of the tested object, for example, the amplitude range of the initial test signal may be 20mV to 200mV.

And step 820, acquiring signals from other positions of the tested object to obtain an acquisition test signal.

Collecting the test signal may refer to collecting a propagated signal of the initial test signal input at the position at another position. Other locations may be other electrode points. Correspondingly, acquiring the test signal may include acquiring signals at other electrode points based on the electrode point at the location. For example, the electrode points a to D may be provided on the object to be measured, where the position where the initial test signal is input is the electrode point a, and the other positions may be the motor points B, C, D. Correspondingly, a signal can be acquired from the motor point B, C, D to obtain an acquired test signal.

It will be appreciated that the initial test signal is affected by other electrode points as it propagates between them, so the collected test signal may be different from the initial test signal.

Step 830, determining a signal transfer relationship based on the initial test signal and the collected test signal.

In some embodiments, the initial test signal and the collected test signal may be modeled or analyzed using various data analysis algorithms, such as regression analysis, discriminant analysis, and the like, to determine the signal transfer relationship.

In some embodiments, when the elements in the signal transfer matrix may be real, the individual elements in the matrix may be determined by based on the following formula:

S _j (t)＝βs _i (t) (14)

wherein S is _j (t) represents the collected test signal collected at electrode point J, β is the adjustment parameter for the signal to propagate from electrode point i to electrode point J, s _i (t) represents an initial test signal input from the electrode point i.

In some embodiments, the signal transfer relationship may include a plurality of simple signal transfer relationships. The simple signal transfer relationship may refer to a change relationship of a simple signal with a relatively regular signal change when the signal is transmitted between a plurality of positions of the object to be measured. In some embodiments, the input initial test signal may be transformed into a plurality of initial simple test signals of different frequencies and intensities, and the collected test signals collected at other respective electrode points may be transformed into a plurality of collected simple test signals of different frequencies and intensities; for each initial simple test signal, determining a simple signal transfer relationship corresponding to the initial simple test signal based on the initial simple test signal and a corresponding acquired simple test signal thereof; and synthesizing the simple signal transfer relations to obtain a signal transfer relation containing a plurality of simple signal transfer relations. The initial simple test signal may be a simple signal obtained by transforming the initial test signal, and the collected simple test signal may be a simple signal obtained by transforming the collected test signal, wherein a transformation method of the signal may be fourier transformation. For example, the initial simple test signal and the collected simple test signal may both be sine wave signals.

In some embodiments, the initial test signal input at location i may be fourier transformed to obtain an initial set S of simple test signals of M different phases and frequencies, where S is a matrix of N × M and N is the number of electrode points. And under the condition that no signal is input at other positions of the object to be tested and the initial test signal is not propagated, each element in the ith row in the S respectively adopts the initial simple test signal, and other positions in the S can be 0, so that the representation that other positions do not contain signals is realized. After the initial test signal is propagated, fourier transform is performed on the collected test signals collected at other positions, so that a collection S ' including a plurality of collected simple test signals can be obtained, where S ' is also a matrix of N × M, each element in S ' represents a collected simple test signal collected at a corresponding electrode point, and the collected simple test signal is a signal obtained by propagating a corresponding initial simple test signal. A calculation can then be made based on S and S' to determine the signal transfer matrix H such that the following equation holds:

S′＝HS (15)

the elements of H are all conduction functions, and represent the change relation of signals when the signals are transmitted at corresponding positions. E.g. h _ij May be an element in H that represents the transfer function of a signal propagating from location i to location j. In some embodiments, the conductance function may take a variety of forms. The transfer function h is shown by the following formula _ij It can be equivalent to an FIR (Finite Impulse Response) filter:

y[n]＝b ₀ x[n]+b ₁ x[n-1]+…+b _L x[n-L] (16)

wherein, y [ n ]]For collecting simple measurementsTest signal, x [ n ]]The initial simple test signal is used, n is the signal length, L is the parameter number, the initial simple test signal can be determined through presetting, and when L is less than or equal to M, the conduction function h can be determined through solving a multivariate linear equation set _ij All of the parameters in (1).

In some embodiments, the signal transfer model may also be trained based on the initial test signal and the collected test signal, resulting in a trained signal transfer model. For more on the signal transfer model, see fig. 9 and its associated description.

It should be understood that the brain structure of the subject is nearly fixed, so the relationship of the signal changes that propagate between the locations of the subject should be the same even if the two signals differ in intensity. Therefore, some embodiments of the present disclosure may determine a signal transfer relationship of the object to be tested by analyzing the initial test signal and the collected test signal, and the signal transfer relationship may also represent a signal variation relationship of the initial noise signal when propagating between various positions of the object to be tested.

Some embodiments of the present description can quickly determine the signal transfer relationship of each measured object by an initial test signal and a collected test signal, and ensure the accuracy of the signal transfer relationship, thereby avoiding errors caused by differences of individual measured objects during denoising.

FIG. 9 is a schematic diagram of a signal transfer model according to some embodiments described herein.

In some embodiments, the initial noise signal may be processed based on a signal transfer model to obtain a target noise signal. As shown in FIG. 9, the inputs to the signal transfer model 930 may include an initial noise signal 910 and a location 920 of the initial noise signal, and the output may include a target noise signal 940. In some embodiments, the signal transfer model may include, but is not limited to, a deep neural network model, a support vector machine model, and the like.

In some embodiments, the signal transfer model may be obtained by training a machine learning model with training samples. As shown in fig. 9, the signal transition model may be obtained by training the initial signal transition model 950 by using the training sample 960 and the label 970, wherein the initial signal transition model may be a signal transition model with no parameter set. The training sample 960 may include an initial test signal and the location of the initial test signal and the tag 970 may include a collection test signal. The training samples and labels can be obtained in the manner shown in fig. 8 and the related description. Inputting a plurality of groups of training samples 960 with labels 970 into an initial signal transfer model 950, constructing a loss function based on the output of the initial signal transfer model 950 and the labels 970, iteratively updating parameters of the initial signal transfer model 950 based on the loss function until preset conditions are met, finishing training, and obtaining a trained signal transfer model 930. The preset conditions may include, but are not limited to, the loss function being less than a threshold, convergence, or the training period reaching a threshold.

In some embodiments, the signal transfer model may include an object information embedding layer for performing feature extraction on shape information and hair information of the measured object to obtain a head feature, and a signal determination layer for performing position information processing on the head feature, the initial noise signal, and the initial noise signal to obtain a target noise signal. The shape information may include, but is not limited to, the shape, size, etc. of the object, and the hair information may include, but is not limited to, the length of the hair, the hardness of the hair, the degree of bending of the hair, the condition of the hair oil, etc. of the object. The shape information and hair information of the object to be measured can be determined by setting in advance.

As shown in fig. 10, the signal transfer model 930 may further include an object information embedding layer 930-1 and a signal determination layer 930-2 connected in sequence, the input of the object information embedding layer 930-1 may include shape information 1010 and hair information 1020 and output as a head feature 931, the input of the signal determination layer may include the head feature 931, an initial noise signal 910 and a position 920 of the initial noise signal, and the output may be a target noise signal 940. The object information embedding layer can be a naive Bayesian model, and the signal determining layer can be a deep neural network model.

In some embodiments, the object information embedding layer may be obtained by training: the training sample may include historical shape information and historical hair condition information of the test subject, and the label may include historical head features of the test subject, where the test subject may refer to a subject for obtaining data thereof to train the subject information embedding layer. The test object may be an organism or a non-organism, similar to the test object. The training samples and labels can be obtained by manually representing the relevant information of the test object. Multiple groups of training samples can be input into the initial object information embedding layer, a loss function is constructed based on the output of the initial object information embedding layer and the label, and parameters of the initial object information embedding layer are updated iteratively based on the loss function until preset conditions are met, so that the trained object information embedding layer is obtained. The preset conditions may include, but are not limited to, the loss function being less than a threshold, convergence, or the training period reaching a threshold.

In some embodiments, the layers may be determined by training the acquisition signals: the training sample may include historical head features, an initial test signal of the object to be tested, and a position of the initial test signal, and the label may include a collected test signal, and the aforementioned training sample and the label thereof may be obtained in a manner as described above with reference to this specification. Multiple sets of training samples can be input into the initial signal determination layer, a loss function is constructed based on the output of the initial signal determination layer and the label, and parameters of the initial signal determination layer are updated iteratively based on the loss function until preset conditions are met, so that a trained signal determination layer is obtained. The preset conditions may include, but are not limited to, the loss function being less than a threshold, convergence, or the training period reaching a threshold.

It should be appreciated that there is a discrepancy in the objects used for the training object information embedding layer corresponding to the training data of the signal determination layer. The trained object information embedding layer can extract the head characteristics of all objects (including the test object and the tested object), and the object information embedding layer is a model common to all tested objects, so that the object information embedding layer obtained by training based on the related data of the test object can extract the head characteristics of different tested objects. And the trained signal determination layer is used for determining the signal transfer relation of a certain tested object. As described above, when the measured objects are different, the corresponding signal transfer relationships are also different. Therefore, a signal determination layer obtained by training based on a measured object can only be used for determining the signal transfer relationship of the measured object. When the signal transfer relationship of another tested object needs to be determined, training can be performed based on the initial test signal of the tested object and the position of the initial test signal and the acquisition test signal on the basis of the trained object information embedding layer.

Some embodiments of the present specification may determine the signal transfer relationship of the measured object more quickly and conveniently through the signal transfer model, so as to perform denoising processing on the acquired electroencephalogram signal. In addition, some embodiments of the present specification may further extract relevant features of the head of the object to be measured, and determine the influence of the difference of the relevant information of the head of the object to be measured on the signal transfer relationship of the object to be measured, so that the denoising processing on the acquired electroencephalogram signals is more accurate.

Fig. 11 is an exemplary flow chart illustrating the determination of a target noise signal according to some embodiments of the present description. In some embodiments, the flow 1100 may be performed by a second determination module.

In some embodiments, the signal transfer relationship may include a plurality of simple signal transfer relationships. The simple signal transfer relationship represents the signal variation relationship of a first simple noise signal as it propagates between the various locations of the object under test. For more on simple signal transition relationships, see fig. 8 and its associated description. The first simple noise signal is obtained by transforming the original noise signal.

When the signal transfer relationship comprises a plurality of simple signal transfer relationships, the process 1100 may be performed to determine a target noise signal. As shown in fig. 11, the process 1100 may include the following steps:

step 1110 transforms the initial noise signal into a first simple noise signal of a plurality of different frequencies and intensities.

The first simple noise signal may be a simple signal obtained by transforming the original noise signal. For example, the first simple noise signal may include any one or a combination of a sine wave signal, a cosine wave signal, a square wave signal, and the like. The initial noise signal may be processed using fourier transform, laplace transform, discrete cosine transform, and the like to obtain a plurality of first simple noise signals of different frequencies and intensities.

Step 1120, for each first simple noise signal, processing the first simple noise signal based on a simple signal transfer relationship corresponding to the first simple noise signal, and determining a second simple noise signal corresponding to the first simple noise signal.

The second simple noise signal may refer to a noise signal when the first simple noise signal is propagated to other positions of the object to be measured.

In some embodiments, for each first simple noise signal, a second simple noise signal corresponding to the first simple noise signal at other locations may be determined based on the first simple noise signal, an input location of the first simple noise signal, and a simple signal transfer relationship. The transfer function of the input position to be transmitted to other positions can be determined according to the simple signal transfer relation and the input position of the first simple noise signal, and the second simple noise signal of other positions can be determined based on the transfer functions and the first simple noise signal.

In step 1130, each of the second simple noise signals is synthesized to obtain a target noise signal.

In some embodiments, for each measured object position, the second simple noise signals at the position can be synthesized to obtain a target noise signal at the position. The synthesis method may be an inverse transform of the transform method for processing the initial noise signal into the first simple noise signal to obtain the target noise signal. For example, when the initial noise signal is processed into the first simple noise signal based on the fourier transform, the respective second simple noise signals may be synthesized based on the inverse fourier transform to obtain the target noise signal.

Some embodiments of the present disclosure may conveniently obtain the target noise signal by using a simple noise signal and a simple signal transfer relationship, so as to simplify the operation and improve the calculation efficiency.

The embodiment of the specification provides a generation device for simulating acquired brain electrical signals, which comprises a processor, wherein the processor is used for executing the generation method for simulating the acquired brain electrical signals.

The embodiment of the present specification provides a computer-readable storage medium, where the aforementioned storage medium stores computer instructions, and after the computer reads the computer instructions in the storage medium, the computer executes the generation method for simulating the acquisition of electroencephalogram signals according to any one of the embodiments of the present specification.

The embodiment of the present specification further provides a collected electroencephalogram signal denoising device, which includes a processor, and the processor is used for executing the collected electroencephalogram signal denoising method according to any one of the embodiments of the present specification.

The embodiment of the present specification provides a computer-readable storage medium, where the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the method for denoising an acquired electroencephalogram signal according to any one of the embodiments of the present specification.

It should be noted that the above descriptions about the respective flows are only for illustration and explanation, and do not limit the applicable scope of the present specification. Various modifications and changes to the individual processes may be made by those skilled in the art in light of the present disclosure. However, such modifications and variations are intended to be within the scope of the present description.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting, of the present invention. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested in this specification, and are intended to be within the spirit and scope of the exemplary embodiments of this specification. Also, the description uses specific words to describe embodiments of the specification. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While certain presently contemplated useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein described. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the foregoing description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit-preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A generation method for simulating an acquired electroencephalogram signal is characterized by comprising the following steps:

obtaining a noiseless simulated electroencephalogram signal based on the basic signal and the first parameter group, wherein the simulated electroencephalogram signal is used as the noiseless electroencephalogram signal in the human body to participate in the analysis and research of the electroencephalogram signal;

obtaining a target noise signal based on an initial noise signal and a second parameter set, wherein the target noise signal is used as a noise signal in an acquired electroencephalogram signal of a human body to participate in analysis and research of the electroencephalogram signal, the second parameter set comprises an interested region transfer matrix and an electrode point transfer matrix, the initial noise signal comprises a simulated brain noise signal and an electrode point noise signal, and the obtaining the target noise signal based on the initial noise signal and the second parameter set comprises:

determining a first sub-noise signal based on the region of interest transfer matrix and the simulated brain noise signal, wherein elements in the region of interest transfer matrix represent transfer relationships of signals when the signals are propagated between different regions of interest;

determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal, wherein elements in the electrode point transfer matrix represent a transfer relationship of signals when the signals are transmitted between different electrode points;

obtaining the target noise signal based on the first sub-noise signal and the second sub-noise signal;

acquiring a first weight of the analog electroencephalogram signal and a second weight of the target noise signal;

and obtaining a target analog acquisition electroencephalogram signal based on the analog electroencephalogram signal, the target noise signal, the first weight and the second weight, wherein the target analog acquisition electroencephalogram signal is used as the acquisition electroencephalogram signal to participate in the analysis and research of the electroencephalogram signal and reduce the acquisition of real electroencephalogram signals.

2. The method of claim 1, wherein the first parameter set includes the region of interest transfer matrix and a signal source transfer matrix, elements of the signal source transfer matrix characterizing a transfer relationship when propagating between signal sources and electrode points in the same region of interest, wherein the different regions of interest represent different brain functional regions in a human brain;

the obtaining a noise-free simulated brain electrical signal based on the base signal and the first parameter group comprises:

for each signal source in the virtual brain model, determining a target basic signal of the signal source based on the region of interest transfer matrix and the basic signal emitted by the signal source;

aiming at each region of interest in the virtual brain model, determining an initial sub-simulation brain electrical signal corresponding to each region of interest based on the target basic signal and the signal source transfer matrix of each signal source in the region of interest;

and simulating a brain electrical signal based on the initial sub-simulation brain electrical signals corresponding to the regions of interest to obtain the simulated brain electrical signal.

3. The method of claim 1, wherein the first parameter set is obtained based on a first preset parameter interval and the second parameter set is obtained based on a second preset parameter interval.

4. The method of claim 1, wherein the elements in the electrode point transfer matrix are conduction functions.

5. A system for generating an analog acquisition of an electroencephalogram signal, the system comprising:

the first acquisition module is used for acquiring a noiseless simulated electroencephalogram signal based on the basic signal and the first parameter group, and the simulated electroencephalogram signal is used as a noiseless electroencephalogram signal in a human body to participate in analysis and research of the electroencephalogram signal;

a second obtaining module, configured to obtain a target noise signal based on an initial noise signal and a second parameter group, where the target noise signal is used as a noise signal in an acquired electroencephalogram signal of a human body to participate in analysis and research of the electroencephalogram signal, the second parameter group includes an area-of-interest transfer matrix and an electrode point transfer matrix, the initial noise signal includes a simulated brain noise signal and an electrode point noise signal, and the second obtaining module is further configured to:

determining a first sub-noise signal based on the region of interest transfer matrix and the simulated brain noise signal, wherein,

the element in the interesting region transfer matrix represents the transfer relationship of signals when the signals are transmitted among different interesting regions;

determining a second sub-noise signal based on the electrode point transfer matrix and the electrode point noise signal, wherein elements in the electrode point transfer matrix represent a transfer relationship of signals when the signals are transmitted between different electrode points; obtaining the target noise signal based on the first sub-noise signal and the second sub-noise signal;

the third acquisition module is used for acquiring the first weight of the analog electroencephalogram signal and the second weight of the target noise signal;

and the fourth acquisition module is used for acquiring a target simulation acquisition electroencephalogram signal based on the simulation electroencephalogram signal, the target noise signal, the first weight and the second weight, wherein the target simulation acquisition electroencephalogram signal is used as the acquisition electroencephalogram signal to participate in analysis and research of the electroencephalogram signal and reduce acquisition of a real electroencephalogram signal.

6. The system of claim 5, wherein the first parameter set includes the region of interest transfer matrix and a signal source transfer matrix, elements of the signal source transfer matrix characterizing a transfer relationship when propagating between a signal source and an electrode point in the same region of interest, wherein the different regions of interest represent different functional brain regions in a human brain;

the first obtaining module is further configured to:

and simulating a brain electrical signal based on the initial sub-simulation brain electrical signals corresponding to the interested areas to obtain the simulated brain electrical signal.

7. The system of claim 5, wherein the first set of parameters is obtained based on a first preset parameter interval and the second set of parameters is obtained based on a second preset parameter interval.

8. The system of claim 5, wherein the elements in the electrode point transfer matrix are conduction functions.

9. A device for generating an analog acquired brain electrical signal, comprising a processor, characterized in that the processor is adapted to perform the method for generating an analog acquired brain electrical signal according to any one of claims 1 to 4.

10. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer executes the method for generating analog acquired brain electrical signals according to any one of claims 1 to 4.