Biological signal frequency identification method and system, storage medium and electronic equipment

Abstract

The invention provides a biological signal frequency identification method and system, electronic equipment and a medium. The method comprises the following steps: storing an obtained reference signal orthogonal matrix; acquiring a biological original signal; obtaining a biological original signal orthogonal matrix based on the biological original signal; obtaining a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix; and taking the frequency with the maximum correlation coefficient as the biological signal frequency. The method can flexibly adapt to multiple scenes, the maximum correlation coefficient is obtained under the conditions of low power consumption and low time complexity, the biological signal frequency is recognized according to the maximum correlation coefficient, and the calculation precision and the information transmission rate are not affected.

Description

Technical Field

[0001] The present application belongs to the field of biotechnology, and in particular, relates to a method, system, storage medium and electronic device for identifying biological signal frequency. Background Art

[0002] At present, the transformation of human-computer interaction represented by brain-computer interaction has become a hot topic. Among them, the brain-computer interface (BCI) is a technology that directly interacts with external devices by interpreting brain activity signals. The core of this technology is to collect and process electroencephalogram (EEG) signals, extract meaningful information from them to control various external devices, including computers, robotic arms, wheelchairs, etc. EEG signals are a direct reflection of brain neuron activity. They are collected from the scalp through an electrode array, and then converted into understandable instructions through a series of complex signal processing algorithms.

[0003] With the rapid development of neuroscience and computing technology, BCI technology has shown broad application prospects in many fields. In the fields of medicine and rehabilitation, BCI systems can help paraplegic patients control prostheses, assist stroke patients in neurological rehabilitation training, and even be used for communication and communication with patients with impaired consciousness. In the field of entertainment, BCI technology can provide a more immersive experience for virtual reality games, allowing users to control characters in games through brain waves. In the field of communications, BCI can also help patients who have lost their language and motor skills to communicate and transmit information through EEG signals.

[0004] Existing brain-computer interface (BCI) technologies cover a variety of interaction methods based on EEG signals, mainly including three systems: event-related potential (ERP), steady-state visual evoked potential (SSVEP) and motor imagery (MI). The ERP system achieves device control and communication by detecting changes in the brain's potential to specific events (such as the P300 wave). The SSVEP system achieves high-precision device control and signal recognition by identifying the brain's response signals to visual stimuli of different frequencies. The MI system uses the EEG signals generated by users when they imagine movements and is widely used in the control operations of prosthetic limbs and wheelchairs. Among them, for the SSVEP system, identifying the frequency of EEG signals requires a lot of calculations, especially in real-time processing environments, the operation time and power consumption are high, which limits its application in portable devices. Summary of the invention

[0005] The purpose of this application is to provide a method, system, storage medium and electronic device for identifying biological signal frequency, which can efficiently identify biological signal frequency.

[0006] In a first aspect, the present application provides a method for identifying the frequency of a biological signal, the method comprising: storing an acquired reference signal orthogonal matrix; acquiring a biological original signal; acquiring a biological original signal orthogonal matrix based on the biological original signal; acquiring a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix; and using the frequency with the maximum correlation coefficient as the biological signal frequency.

[0007] In an implementation manner of the first aspect, before storing the acquired reference signal orthogonal matrix, the method includes: acquiring multiple reference signals; performing orthogonal triangular decomposition on the multiple reference signals to acquire the reference signal orthogonal matrices corresponding to the multiple reference signals respectively.

[0008] In an implementation manner of the first aspect, a dimension of each of the reference signals is 500×8.

[0009] In an implementation of the first aspect, obtaining the biological original signal includes: collecting the biological signal; filtering the biological signal through Fourier transform and inverse Fourier transform to obtain the biological original signal.

[0010] In an implementation of the first aspect, obtaining the biological original signal orthogonal matrix based on the biological original signal includes: performing orthogonal triangular decomposition on the biological original signal to obtain the biological original signal orthogonal matrix.

[0011] In an implementation of the first aspect, obtaining the maximum correlation coefficient includes: calling the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix to calculate the correlation matrix; and performing singular value decomposition on the correlation matrix to obtain the maximum correlation coefficient.

[0012] In an implementation manner of the first aspect, the acquired reference signal orthogonal matrix is ​​stored using an MCU chip.

[0013] In a second aspect, the present application provides a biological signal frequency identification system, the system comprising: a storage module for storing an acquired reference signal orthogonal matrix; a first acquisition module for acquiring a biological original signal; a second acquisition module for acquiring a biological original signal orthogonal matrix based on the biological original signal; a maximum correlation coefficient module for acquiring a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix; and an identification module for taking a frequency with the maximum correlation coefficient as a biological signal frequency.

[0014] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device performs the above-mentioned method for identifying the frequency of a biological signal.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned method for identifying the frequency of a biological signal.

[0016] As described above, the biological signal frequency recognition method, system, storage medium and electronic device described in the present application have the following beneficial effects: they can flexibly adapt to multiple scenarios, obtain the maximum correlation coefficient with low power consumption and low time complexity, and thereby identify the biological signal frequency, and the calculation accuracy and information transmission rate are not affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic diagram of a scene of an electronic device in an embodiment of the present application.

[0018] Figure 2 Shown is a flow chart of a method for identifying biological signal frequency according to an embodiment of the present application.

[0019] Figure 3 Shown is a schematic diagram of a method for identifying biological signal frequency according to an embodiment of the present application in one embodiment.

[0020] Figure 4 Shown is a schematic diagram of a method for identifying biological signal frequency according to an embodiment of the present application in one embodiment.

[0021] Figure 5 Shown is a schematic diagram of the structure of a biological signal frequency recognition system in one embodiment of the present application.

[0022] Figure 6 Shown is a schematic structural diagram of an electronic device of the present application in one embodiment. DETAILED DESCRIPTION

[0023] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0024] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present application in a schematic manner. Therefore, the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0025] In addition, in this application, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0026] The following embodiments of the present application provide a method for identifying biological signal frequency, which can be applied to Figure 1 The electronic device described in this application may include a mobile phone 11 with wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiments of this application do not impose any restrictions on the specific types of electronic devices.

[0027] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN).

[0028] For example, the electronic device can communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS can include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS) and / or Satellite Based Augmentation Systems (SBAS).

[0029] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0030] like Figure 2 As shown, in one embodiment, the biological signal frequency recognition method of the present application includes steps S1 to S5.

[0031] S1: Store the acquired reference signal orthogonal matrix.

[0032] Specifically, before obtaining the reference signal orthogonal matrix, the method for identifying the biological signal frequency provided by the present application further includes:

[0033] 1) Acquire multiple reference signals;

[0034] 2) Performing orthogonal triangular decomposition (QR decomposition) on the multiple reference signals to obtain the reference signal orthogonal matrices corresponding to the multiple reference signals respectively.

[0035] In some embodiments, the frequency of the EEG signal is identified using the method for identifying the frequency of the biological signal of the present application. The various neural networks distributed in the brain have inherent resonant frequencies. Under normal conditions, these neural networks are not synchronized with each other, and the EEG at this time is spontaneous. When a constant frequency of external visual stimulation is applied, the neural network consistent with the stimulation frequency will resonate, causing the brain's potential activity to change significantly at the stimulation frequency, thereby generating an SSVEP signal.

[0036] The reference signal is a reference signal corresponding to the visual stimulation frequency. Since the visual stimulation frequency is a preset constant frequency, a plurality of corresponding reference signals can be obtained.

[0037] In some embodiments, the dimension of each of the reference signals is 500×8.

[0038] Specifically, Figure 3 As shown, the reference signal orthogonal matrix obtained by using QR decomposition is stored in a memory.

[0039] In some embodiments, the memory may select an MCU chip.

[0040] S2: Obtain original biological signals.

[0041] In some embodiments, the biological raw signal is an electroencephalogram signal.

[0042] Specifically, obtaining the biological original signal includes steps S21-S22.

[0043] S21: Collect biological signals.

[0044] In some embodiments, a Neuroscan SynAmps2 64-256-lead EEG amplifier can be used to connect an EEG cap to collect EEG signals. When in use, the user wears the EEG cap and directly collects EEG signals on the surface of the scalp through the EEG cap. Among them, the Neuroscan SynAmps2 64-256-lead EEG amplifier supports a maximum of 64-lead EEG cap connection, and supports connecting four devices in parallel to collect EEG signals from 256 electrodes.

[0045] It should be noted that, due to the SSVEP system, only the electrode channels corresponding to the brain areas related to vision need to be selected.

[0046] It should be noted that this application does not impose any restrictions on the equipment for collecting biological signals.

[0047] S22: Filter the biological signal through Fourier transform and inverse Fourier transform to obtain the original biological signal.

[0048] Specifically, the biological signal is filtered through Fourier transform and inverse Fourier transform to refine the data, thereby reducing unnecessary calculations, and finally the refined biological original signal is input into the MCU chip in a matrix manner.

[0049] S3: Obtaining a biological original signal orthogonal matrix based on the biological original signal.

[0050] Specifically, the biological original signal is subjected to orthogonal triangular decomposition to obtain an orthogonal matrix of the biological original signal.

[0051] As mentioned above, orthogonal triangular decomposition is a process with a large amount of calculation, especially in embedded systems, such as MCU chips, where the duration of orthogonal triangular decomposition to obtain an orthogonal matrix is ​​relatively long. In the traditional calculation process, each time the biological signal frequency is identified, the original biological signal and the reference signal need to be imported in the form of a matrix, and then orthogonal triangular decomposition is performed to obtain the corresponding orthogonal matrix. This means that each time the biological signal frequency is identified, the reference signal needs to be orthogonal triangular decomposition, which increases the amount of calculation and time. Therefore, since the reference signal corresponds to the external visual stimulus of a preset constant frequency, that is, the reference signal is actually predetermined and fixed, the present application performs orthogonal triangular decomposition on each reference signal in advance, obtains the corresponding reference signal orthogonal matrix, and stores all the reference signal orthogonal matrices in the MCU chip, so that when performing biological signal frequency identification, the reference signal orthogonal matrix can be directly called, and only the input original signal needs to be orthogonal triangular decomposition, thereby greatly reducing the amount of calculation and time, and improving the overall performance and response speed of the system.

[0052] S4: Obtain a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix.

[0053] Specifically, obtaining the maximum correlation coefficient includes step S41 to step S42.

[0054] S41: Call the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix to calculate the correlation matrix.

[0055] S42: Perform singular value decomposition on the correlation matrix to obtain the maximum correlation coefficient.

[0056] In some embodiments, the MCU chip stores a total of forty reference signal orthogonal matrices, namely Y i (i=1,2,...,40), let Q Yi The matrix is ​​the orthogonal matrix of the i-th reference signal pre-stored in the MCU chip, i, Q X The matrix is ​​the orthogonal matrix of the biological original signal calculated in real time, and the correlation matrix is:

[0057]

[0058] Right D i Perform singular value decomposition:

[0059]

[0060] Among them, U i is the m-order orthogonal matrix of the i-th reference signal, V i is the n-th order orthogonal matrix of the i-th reference signal, Σ i It is an m×n rectangular diagonal matrix consisting of non-negative diagonal elements arranged in descending order.

[0061] Furthermore, from Σ i Extract singular values ​​as correlation coefficients (such as Figure 4 p1 to p n ), then the maximum correlation coefficient can be obtained from it.

[0062] S5: The frequency having the maximum correlation coefficient is taken as the biological signal frequency.

[0063] Specifically, the reference signal corresponds to an external visual stimulus of a preset constant frequency, and the stimulus frequency corresponding to the reference signal with the maximum correlation coefficient is actually the frequency at which the user is gazing, that is, the biological signal frequency.

[0064] Taking the above embodiment as an example, if the traditional process is followed, the QR decomposition calculation needs to be performed on the biological original signal and 40 reference signals respectively, and the total operation time is 41 seconds to 82 seconds. Figure 4As shown, using the method for identifying the frequency of biological signals provided in the present application, it is only necessary to perform QR decomposition calculations on the original biological signal and then calculate the correlation coefficient, so the total operation time can be shortened to 1 to 2 seconds. It can be seen that the method for identifying the frequency of biological signals provided in the above embodiments of the present application significantly improves the calculation efficiency and response speed, and achieves the goal of reducing power consumption, calculation complexity and calculation time without changing the information transmission rate (ITR) and calculation accuracy. The present application enables the BCI system to efficiently identify the frequency of EEG signals in a low-cost, low-power environment, and has broad application prospects.

[0065] The protection scope of the method for identifying the frequency of biological signals described in the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0066] The embodiments of the present application also provide a biological signal frequency recognition system, which can implement the biological signal frequency recognition method described in the present application. However, the implementation device of the biological signal frequency recognition system described in the present application includes but is not limited to the structure of the biological signal frequency recognition system listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0067] like Figure 5 As shown, in one embodiment, the biological signal frequency recognition system of the present application includes a storage module 41 , a first acquisition module 42 , a second acquisition module 43 , a maximum correlation coefficient module 44 and a recognition module 45 .

[0068] The storage module 41 is used to store the acquired reference signal orthogonal matrix;

[0069] A first acquisition module 42, used to acquire an original biological signal;

[0070] A second acquisition module 43, used for acquiring a biological original signal orthogonal matrix based on the biological original signal;

[0071] A maximum correlation coefficient module 44 is used to obtain a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix;

[0072] The identification module 45 is used to take the frequency with the maximum correlation coefficient as the biological signal frequency.

[0073] Among them, the structures and principles of the storage module 41, the first acquisition module 42, the second acquisition module 43, the maximum correlation coefficient module 44 and the identification module 45 correspond one by one to the steps in the above-mentioned biological signal frequency identification method, so they are not repeated here.

[0074] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0075] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0076] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0077] The present application embodiment also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by a program to instruct the processor, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state hard disk (SSD)), etc.

[0078] The embodiment of the present application also provides an electronic device, which includes a processor and a memory.

[0079] The memory is used to store computer programs.

[0080] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0081] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned method for identifying the biological signal frequency.

[0082] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0083] like Figure 6As shown, the electronic device of the present application is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 51, a memory 52, and a bus 53 connecting different system components (including the memory 52 and the processing unit 51).

[0084] Bus 53 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0085] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0086] The memory 52 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 523 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 53 via one or more data medium interfaces. The memory 52 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0087] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in the memory 52, such program modules 5241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 5241 generally perform the functions and / or methods of the embodiments described herein.

[0088] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 54. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 55. Figure 6 As shown, the network adapter 55 communicates with other modules of the electronic device via the bus 53. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0089] The present application embodiment may also provide a computer program product, the computer program product including one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer or data center.

[0090] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product may be a software installation package, and when the above method is required, the computer program product may be downloaded and executed on a computer.

[0091] The present application provides a method, system, storage medium and electronic device for identifying the frequency of biological signals, which can flexibly adapt to multiple scenarios, obtain the maximum correlation coefficient with low power consumption and low time complexity, and thereby identify the frequency of biological signals, without affecting the calculation accuracy and information transmission rate.

[0092] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0093] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for identifying biological signal frequency, characterized in that: The method comprises: The obtained reference signal orthogonal matrix is ​​stored; Obtain original biological signals; Acquire a biological original signal orthogonal matrix based on the biological original signal; Obtaining a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix; The frequency having the maximum correlation coefficient is taken as the biological signal frequency.

2. The method for identifying biological signal frequency according to claim 1, characterized in that: Before storing the acquired reference signal orthogonal matrix, the method includes: acquiring multiple reference signals; Orthogonal triangular decomposition is performed on the multiple reference signals to obtain the reference signal orthogonal matrices respectively corresponding to the multiple reference signals.

3. The method for identifying biological signal frequency according to claim 1, characterized in that: The dimension of each reference signal is 500×8.

4. The method for identifying biological signal frequency according to claim 1, characterized in that: Obtaining biological original signals includes: Collect biological signals; The biological signal is filtered through Fourier transform and inverse Fourier transform to obtain the original biological signal.

5. The method for identifying biological signal frequency according to claim 1, characterized in that: Acquiring an orthogonal matrix of the biological original signal based on the biological original signal includes: Performing orthogonal triangular decomposition on the biological original signal to obtain the orthogonal matrix of the biological original signal.

6. The method for identifying biological signal frequency according to claim 1, characterized in that: Obtaining the maximum correlation coefficient includes: Calling the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix to calculate a correlation matrix; Performing singular value decomposition on the correlation matrix to obtain the maximum correlation coefficient.

7. The method for identifying biological signal frequency according to claim 1, characterized in that: The MCU chip is used to store the acquired reference signal orthogonal matrix.

8. A biological signal frequency recognition system, characterized in that: The system comprises: A storage module, used for storing the acquired reference signal orthogonal matrix; The first acquisition module is used to acquire the original biological signal; A second acquisition module, used for acquiring an orthogonal matrix of biological original signals based on the biological original signals; A maximum correlation coefficient module, used for obtaining a maximum correlation coefficient based on the stored reference signal orthogonal matrix and the biological original signal orthogonal matrix; The identification module is used to take the frequency with the maximum correlation coefficient as the biological signal frequency.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the electronic device executes the biological signal frequency recognition method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for identifying the biological signal frequency according to any one of claims 1 to 7 is implemented.

Images