A method for aligning unhealthy neural signals to healthy neural signals

Abstract

A method for aligning neural signals from unhealthy states with those from healthy states. Based on factors such as the target's behavioral health, stimulus response, body temperature, and cross-functional noise cells, the method adjusts the neural signal's response time, corrects the waveform amplitude and width, and removes noise. This method aligns the quality of neural signals from unhealthy states with that from healthy states and cleans up noise. This method addresses the current problem of inaccurate neural signal acquisition when an organism is in an unhealthy state, as neural signals are not collected based on the target's health status.

Description

Technical Field

[0001] The present invention relates to the technical field of neural signal alignment, and in particular to a method for aligning unhealthy neural signals to healthy neural signals. Background Art

[0002] Currently, neural signal acquisition is typically performed using both invasive and non-invasive techniques. However, the target's health status is not considered during neural signal acquisition. When an organism is in an unhealthy state, the central nervous system's response slows, which can directly lead to delayed movement and action. Fever can also cause dilation of brain blood vessels and increase cerebral blood flow. Numerous studies have shown that the brain can directly regulate neural activity through the heartbeat. When the heart pumps blood, brain blood vessels dilate, changing the distance from the electrode to the neuron and the resistance, thereby reducing the measured signal amplitude and lengthening its width. Therefore, the target's health status will affect the response time of its neural signals, the waveform amplitude / width, and other factors, and the acquired signals may contain noise. Therefore, a method to align unhealthy neural signals with healthy neural signals is urgently needed to address this issue. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a method for aligning an unhealthy neural signal with a healthy neural signal, which overcomes the above problems or at least partially solves the above problems.

[0004] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention discloses a method for aligning an unhealthy neural signal with a healthy neural signal, characterized by comprising:

[0006] S100. Evaluate the behavioral health of the target object to obtain a behavioral health coefficient of the target object;

[0007] S200. Identify the stimulus response of the target object, and obtain a response delay coefficient of the neural signal in combination with the behavioral health coefficient; obtain a neural signal after the response time correction based on the response delay coefficient;

[0008] S300. Perform fever analysis on the target object to obtain characteristic information before and after fever;

[0009] S400. Obtaining amplitude and width correction coefficients of the neural signal based on the characteristic information of the target object before and after fever, and performing width and amplitude correction on the neural signal after the response time correction based on the amplitude and width correction coefficients to obtain a neural signal after amplitude and width correction;

[0010] S500. Analyze cells across functional areas of the target object, eliminate noise cells, and obtain the final quality-enhanced neural signal.

[0011] Furthermore, the target's behavioral health assessment includes at least an analysis of the target's eating, defecation, movement, and sleep. Specifically, the target's eating analysis includes:

[0012] S101. Obtaining a video of the target object eating within a preset time period before and after electrode implantation, respectively, and analyzing the video to obtain a video clip of the target object eating;

[0013] S102. Identifying the target object's eating segment, and when consecutive preset frame images in the eating segment are identified as abnormal eating, determining the consecutive preset frame images as abnormal eating images;

[0014] S103. Obtaining a model confidence level corresponding to the image based on the abnormal eating image;

[0015] S104. According to the model confidence corresponding to the image, the eating status results of the target object before electrode implantation and the eating status results after electrode implantation are obtained respectively.

[0016] Furthermore, the target's defecation analysis, movement analysis, and sleep analysis are performed to obtain the target's behavioral health coefficient. The specific methods include:

[0017] S105. Obtain the target object's defecation, movement, and sleep videos within a preset period before and after electrode implantation, and repeat S101-S104 to obtain the defecation status results before and after electrode implantation, the electric defecation status results, and the sleep status results, respectively;

[0018] S106. Obtain the health coefficient of the target object based on the target object's eating results, defecation results, electric defecation results and sleeping results before and after electrode implantation.

[0019] Furthermore, in S200, multiple stimulations are performed on the target object to obtain the stimulation reaction time of the target object before and after electrode implantation. The response delay coefficient of the neural signal is obtained by combining the behavioral health coefficient. The neural signal collected by the electrode is corrected based on the response delay coefficient to obtain a neural signal with a corrected response time. The specific method includes:

[0020] S201. Stimulating the target object before and after electrode implantation, wherein the stimulation includes at least motion stimulation, visual stimulation, auditory stimulation, and tactile stimulation, obtaining a stimulation video of the target object, loading a stimulation recognition model, and when continuous preset frame images are identified as stimulation, determining the continuous preset frame images as stimulation images, and determining a stimulation implementation frame based on the stimulation images;

[0021] S202. Training the target stimulus response recognition model. When the continuous preset frame images are recognized as normal responses, determining the continuous preset frame images as normal response images, and determining the normal stimulus response frames according to the normal response images;

[0022] S203. Calculating the target stimulation response time before and after electrode implantation based on the stimulation implementation frame and the normal stimulation response frame;

[0023] S204. Calculating a response delay coefficient based on the target stimulation reaction time before and after electrode implantation and the behavioral health coefficient;

[0024] S205. Obtain a corrected response moment according to the neural signal acquisition time and the response delay coefficient, and obtain a neural signal corrected for the response moment according to the corrected response moment.

[0025] Furthermore, in S300, the characteristic information of the target object before fever includes the average head temperature before fever, the blood vessel diameter before fever, the equivalent center of mass position of the electrode before fever, and the spatial position of each neuron before fever; the characteristic information of the target object after fever includes the average head temperature after fever, the blood vessel diameter after fever, the equivalent center of mass position of the electrode after fever, and the spatial position of each neuron after fever.

[0026] Furthermore, the average head temperature of the target object before and after fever, and the blood vessel diameter before and after fever are obtained. The specific method includes: based on infrared imaging equipment, obtaining the average head temperature of the target object before and after fever; fusing the acquired CT tomography image and MRI tomography blood vessel diameter scanning image to obtain a fused image, segmenting the blood vessels in the fused image, and marking the blood vessel area to obtain a marked image; performing three-dimensional reconstruction on the marked image to obtain a three-dimensional reconstructed image, and obtaining the blood vessel diameter before and after fever according to the three-dimensional reconstructed image.

[0027] Furthermore, the equivalent center of mass position of the electrode before and after fever, and the spatial position of each neuron before and after fever are obtained. The specific method includes: obtaining the equivalent center of mass position of the electrode before and after fever based on the three-dimensional reconstructed image; extracting the spike signal of each acquired neural signal by the threshold method, and calculating the three-dimensional coordinate value of each spike segment based on a preset formula and an optimization algorithm, clustering the three-dimensional coordinate values ​​of the spike segments, and obtaining the spatial position of each neuron before and after fever.

[0028] Furthermore, in S400, after obtaining the amplitude and width correction coefficients, the corrected width and amplitude are obtained according to the positional relationship between the cells, electrodes and blood vessels. The specific method includes: when the cells and electrodes are on both sides of the blood vessel, the time span between the peak and the trough of the spike signal collected after fever is defined as the waveform width, and the time width after fever is obtained, and the corrected time width is the product of the time width after fever and the amplitude and width correction coefficient; the maximum amplitude after fever is obtained, and the corrected maximum amplitude is the quotient of the maximum amplitude after fever and the amplitude and width correction coefficient; when the cells and electrodes are on the same side of the blood vessel, the corrected time width is the quotient of the time width after fever and the amplitude and width correction coefficient, and the corrected maximum amplitude is the product of the maximum amplitude after fever and the amplitude and width correction coefficient; and the neural signal with amplitude and width correction is obtained according to the corrected time width and the corrected maximum amplitude.

[0029] Furthermore, in S500, the target object is analyzed across functional areas of cells, specifically including stimulation pattern recognition, functional area segmentation and neuron distribution. The specific method includes: when conducting a visual stimulation experiment with or without light and generating high-decibel noise, first identify the stimulation pattern and train the abnormal sound recognition model. When the abnormal sound reaches the stress response threshold of the target object's auditory functional area neurons, the abnormal sound recognition model automatically runs and prompts; then segment the functional area, train the functional area segmentation model, segment each functional area in the fused image, obtain a segmented image with functional area boundary lines, and then perform three-dimensional reconstruction on the segmented image; finally, match and divide the neuron spatial coordinates with each functional area in the three-dimensional reconstructed image to obtain neurons in the auditory functional area, and form a neural signal set based on the neurons; based on the RNN neural network model, train the visual or auditory response recognition model, classify the neural signals that form the neural signal set, and obtain an auditory response pattern neural signal set; remove the auditory response pattern neural signal set from the amplitude and width corrected neural signal to obtain the final quality-enhanced neural signal set.

[0030] In a second aspect, an embodiment of the present invention discloses an electronic device, including:

[0031] one or more processors;

[0032] a memory for storing one or more programs;

[0033] When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the signal alignment method.

[0034] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0035] The present invention discloses a method for aligning an unhealthy neural signal with a healthy neural signal, comprising: evaluating the behavioral health of a target object to obtain a behavioral health coefficient of the target object; identifying the stimulus response of the target object, and combining the behavioral health coefficient to obtain a response delay coefficient of the neural signal; obtaining a neural signal corrected at the response time based on the response delay coefficient; performing a fever analysis on the target object to obtain characteristic information before and after fever of the target object; obtaining an amplitude and width correction coefficient of the neural signal based on the characteristic information before and after fever of the target object, performing width and amplitude correction on the neural signal corrected at the response time based on the amplitude and width correction coefficient to obtain an amplitude and width corrected neural signal; performing a cross-functional area cell analysis on the target object, eliminating noise cells, and obtaining a final quality-enhanced neural signal.

[0036] Based on factors such as the subject's behavioral health status, stimulus response, body temperature, and cross-functional noise cells, the present invention adjusts the response time of neural signals, corrects waveform amplitude and width, and removes noise. This aligns the quality of neural signals in unhealthy conditions with that in healthy conditions and eliminates noise. This addresses the current problem of inaccurate neural signal acquisition when the subject's health status is not considered, leading to inaccurate neural signal acquisition when the subject is in an unhealthy state.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 This is a flow chart of a method for aligning an unhealthy neural signal with a healthy neural signal in Example 1 of the present invention;

[0040] Figure 2 Schematic diagram of neural signal response delay correction in Example 1 of the present invention;

[0041] Figure 3 Schematic diagram of correction of width and amplitude height of neural signal waveform in Example 1 of the present invention;

[0042] Figure 4 This is a structural diagram of an electronic device in Example 2 of the present invention. DETAILED DESCRIPTION

[0043] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0044] In order to solve the problems existing in the prior art, an embodiment of the present invention provides a method for aligning an unhealthy neural signal with a healthy neural signal.

[0045] Example 1

[0046] The present invention discloses a method for aligning an unhealthy neural signal to a healthy neural signal. Figure 1 ,include:

[0047] S100. Evaluate the behavioral health of the target object to obtain the behavioral health coefficient of the target object; specifically, evaluate the behavioral health of the target object; obtain the behavioral health coefficient of the target object by analyzing the target object's eating, defecation, movement and sleep.

[0048] Specifically, in this embodiment S100, by analyzing the target object's eating, the specific method includes:

[0049] S101. Obtaining a video of the target object eating within a preset time period before and after electrode implantation, respectively, and analyzing the video to obtain a video clip of the target object eating;

[0050] Specifically, a target object eating video is obtained within a preset time period before and after electrode implantation, and the video is parsed to obtain a total of N1 target object eating video clips, and the i-th video clip obtains M i Frame continuous frame image;

[0051] S102. Identify the target object eating segments. When there are consecutive preset frame images in the eating segments that are identified as eating abnormalities, determine that the consecutive preset frame images are eating abnormality images. Specifically, identify N1 target object eating segments using a deep learning classification model (such as VGG / Resnet, etc.). When the i-th segment M i When there are consecutive preset frame images in the frame image that are identified as eating abnormalities, this segment is identified as eating abnormality judgment. At this time, the number of segments belonging to eating abnormalities is N 11 ;

[0052] S103. According to the abnormal eating image, obtain the model confidence corresponding to the image; when the i-th video segment M iIf the jth frame image in the frame image is identified as eating abnormality, the model confidence score corresponding to the image is score ij ;

[0053] S104. Obtain the eating status result before electrode implantation and the eating status result after electrode implantation according to the model confidence level corresponding to the image. Calculate the eating status result score before electrode implantation 1q , the score 1q The calculation formula is:

[0054]

[0055] Among them, N1 is the number of target object eating video clips, N 11 is the number of segments with abnormal eating, M i1 is the i-th video segment M i The number of abnormal eating patterns identified in the frame image; similarly, the score of eating status after electrode implantation can be obtained. 1h .

[0056] After analyzing the target's eating habits, we then analyze its defecation, movement, and sleep to obtain its behavioral health coefficient. Specific methods include:

[0057] S106. Obtain the target object's defecation, movement, and sleep videos within the preset time before electrode implantation. Repeat S101-S105 to obtain the defecation status scores before and after electrode implantation. 2q and score 2h , defecation score before and after electrode implantation 3q and score 3h , sleep status score before and after electrode implantation 4q and score 4h ;

[0058] S107. Score based on eating status before and after electrode implantation 1q and score 1h , defecation score before and after electrode implantation 2q and score 2h , defecation score before and after electrode implantation 3q and score 3h , sleep status score before and after electrode implantation 4q and score 4h , calculate the target object health coefficient

[0059] S200. Identify the stimulus response of the target object; perform multiple stimulations on the target object, obtain the stimulus response time of the target object before and after electrode implantation, and combine the behavioral health coefficient to obtain the response delay coefficient of the neural signal; based on the response delay coefficient, correct the neural signal collected by the electrode to obtain the neural signal U1 after the response time correction;

[0060] In S200 of this embodiment, multiple stimulations are performed on the target object to obtain the stimulation reaction time of the target object before and after electrode implantation. The response delay coefficient of the neural signal is obtained by combining the behavioral health coefficient. The specific method includes:

[0061] S201. Stimulating the target object before and after electrode implantation, wherein the stimulation includes at least motion stimulation, visual stimulation, auditory stimulation, and tactile stimulation, obtaining a stimulation video of the target object, loading a stimulation recognition model, and when continuous preset frame images are identified as stimulation, determining the continuous preset frame images as stimulation images, and determining a stimulation implementation frame based on the stimulation images;

[0062] S202. Training the target stimulus response recognition model. When the continuous preset frame images are recognized as normal responses, determining the continuous preset frame images as normal response images, and determining the normal stimulus response frames according to the normal response images;

[0063] S203. Calculating the target stimulation response time before and after electrode implantation based on the stimulation implementation frame and the normal stimulation response frame;

[0064] S204. Calculating a response delay coefficient based on the target stimulation reaction time before and after electrode implantation and the behavioral health coefficient;

[0065] S205. Obtain a corrected response moment according to the neural signal acquisition time and the response delay coefficient, and obtain a neural signal corrected for the response moment according to the corrected response moment.

[0066] Specifically, before electrode implantation, the target is stimulated, the stimulation includes at least motion stimulation, visual stimulation, auditory stimulation and tactile stimulation, the target stimulation video is obtained, the stimulation recognition model is loaded, and when the continuous τ frames are identified as having stimulation, the τ frame picture is obtained [P c0 , P c1 …P ci …P cτ ], among which, the P c0The frame is the behavioral stimulus implementation frame; for example, in the roller motion stimulation, the rolling rotation recognition model (which can be Resnet, etc.) is trained, and the model recognizes that the roller starts to work; in the light / no light visual stimulation, the light source recognition model (which can be VGG, etc.) is trained, and the model recognizes that there is a stimulating light source in the shooting picture; in the auditory stimulation, the speech recognition model (which can be LSTM, etc.) is trained, and the model recognizes that there is a stimulating speech.

[0067] Train the target stimulus response recognition model (for example, in the case of roller motion stimulation, the target response recognition model can be a classification network such as ResNet, which recognizes that the target starts walking / running; in the case of light / no light visual stimulation, the target response recognition model can be a classification network such as Vgg, which recognizes that the target has closed its eyes). When η consecutive frames are recognized as normal action responses, obtain η frames of images. Among them, the P d0 Frame is the stimulus-response normal frame;

[0068] Calculate the target stimulation reaction time before electrode implantation Where FPS is the frame rate of the shooting device, t dq =[t dq1 ,t dq2 ,t dq3 ,t dq4 ], t dq1 For exercise stimulation, dq2 For visual stimulation, t dq3 For auditory stimulation, t dq4 For tactile stimulation;

[0069] Implant the electrodes, repeat S201-S203, and calculate the target stimulation reaction time t after electrode implantation. dh ;

[0070] According to the target stimulation reaction time t before and after electrode implantation dq , t dh and behavioral health coefficient Calculate the response delay coefficient t υ , I is the number of delayed response experiment types;

[0071] When the response delay coefficient t is obtained υ Then, the neural signal collected by the electrode is corrected based on the response delay coefficient to obtain the neural signal U1 after the response time correction; the specific method includes:

[0072] like Figure 2 , according to the neural signal acquisition time t 采集 and response delay coefficient t υ , and obtain the corrected response time t 修正 =t采集 -t υ , according to the corrected response time t 修正 The neural signal U1 after correction at the response time is obtained.

[0073] S300. Perform fever analysis on the target object to obtain characteristic information of the target object before and after fever. The characteristic information of the target object before fever includes the average head temperature before fever, the blood vessel diameter before fever, the equivalent center of mass position of the electrode before fever, and the spatial position of each neuron before fever; the characteristic information of the target object after fever includes the average head temperature after fever, the blood vessel diameter after fever, the equivalent center of mass position of the electrode after fever, and the spatial position of each neuron after fever.

[0074] Get the average head temperature T before and after fever ZC and T FR , blood vessel diameter d before and after fever q and d h , the equivalent center of mass position of the electrode before and after heating p q and p h , the spatial position of each neuron before and after fever o q and o h ;

[0075] Specifically, firstly, the average head temperature before fever is obtained based on infrared imaging equipment. ZC Similarly, the average head temperature after fever is obtained FR Then the CT scan image and the MRI vascular diameter scan image are fused to obtain the image img ctmri-j , segment the blood vessels in the image and mark the blood vessel area to obtain the marked image b_img ctmri-j ; Perform three-dimensional reconstruction on the multi-layer images to obtain the blood vessel diameter d in the three-dimensional image before and after fever. q and d h Finally, according to the three-dimensional reconstructed image, the equivalent centroid position p of the electrode before and after heating is obtained in the three-dimensional image. q and ph; for each acquired neural signal, the spike signal is extracted by the threshold method, based on the formula The optimization algorithm is used to solve the minimum problem and calculate the x, y, and z values ​​of each spike segment, where ptp c is the peak-to-peak value of each spike signal, α is the maximum amplitude, and x c ,y c ,z c The channel position on the electrode is clustered to obtain the spatial position of each neuron before and after fever. q and o h .

[0076] S400. Based on the average head temperature T before and after heating ZC and T FR , blood vessel diameter d before and after fever q and d h , the equivalent center of mass position of the electrode before and after heating p q and p h , the spatial position of each neuron before and after fever o q and o h , get the amplitude and width correction coefficient of the neural signal Correction factor based on the amplitude and width Performing width and amplitude correction on the neural signal U1 to obtain an amplitude and width corrected neural signal U2;

[0077] Average head temperature T before and after fever ZC and T FR , blood vessel diameter d before and after fever q and d h , the equivalent center of mass position of the electrode before and after heating p q and p h , the spatial position of each neuron before and after fever o q and o h After that, the amplitude and width correction factors Among them, d q and d h is the diameter of blood vessels before and after fever, p q and p h is the equivalent center of mass position of the electrode before and after heating, o q and o h is the spatial position of each neuron before and after fever, T ZC and T FR is the average head temperature before and after fever; obtain the amplitude and width correction coefficient Then, the corrected width and amplitude are obtained according to the positional relationship between the cells, electrodes and blood vessels. The specific methods include: Figure 3 When cells and electrodes are on different sides of a blood vessel, the thickening of the blood vessel squeezes the surrounding cells and electrodes to the sides of the vessel (when the distance is sufficient and the blood vessel diameter is appropriate, the electrode can still collect cell signals). The time span between the peak and trough of the spike signal collected after heating is defined as the waveform width, and the time width after heating Δt is obtained. h , then the corrected width is Get the maximum amplitude including the negative maximum amplitude and the maximum positive amplitude Amplitude correction When the cell and the electrode are on the same side of the blood vessel, when the blood vessel thickens, the distance between the cell and the electrode will become closer (the electrode is fixed on the skull, and its movement distance is relatively small compared to the cell movement distance, so the cell tends to move closer to the electrode). When the distance between the cell and the electrode decreases, the amplitude of the signal collected after the distance is shortened increases and the waveform width becomes narrower compared to the signal collected before the distance changes. The corrected width is Amplitude correction The neural signal U2 after amplitude and width correction is obtained.

[0078] S500. Analyze the target object's cells across functional areas; by identifying the stimulation pattern, segmenting the functional areas and distributing the neurons, remove the noise cells and obtain the final quality-enhanced neural signal set U3.

[0079] In S500 of this embodiment, by identifying the stimulation pattern, segmenting the functional area and distributing the neurons, and eliminating the noise cells, the final quality-enhanced neural signal set U3 is obtained. The specific method includes: when conducting a light / no light visual stimulation experiment and generating a high-decibel noise, firstly identifying the stimulation pattern, training the abnormal sound recognition model, when the abnormal sound reaches the stress response threshold of the neurons in the auditory functional area of ​​the test target object, the model automatically runs and prompts; then segmenting the functional area, training the functional area segmentation model, and performing the fusion of the tomographic image img ctmri-j Each functional area is segmented to obtain b_img with functional area boundary lines ctmri-j , then multi-layer image b_img ctmri-j Perform three-dimensional reconstruction; finally, the neuron spatial coordinates o h Match and divide the functional areas in the 3D reconstructed 3D image and find the neurons in the auditory functional area. hT , and form a neural signal set U 2T Based on the RNN neural network model, the visual or auditory response recognition model is trained to identify the U 2T The neural signals contained are classified to obtain the auditory response pattern neural signal set U 2T_T ;Change U 2T_T Eliminate them from U2 and obtain the final quality-enhanced neural signal set U3, where U3∈U2.

[0080] The present invention discloses a method for aligning an unhealthy neural signal to a healthy neural signal, comprising: evaluating the behavioral health of a target object; obtaining the behavioral health coefficient of the target object by analyzing the target object's eating, defecation, movement, and sleep. Identify the stimulus response of the target object; perform multiple stimulations on the target object to obtain the stimulus response time of the target object before and after electrode implantation, and combine it with the behavioral health coefficient to obtain the response delay coefficient of the neural signal; based on the response delay coefficient, correct the neural signal collected by the electrode to obtain the neural signal U1 after the response time correction; analyze the fever of the target object; obtain the average head temperature T before and after fever ZC and T FR , blood vessel diameter d before and after fever q and d h , the equivalent center of mass position of the electrode before and after heating p q and p h , the spatial position of each neuron before and after fever o q and o h According to the average head temperature T before and after the fever ZC and T FR , blood vessel diameter d before and after fever q and d h , the equivalent center of mass position of the electrode before and after heating p q and p h , the spatial position of each neuron before and after fever o q and o h , get the amplitude and width correction coefficient of the neural signal Correction factor based on the amplitude and width The width and amplitude of the neural signal U1 are corrected to obtain the amplitude and width corrected neural signal U2; the target object is analyzed across functional area cells; through stimulation pattern recognition, functional area segmentation and neuron distribution, noise cells are eliminated to obtain the final quality-enhanced neural signal set U3.

[0081] This embodiment adjusts the response time of neural signals, corrects waveform amplitude and width, and removes noise based on factors such as the target's behavioral health status, stimulus response, body temperature, and cross-functional noise cells. This aligns the quality of neural signals in unhealthy conditions with that in healthy conditions and eliminates noise. This addresses the current problem of inaccurate neural signal acquisition when the subject's health status is not considered, resulting in inaccurate neural signal acquisition when the subject is in an unhealthy state.

[0082] Example 2

[0083] Based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device. Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Figure 2As shown, an embodiment of the present disclosure provides an electronic device comprising: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement any of the optimization methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory and are configured to implement information exchange between the processor and the memory.

[0084] Among them, the processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically such as SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus), etc.

[0085] In some embodiments, the processor 101 , the memory 102 , and the I / O interface 103 are connected to each other via a bus 104 , and further connected to other components of the computing device.

[0086] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0087] According to an embodiment of the present disclosure, a computer-readable medium is further provided, wherein a computer program is stored on the computer-readable medium, wherein when the program is executed by a processor, the steps of any optimization method in the above-mentioned embodiment are implemented.

[0088] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0089] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0090] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0091] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0092] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0093] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A method for aligning an unhealthy neural signal to a healthy neural signal, characterized in that: include: S100. Evaluate the behavioral health of the target object to obtain a behavioral health coefficient of the target object; S200. Identify the stimulus response of the target object, and obtain a response delay coefficient of the neural signal in combination with the behavioral health coefficient; obtain a neural signal after the response time correction based on the response delay coefficient; S300. Perform fever analysis on the target object to obtain characteristic information before and after fever; S400. Obtaining amplitude and width correction coefficients of the neural signal based on the characteristic information of the target object before and after fever, and performing width and amplitude correction on the neural signal after the response time correction based on the amplitude and width correction coefficients to obtain a neural signal after amplitude and width correction; S500. Analyze cells across functional areas of the target object, eliminate noise cells, and obtain the final quality-enhanced neural signal.

2. The method for aligning an unhealthy neural signal to a healthy neural signal according to claim 1, wherein: The target's behavioral health assessment includes at least an analysis of the target's eating, defecation, movement, and sleep. Specifically, the target's eating analysis includes: S101. Obtaining a video of the target object eating within a preset time period before and after electrode implantation, respectively, and analyzing the video to obtain a video clip of the target object eating; S102. Identifying the target object's eating segment, and when consecutive preset frame images in the eating segment are identified as abnormal eating, determining the consecutive preset frame images as abnormal eating images; S103. Obtaining a model confidence level corresponding to the image based on the abnormal eating image; S104. According to the model confidence corresponding to the image, the eating status results of the target object before electrode implantation and the eating status results after electrode implantation are obtained respectively.

3. The method for aligning an unhealthy neural signal to a healthy neural signal according to claim 2, wherein: Analyze the defecation, movement, and sleep of the target to obtain the target's behavioral health coefficient. The specific methods include: S105. Obtain the target object's defecation, movement, and sleep videos within a preset period before and after electrode implantation, and repeat S101-S104 to obtain the defecation status results before and after electrode implantation, the electric defecation status results, and the sleep status results, respectively; S106. Obtain the health coefficient of the target object based on the target object's eating results, defecation results, electric defecation results and sleeping results before and after electrode implantation.

4. The method for aligning an unhealthy neural signal to a healthy neural signal according to claim 1, wherein: In S200, multiple stimulations are applied to the target object to obtain the stimulation reaction time of the target object before and after electrode implantation. The response delay coefficient of the neural signal is obtained by combining the behavioral health coefficient. The neural signal collected by the electrode is corrected based on the response delay coefficient to obtain a neural signal with a corrected response time. The specific method includes: S201. Stimulating the target object before and after electrode implantation, wherein the stimulation includes at least motion stimulation, visual stimulation, auditory stimulation, and tactile stimulation, obtaining a stimulation video of the target object, loading a stimulation recognition model, and when continuous preset frame images are identified as stimulation, determining the continuous preset frame images as stimulation images, and determining a stimulation implementation frame based on the stimulation images; S202. Training the target stimulus response recognition model. When the continuous preset frame images are recognized as normal responses, determining the continuous preset frame images as normal response images, and determining the normal stimulus response frames according to the normal response images; S203. Calculating the target stimulation response time before and after electrode implantation based on the stimulation implementation frame and the normal stimulation response frame; S204. Calculating a response delay coefficient based on the target stimulation reaction time before and after electrode implantation and the behavioral health coefficient; S205. Obtain a corrected response moment according to the neural signal acquisition time and the response delay coefficient, and obtain a neural signal corrected for the response moment according to the corrected response moment.

5. The method for aligning an unhealthy neural signal to a healthy neural signal according to claim 1, wherein: In S300, the characteristic information of the target object before fever includes the average head temperature before fever, the blood vessel diameter before fever, the equivalent center of mass position of the electrode before fever, and the spatial position of each neuron before fever; the characteristic information of the target object after fever includes the average head temperature after fever, the blood vessel diameter after fever, the equivalent center of mass position of the electrode after fever, and the spatial position of each neuron after fever.

6. The method for aligning an unhealthy neural signal with a healthy neural signal according to claim 5, characterized in that: The average head temperature and blood vessel diameter of the target object before and after fever are obtained. The specific method includes: obtaining the average head temperature of the target object before and after fever based on infrared imaging equipment; fusing the obtained CT tomography image and MRI tomography blood vessel diameter scanning image to obtain a fused image, segmenting the blood vessels in the fused image, and marking the blood vessel area to obtain a marked image; performing three-dimensional reconstruction on the marked image to obtain a three-dimensional reconstructed image, and obtaining the blood vessel diameters before and after fever based on the three-dimensional reconstructed image.

7. The method for aligning an unhealthy neural signal to a healthy neural signal according to claim 6, characterized in that: The equivalent centroid position of the electrode before and after fever, and the spatial position of each neuron before and after fever are obtained. The specific method includes: obtaining the equivalent centroid position of the electrode before and after fever based on the three-dimensional reconstructed image; extracting the spike signal from each acquired neural signal by a threshold method, calculating the three-dimensional coordinate value of each spike segment based on a preset formula and an optimization algorithm, clustering the three-dimensional coordinate values ​​of the spike segments, and obtaining the spatial position of each neuron before and after fever.

8. The method for aligning an unhealthy neural signal with a healthy neural signal according to claim 7, characterized in that: In S400, after obtaining the amplitude and width correction coefficients, the corrected width and amplitude are obtained according to the positional relationship between the cells, electrodes and blood vessels. The specific method includes: when the cells and electrodes are on both sides of the blood vessel, the time span between the peak and the trough of the spike signal collected after fever is defined as the waveform width, and the time width after fever is obtained, and the corrected time width is the product of the time width after fever and the amplitude and width correction coefficient; the maximum amplitude after fever is obtained, and the corrected maximum amplitude is the quotient of the maximum amplitude after fever and the amplitude and width correction coefficient; when the cells and electrodes are on the same side of the blood vessel, the corrected time width is the quotient of the time width after fever and the amplitude and width correction coefficient, and the corrected maximum amplitude is the product of the maximum amplitude after fever and the amplitude and width correction coefficient; and the neural signal with amplitude and width correction is obtained according to the corrected time width and the corrected maximum amplitude.

9. The method for aligning an unhealthy neural signal to a healthy neural signal according to claim 7, wherein: In S500, the target object is analyzed across functional areas, specifically including stimulation pattern recognition, functional area segmentation and neuron distribution. The specific method includes: when a visual stimulation experiment with or without light is conducted and a high-decibel noise is generated, the stimulation pattern is first recognized and an abnormal sound recognition model is trained. When the abnormal sound reaches the stress response threshold of the target object's auditory functional area neurons, the abnormal sound recognition model automatically runs and prompts; then the functional area is segmented, the functional area segmentation model is trained, each functional area in the fused image is segmented, and a segmented image with functional area boundary lines is obtained, and then the segmented image is three-dimensionally reconstructed; finally, the neuron spatial coordinates are matched and divided with each functional area in the three-dimensional reconstructed image to obtain neurons in the auditory functional area, and a neural signal set is formed according to the neurons; based on the RNN neural network model, a visual or auditory response recognition model is trained to classify the neural signals that form the neural signal set to obtain an auditory response pattern neural signal set; the auditory response pattern neural signal set is eliminated from the amplitude and width corrected neural signals to obtain a final quality-enhanced neural signal set.

10. An electronic device comprising: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the signal alignment method of claims 1-9.

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