Electrode design method, manufacturing method, and electrode

Abstract

The application discloses an electrode design method, a manufacturing method and an electrode. The electrode design method comprises the following steps: obtaining medical image data and neural signal decoding requirements; constructing a target cerebral cortex model according to the medical image data; determining electrode parameters according to the neural signal decoding requirements and the target cerebral cortex model; and determining an electrode mask file according to the electrode parameters. The application solves the technical problem that the electrode in the prior art cannot meet the personalized customization requirements in different medical scenarios.

Description

Technical Field

[0001] This invention relates to the field of electrode technology, and more specifically, to an electrode design method, a manufacturing method, and an electrode. Background Technology

[0002] In the field of brain-computer interfaces, intracranial cortical ECoG electrodes, as key components connecting the cerebral cortex to external devices, directly affect the acquisition and clinical decoding effectiveness of electroencephalogram (EEG) signals. Traditional ECoG electrode designs mainly fall into two categories: one is metal electrodes based on rigid materials, and the other is flexible electrodes manufactured using microelectromechanical systems (MEMS) technology. However, both types of electrodes have certain limitations, restricting their widespread application in personalized medicine scenarios.

[0003] The main problems with rigid metal electrode sheets are: fixed site layout and size: the site layout of these electrodes is usually preset and cannot be adjusted according to the brain sulci and gyri of a specific patient, which limits the adhesion between the electrode and the brain surface, thus affecting the efficiency and quality of signal acquisition; poor biocompatibility and adhesion effect: the rigidity of the metal material itself makes it difficult for the electrode to form close contact with the soft and complex brain surface, which not only leads to signal loss or absence, but may also cause immune response, and may cause tissue damage during long-term implantation, as well as a decrease in signal-to-noise ratio.

[0004] While MEMS flexible electrodes have solved the adhesion problem of rigid electrodes to some extent, they still face the following challenges: the fixation of electrode site configuration: despite the use of a flexible substrate, the size and layout of the electrode sites are still limited by the pre-designed template, making it impossible to achieve truly personalized adjustments; and the limited range of applications: for different indications, such as limb movement recovery and improvement of language disorders, signals need to be collected from different brain regions, and a single electrode design is difficult to meet diverse needs.

[0005] In summary, the electrodes in the existing technology cannot meet the personalized customization needs of different medical scenarios. Summary of the Invention

[0006] This invention provides an electrode design method, a manufacturing method, and an electrode, to at least solve the technical problem that electrodes in the prior art cannot meet the personalized customization needs of different medical scenarios.

[0007] According to one aspect of the present invention, an electrode design method is provided, comprising: acquiring medical imaging data and neural signal decoding requirements; constructing a target cerebral cortex model based on the medical imaging data; determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; and determining an electrode mask file based on the electrode parameters.

[0008] Optionally, the medical imaging data includes structural images and functional images of the cerebral cortex. Based on the medical imaging data, a target cerebral cortex model is constructed, including: adding the functional images of the cerebral cortex to the structural images of the cerebral cortex to obtain the target image, wherein the target image includes structural features and functional area features of the cerebral cortex; and determining the cerebral cortex model based on the target image.

[0009] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: determining the type of neural signal requirement based on the neural signal decoding requirements; determining initial parameters based on the type of neural signal requirement; and optimizing the initial parameters based on the target cerebral cortex model to obtain the electrode parameters.

[0010] Optionally, the initial parameters include electrode site size and site density. Based on the target cerebral cortex model, the initial parameters are optimized to obtain electrode parameters, including: determining the functional characteristics of functional areas in the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode site size and site density based on the functional characteristics to obtain electrode parameters.

[0011] Optionally, the initial parameters include electrode shape and electrode size. Based on the target cerebral cortex model, the initial parameters are optimized to obtain electrode parameters, including: determining the structural features of the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode shape and electrode size based on the structural features to obtain electrode parameters.

[0012] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: inputting neural signal decoding requirements and the target cerebral cortex model into a pre-constructed electrode parameter determination model, and using the electrode parameter determination model to determine the electrode parameters, wherein the electrode parameter determination model includes the correspondence between neural signal decoding requirements, the target cerebral cortex model, and the electrode parameters.

[0013] Optionally, the electrode design method further includes: using preset simulation software to simulate the interaction between the electrode corresponding to the electrode parameters and the target cerebral cortex and to acquire EEG signals; determining the parameter validity of the electrode parameters based on the EEG signals; and iteratively optimizing the electrode parameter determination model based on the parameter validity.

[0014] According to another aspect of the present invention, an electrode design method is also provided, comprising: acquiring medical image data and neural signal decoding requirements input by a user from a graphical user interface; constructing a target cerebral cortex model based on the medical image data; determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; determining an electrode mask file based on the electrode parameters; and outputting the electrode mask file to the graphical user interface.

[0015] According to another aspect of the present invention, an electrode design system is also provided, comprising: an acquisition module for acquiring medical imaging data and neural signal decoding requirements; a construction module for constructing a target cerebral cortex model based on the medical imaging data; a first determination module for determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; and a second determination module for determining an electrode mask file based on the electrode parameters.

[0016] According to another aspect of the present invention, an electrode manufacturing method is also provided, comprising: obtaining an electrode mask file determined by the electrode design method described in any of the above embodiments; and manufacturing a target electrode based on the electrode mask file using a preset electrode manufacturing process.

[0017] Optionally, the preset electrode manufacturing process is a microelectromechanical system (MEMS) manufacturing process.

[0018] According to another aspect of the present invention, an electrode is also provided, which is manufactured based on the electrode manufacturing method described in any of the above embodiments.

[0019] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the executable program, wherein the executable program executes the electrode design methods of various embodiments of the present invention when it runs.

[0020] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to execute the electrode design method of various embodiments of the present invention.

[0021] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the electrode design methods of various embodiments of the present invention.

[0022] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the electrode design methods of various embodiments of the present invention.

[0023] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the electrode design methods in various embodiments of the present invention.

[0024] In this embodiment of the invention, an electrode design method is provided, comprising: acquiring medical imaging data and neural signal decoding requirements; constructing a target cerebral cortex model based on the medical imaging data; determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; and determining an electrode mask file based on the electrode parameters. This invention constructs a target cerebral cortex model using medical imaging data, taking into account the medical imaging data of actual patients, resulting in a targeted model. Furthermore, after constructing the target cerebral cortex model, it also considers neural signal decoding requirements. Electrode parameters are determined under the combined effect of the target cerebral cortex model and neural signal decoding requirements, thereby achieving personalized customization of the electrode design process for different patients and different neural signal decoding requirements. This solves the technical problem that electrodes in the prior art cannot meet the personalized customization needs of different medical scenarios. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 This is a flowchart illustrating an electrode design method according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of another electrode design method provided according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of an electrode design system provided according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] According to an embodiment of this application, an embodiment of an electrode design method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This method embodiment can also be executed in an electronic device, similar control device, or terminal that includes a memory and a processor. Taking an in-vehicle terminal as an example, the in-vehicle terminal may include one or more processors and a memory for storing data. Optionally, the in-vehicle terminal may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the in-vehicle terminal. For example, the in-vehicle terminal may include more or fewer components than described above, or have a different configuration than described above.

[0033] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), and artificial intelligence (AI) type processors. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0034] The memory can be used to store computer programs, such as the computer program corresponding to the electrode design method in the embodiments of this application. The processor implements the electrode design method by running the computer program stored in the memory. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the electronic device via a grid. Examples of such grids include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0035] The communication device is used to receive or transmit data via a grid. Specific examples of the aforementioned grid may include a wireless grid provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other grid devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet. In some embodiments of this solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, enabling the mobile device to send commands to the vehicle-mounted terminal.

[0036] The display device can be a touchscreen liquid crystal display (LCD) or a touch display (also referred to as a "touchscreen" or "touch screen"). This LCD allows the user to interact with the user interface of the in-vehicle terminal. In some embodiments, the in-vehicle terminal has a graphical user interface (GUI), allowing the user to interact with the GUI through finger contact and / or gestures on a touch surface. This human-machine interaction function may include vehicle gear shifting functionality. Executable instructions for performing these human-machine interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0037] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display and data used for analysis) involved in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with relevant laws, regulations and standards, necessary confidentiality measures have been taken, they do not violate public order and good morals, and responsive operation interfaces are provided for users to choose to authorize and refuse.

[0038] Figure 1 This is a flowchart illustrating an electrode design method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0039] Step S101: Obtain medical imaging data and neural signal decoding requirements.

[0040] Specifically, medical imaging data obtained from patients mainly includes fMRI (functional magnetic resonance imaging), MRI (magnetic resonance imaging), and CT (computed tomography) images. MRI and CT images clearly show brain structures, especially details of cortical regions, while fMRI provides the location of target brain regions, i.e., the location of functional areas. Additionally, decoding needs are obtained to determine which specific neural signals the target is to decode, such as limb movement commands or language comprehension signals.

[0041] Optionally, the requirements for neural signal decoding may also include the frequency range of the signal, the signal-to-noise ratio, and related brain region information.

[0042] Step S102: Construct a target cerebral cortex model based on medical imaging data.

[0043] Specifically, using pre-set medical image processing software, the acquired MRI and CT images are converted into three-dimensional visualization models to obtain a target cerebral cortex model, accurately presenting the geometric features and surface morphology of the patient's cerebral cortex.

[0044] Optionally, when constructing the target cerebral cortex model, fMRI (functional magnetic resonance imaging) data is also used to mark the target functional areas on the cerebral cortex model. These areas are the focus of subsequent electrode design to ensure that the electrodes accurately cover the target areas.

[0045] Understandably, the constructed target cerebral cortex model includes the target functional area.

[0046] Step S103: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model.

[0047] Specifically, electrode parameters include electrode size and layout. Based on the characteristics of the target brain region in the target cerebral cortex model and decoding requirements, the size (including the size and shape of the electrode sites), layout (the spacing and arrangement between electrodes), and density (the number of electrodes per unit area) of the electrodes are determined. This step is highly personalized and aims to optimize signal acquisition efficiency and signal-to-noise ratio.

[0048] Optionally, when determining electrode parameters, appropriate electrode materials may be selected based on signal type and patient safety considerations.

[0049] Step S104: Determine the electrode mask file based on the electrode parameters.

[0050] Specifically, after determining the electrode parameters, a mask is designed using preset mask design software. A mask is a template used for patterning during MEMS manufacturing, determining the shape and layout of the electrodes. Mask file output: The completed mask design is output in a specific format (such as GDSII) to form an electrode mask file.

[0051] It should be noted that the mask design must cover all electrode sites, interconnects, and necessary control circuitry. Each part must be precise to the micrometer level to ensure the accuracy of the photolithography steps.

[0052] In this embodiment of the invention, an electrode design method is provided, comprising: acquiring medical imaging data and neural signal decoding requirements; constructing a target cerebral cortex model based on the medical imaging data; determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; and determining an electrode mask file based on the electrode parameters. This invention constructs a target cerebral cortex model using medical imaging data, taking into account the medical imaging data of actual patients, resulting in a targeted model. Furthermore, after constructing the target cerebral cortex model, it also considers neural signal decoding requirements. Electrode parameters are determined under the combined effect of the target cerebral cortex model and neural signal decoding requirements, thereby achieving personalized customization of the electrode design process for different patients and different neural signal decoding requirements. This solves the technical problem that electrodes in the prior art cannot meet the personalized customization needs of different medical scenarios.

[0053] Optionally, the medical imaging data includes structural images and functional images of the cerebral cortex. In step S102 above, constructing a target cerebral cortex model based on the medical imaging data may include the following steps:

[0054] Step S1021: Add the functional image of the cerebral cortex to the structural image of the cerebral cortex to obtain the target image, wherein the target image includes the structural features and functional area features of the cerebral cortex.

[0055] Specifically, structural images of the cerebral cortex (such as high-resolution MRI scans) are overlaid and fused with functional images (such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). Structural images provide the brain's anatomical structure, while functional images reveal the activity of brain regions during specific tasks. During the fusion process, ensuring precise alignment between the two sets of images is crucial. Image processing techniques can be used to adjust the spatial positioning of the images, ensuring accurate matching of structural features and functional area features within the target image. The fused target image includes structural features of the cerebral cortex (such as sulci and gyri, and the boundary between white and gray matter) and functional areas (such as the motor cortex, sensory cortex, and language cortex).

[0056] Step S1022: Determine the cerebral cortex model based on the target image.

[0057] For example, a 3D reconstruction algorithm is used to transform a 2D target image into a 3D model of the cerebral cortex. The software constructs the 3D geometry of the cerebral cortex, including details such as sulci, folds, and wrinkles, based on the grayscale values ​​or labeling information in the target image. Simultaneously, functional areas are precisely mapped onto the 3D model based on the functional area features in the target image.

[0058] Optionally, in some embodiments of the present invention, model correction and optimization are included, based on clinical experience and anatomical knowledge, to correct the reconstructed model, for example, by filling gaps in the image and smoothing unnatural surfaces, to ensure the accuracy of the model and its usability for subsequent steps.

[0059] Optionally, in some embodiments of the present invention, the completed cerebral cortex model is validated by comparing it with the original medical imaging data to confirm the accuracy of the model.

[0060] Optionally, in step S103 above, determining the electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model may include the following steps:

[0061] Step S1031: Determine the type of neural signal requirement based on the neural signal decoding requirements.

[0062] Specifically, the need for decoding neural signals can include action potentials, local field potentials, electromyography signals, etc., depending on the application of the target therapy, such as motor control assistance, pain management, epilepsy monitoring, etc. Based on the type of need, the required type of decoded neural signal (including but not limited to frequency range, amplitude characteristics, and spatiotemporal pattern of the signal) is determined. Different signal types have different requirements for the size, shape, and density of electrodes.

[0063] Step S1032: Determine the initial parameters according to the type of neural signal demand.

[0064] For example, initial parameters include electrode site size and site density. For high-frequency, high-amplitude neural signals, a larger electrode contact area is required to acquire the relevant signals; while for low-frequency signals, the electrode size needs to be reduced to improve spatial resolution. At the same time, considering signal transmission, the electrode thickness and the size of the transmission wires also need to be set.

[0065] The electrode array layout is designed based on the spatial distribution of decoding requirements. For areas requiring high-resolution signal acquisition, the electrode spacing should be smaller and the density higher; while for areas with less stringent signal acquisition requirements or a wider range of signal sources, the electrode spacing can be appropriately increased to reduce potential signal crosstalk.

[0066] Based on considerations of signal characteristics and patient safety, appropriate conductive materials are selected. For example, highly conductive and chemically stable materials such as graphene, titanium, platinum, or gold are often used as electrode site materials that come into direct contact with brain tissue, while insulating and stable materials such as parylene and polyimide (PI) are used as the main electrode materials to improve the biocompatibility of the electrode with brain tissue and the purity of the signal.

[0067] Step S1033: Optimize the initial parameters based on the target cerebral cortex model to obtain electrode parameters.

[0068] Specifically, the initial parameters obtained in step S1032 are aligned with the target cerebral cortex model to ensure that the electrode array layout accurately covers the functional areas identified in the model, while avoiding unnecessary interference with brain regions. Based on the target cerebral cortex model, the electrode shape is adjusted to better adapt to the curved structure of the cerebral cortex, the electrode spacing and density are optimized to adapt to changes in neural signal intensity in different regions, and further screening of electrode materials is conducted to ensure that good signal transmission is maintained while minimizing the impact on brain tissue.

[0069] Understandably, through the above steps, it is possible to accurately determine various parameters of the electrodes based on specific neural signal decoding needs and individualized three-dimensional models of the patient's cerebral cortex.

[0070] Optionally, the initial parameters include electrode site size and site density. In step S1033 above, optimizing the initial parameters based on the target cerebral cortex model to obtain the electrode parameters may include the following steps:

[0071] Step S1033a: Based on the target cerebral cortex model, determine the functional characteristics of the functional areas in the target cerebral cortex.

[0072] Specifically, by using a constructed model of the target cerebral cortex and the marked functional areas within the model, combined with the patient's specific fMRI data, the characteristics of each functional area are analyzed. This includes, but is not limited to, the activity level of the functional area, the specific neural functions it is responsible for, and its interaction and connection patterns with other functional areas.

[0073] Step S1033b: Based on the functional characteristics, the electrode site size and site density are optimized to obtain the electrode parameters.

[0074] Specifically, based on the characteristics of the functional area, the type of neural signals generated or received in that area and its main frequency range can be determined.

[0075] The size of the electrode sites is adjusted according to the signal type and frequency range of each functional area. For high-frequency signals, larger electrode sites are needed to acquire the relevant signals; for low-frequency signals, smaller electrode sites are needed to improve spatial resolution.

[0076] Ensure that the size of the electrode sites matches the micro-topography of the cerebral cortex, such as adapting to the depth and width of the sulci and gyri, to guarantee good mechanical contact and signal transmission.

[0077] For areas with dense functionalities or requiring high-resolution signal acquisition, increasing the density of electrode sites can capture more detailed information and improve decoding accuracy.

[0078] At the same time, considering the biocompatibility of brain tissue and the stability of long-term implantation, it is not advisable to use high-density configuration in all areas. It is necessary to balance signal crosstalk and decoding requirements, as well as electrode adhesion and patient safety and comfort.

[0079] Understandably, based on the above steps, the size and density of electrode sites can be precisely adjusted according to the functional characteristics of a patient's specific cerebral cortex model, thereby obtaining a highly customized set of electrode parameters. These parameters not only maximize the efficiency of neural signal acquisition and decoding but also ensure the safety of electrode implantation and the individual patient's comfort.

[0080] Optionally, the initial parameters include electrode shape and electrode size. In step S1033 above, optimizing the initial parameters based on the target cerebral cortex model to obtain electrode parameters may include the following steps:

[0081] Step S1033c: Based on the target cerebral cortex model, determine the structural features of the target cerebral cortex.

[0082] For example, for a three-dimensional target cerebral cortex model, the structural features of the cerebral cortex most relevant to the decoding requirements of neural signals are identified and confirmed, and these features are used as the basis for subsequent parameter optimization. These features include, for example, the curvature, thickness, distribution of sulci and gyri of the cerebral cortex, and the geometry and location of specific functional areas.

[0083] During feature extraction, computer vision and machine learning algorithms are used to extract specific numerical values ​​or descriptions of the aforementioned structural features from the model. For example, edge detection algorithms are used to identify the boundaries of grooves and troughs; morphological analysis is used to quantify curvature and thickness; and region segmentation techniques are used to determine the size and shape of functional areas.

[0084] In step S1333d, the electrode shape and size are optimized based on the structural characteristics to obtain the electrode parameters.

[0085] Specifically, based on the curvature and geometry of the cerebral cortex, the shape of the electrodes is optimized to ensure that they fit closely to the cortical surface, reducing mechanical stress and protecting brain tissue. For example, smaller electrode shapes are needed for areas with greater curvature, while larger electrode shapes can be chosen to cover a wider area for flat or open regions. Electrode sizes are adjusted according to the structural features in the target cerebral cortex model to ensure that electrode sites cover all key areas while avoiding interference between electrodes.

[0086] Optionally, in step S103, the electrode parameters are determined according to the neural signal decoding requirements and the target cerebral cortex model, including: inputting the neural signal decoding requirements and the target cerebral cortex model into a pre-constructed electrode parameter determination model, and using the electrode parameter determination model to determine the electrode parameters, wherein the electrode parameter determination model includes the correspondence between the neural signal decoding requirements, the target cerebral cortex model and the electrode parameters.

[0087] For example, a large amount of known neural signal decoding requirements, cerebral cortex model data, and corresponding electrode parameter settings are collected.

[0088] Feature extraction: Extract key features from the cerebral cortex model, such as the curvature of the cortex, the sulcus, and the location of functional areas; at the same time, extract key elements such as signal type, frequency range, and decoding accuracy requirements from the neural signal decoding needs.

[0089] Model training: Machine learning algorithms (such as support vector machines, neural networks, decision trees, etc.) are used to construct a model for determining electrode parameters. The above features are used as inputs, and the electrode parameters (including electrode size, site density, shape, etc.) are used as outputs to train the model and establish the correlation between them.

[0090] The specific neural signal decoding requirements are input into the model, including the type of signal to be decoded (e.g., motor, sensory, and language signals), the frequency range of the signals to be acquired, and the required decoding accuracy. Simultaneously, the constructed 3D model data of the target cerebral cortex is used as another set of inputs, including the geometry of the cerebral cortex, the precise location and size of each functional area, and other information. The electrode parameter determination model, based on the input decoding requirements and the cerebral cortex model, automatically analyzes the correlation between the two and dynamically adjusts and optimizes the electrode parameters through internal algorithms and a pre-trained model.

[0091] The model outputs a series of electrode parameters, including but not limited to the size of the electrode sites, the density and distribution between sites, the selection of electrode materials, and the overall layout of the electrode array. These parameters are based on the model's deep understanding of input requirements and brain structure, and aim to maximize the acquisition quality and decoding efficiency of neural signals.

[0092] Optionally, the electrode design method further includes: using preset simulation software to simulate the interaction between the electrode corresponding to the electrode parameters and the target cerebral cortex and to acquire EEG signals; determining the parameter validity of the electrode parameters based on the EEG signals; and iteratively optimizing the electrode parameter determination model based on the parameter validity.

[0093] For example, based on the electrode parameters optimized in step S103, such as electrode site size, site density, shape, and material properties, a complete electrode model is established in the simulation software. The target cerebral cortex model obtained in step S102 is imported into the simulation environment to ensure the accuracy and completeness of the model. The simulation boundary conditions and physical parameters are set, such as signal source type, frequency, cerebral cortex conductivity, dielectric constant, etc., and then the simulation program is run to simulate the process of electrodes collecting EEG signals on the target cerebral cortex, observing and recording key information such as signal quality and the contact state between the electrodes and the cerebral cortex.

[0094] The EEG signals obtained from the simulation were analyzed to evaluate whether key indicators such as signal-to-noise ratio, frequency response, and spatial resolution met the requirements for neural signal decoding. The quality of the signal directly affects the accuracy of the decoding.

[0095] Based on the signal quality analysis results, evaluate the effectiveness of the current electrode parameter settings. If the signal quality does not meet expectations, further analyze which parameters are problematic, such as electrode size, density, or material selection.

[0096] The validity evaluation results of the parameters determined in the above steps are fed back to the electrode parameter determination model as input data for model optimization.

[0097] Based on the feedback data, adjust the model's algorithm framework or parameter settings, including updating weights, introducing new feature variables, changing the learning rate, etc., and then retrain the model to make it more accurate in predicting electrode parameters.

[0098] Figure 2 This is a schematic flowchart of another electrode design method provided according to an embodiment of the present invention, with reference to... Figure 2 An electrode design method is also provided, including:

[0099] Step S201: Obtain the medical image data and neural signal decoding requirements input by the user from the graphical user interface;

[0100] Step S202: Construct a target cerebral cortex model based on medical imaging data;

[0101] Step S203: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model;

[0102] Step S204: Determine the electrode mask file based on the electrode parameters;

[0103] Step S205: Output the electrode mask file to the graphical user interface.

[0104] It is understood that the above steps S201 to S205 are implemented in the context of a graphical user interface, and the specific implementation steps are the same as those described in the above embodiments, and will not be repeated here.

[0105] It should be noted that any intermediate data generated in steps S201 to S205 above can be displayed in the graphical user interface.

[0106] Reference Figure 2 The present invention also provides an electrode manufacturing method, comprising: obtaining an electrode mask file determined by the electrode design method described in any of the above embodiments; and manufacturing a target electrode based on the electrode mask file using a preset electrode manufacturing process.

[0107] Optionally, the preset electrode manufacturing process is a microelectromechanical system (MEMS) manufacturing process.

[0108] In embodiments of the present invention, the above-described electrode manufacturing method is implemented as follows:

[0109] Preparations before manufacturing:

[0110] Mask file generation: Based on the determined electrode parameters, the software generates an electrode mask file, which guides the subsequent micro-nano fabrication process.

[0111] Step 1: Substrate processing:

[0112] Substrate selection: Select a substrate material with excellent biocompatibility and flexibility, such as polyimide (PI), which can adapt to the complex topography of the cerebral cortex.

[0113] Substrate preparation: The PI substrate is pretreated, including cleaning and surface activation, to ensure the adhesion of subsequent materials and the stability of the electrodes.

[0114] For example, the PI layer thickness is 2-5 μm.

[0115] Step 2: Metal layer deposition and patterning:

[0116] Metal sputtering: A Cr / Au (chromium / gold) alloy layer with a thickness of approximately 300 nm is uniformly deposited on the PI substrate using metal sputtering technology. Cr serves as the adhesion layer, while Au provides good conductivity and biocompatibility.

[0117] Photolithography patterning: Using photolithography, the shape and size of the electrode sites are defined based on the mask file. This step requires extremely high precision to ensure that each electrode site is accurately placed in the predetermined position.

[0118] Step 3: Preparation of insulating layer at electrode sites:

[0119] Insulating layer deposition: By spin-coating a PI layer over the metal electrode layer, an insulating layer is formed to protect the electrode from external interference and ensure good contact between the electrode and the cerebral cortex.

[0120] RIE etching: Reactive ion etching (RIE) technology is used to precisely create holes in the PI insulating layer to expose electrode sites, but the surrounding insulating layer is preserved to prevent short circuits or corrosion of the electrodes by biological fluids.

[0121] Step 4: Packaging and post-processing of the electrode array:

[0122] Flexible encapsulation: After the above procedures are completed, the entire electrode array needs to be flexibly encapsulated to ensure that the electrode array can maintain the necessary flexibility and mechanical strength when implanted into the cerebral cortex, thereby reducing damage to brain tissue.

[0123] Electrode array testing: Perform electrical performance tests on the electrode array to check the connectivity between electrodes, the impedance values ​​of the electrodes, and the overall signal transmission capability.

[0124] Step 5: Electrode release and cleaning:

[0125] Electrode release: Through a specific release process, the formed electrode array is separated from the substrate, making it an independent flexible electrode.

[0126] Thorough cleaning: The separated flexible electrodes are cleaned to remove any residual chemicals or impurities to ensure the safety and reliability of the electrodes when implanted in the brain.

[0127] The present invention also provides an electrode, which is manufactured based on the electrode manufacturing method described in any of the above embodiments.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0129] Reference Figure 3The present invention also provides an electrode design system, comprising: an acquisition module 301 for acquiring medical image data and neural signal decoding requirements; a construction module 302 for constructing a target cerebral cortex model based on the medical image data; a first determination module 303 for determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; and a second determination module 304 for determining an electrode mask file based on the electrode parameters.

[0130] In this embodiment of the invention, an electrode design system is provided. An acquisition module 301 acquires medical imaging data and neural signal decoding requirements; a construction module 302 constructs a target cerebral cortex model based on the medical imaging data; a first determination module 303 determines electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model; and a second determination module 304 determines an electrode mask file based on the electrode parameters. The various modules of this invention cooperate to construct a target cerebral cortex model using medical imaging data, taking into account the medical impact data of actual patients. The constructed model is targeted. Furthermore, after constructing the target cerebral cortex model, neural signal decoding requirements are also considered. Electrode parameters are determined under the combined effect of the target cerebral cortex model and neural signal decoding requirements, thereby achieving personalized customization for different patients and different neural signal decoding requirements during the electrode design process. This solves the technical problem that electrodes in the prior art cannot meet the personalized customization needs of different medical scenarios.

[0131] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the executable program, wherein the executable program executes the methods in various embodiments of the present invention during runtime.

[0132] Optionally, in this embodiment, the processor in the above-described electronic device can be configured to run an executable program to perform the following steps:

[0133] Step S101: Obtain medical imaging data and neural signal decoding requirements.

[0134] Step S102: Construct a target cerebral cortex model based on medical imaging data.

[0135] Step S103: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model.

[0136] Step S104: Determine the electrode mask file based on the electrode parameters.

[0137] Optionally, in this embodiment, the processor in the above-described electronic device can be configured to run an executable program to perform the following steps:

[0138] Step S201: Obtain the medical image data and neural signal decoding requirements input by the user from the graphical user interface;

[0139] Step S202: Construct a target cerebral cortex model based on medical imaging data;

[0140] Step S203: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model;

[0141] Step S204: Determine the electrode mask file based on the electrode parameters;

[0142] Step S205: Output the electrode mask file to the graphical user interface.

[0143] Optionally, the medical imaging data includes structural images and functional images of the cerebral cortex. Based on the medical imaging data, a target cerebral cortex model is constructed, including: adding the functional images of the cerebral cortex to the structural images of the cerebral cortex to obtain the target image, wherein the target image includes structural features and functional area features of the cerebral cortex; and determining the cerebral cortex model based on the target image.

[0144] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: determining the type of neural signal requirement based on the neural signal decoding requirements; determining initial parameters based on the type of neural signal requirement; and optimizing the initial parameters based on the target cerebral cortex model to obtain the electrode parameters.

[0145] Optionally, the initial parameters include electrode site size and site density. Based on the target cerebral cortex model, the initial parameters are optimized to obtain electrode parameters, including: determining the functional characteristics of functional areas in the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode site size and site density based on the functional characteristics to obtain electrode parameters.

[0146] Optionally, the initial parameters include electrode shape and electrode size. Based on the target cerebral cortex model, the initial parameters are optimized to obtain electrode parameters, including: determining the structural features of the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode shape and electrode size based on the structural features to obtain electrode parameters.

[0147] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: inputting neural signal decoding requirements and the target cerebral cortex model into a pre-constructed electrode parameter determination model, and using the electrode parameter determination model to determine the electrode parameters, wherein the electrode parameter determination model includes the correspondence between neural signal decoding requirements, the target cerebral cortex model, and the electrode parameters.

[0148] Optionally, the electrode design method further includes: using preset simulation software to simulate the interaction between the electrode corresponding to the electrode parameters and the target cerebral cortex and to acquire EEG signals; determining the parameter validity of the electrode parameters based on the EEG signals; and iteratively optimizing the electrode parameter determination model based on the parameter validity.

[0149] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0150] Optionally, in this embodiment, the executable program can be configured to store an executable program for performing the following steps:

[0151] Step S101: Obtain medical imaging data and neural signal decoding requirements.

[0152] Step S102: Construct a target cerebral cortex model based on medical imaging data.

[0153] Step S103: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model.

[0154] Step S104: Determine the electrode mask file based on the electrode parameters.

[0155] Optionally, in this embodiment, the executable program can be configured to store an executable program for performing the following steps:

[0156] Step S201: Obtain the medical image data and neural signal decoding requirements input by the user from the graphical user interface;

[0157] Step S202: Construct a target cerebral cortex model based on medical imaging data;

[0158] Step S203: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model;

[0159] Step S204: Determine the electrode mask file based on the electrode parameters;

[0160] Step S205: Output the electrode mask file to the graphical user interface.

[0161] Optionally, the medical imaging data includes structural images and functional images of the cerebral cortex. Based on the medical imaging data, a target cerebral cortex model is constructed, including: adding the functional images of the cerebral cortex to the structural images of the cerebral cortex to obtain the target image, wherein the target image includes structural features and functional area features of the cerebral cortex; and determining the cerebral cortex model based on the target image.

[0162] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: determining the type of neural signal requirement based on the neural signal decoding requirements; determining initial parameters based on the type of neural signal requirement; and optimizing the initial parameters based on the target cerebral cortex model to obtain the electrode parameters.

[0163] Optionally, the initial parameters, including electrode site size and site density, are optimized based on the target cerebral cortex model to obtain electrode parameters, including: determining the functional characteristics of functional areas in the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode site size and site density based on the functional characteristics to obtain electrode parameters.

[0164] Optionally, the initial parameters include electrode shape and electrode size. Based on the target cerebral cortex model, the initial parameters are optimized to obtain electrode parameters, including: determining the structural features of the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode shape and electrode size based on the structural features to obtain electrode parameters.

[0165] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: inputting neural signal decoding requirements and the target cerebral cortex model into a pre-constructed electrode parameter determination model, and using the electrode parameter determination model to determine the electrode parameters, wherein the electrode parameter determination model includes the correspondence between neural signal decoding requirements, the target cerebral cortex model, and the electrode parameters.

[0166] Optionally, the electrode design method further includes: using preset simulation software to simulate the interaction between the electrode corresponding to the electrode parameters and the target cerebral cortex and to acquire EEG signals; determining the parameter validity of the electrode parameters based on the EEG signals; and iteratively optimizing the electrode parameter determination model based on the parameter validity.

[0167] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0168] Optionally, in this embodiment, the computer program, when executed by the processor, performs the following steps:

[0169] Step S101: Obtain medical imaging data and neural signal decoding requirements.

[0170] Step S102: Construct a target cerebral cortex model based on medical imaging data.

[0171] Step S103: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model.

[0172] Step S104: Determine the electrode mask file based on the electrode parameters.

[0173] Optionally, in this embodiment, the computer program, when executed by the processor, performs the following steps:

[0174] Step S201: Obtain the medical image data and neural signal decoding requirements input by the user from the graphical user interface;

[0175] Step S202: Construct a target cerebral cortex model based on medical imaging data;

[0176] Step S203: Determine electrode parameters based on neural signal decoding requirements and the target cerebral cortex model;

[0177] Step S204: Determine the electrode mask file based on the electrode parameters;

[0178] Step S205: Output the electrode mask file to the graphical user interface.

[0179] Optionally, the medical imaging data includes structural images and functional images of the cerebral cortex. Based on the medical imaging data, a target cerebral cortex model is constructed, including: adding the functional images of the cerebral cortex to the structural images of the cerebral cortex to obtain the target image, wherein the target image includes structural features and functional area features of the cerebral cortex; and determining the cerebral cortex model based on the target image.

[0180] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: determining the type of neural signal requirement based on the neural signal decoding requirements; determining initial parameters based on the type of neural signal requirement; and optimizing the initial parameters based on the target cerebral cortex model to obtain the electrode parameters.

[0181] Optionally, the initial parameters, including electrode site size and site density, are optimized based on the target cerebral cortex model to obtain electrode parameters, including: determining the functional characteristics of functional areas in the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode site size and site density based on the functional characteristics to obtain electrode parameters.

[0182] Optionally, the initial parameters include electrode shape and electrode size. Based on the target cerebral cortex model, the initial parameters are optimized to obtain electrode parameters, including: determining the structural features of the target cerebral cortex based on the target cerebral cortex model; and optimizing the electrode shape and electrode size based on the structural features to obtain electrode parameters.

[0183] Optionally, electrode parameters are determined based on neural signal decoding requirements and the target cerebral cortex model, including: inputting neural signal decoding requirements and the target cerebral cortex model into a pre-constructed electrode parameter determination model, and using the electrode parameter determination model to determine the electrode parameters, wherein the electrode parameter determination model includes the correspondence between neural signal decoding requirements, the target cerebral cortex model, and the electrode parameters.

[0184] Optionally, the electrode design method further includes: using preset simulation software to simulate the interaction between the electrode corresponding to the electrode parameters and the target cerebral cortex and to acquire EEG signals; determining the parameter validity of the electrode parameters based on the EEG signals; and iteratively optimizing the electrode parameter determination model based on the parameter validity.

[0185] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0186] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0187] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0188] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0189] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0190] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0192] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0193] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An electrode design method, characterized in that, include: The need to acquire medical imaging data and decode neural signals; Based on the medical imaging data, construct a target cerebral cortex model; Based on the neural signal decoding requirements and the target cerebral cortex model, the electrode parameters are determined, including: Based on the target cerebral cortex model, determine the functional characteristics of the functional areas in the target cerebral cortex; Based on the target cerebral cortex model, the structural features of the target cerebral cortex are determined; Based on the functional characteristics, the electrode site size and site density in the initial parameters are optimized, and based on the structural features, the electrode shape and electrode size in the initial parameters are optimized to obtain the electrode parameters. The initial parameters are determined based on the neural signal decoding requirements. The functional characteristics include the signal frequency of the functional area in the target cerebral cortex; the higher the signal frequency of the functional area in the target cerebral cortex, the larger the electrode site size; the lower the signal frequency of the functional area in the target cerebral cortex, the smaller the electrode site size. The structural features represent the curvature and geometry of the target cerebral cortex; the greater the curvature of the target cerebral cortex, the smaller the electrode size; the smaller the curvature of the target cerebral cortex, the larger the electrode size. Based on the electrode parameters, determine the electrode mask file.

2. The electrode design method according to claim 1, characterized in that, The medical imaging data includes structural images and functional images of the cerebral cortex. The step of constructing a target cerebral cortex model based on the medical imaging data includes: The functional image of the cerebral cortex is added to the structural image of the cerebral cortex to obtain a target image, wherein the target image includes structural features and functional area features of the cerebral cortex; The cerebral cortex model is determined based on the target image.

3. The electrode design method according to claim 1, characterized in that, The step of determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model includes: Based on the neural signal decoding requirements, determine the type of neural signal requirement; Determine the initial parameters based on the type of neural signal requirement; Based on the target cerebral cortex model, the initial parameters are optimized to obtain the electrode parameters.

4. The electrode design method according to claim 1, characterized in that, The step of determining electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model includes: The neural signal decoding requirements and the target cerebral cortex model are input into a pre-constructed electrode parameter determination model. The electrode parameters are determined using the electrode parameter determination model, wherein the electrode parameter determination model includes the correspondence between the neural signal decoding requirements, the target cerebral cortex model, and the electrode parameters.

5. The electrode design method according to claim 4, characterized in that, Also includes: Using preset simulation software, the interaction between the electrodes corresponding to the electrode parameters and the target cerebral cortex is simulated and brain signals are acquired. The validity of the electrode parameters is determined based on the electroencephalogram (EEG) signals. Based on the validity of the parameters, the electrode parameter determination model is iteratively optimized.

6. An electrode design method, characterized in that, include: The need to acquire medical imaging data and decode neural signals by obtaining user input from a graphical user interface; Based on the medical imaging data, construct a target cerebral cortex model; Based on the neural signal decoding requirements and the target cerebral cortex model, electrode parameters are determined, including: determining the functional characteristics of functional areas in the target cerebral cortex based on the target cerebral cortex model; determining the structural features of the target cerebral cortex based on the target cerebral cortex model; optimizing the electrode site size and site density in the initial parameters based on the functional characteristics; and optimizing the electrode shape and electrode size in the initial parameters based on the structural features to obtain the electrode parameters. The initial parameters are determined based on the neural signal decoding requirements. The functional characteristics include the signal frequency of the functional areas in the target cerebral cortex; the higher the signal frequency of the functional areas in the target cerebral cortex, the larger the electrode site size; the lower the signal frequency of the functional areas in the target cerebral cortex, the smaller the electrode site size. The structural features represent the curvature and geometry of the target cerebral cortex; the greater the curvature of the target cerebral cortex, the smaller the electrode size; and the smaller the curvature of the target cerebral cortex, the larger the electrode size. Based on the electrode parameters, determine the electrode mask file; The electrode mask file is output to the graphical user interface.

7. An electrode design system, characterized in that, include: The acquisition module is used to acquire medical image data and meet the decoding requirements of neural signals; A construction module is used to construct a target cerebral cortex model based on the medical imaging data; The first determining module is used to determine electrode parameters based on the neural signal decoding requirements and the target cerebral cortex model, including: determining the functional characteristics of functional areas in the target cerebral cortex based on the target cerebral cortex model; determining the structural features of the target cerebral cortex based on the target cerebral cortex model; optimizing the electrode site size and site density in the initial parameters according to the functional characteristics; and optimizing the electrode shape and electrode size in the initial parameters according to the structural features to obtain the electrode parameters, wherein the initial parameters are determined based on the neural signal decoding requirements, the functional characteristics include the signal frequency of the functional areas in the target cerebral cortex, the higher the signal frequency of the functional areas in the target cerebral cortex, the larger the electrode site size, the lower the signal frequency of the functional areas in the target cerebral cortex, the smaller the electrode site size, and the structural features are used to represent the curvature and geometry of the target cerebral cortex, the greater the curvature of the target cerebral cortex, the smaller the electrode size, and the smaller the curvature of the target cerebral cortex, the larger the electrode size; The second determining module is used to determine the electrode mask file based on the electrode parameters.

8. A method for manufacturing an electrode, characterized in that, include: Obtain the electrode mask file determined by the electrode design method according to any one of claims 1 to 6; A target electrode is manufactured using a preset electrode manufacturing process based on an electrode mask file. The preset electrode manufacturing process includes photolithography patterning and reactive ion etching. The photolithography patterning is used to define the shape and size of the electrode sites according to the electrode mask file based on photolithography technology. The reactive ion etching is used to precisely open holes in the PI insulating layer to expose the electrode sites.

9. The electrode manufacturing method according to claim 8, characterized in that, The preset electrode manufacturing process is a microelectromechanical system (MEMS) manufacturing process.

10. An electrode, characterized in that, The electrode is manufactured based on the electrode manufacturing method according to any one of claims 8 to 9.

11. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 6.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 6.

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