Flexible implantable electroencephalography electrode

Abstract

The application relates to the technical field of biology, and particularly discloses a flexible implantable electroencephalogram electrode, a smart strain material can dynamically adjust the hardness through electric signal instructions, a stress sensor is matched to monitor rigidity data in real time, the hardness is increased during implantation to avoid flexural deformation, the flexible implantable electroencephalogram electrode can accurately reach a deep target brain area, the hardness is reduced after implantation to adapt to the mechanical properties of brain tissue, inflammation and immune responses caused by long-term compression are reduced, implantation accuracy and biocompatibility can be considered; a signal processing module performs feature correlation analysis on real-time rigidity data and a multimodal original data set, so that the monitoring result not only contains physiological signal information, but also fuses the adaptation state data of the electrode and brain tissue, signal interference caused by unsuitable electrode rigidity is eliminated, and the accuracy and reliability of multi-dimensional physiological state evaluation of the target brain area are improved; in this way, multi-dimensional acquisition and fusion are realized, dynamic adaptation and accurate implantation can be realized, and data correlation and evaluation accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a flexible implantable electroencephalogram (EEG) electrode. Background Technology

[0002] Implantable EEG electrodes are key tools for neuroscience research and the treatment of neurological diseases. Their core function is to achieve precise acquisition and transmission of electrophysiological signals from brain tissue. Traditional implantable electrodes mostly use rigid or semi-rigid materials, such as metal microfilaments and silicon-based probes. Although they can achieve high signal acquisition accuracy, they have poor biocompatibility with brain tissue and are prone to causing inflammatory reactions and mechanical damage.

[0003] In response, flexible implantable electrodes have become a research hotspot. These electrodes utilize flexible materials such as polyimide and hydrogel to reduce mechanical stimulation of brain tissue. However, existing flexible electrodes still face two major technical bottlenecks: First, the acquisition channels are limited in function, often only capable of acquiring electrophysiological signals and unable to simultaneously obtain physical parameters such as temperature and pressure. This results in insufficient dimensions for assessing the physiological state of the brain, affecting the accuracy of disease diagnosis and treatment. Second, the mechanical properties of flexible materials are difficult to dynamically adjust. During implantation, excessively low stiffness can cause the electrode to easily bend and deform, making it difficult to accurately reach the target area in deep brain tissue. Conversely, if the stiffness cannot be reduced after implantation, it will exert chronic pressure on brain tissue over a long period, triggering an immune response.

[0004] Some approaches attempt to increase the signal dimensionality by increasing the number of acquisition channels, but these fail to achieve integrated acquisition of electrophysiological and physical signals, and the lack of coordinated channel layout design makes them prone to signal interference. Other approaches incorporate strain materials such as shape memory alloys into flexible substrates to adjust stiffness, but due to the slow material response speed and limited control range, they still cannot meet the dual requirements of precise implantation and long-term compatibility. Therefore, developing a flexible implantable EEG electrode that combines multi-physical channel collaborative acquisition capabilities with dynamic mechanical control functions has become a pressing technical problem in this field. Summary of the Invention

[0005] This invention provides a flexible implantable EEG electrode that combines multi-physical channel collaborative acquisition capability with dynamic mechanical control function.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0007] A flexible implantable EEG electrode includes a flexible substrate, a multi-physical acquisition module, a strain modulation module, and a signal processing module;

[0008] The multi-physical acquisition module includes several acquisition channels integrated within a flexible substrate. These acquisition channels are divided into electrophysiological signal channels and physical parameter channels. The multi-physical acquisition module performs noise reduction processing on the raw EEG signals acquired through the electrophysiological signal channels using a preamplifier circuit to obtain preprocessed electrical signals. It then performs baseline correction on the temperature and pressure acquired through the physical parameter channels using a filtering algorithm to obtain standardized physical parameters. Finally, it fuses the preprocessed electrical signals and standardized physical parameters using a time-division multiplexing method to obtain a multimodal raw dataset.

[0009] The strain control module includes a smart strain material and a stress sensor dispersed within a flexible substrate. The smart strain material is in the form of micron-sized particles or a nanofiber mesh, uniformly dispersed in the flexible substrate, and gradient-distributed along the length of the flexible substrate. The dispersion density of the smart strain material at the front end of the flexible substrate is higher than that at the rear end. The stress sensor is a micro-thin film type, embedded in the flexible substrate at equal intervals along its length. The sensing surface of the stress sensor is parallel to the thickness direction of the flexible substrate. The stress sensor is electrically connected to the signal processing module.

[0010] The hardness of the intelligent strain material is dynamically adjusted by receiving electrical signal commands from the strain control module. The mechanical parameters of the adjusted flexible substrate are monitored in real time by a stress sensor to obtain real-time stiffness data. The real-time stiffness data and the multimodal raw dataset are subjected to feature correlation analysis by a signal processing module to obtain fusion monitoring results. The fusion monitoring results are used to assess the multidimensional physiological state of the target brain region.

[0011] The basic principle and beneficial effects of the solution are as follows: Through the integrated design of electrophysiological signal channels and physical parameter channels, raw EEG signals, temperature, and pressure are acquired simultaneously. After noise reduction, baseline correction, and time-sharing multiplexing and fusion, a multimodal raw dataset is formed, enabling comprehensive capture of the physiological state of the target brain region and improving data dimensionality and assessment comprehensiveness. The intelligent strain material can dynamically adjust its hardness through electrical signal commands, and with the help of stress sensors to monitor stiffness data in real time, the hardness is increased during implantation to avoid buckling deformation and accurately reach the deep target brain region. After implantation, the hardness is reduced to adapt to the mechanical properties of brain tissue, reducing inflammation and immune responses caused by long-term pressure, thus balancing implantation accuracy and biocompatibility.

[0012] The signal processing module performs feature correlation analysis on real-time stiffness data and multimodal raw datasets, enabling the monitoring results to include not only physiological signal information but also data on the adaptation status of electrodes and brain tissue. Simultaneously, the intelligent strain material, as the core of the flexible substrate's mechanical function, constitutes the main body bearing the mechanical properties of the flexible substrate. Stress sensors are embedded in the dispersed matrix of the intelligent strain material, with their sensing ends in direct contact with the particles or fibers of the intelligent strain material. This allows for real-time sensing of the mechanical deformation of the matrix caused by changes in the hardness of the intelligent strain material, ensuring that changes in the hardness of the intelligent strain material are synchronously transmitted to the entire flexible substrate, preventing local mechanical property deterioration. This eliminates signal interference caused by unsuitable electrode stiffness, improving the accuracy and reliability of multi-dimensional physiological state assessment of the target brain region, and combining multi-physical channel collaborative acquisition capabilities with dynamic mechanical regulation functions.

[0013] Furthermore, the flexible substrate has a layered composite structure, which consists of a signal transmission middle layer and a strain control inner layer stacked sequentially. The signal transmission middle layer embeds the acquisition channels of a multi-physical acquisition module, and the strain control inner layer disperses intelligent strain material and stress sensors. The layers are connected by bonding. The intelligent strain material is uniformly dispersed throughout the entire area of ​​the strain control inner layer of the flexible substrate and is fixed to the strain control inner layer through both physical intercalation and chemical cross-linking. The signal pins of the stress sensor penetrate the signal transmission middle layer and are electrically connected to the signal processing module. The outer side of the pins is covered with a polyimide insulating layer.

[0014] The beneficial effects are as follows: the signal conduction middle layer provides stable support for the acquisition channel; if a material with low dielectric constant is used, capacitive interference in signal transmission can be reduced, ensuring the fidelity of the transmission of the original EEG signal and physical parameters; the strain regulation inner layer enhances the mechanical response sensitivity of the intelligent strain material and improves the accuracy of hardness adjustment; the high-strength interlayer bonding prevents delamination during implantation, so that the overall structure maintains the bending compliance of the flexible substrate and optimizes the performance of each module through functional layering, thereby improving the long-term stability of the electrode and the reliability of multi-dimensional monitoring; the intelligent strain material is fixed to the strain regulation inner layer through physical intercalation and chemical cross-linking, ensuring no relative displacement and improving the continuity of mechanical performance transmission; the outer side of the pin is covered with a polyimide insulating layer to avoid crosstalk with the acquisition channel signal.

[0015] Furthermore, the layered composite structure also includes a biocompatible outer layer, which is sequentially stacked with a signal transduction middle layer and a strain regulation inner layer.

[0016] The beneficial effects are: the biocompatible outer layer reduces the interfacial reaction between the electrode and brain tissue, reduces the release of inflammatory factors, and improves the biosafety of long-term implantation.

[0017] Furthermore, the formula for the hardness adjustment amount ΔH of the intelligent strain material is as follows:

[0018] ,

[0019] Where k is the material response coefficient, I is the control current intensity, and t is the control time. is the temperature influence factor, and T is the ambient temperature.

[0020] The beneficial effects are as follows: Since the stiffness adjustment of intelligent strain materials largely relies on empirical parameters, the adjustment accuracy is low and the response is unstable. This method allows for dynamic calculation of the required stiffness adjustment based on the depth of the target brain region (e.g., low stiffness is required for the superficial cortex and high stiffness for deep nuclei), reducing stiffness adjustment errors. At the same time, the exp (-α·T) term in the formula can compensate for the influence of ambient temperature (e.g., the difference between the normal body temperature of brain tissue of 37°C and the room temperature during implantation) on the material stiffness in real time, avoiding adjustment failure caused by temperature drift. This quantitative control allows the flexible substrate to quickly increase its stiffness during implantation to resist buckling deformation and accurately reach the target area. After implantation, the stiffness can be reduced as needed to adapt to the mechanical properties of brain tissue, reducing inflammatory responses caused by long-term compression.

[0021] Furthermore, the time slot allocation period of the time division multiplexing method The formula is as follows:

[0022] ,

[0023] Where n is the number of electrophysiological signal channels. Let m be the time slot width of the i-th electrophysiological signal channel, and m be the number of physical parameter channels. Let be the time slot width of the j-th physical parameter channel.

[0024] The beneficial effects are: providing a quantitative allocation basis for the efficient fusion of electrophysiological signals and physical parameter signals, preventing signal interference and data loss caused by disordered time slot allocation during signal fusion, and being able to dynamically adjust according to the number of channels compared to using a fixed time slot allocation mode. It is less likely to cause waveform distortion due to insufficient time slots for electrophysiological signals or resource waste caused by redundant time slots for physical parameter signals, thereby improving the signal integrity of the multimodal raw dataset and shortening the signal fusion delay.

[0025] Furthermore, the signal acquisition end of the electrophysiological signal channel is equipped with a micro-nano point conduction array, which consists of several conductive microneedles distributed in a regular hexagonal shape, with adjacent conductive microneedles arranged at a preset center-to-center distance; the surface of the conductive microneedles is coated with a titanium conductive layer, and the original EEG signal is acquired through single-point contact acquisition by the conductive microneedles of the micro-nano point conduction array and then transmitted to the signal conduction circuit in the electrophysiological signal channel.

[0026] The beneficial effects are as follows: the point conduction structure can improve signal conduction efficiency and acquisition stability; the hexagonal distribution of micro-nano point conduction array can increase the contact area with brain tissue; the titanium conductive layer has excellent conductivity and biocompatibility, which can reduce signal conduction impedance, reduce the attenuation of the original EEG signal during the conduction process, and avoid biological rejection reactions caused by conductive materials; the adjacent spacing can avoid signal crosstalk, ensure that the original EEG signals acquired by each conductive microneedle are independent and accurate, and guarantee the signal quality of the multimodal original dataset.

[0027] Furthermore, the electrophysiological signal channel also includes an impedance compensation unit, which is connected in series at the input of the preamplifier circuit. The original EEG signal is transmitted to the impedance compensation unit through a micro-nano dot conduction array, and then dynamically adjusted by an impedance matching algorithm to obtain an impedance normalized signal. The impedance normalized signal is transmitted to the preamplifier circuit for noise reduction to obtain a preprocessed electrical signal. The impedance matching algorithm calculates the compensation impedance value ΔZ = Z0 - Z1 by real-time monitoring of the input impedance value Z1 of the electrophysiological signal channel and comparing it with a preset reference impedance Z0, and adjusts the variable resistance network of the impedance compensation unit.

[0028] The beneficial effects are: it can avoid impedance fluctuations caused by changes in electrode-tissue contact state during EEG signal acquisition, such as implantation depth adjustment and brain tissue peristalsis; by calculating and compensating for impedance differences in real time through impedance matching algorithms, the input impedance of the electrophysiological signal channel is stabilized near the preset reference value, reducing reflection loss and energy attenuation during signal transmission, ensuring that the original EEG signal maintains stable amplitude and phase characteristics before entering the preamplifier circuit, avoiding signal distortion caused by impedance drift, and improving the signal-to-noise ratio of the preprocessed electrical signal.

[0029] Furthermore, the multi-physical acquisition module also includes a channel optimization unit, which is connected to each acquisition channel. The channel optimization unit performs real-time quality scoring on the preprocessed electrical signals of the electrophysiological signal channel and the standardized physical parameters of the physical parameter channel through a signal quality assessment algorithm, and removes invalid channel data with scores below a preset threshold. The filtered valid channel data is then fused using a time-division multiplexing method to obtain a multimodal raw dataset.

[0030] The beneficial effects are as follows: through dynamic evaluation and screening mechanisms, invalid data caused by poor contact, noise interference, etc. are effectively eliminated, reducing the interference of redundant information on subsequent processing, ensuring that the data from effective channels has high fidelity, and improving the information effectiveness of the multimodal raw dataset; at the same time, the elimination of invalid channels reduces the computational load, shortens the response time, improves data quality and processing efficiency, and increases the sensitivity of fusion monitoring results to abnormal changes in the physiological state of the target brain region.

[0031] Furthermore, the acquisition channels are arranged in a center-periphery distributed layout on the flexible substrate. The central region is the core acquisition area of ​​the target brain region, in which electrophysiological signal channels and physical parameter channels are arranged alternately. The peripheral region is the auxiliary acquisition area, which is only equipped with electrophysiological signal channels. The channel density of the core acquisition area is higher than that of the auxiliary acquisition area, and the shape of the core acquisition area is adapted to the anatomical structure of the target brain region.

[0032] The beneficial effects are as follows: the core acquisition area adopts a high-density alternating layout, which can intensively capture signals from the core parts of the target brain region; the alternating arrangement of electrophysiological and physical parameter channels can simultaneously acquire multi-dimensional data from the core region, improving the spatiotemporal correlation of the data; the auxiliary acquisition area ensures a certain signal coverage range while avoiding waste of channel resources; the density difference between the core area and the auxiliary area allows the electrodes to focus on key areas while taking into account the surrounding range, ensuring high resolution of core data while reducing overall power consumption; the shape design adapted to the anatomical structure of the target brain region makes the acquisition channel more closely fit the brain tissue morphology, reducing signal loss due to layout mismatch, and ultimately improving the spatial coverage accuracy of the multimodal raw dataset. Attached Figure Description

[0033] Figure 1 A schematic diagram of the structure of a flexible substrate for an embodiment of a flexible implantable EEG electrode;

[0034] Figure 2 This is a structural block diagram of an embodiment of a flexible implantable EEG electrode.

[0035] The attached figures are labeled as follows: 1. Biocompatible outer layer; 2. Signal transduction middle layer; 3. Strain regulation inner layer; 4. Smart strain material micron-sized particles; 5. Stress sensor; 6. Acquisition channel. Detailed Implementation

[0036] The following detailed description illustrates the specific implementation method:

[0037] This embodiment describes a flexible implantable EEG electrode, as shown in the attached image. Figure 1 With appendix Figure 2 As shown, the specific implementation process is as follows: This flexible implantable EEG electrode includes a flexible base, a multi-physical acquisition module, a strain modulation module, a signal processing module, and a physical location module. The flexible base adopts a layered composite structure, consisting of a biocompatible outer layer 1, a signal transmission middle layer 2, and a strain modulation inner layer 3 stacked sequentially. The biocompatible outer layer 1 comprises two layers located above and below each other: the biocompatible outer layer 1 is made of polyethylene glycol hydrogel material, which has excellent tissue affinity and can reduce post-implantation inflammatory response; the signal transmission middle layer 2 is made of polyimide film, embedding the acquisition channels of the multi-physical acquisition module, and has a low dielectric constant (ε). r≤3.5) can reduce signal transmission loss; the strain regulation inner layer 3 is a liquid metal-doped polydimethylsiloxane material, in which smart strain materials and stress sensors are dispersed. Each layer is bonded by a biomedical epoxy adhesive, and the interlayer peel strength reaches 0.8 N / cm, which meets the mechanical reliability requirements of implantation operation.

[0038] In this embodiment, as shown in the appendix Figure 1 As shown, the micron-sized smart strain material particles 4 are uniformly dispersed throughout the entire strain control inner layer 3 of the flexible substrate 1, exhibiting a gradient distribution along the length of the flexible substrate 1. The dispersion density of the micron-sized smart strain material particles 4 at the front end of the flexible substrate 1 is higher than that at the rear end of the flexible substrate 1. The stress sensor 5 is a micro-film type, embedded in the flexible substrate 1 at equal intervals along its length. The sensing surface of the stress sensor 5 is parallel to the thickness direction of the flexible substrate 1, and the stress sensor 5 is electrically connected to the signal processing module. The micron-sized smart strain material particles 4 are fixed to the strain control inner layer 3 through both physical intercalation and chemical cross-linking. The signal pins of the stress sensor 5 penetrate the signal transmission middle layer 2 and are electrically connected to the signal processing module. The outer side of the pins is covered with a polyimide insulating layer. The micron-sized particles 4 of the intelligent strain material serve as the core of the mechanical function of the flexible substrate 1, constituting the main body bearing the mechanical properties of the flexible substrate 1. The stress sensor 5 is embedded in the matrix in which the micron-sized particles 4 of the intelligent strain material are dispersed. Its sensing end is in direct contact with the micron-sized particles 4 of the intelligent strain material, which can sense the mechanical deformation of the matrix caused by the change in hardness of the intelligent strain material in real time. This ensures that the change in hardness of the intelligent strain material can be synchronously transmitted to the entire flexible substrate 1, preventing the local mechanical properties from deteriorating. This eliminates the signal interference caused by unsuitable electrode stiffness.

[0039] The multi-physical acquisition module includes 12 acquisition channels 6 integrated into the signal transduction layer 2, including 9 electrophysiological signal channels and 3 physical parameter channels (1 temperature channel and 2 pressure channels). The acquisition channels 6 are distributed in a center-periphery pattern on a flexible substrate: the central core acquisition area is circular, adapted to the anatomical morphology of the subthalamic nucleus, with 3 electrophysiological signal channels and 1 physical parameter channel arranged alternately within the area, with an adjacent channel spacing of 0.05 mm; the peripheral auxiliary acquisition area surrounds the core area, with 6 electrophysiological signal channels and 2 physical parameter channels, with an adjacent channel spacing of 0.12 mm. The channel density in the core area (4 channels / mm²) is 5 times that in the auxiliary area (0.8 channels / mm²), ensuring high-resolution acquisition of signals from the core brain region. The electrophysiological signal acquisition end features a micro-nano dot conduction array, composed of 36 hexagonally distributed conductive microneedles made of platinum-iridium alloy. The center-to-center spacing between adjacent microneedles is 50 μm. Each microneedle is coated with a titanium conductive layer, which, after plasma treatment, achieves a surface roughness Ra ≤ 0.5 μm, enhancing both conductivity and biocompatibility. Simultaneously, the electrophysiological signal channel also incorporates an impedance compensation unit connected in series. This unit utilizes a variable resistor network (adjustable resistance range) connected to the input of the preamplifier circuit. The multi-physics acquisition module further includes a channel optimization unit. This unit incorporates a signal quality assessment algorithm that scores the signal quality by calculating the weighted values ​​of the signal-to-noise ratio (SNR), baseline drift (D), and signal integrity coefficient (C) (weights: SNR 0.5, D 0.3, C 0.2). The preset effective channel threshold is a weighted value ≥ 0.7. For example, when an electrophysiological signal channel has an SNR of 25dB, D of 0.1mV, and C of 0.9, the weighted value is 12.71 (≥0.7 after normalization), and it is determined to be a valid channel. If a physical parameter channel has an SNR of 8dB, D of 0.5mV, and C of 0.4 due to poor contact, the weighted value is 4.23 (<0.7 after normalization), and it is determined to be an invalid channel and its data is removed.

[0040] The electrophysiological signal channel collects raw EEG signals from the subthalamic nucleus region via conductive microneedles of a micro-nano dot conduction array. The raw EEG signals are first transmitted to an impedance compensation unit. An impedance matching algorithm monitors the channel input impedance value Z1 in real time. For example, if Z1 = 0.15Ω is detected, it is compared with the preset reference impedance Z0 = 0.10Ω, and the compensation impedance value ΔZ = Z0 - Z1 = -0.05Ω is calculated. Then, a variable resistor network is adjusted to connect a 0.05Ω compensation resistor to obtain an impedance normalized signal. The impedance normalized signal is transmitted to a preamplifier circuit for noise reduction processing to suppress power frequency interference, improve the signal-to-noise ratio, and obtain a preprocessed electrical signal. In the physical parameter channels, the temperature channel acquires raw brain tissue temperature data through a platinum resistance sensor, with a measurement range of 36℃-38℃ and an accuracy of ±0.1℃. The pressure channel acquires brain tissue pressure through a piezoresistive sensor (measurement range of 0kPa-5kPa and accuracy of ±0.05kPa). Both types of raw data undergo baseline correction using a Kalman filter algorithm (filter coefficient set to 0.02) to eliminate baseline drift. The drift is controlled within ±0.03℃ / h and ±0.02kPa / h, respectively, to obtain standardized physical parameters, such as a standardized temperature value of 37.2℃ and a standardized pressure value of 1.8kPa.

[0041] The effective channel data, after being filtered by the channel optimization unit (for example, retaining 10 effective channels: 8 electrophysiological signal channels and 2 physical parameter channels), is then fused using a time-division multiplexing method. This is based on the time slot allocation period formula. Where n=8 (number of effective electrophysiological signal channels), and assuming the time slot width t of each electrophysiological signal channel. e i = 100 μs, therefore m=2 (number of effective physical parameter channels), assuming the time slot width t of each physical parameter channel. p j=60μs, therefore The total time slot period T is calculated. s =920μs, ensuring no conflict or loss during the fusion of the two types of signals, ultimately obtaining the multimodal raw dataset. In the strain control module, the smart strain material is a shape memory polymer with a glass transition temperature of 32-38℃, and its hardness adjustment is achieved through electrical signal commands sent by the strain control module. According to the hardness adjustment formula... The material response coefficient k = 0.03 MPa / (A·s) (obtained from the material properties test of the shape memory polymer polycaprolactone-polyurethane copolymer). The current intensity I is set according to the implantation requirements: during the implantation process, the stiffness needs to be increased to resist buckling. Assuming I = 0.5 A, the adjustment time t = 5 s, the ambient temperature T = 37 ℃ (brain tissue temperature), and the temperature influence factor α = 0.02 / ℃ (obtained through temperature-stiffness characteristic experiment fitting), the stiffness adjustment amount ΔH = 0.036 MPa can be calculated, which increases the stiffness of the flexible substrate from the initial 0.1 MPa to 0.136 MPa, meeting the stiffness requirements for deep brain region implantation. After implantation, the stiffness needs to be reduced to adapt to the brain tissue. Assuming I = 0.2 A, t = 3 s, T = 37 ℃, then ΔH = 0.00864 MPa, and the stiffness drops back to 0.108 MPa, reducing long-term pressure on the brain tissue. Meanwhile, the stress sensor monitors the mechanical parameters of the flexible substrate in real time, obtains real-time stiffness data, such as 0.11 MPa after implantation, and transmits it to the signal processing module.

[0042] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A flexible implantable EEG electrode, comprising a flexible substrate, a multi-physical acquisition module, a strain modulation module, and a signal processing module, characterized in that, The multi-physical acquisition module includes several acquisition channels integrated within a flexible substrate. These acquisition channels are divided into electrophysiological signal channels and physical parameter channels. The multi-physical acquisition module performs noise reduction processing on the raw EEG signals acquired through the electrophysiological signal channels using a preamplifier circuit to obtain preprocessed electrical signals. It then performs baseline correction on the temperature and pressure acquired through the physical parameter channels using a filtering algorithm to obtain standardized physical parameters. Finally, it fuses the preprocessed electrical signals and standardized physical parameters using a time-division multiplexing method to obtain a multimodal raw dataset. The strain control module includes a smart strain material and a stress sensor dispersed in a flexible substrate. The smart strain material is in the form of micron-sized particles or nanofiber mesh, uniformly dispersed in the flexible substrate, and distributed in a gradient along the length of the flexible substrate. The dispersion density of the smart strain material at the front end of the flexible substrate is higher than that at the rear end of the flexible substrate. The stress sensor is a miniature thin-film type, embedded in the flexible substrate at equal intervals along the length of the flexible substrate. The sensing surface of the stress sensor is parallel to the thickness direction of the flexible substrate. The stress sensor is electrically connected to the signal processing module. The hardness of the intelligent strain material is dynamically adjusted by the electrical signal command received by the strain control module. The mechanical parameters of the adjusted flexible substrate are monitored in real time by the stress sensor to obtain real-time stiffness data. The real-time stiffness data and the multimodal raw dataset are subjected to feature correlation analysis through a signal processing module to obtain fusion monitoring results, which are used to evaluate the multidimensional physiological state of the target brain region. When the hardness of the smart strain material is dynamically adjusted by the electrical signal command received by the strain control module, the formula for the hardness adjustment amount of the smart strain material is as follows: ， Where ΔH is the hardness adjustment amount, k is the response coefficient of the smart strain material, I is the control current intensity, and t is the control time. is the temperature influence factor, and T is the ambient temperature.

2. The flexible implantable EEG electrode according to claim 1, characterized in that, The flexible substrate has a layered composite structure, which consists of a signal transmission middle layer and a strain control inner layer stacked sequentially. The signal transmission middle layer embeds the acquisition channels of a multi-physical acquisition module. The strain control inner layer disperses intelligent strain material and stress sensors, and the layers are connected by bonding. The intelligent strain material is uniformly dispersed throughout the entire area of ​​the strain control inner layer of the flexible substrate and is fixed to the strain control inner layer by both physical intercalation and chemical cross-linking. The signal pins of the stress sensor penetrate the signal transmission middle layer and are electrically connected to the signal processing module. The outer side of the pins is covered with a polyimide insulating layer.

3. The flexible implantable EEG electrode according to claim 2, characterized in that, The layered composite structure also includes a biocompatible outer layer, which is sequentially stacked with a signal transduction middle layer and a strain regulation inner layer.

4. The flexible implantable EEG electrode according to claim 3, characterized in that, The time slot allocation period of the time division multiplexing method The formula is as follows: ， Where n is the number of electrophysiological signal channels. Let m be the time slot width of the i-th electrophysiological signal channel, and m be the number of physical parameter channels. Let be the time slot width of the j-th physical parameter channel.

5. The flexible implantable EEG electrode according to claim 4, characterized in that, The signal acquisition end of the electrophysiological signal channel is equipped with a micro-nano point conduction array, which consists of several conductive microneedles distributed in a regular hexagonal shape, with adjacent conductive microneedles arranged at a preset center-to-center distance; the surface of the conductive microneedles is covered with a titanium conductive layer, and the original EEG signal is acquired by single-point contact through the conductive microneedles of the micro-nano point conduction array and then transmitted to the signal conduction circuit in the electrophysiological signal channel.

6. The flexible implantable EEG electrode according to claim 5, characterized in that, The electrophysiological signal channel also includes an impedance compensation unit, which is connected in series at the input of the preamplifier circuit. The original EEG signal is transmitted to the impedance compensation unit through a micro-nano dot conduction array, and then dynamically adjusted by an impedance matching algorithm to obtain an impedance normalized signal. The impedance normalized signal is transmitted to the preamplifier circuit for noise reduction to obtain a preprocessed electrical signal. The impedance matching algorithm calculates the compensation impedance value ΔZ = Z0 - Z1 by real-time monitoring of the input impedance value Z1 of the electrophysiological signal channel and comparing it with a preset reference impedance Z0, and adjusts the variable resistance network of the impedance compensation unit.

7. The flexible implantable EEG electrode according to claim 6, characterized in that, The flexible substrate is a layered composite structure, which consists of a biocompatible outer layer, a signal transduction middle layer, and a strain regulation inner layer stacked sequentially. The signal transduction middle layer has an embedded acquisition channel of a multi-physics acquisition module, and the strain regulation inner layer contains intelligent strain materials and stress sensors. The layers are connected by bonding.

8. The flexible implantable EEG electrode according to claim 7, characterized in that, The multi-physical acquisition module also includes a channel optimization unit, which is connected to each acquisition channel. The channel optimization unit performs real-time quality scoring on the preprocessed electrical signals of the electrophysiological signal channel and the standardized physical parameters of the physical parameter channel through a signal quality assessment algorithm, and removes invalid channel data with scores below a preset threshold. The filtered valid channel data is fused using a time-division multiplexing method to obtain a multimodal raw dataset.

9. The flexible implantable EEG electrode according to claim 8, characterized in that, The acquisition channels are distributed in a center-periphery pattern on a flexible substrate. The central region is the core acquisition area of ​​the target brain region, in which electrophysiological signal channels and physical parameter channels are arranged alternately. The peripheral region is the auxiliary acquisition area, which is only equipped with electrophysiological signal channels. The channel density of the core acquisition area is higher than that of the auxiliary acquisition area, and the shape of the core acquisition area is adapted to the anatomical structure of the target brain region.

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