Bioelectric signal acquisition electrode and efficient electroencephalogram signal acquisition method

Abstract

The present application discloses a bioelectric signal acquisition electrode and a method for efficient electroencephalogram (EEG) signal acquisition, which relates to the technical field of signal processing. The method includes: Step 1: Based on ion diffusion and capacitance effect, perform dynamic impedance modeling of the electrode-tissue interface; calculate the density distribution of the conductive coupling agent based on the impedance function; Step 2: Calculate the signal quality index; perform signal enhancement according to the signal quality index to obtain an enhanced signal; Step 3: Extract features from the enhanced signal to obtain a feature signal; perform dynamic noise suppression according to the feature signal to obtain a noise suppression result; Step 4: Perform signal reconstruction according to the noise suppression result to obtain a reconstructed signal, and complete the acquisition of the EEG signal. The electrode design of the present invention improves the contact quality, and the distribution of the conductive coupling agent is more uniform, ensuring long-term stable monitoring. At the same time, through the adaptive noise suppression and signal reconstruction mechanisms, the clarity and fidelity of the signal are effectively improved.

Description

Technical Field

[0001] The present disclosure relates to, but is not limited to, the technical field of bioelectrical signal acquisition and processing, and particularly relates to a bioelectrical signal acquisition electrode and a method for efficiently acquiring electroencephalogram signals. Background Art

[0002] In the existing technical field, the acquisition technology of electroencephalogram (EEG) signals has been widely applied in medical diagnosis, neuroscience research, intelligent human-computer interaction, etc. EEG signals are electrical activity data collected from the scalp surface through electrodes, and these data can reflect the neural activity state of the brain, which is an important basis for evaluating the health status of the nervous system, studying the characteristics of brain waves, and even developing brain-computer interfaces. With the continuous increase in application requirements, the need to acquire high-quality and stable EEG signals has become more urgent, especially in long-term monitoring and dynamic acquisition environments, where the stability and accuracy of signals are particularly important. However, the existing technologies face many challenges in the process of EEG signal acquisition, especially in aspects such as the repeated use of electrodes, stable signal conduction, and noise suppression, and there are still significant problems.

[0003] First of all, most of the existing EEG signal acquisition devices use wet electrodes, and their working principle is to rely on coupling agents such as conductive gels to enhance the contact quality between the electrodes and the skin, so as to obtain clear signals. However, the use of wet electrodes has obvious defects. The conductive gel will gradually dry out over time, resulting in a decrease in the contact quality between the electrodes and the skin, thereby deteriorating the signal quality and making it difficult to meet the requirements of long-term monitoring. In addition, the problem of gel residue on the skin increases the inconvenience of cleaning and use, which is particularly obvious in clinical applications. To overcome the deficiencies of wet electrodes, some research and commercial applications have introduced dry electrode technologies and tried to acquire EEG signals under gel-free conditions. However, the contact quality of dry electrodes is poor and is easily affected by factors such as scalp surface oil and sweating, resulting in unstable signal conduction and further affecting the quality of the acquired signals. Secondly, the conduction and quality stability of signals have always been key issues in EEG signal acquisition. In the existing technologies, due to the high impedance of the scalp surface, poor or uneven contact between the electrodes and the skin easily leads to excessive contact resistance, thereby affecting signal transmission. This problem is more prominent in dynamic environments. For example, when the patient moves, the head posture changes, or sweating occurs, the contact quality of the electrodes is extremely likely to change, resulting in fluctuations or even distortion of EEG signals. For this reason, some technical solutions have tried to improve the contact quality between the electrodes and the skin by improving electrode materials, designing flexible structures, etc., but these measures have not fundamentally solved the signal instability caused by fluctuations in contact resistance. In addition, in a dynamic environment, the friction and displacement between the electrodes and the skin will also cause electrical noise interference, further reducing the clarity of the signals.

[0004] During the process of electroencephalogram (EEG) signal acquisition, noise interference is another important factor affecting signal quality. The amplitude of EEG signals is generally at the microvolt level, extremely weak, and vulnerable to various factors such as external electromagnetic interference, electromyographic noise, and equipment noise. Existing technologies mostly use methods such as hardware shielding, low-pass filtering, and digital signal processing to suppress noise. However, these methods are difficult to completely filter out noise in complex environments, and often weaken the effective components of the signal while suppressing noise, affecting the overall clarity and fidelity of the signal. In addition, traditional noise suppression methods usually have difficulty adapting to dynamically changing noise environments, resulting in poor suppression effects during long-term or dynamic monitoring. Summary of the Invention

[0005] The present disclosure aims to provide a bioelectric signal acquisition electrode and an efficient EEG signal acquisition method.

[0006] To solve the above problems, the technical solution of the present invention is realized as follows:

[0007] A bioelectric signal acquisition electrode includes: a conductive hollow catheter, an airbag structure, and a flexible buffer head. The buffer head and the airbag structure are respectively arranged at both ends of the hollow catheter, and a gap structure is provided at one end of the buffer head away from the hollow catheter; a threaded structure is provided on the outer surface of the hollow catheter, so that the acquisition electrode can be screwed into or out of a matching electrical signal processing device during use; a conductive coupling agent is suitable for being stored in the hollow structure of the hollow catheter, and by squeezing the airbag structure, the conductive coupling agent flows out from the gap structure of the buffer head.

[0008] Further, the conductive coupling agent is conductive paste; the material of the hollow catheter is selected as medical grade 304 stainless steel.

[0009] Further, the airbag structure is set as an automatically adjustable airbag. The airbag structure includes an airbag and a micro-valve, and the micro-valve is used to regulate the internal air pressure of the airbag to control the outflow amount of the conductive coupling agent.

[0010] Further, the buffer head is set as an eight-shaped claw structure; a airbag fixing seat is connected between the airbag structure and the hollow catheter. The micro-valve includes an electric control switch, and an electric connection contact is provided on the airbag fixing seat. The electric control switch is electrically connected to the electric connection contact, and the electric control switch is used to automatically control the outflow of the conductive coupling agent.

[0011] An efficient EEG signal acquisition method for a bioelectric signal acquisition electrode, characterized in that the method includes:

[0012] Step 1: Based on ion diffusion and capacitance effect, perform dynamic impedance modeling of the electrode-tissue interface to obtain an interface impedance function; based on the impedance function, calculate the density distribution of the conductive coupling agent.

[0013] Step 2: Calculate the signal quality index according to the impedance function and the conductive coupling agent density distribution; perform signal enhancement based on the signal quality index to obtain an enhanced signal;

[0014] Step 3: Extract features from the enhanced signal to obtain a feature signal; perform dynamic noise suppression based on the feature signal to obtain a noise suppression result;

[0015] Step 4: Perform signal reconstruction based on the noise suppression result to obtain a reconstructed signal, and complete the electroencephalogram signal acquisition.

[0016] Further, in Step 1, based on ion diffusion and capacitance effects, the dynamic impedance of the electrode-tissue interface is modeled through the following formula to obtain the interface impedance function:

[0017]

[0018] where, Z interface (t, ω) is the interface impedance function, representing the impedance of the electrode-tissue interface at time t and angular frequency ω; R ct is the charge transfer resistance, representing the resistance of electron transfer between the electrode and the tissue; ω is the angular frequency, representing the frequency of the AC signal; τ is the time constant, used to describe the time characteristics of the charge transfer process. The larger the time constant, the slower the speed of charge passing through the interface; α is the phase factor; D i is the ion diffusion coefficient, representing the diffusion rate of the i-th ion on the interface; is the Laplacian operator of the ion concentration, representing the spatial distribution gradient of the concentration c i (x, t) of the i-th ion on the electrode surface; β is the ion diffusion influence factor, used to adjust the influence degree of the ion concentration gradient on the impedance; C dl is the double-layer capacitance, representing the capacitance value of the double layer formed at the electrode-tissue interface; j is the imaginary symbol; N is the number of ion species; x is the spatial position coordinate.

[0019] Further, based on the impedance function, the conductive coupling agent density distribution is calculated through the following formula:

[0020]

[0021] where, ρ(x, t) is the conductive coupling agent density distribution, representing the conductive coupling agent density at spatial position coordinate x and time t, and ρ(x, t - 1) represents the conductive coupling agent density at spatial position coordinate x and time t - 1; ρ0 is the initial density of the initial conductive coupling agent; |Z interface (t, ω)| is the modulus value of the interface impedance function; Z0 is the standard impedance value; D gis the diffusion coefficient of the conductive coupling agent; ∈0 is the vacuum permittivity; ∈ r is the relative permittivity; E(x, t) represents the local electric field strength at the spatial position coordinate x and time t; is the Laplace operator.

[0022] Further, in step 2, according to the following formula, the signal quality index is calculated based on the impedance function and the conductive coupling agent density distribution:

[0023]

[0024] where Q(t) represents the signal quality index at time t; V is the electrode coverage volume; S(t) is the original electroencephalogram signal at time t; λ is the smoothing factor.

[0025] Further, in step 2, according to the following formula, signal enhancement is performed based on the signal quality index to obtain the enhanced signal:

[0026]

[0027] where S enhanced (t) represents the enhanced signal at time t; v is the time integration variable; ρ(x, v) is the representation of ρ(x, t) under the time integration variable; Z interface (v, ω) is the representation of Z interface (t, ω) under the time integration variable.

[0028] Further, in step 3, according to the following formula, feature extraction is performed on the enhanced signal to obtain the feature signal:

[0029]

[0030] where is the Hilbert transform operator; F(t) represents the feature signal at time t.

[0031] Further, in step 3, according to the following formula, dynamic noise suppression is performed based on the feature signal to obtain the noise suppression result:

[0032]

[0033] where σ is the Gaussian noise variance; max(|Z interface (t, ω)|, Z0) represents taking the maximum value of |Z interface (t, ω)| and Z0; S denoised (t) is the noise suppression result at time t.

[0034] Further, in step 4, according to the following formula, signal reconstruction is performed based on the noise suppression result to obtain the reconstructed signal:

[0035]

[0036] Among them, S final (t) is the reconstructed signal at time t; ||·||2 is the L2 norm.

[0037] A bioelectric signal acquisition electrode and an electroencephalogram signal efficient acquisition method of the present invention have the following beneficial effects: The present invention adopts a reusable electrode structure, which greatly improves the contact quality between the electrode and the skin, making the signal conduction more stable. This electrode design combines a hollow structure with an injection method of a conductive coupling agent. By adjusting the distribution of the conductive coupling agent, it is ensured that the electrode can maintain sufficient conductivity during long-term contact, thereby reducing the discontinuity of signal transmission. In addition, the structural design of the electrode takes into account the requirements of repeated use, reduces the performance degradation problem caused by multiple uses, and at the same time reduces the maintenance cost. Compared with traditional wet electrodes, the electrode of the present invention can still maintain a good contact effect in a dry environment, so that stable signals can be obtained without using a conductive gel, which is particularly important in dynamic and portable electroencephalogram acquisition devices. Secondly, the dynamic impedance modeling technology proposed by the present invention significantly improves the stability and accuracy of the signal. The impedance of the traditional electrode and skin contact interface fluctuates significantly at different times and in different environments, while the present invention constructs a dynamic impedance model through ion diffusion and capacitance effects, which can adjust the evaluation value of the interface impedance in real time. This modeling technology enables the system to dynamically adjust the signal transmission path according to the current contact conditions, thereby reducing the signal distortion problem caused by poor contact or impedance fluctuation. Especially in a dynamic environment, this modeling method can effectively identify and compensate for the impedance changes caused by small head movements, sweating or skin oil, ensuring the stability and continuity of signal acquisition. In terms of noise suppression, the present invention introduces a dynamic noise suppression mechanism based on signal quality indicators, further improving the clarity and fidelity of the signal. Traditional noise suppression methods are usually difficult to adapt to a dynamic noise environment, while the present invention adjusts the intensity of noise suppression in real time according to the evaluation results of signal quality indicators. This adaptive suppression mechanism ensures that the noise suppression effect is enhanced when the signal quality is low, and signal loss is reduced when the signal quality is high, thereby maintaining the clarity of the signal in a complex noise environment. The parameter of the Gaussian noise variance σ is introduced into the noise suppression formula, ensuring special suppression of Gaussian distributed noise, and can significantly reduce the influence of common noise sources such as electromagnetic interference and muscle noise on electroencephalogram signals. This dynamic suppression method is particularly suitable for long-term monitoring, and can adapt to changes in noise levels at different acquisition stages, making the signal-to-noise ratio of the acquired signal significantly improved. Description of the Drawings

[0038] Figure 1A three-dimensional structure diagram of a bioelectric signal acquisition electrode provided by an embodiment of the present invention;

[0039] Figure 2 A cross-sectional structure diagram of a bioelectric signal acquisition electrode provided by an embodiment of the present invention;

[0040] Figure 3 An exploded structure diagram of a bioelectric signal acquisition electrode provided by an embodiment of the present invention;

[0041] Figure 4 A schematic flowchart of a method for efficiently acquiring electroencephalogram signals of a bioelectric signal acquisition electrode provided by an embodiment of the present invention. Detailed implementation manners

[0042] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present disclosure clearer and more understandable, the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0043] Embodiment 1, referring to Figure 1 、 Figure 2 、 Figure 3 : A bioelectric signal acquisition electrode 100, which includes: a hollow stainless steel hollow catheter 110, an airbag structure 120, and a silicone buffer head 130. The buffer head 130 and the airbag structure 120 are respectively arranged at both ends of the hollow catheter 110. A gap structure is provided at one end of the buffer head 130 away from the hollow catheter 110. A threaded structure is provided on the outer surface of the hollow catheter 110, so that the acquisition electrode 100 can be screwed into or out of a matching electric signal processing device during use and assembly; a conductive coupling agent is injected into the hollow structure of the hollow catheter 110. By squeezing the airbag structure 120, the conductive coupling agent flows out from the gap structure of the buffer head 130, so as to be infiltrated into the scalp through the user's hair. A gap structure is provided at the tail of the buffer head 130, so that the conductive coupling agent can flow out from the gap, and at the same time provide buffering for contact with the skin; the acquisition electrode 100 is assembled on an electroencephalogram cap.

[0044] In this embodiment, the conductive coupling agent is conductive paste, and the material of the hollow catheter 110 is selected as medical-grade 304 stainless steel. The hollow stainless-steel hollow catheter 110 serves as the main body of the electrode. This selection is based on the electrical conductivity, corrosion resistance, and structural strength of the stainless-steel material. The electrical conductivity of the stainless steel ensures that the electrode can stably collect electroencephalogram (EEG) signals, while its corrosion-resistant property extends the service life of the electrode, laying a solid foundation for reusable use. The design of the hollow structure not only reduces the weight of the electrode but also provides space for filling the conductive coupling agent, facilitating the direct arrival of the conductive medium to the skin surface during signal acquisition. By adding a threaded structure on the outer surface of the hollow catheter 110, the adjustability and connection stability of the electrode on the EEG cap can be ensured, avoiding poor contact caused by accidental movement or position deviation, thereby reducing noise interference during the acquisition process and improving the accuracy of signal acquisition. The conductive coupling agent filled inside the hollow catheter 110 plays an important role during signal acquisition. Different from traditional electrodes that need to directly contact the skin, the present invention improves the quality of signal acquisition to a higher level through the action of the conductive coupling agent. The conductive coupling agent is a conductive medium between the electrode and the skin, which can effectively reduce the contact resistance, thereby optimizing the signal conduction efficiency. Since the scalp itself has a large impedance and the hair layer is also likely to block the direct contact between the electrode and the skin, it is difficult to obtain stable signals only by the direct contact between the electrode and the skin. In the present invention, the airbag structure 120 is connected to the hollow catheter 110 through the airbag plastic seat, and the conductive coupling agent is controlled to flow out through the airbag structure 120 at the tail of the electrode, thereby forming a uniform conductive medium between the electrode and the skin. This not only solves the problem of contact resistance but also reduces the energy loss in signal conduction. In addition, the design of the airbag structure 120 provides precise control over the outflow volume of the conductive coupling agent. The user can squeeze the airbag as needed to make the outflow volume of the conductive coupling agent adapt to the requirements of different scalp parts, thereby ensuring the consistency and stability of the acquired signals.

[0045] The silicone buffer head 130 at the front end of the electrode is also an important part of this design. In the process of wearing traditional electrodes, due to direct contact with the skin, they often cause relatively large pressure, resulting in discomfort after long-term wearing and even affecting the signal stability. In this invention, by setting the silicone buffer head 130 at the front end of the electrode, the elastic property of silicone is utilized to buffer the direct pressure between the electrode and the skin, thus enhancing the wearing comfort. A gap structure is also designed at the tail of the silicone buffer head 130, enabling the conductive coupling agent to flow out through the gap and slowly penetrate to the skin surface. This design of the gap structure ensures that the conductive coupling agent can be evenly and slowly transmitted to the skin surface, avoiding the problems of excessive outflow or uneven distribution of the conductive coupling agent, thereby effectively improving the contact quality between the electrode and the skin and enhancing the stability of signal conduction. In addition, this design also solves the problem of signal interference caused by uneven contact pressure, enabling the electrode to maintain a stable signal output during long-term EEG signal acquisition. Through multi-level structural optimization, the overall design of this invention enables the electrode to not only have high conductivity and signal conduction efficiency but also have wearing comfort. During long-term EEG acquisition, traditional electrodes often suffer from signal fluctuations due to poor contact or uneven pressure distribution. However, this invention effectively solves these problems through the design of the airbag structure 120 and the silicone buffer head 130 at the tail of the electrode. Specifically, the conductive coupling agent control mechanism of the airbag structure 120 ensures a stable signal output, and the buffering characteristics of the silicone buffer head 130 solve the problems of wearing discomfort and signal instability caused by poor contact or excessive pressure. Due to the multi-level design of the electrode in this invention, the performance of the electrode has been optimized in all aspects, not only improving the quality of signal acquisition but also ensuring the comfort of users during long-term monitoring. In addition, the electrode structure design of this invention makes it highly reusable. The selection of the stainless steel hollow catheter 110 ensures the anti-corrosion and anti-oxidation properties of the electrode, enabling the electrode to maintain good conductivity during multiple uses. The conductive coupling agent can be removed through simple cleaning steps after use, and the hollow catheter 110 and the silicone buffer head 130 can also be processed through disinfection and other means for reuse. This design not only reduces resource waste but also lowers the usage cost, meeting the requirements of environmental protection. Generally speaking, this invention provides an electrode device with reasonable structure and excellent performance, which can provide efficient and stable signal conduction during EEG acquisition, taking into account the user's wearing experience and the reusability of the electrode. In practical applications, this electrode device is particularly suitable for scenarios that require long-term EEG monitoring, such as sleep monitoring, neuroscience experiments, etc., providing users with high-quality data acquisition means and effectively improving the wearing comfort of users.

[0046] Example 2: In this example, the conductive coupling agent is conductive paste; the material of the hollow catheter 110 is selected as medical-grade 304 stainless steel; the airbag structure 120 is an automatically adjustable pneumatic bag, and the internal air pressure is regulated by a micro-valve 121 provided on the pneumatic bag. The valve releases pressure through an electric control switch to achieve automatic control of the outflow rate of the conductive coupling agent. Specifically, the airbag structure 120 includes a pneumatic bag 122 and a micro-valve 121. The micro-valve 121 is used to regulate the internal air pressure of the pneumatic bag 122 to control the outflow rate of the conductive coupling agent. The buffer head 130 is set as an octagonal claw-like structure; there is an airbag fixing seat 140 connected between the airbag structure 120 and the hollow catheter 110. The micro-valve 121 includes an electric control switch, and the airbag fixing seat 140 is provided with an electrical contact. The electric control switch is electrically connected to the electrical contact, and the electric control switch is used to automatically control the outflow of the conductive coupling agent.

[0047] Specifically, in this embodiment, the electrode core is composed of a stainless steel tube body, and its surface is treated with a special process to enhance the durability of the electrode and reduce the problem of conductive performance attenuation after multiple uses. Due to the high corrosion resistance and oxidation resistance of the stainless steel material itself, the electrode can maintain stable conductive performance even in different temperature and humidity environments. At the same time, the high strength and wear resistance of stainless steel support the structural stability of the electrode during long-term use, which is crucial for the signal acquisition accuracy in long-term monitoring tasks. To achieve efficient adaptation to electroencephalogram signal acquisition, the design of the electrode not only considers conductivity but also, through precise structural regulation, enables the conductive coupling agent to contact the scalp in an optimal manner, thereby improving the signal conduction efficiency.

[0048] To further reduce the contact resistance and enhance the contact stability between the electrode and the skin, a conductive coupling agent is injected into the electrode, and this conductive medium plays a crucial role in the electrode structure. In the design of the present invention, the conductive coupling agent is specifically filled in the hollow part of the electrode and is controlled by a special airbag system. This airbag system can not only precisely adjust the outflow amount of the conductive coupling agent but also be adjusted at any time as needed during use, enabling the conductive coupling agent to uniformly penetrate to the scalp surface, thereby effectively improving the stability and quality of the signal. The conductive coupling agent has dual advantages in signal conduction. On the one hand, it reduces the contact resistance between the electrode and the scalp, ensuring that the signal can be conducted more efficiently; on the other hand, it forms a continuous conductive path, reducing the signal fluctuation problem caused by changes in the skin surface impedance. In addition, the electrode of the present invention is equipped with a silicone buffer head 130 at the front end. This design not only greatly improves the wearing comfort but also provides flexible buffering during the process of the electrode contacting the scalp. Different from hard materials, silicone has good flexibility and deformation adaptability and can be evenly distributed under pressure, thus relieving the discomfort caused by long-term wearing of the electrode. The silicone buffer head 130 is also provided with a gap structure, which can guide the conductive coupling agent to flow from the hollow conduit 110 to the scalp surface and slowly release in the gap to ensure that the conductive coupling agent covers the contact area. This gap release design not only avoids the problem of excessive outflow of the coupling agent but also effectively ensures the signal conduction quality when the electrode contacts the skin. This design is crucial for the long-term stability and efficiency of electroencephalogram signal acquisition, especially in dynamic scenarios, which can significantly reduce signal fluctuations and make the collected electroencephalogram data more reliable and accurate.

[0049] Example 3, refer to Figure 4 , A method for efficiently collecting electroencephalogram signals of a bioelectric signal acquisition electrode, the method comprising:

[0050] Step 1: Based on ion diffusion and capacitance effect, perform dynamic impedance modeling of the electrode-tissue interface to obtain an interface impedance function; based on the impedance function, calculate the density distribution of the conductive coupling agent;

[0051] At the moment when the electrode contacts the skin, the ions in the conductive coupling agent will penetrate into the microscopic structures on the skin surface, such as sweat glands, pores, and skin folds. This diffusion process causes the ions to form a dynamic distribution layer on the skin surface, and then generates a unique impedance characteristic at the electrode-skin interface. The diffusion of ions not only affects the absolute value of the interface impedance, but also directly affects the time characteristics of the impedance through the capacitance effect. The ion layer formed on the interface is equivalent to a microscopic capacitor, which will form a charge separation effect between the electrode and the skin, thus generating a sensitive response to the tiny charge changes during the signal acquisition process. The capacitance effect in this process is the key to achieving efficient acquisition of electroencephalogram signals, because it enables the electrode to more sensitively capture the weak bioelectric signals on the scalp surface. As the ions continue to diffuse to the skin surface, the interface capacitance will gradually increase, enabling the electrode to maintain a stable electrical response ability under the change of the tiny contact area. Through the dynamic impedance model, the system can monitor and adjust the distribution of ions and the change of capacitance in real time, thus ensuring that the signal conduction channel between the electrode and the skin is always in the best state.

[0052] In addition, this dynamic impedance model also enables the system to judge the distribution density of the conductive coupling agent by analyzing the change of the interface impedance. As a key medium for signal conduction, the distribution density of the conductive coupling agent directly affects the signal conduction path and the conductivity stability. During the process of the electrode contacting the skin, the conductive coupling agent acts as a bridge between the electrode and the skin, enabling the smooth transmission of electrical signals. However, due to the non-uniformity of the skin surface structure and the influence of factors such as hair and grease, the distribution density of the conductive coupling agent varies at different contact sites, resulting in local impedance fluctuations. In this case, the dynamic impedance model can monitor these impedance fluctuations in real time and perform automatic correction based on the density distribution of the conductive coupling agent to ensure the stability and accuracy of signal acquisition. This real-time adjustment mechanism enables the conductive coupling agent to optimize its distribution according to the dynamic changes of the electrode-tissue interface at any time, thus maintaining the high efficiency of signal conduction under complex conditions. It should be noted that the dynamic impedance model not only considers the distribution density of the conductive coupling agent, but also constructs a complete impedance change mapping mechanism through the comprehensive action of the dynamic characteristics of ion diffusion and the capacitance effect. The interaction between the conductivity change caused by ion diffusion and the impedance adjustment caused by the capacitance effect provides the system with the ability to comprehensively monitor the changes of the electrode-tissue interface, and this design is particularly important in long-term signal acquisition. Since EEG signal acquisition usually needs to last for several hours or even days, slight fluctuations in the interface impedance may affect the signal stability, and the traditional static impedance model cannot meet this requirement. By introducing the dynamic impedance model, the present invention can automatically adapt to the interface impedance fluctuations caused by movement, environmental humidity change, etc., so as to maintain high-quality signal output throughout the acquisition process. This design greatly enhances the adaptability of the system, enabling the acquisition of EEG signals to maintain high accuracy and stability in different environments.

[0053] Step 2: Calculate the signal quality index according to the impedance function and the distribution density of the conductive coupling agent; perform signal enhancement according to the signal quality index to obtain an enhanced signal;

[0054] In practical applications, signal quality indicators mainly focus on parameters such as whether the contact interface is uniform, whether the conductive coupling agent is stable enough, and the degree of noise interference. By evaluating these parameters in real time, the system can effectively judge the feasibility and reliability of signal acquisition and perform signal enhancement when necessary. The core of the signal enhancement process lies in adaptively adjusting the contact parameters of the electrodes and the distribution density of the conductive coupling agent to optimize the signal acquisition path, thereby generating a clearer and more stable signal at the contact interface. Signal enhancement is not just a simple amplification of the signal intensity, but a multi-dimensional optimization based on the actual signal environment, including suppressing invalid noise, improving the resolution of the target signal, and reducing the fluctuation of the interface contact impedance. During the signal enhancement process, the system also optimizes the uniformity and consistency of the signal path. This process can ensure that the conductive coupling agent forms a stable and uniform covering layer at the electrode-skin interface, reducing signal fluctuation problems caused by poor contact or insufficient conductivity. This adaptive enhancement method is not only applicable to EEG signal acquisition under static conditions but also has strong robustness for signal acquisition in dynamic environments (such as slight movement of the user's head). This signal enhancement mechanism can automatically adjust when the electrode contact is disturbed, ensuring that the acquired signal meets the expectations in terms of clarity and accuracy, and maintaining the stable transmission of EEG signals for a long time. In addition, the introduction of signal quality indicators not only provides an accurate reference for signal enhancement but also improves the overall intelligence of the signal acquisition system. By continuously monitoring and evaluating the signal quality, the system can identify external interference factors that may affect the signal, such as scalp oil, sweat, and environmental temperature, which often have an adverse impact on the stability of signals in traditional EEG signal acquisition. By adjusting the parameters of signal enhancement in real time, the system can actively adapt to these environmental changes and minimize interference, thereby laying a reliable foundation for subsequent signal processing and analysis. The dynamic evaluation of signal quality indicators enables the system to achieve more efficient and accurate signal acquisition in complex application scenarios, ensuring the validity of data for both long-term static monitoring and short-term dynamic acquisition.

[0055] Step 3: Extract features from the enhanced signal to obtain a feature signal; perform dynamic noise suppression based on the feature signal to obtain a noise suppression result;

[0056] After the feature extraction is completed, the system enters the dynamic noise suppression stage. During the acquisition process, EEG signals are vulnerable to various interference factors, such as external interferences like power supply noise and muscle activities. These factors introduce different degrees of random noise into the signals, thereby affecting the accuracy and usability of the signals. Through real-time monitoring and processing of the extracted feature signals, dynamic noise suppression can efficiently identify and suppress non-target signal components. Different from traditional static noise suppression methods, dynamic noise suppression can adaptively adjust according to changes in the signal acquisition environment, ensuring high-efficiency noise suppression effects under different environments and conditions. This real-time adjustment suppression method can not only accurately identify the characteristics of environmental noise but also filter it in a timely manner when the noise appears, enabling the collected EEG signals to maintain a high degree of clarity and stability. To achieve more effective dynamic noise suppression, the system analyzes the time-frequency characteristics of the signals during the feature extraction process and establishes a signal feature model based on frequency and time. This model can distinguish different frequency components of noise and target signals, thereby precisely suppressing the noise components through methods such as band filtering. For example, power supply noise usually fluctuates within a specific frequency range, and interferences such as muscle activities also have their frequency characteristics. By establishing a frequency discrimination model, the system can effectively filter out these non-target signals. During this process, the frequency components and amplitude characteristics of the signals are accurately captured and separated, enabling the true EEG signals to be clearly presented from the noise, ensuring the high resolution of the signals. Another important aspect of this dynamic noise suppression process lies in its adaptive ability, which enables the system to automatically adjust the noise suppression parameters according to changes in the signal acquisition environment. The acquisition environment of EEG signals may change due to the user's actions, postures, or changes in the external environment. Static noise suppression methods tend to be limited in dealing with these changes, while dynamic noise suppression can automatically identify and adapt to environmental changes. For example, when the user's head makes a small movement, the impedance change at the contact interface may introduce additional noise. The dynamic noise suppression system can identify this change and immediately adjust the filter parameters to adapt to the new signal state, thereby maintaining the stability of signal acquisition. Through the dual processing of feature extraction and dynamic noise suppression, the system obtains a noise-suppressed pure signal in step 3, which provides a high-quality data basis for the next signal reconstruction. This multi-level signal optimization method not only greatly improves the acquisition quality of EEG signals but also enhances the robustness of the system, enabling it to still obtain clear and accurate EEG signals in complex and variable environments. The combination of feature extraction and dynamic noise suppression enables the system to always maintain the ability to accurately identify signals and suppress interferences during the processing, ultimately achieving the efficient acquisition of EEG signals.

[0057] Step 4: Perform signal reconstruction based on the noise suppression result to obtain a reconstructed signal and complete the EEG signal acquisition.

[0058] Example 4: In step 1, based on ion diffusion and capacitance effect, the dynamic impedance of the electrode-tissue interface is modeled through the following formula to obtain the interface impedance function:

[0059]

[0060] where Z interface (t, ω) is the interface impedance function, representing the impedance of the electrode-tissue interface at time t and angular frequency ω; R ct is the charge transfer resistance, representing the resistance of electron transfer between the electrode and the tissue; ω is the angular frequency, representing the frequency of the AC signal; τ is the time constant, used to describe the time characteristics of the charge transfer process. The larger the time constant, the slower the charge passes through the interface; α is the phase factor; D i is the ion diffusion coefficient, representing the diffusion rate of the i-th ion on the interface; is the Laplace operator of the ion concentration, representing the spatial distribution gradient of the concentration c i (x, t) of the i-th ion on the electrode surface; β is the ion diffusion influence factor, used to adjust the influence degree of the ion concentration gradient on the impedance; C dl is the double-layer capacitance, representing the capacitance value of the double layer formed at the electrode-tissue interface; j is the imaginary symbol; N is the number of ion species; x is the spatial position coordinate.

[0061] Specifically, the design of the formula introduces the ratio of the charge transfer resistance R ct and the time constant τ This part reveals the rate and difficulty of charge transfer from the electrode to the tissue surface. The charge transfer resistance R c t is a key factor in the electrochemical reaction at the electrode-tissue interface. It represents the resistance of charge transfer at the electrode-skin interface and reflects the difficulty encountered by electrons when passing through the interface. The greater this resistance, the more restricted the flow of electrons at the interface, which may lead to signal attenuation and a decrease in acquisition accuracy. Correspondingly, the time constant τ determines the accumulation and flow rate of charge at the interface. In the case of a smaller time constant, charge can pass through the interface more quickly, which is crucial for rapidly changing high-frequency EEG signals; while at a larger τ value, the charge transfer speed slows down, making it more suitable for the acquisition of low-frequency stable signals, enabling the electrode to have a wide adaptability to different frequency signals. The introduction of the phase factor α also enhances the flexibility of this part. It is used to adjust the phase response of the electrode at different frequencies to ensure accurate signal acquisition within different signal frequency ranges. Next, the influence of ion diffusion is expressed through to further enrich the dynamic characteristics of the interface impedance. The ion diffusion coefficient D iDescribes the diffusion rates of different ions on the interface, while the Laplace operator characterizes the spatial distribution gradient of ion concentration. Ion diffusion is crucial for electroencephalogram (EEG) signal acquisition because it determines the penetration depth and distribution uniformity of the conductive coupling agent between the electrode and the skin. The gradient change of ion concentration forms a non-uniform electric field, resulting in differences in local impedance. This spatial distribution characteristic of ion diffusion enables the formula to dynamically capture the behavior of different ions on the interface, ensuring the consistency and stability of signal acquisition under multi-ion conditions. By adjusting the β parameter, the formula can also control the influence weight of ion concentration on impedance to adapt to different acquisition requirements. If the β value is high, the influence of the concentration gradient is enhanced, and the interface impedance is more sensitive to ion diffusion, which is particularly suitable for acquisition environments that require long-term conductive uniformity. The double-layer capacitance C dl added to the formula adds a capacitive effect, which is inversely proportional to the frequency component jω, reflecting the supporting effect of the double-layer capacitance on signal conduction under low-frequency conditions. The double-layer capacitance is a thin-layer capacitive structure on the interface, formed by the charge separation between the ions adsorbed on the electrode surface and the skin. This capacitive effect enables the interface to increase charge retention in low-frequency signal acquisition, thereby enhancing signal stability. In actual EEG signal acquisition, low-frequency signals are usually vulnerable to impedance changes due to their low frequencies, but the presence of the double-layer capacitance provides additional support for signal conduction, enabling the signal to maintain a certain clarity even at low frequencies. At the same time, the term in the formula further reflects the influence of time variation on the concentrations of different ions. By incorporating the concentration changes of multiple ions over time into the model, it ensures that impedance fluctuations can still be accurately reflected in a dynamic environment (such as user movement, environmental temperature changes, etc.).

[0062] Example 5: Calculate the density distribution of the conductive coupling agent based on the impedance function using the following formula:

[0063]

[0064] where ρ(x, t) is the density distribution of the conductive coupling agent, representing the density of the conductive coupling agent at the spatial position coordinate x and time t, ρ(x, t - 1) represents the density of the conductive coupling agent at the spatial position coordinate x and time t - 1; ρ0 is the initial density of the initial conductive coupling agent; |Z interface (t, ω)| is the modulus value of the interface impedance function; Z0 is the standard impedance value; D f is the diffusion coefficient of the conductive coupling agent; ∈0 is the vacuum permittivity; ∈ r is the relative permittivity; E(x, t) represents the local electric field strength at the spatial position coordinate x and time t; is the Laplace operator.

[0065] Specifically, the core part of the formula consists of two major modules: First is the impedance feedback control mechanism, which adjusts the density of the conductive coupling agent. Among them, |Z interface (t, ω)| represents the modulus of the interface impedance function, which is used to monitor the contact situation between the electrode and the skin interface in real time, and Z0 is a standard impedance value used to normalize the impedance modulus. The introduction of this part of the impedance control mechanism aims to ensure that the density of the conductive coupling agent can be dynamically adjusted according to the change of the interface impedance. If the interface impedance is large, it means that the contact between the electrode and the skin is poor, and the density distribution of the conductive coupling agent will be appropriately suppressed to reduce the consumption of the conductive coupling agent; while when the impedance is small, the distribution of the conductive coupling agent will be appropriately increased to ensure the stable transmission of the signal. This design optimizes the distribution of the conductive coupling agent through impedance feedback, helps to reduce the signal interference between the electrode and the skin, and ensures the continuity and quality of the collected signal. The second module of the formula combines the diffusion mechanism of the conductive coupling agent and the electric field influence, and acts on the density change rate and spatial diffusion through the Laplace operator , further refining the distribution regulation of the conductive coupling agent. The time derivative term captures the density change of the conductive coupling agent at the previous moment, ensuring the continuity and stability of the density distribution in time and avoiding signal fluctuations caused by mutations. The diffusion behavior of the conductive coupling agent is described by the diffusion coefficient D g and the Laplace operator . The diffusion coefficient D g controls the diffusion rate of the conductive coupling agent in space, thereby dynamically balancing the density of the conductive coupling agent in different regions. The diffusion of the conductive coupling agent not only helps to form a uniform distribution on the electrode surface, reducing local impedance fluctuations at the contact interface, but also ensures the stability of signal acquisition during long-term monitoring. A larger diffusion coefficient D g will cause the conductive coupling agent to diffuse quickly to adapt to a wide signal acquisition area, while a smaller diffusion coefficient helps to concentrate the distribution of the conductive coupling agent to achieve high efficiency in signal conduction in a specific area. In addition, the electric field influence is described by the term , reflecting the regulation effect of the local electric field on the density of the conductive coupling agent. Here, the vacuum permittivity ∈0 and the relative permittivity ∈ r are introduced to consider the electrical properties of the interface environment, and the squared electric field E 2(x, t) reflects the non - linear effect of the electric field on the density distribution of the conductive coupling agent. At the electrode - skin interface, due to the electric field applied by the electrode, the ions of the conductive coupling agent are rearranged in the electric field, forming a certain charge distribution trend, which affects the flow and distribution of the conductive coupling agent in different regions. The electric - field term controls the distribution of the conductive - coupling - agent density in time and space, making the conductive coupling agent more concentrated in the region with a stronger local electric field, thereby enhancing the signal in the key area of electrode - skin contact. The Laplace operator The comprehensive effect on the entire density - distribution model ensures that the diffusion and time - variation of the conductive coupling agent are not only affected by the local electric field but also can be uniformly adjusted in space, forming a dynamic and stable density distribution. Through the comprehensive regulation of the above - mentioned items, the impedance feedback, diffusion coefficient, and electric - field effect in the formula jointly constitute the density - distribution model of the conductive coupling agent, enabling the conductive coupling agent at the electrode - skin contact interface to adaptively adjust its density and position according to the acquisition environment. This design significantly improves the efficiency and quality of EEG signal acquisition in practical applications, ensuring that the distribution of the conductive coupling agent can remain stable under different environments and contact conditions to provide a consistent signal - transmission path.

[0066] Example 6: In step 2, according to the impedance function and the density distribution of the conductive coupling agent, the signal - quality index is calculated through the following formula:

[0067]

[0068] Among them, Q(t) represents the signal - quality index at time t; V is the electrode - covered volume; S(t) is the original EEG signal at time t; and λ is the smoothing factor.

[0069] Specifically, the first part of the signal - quality - index formula is used to measure the influence of the electrode - skin interface impedance. The modulus of the interface - impedance function |Z interface (t, ω)| reflects the impedance magnitude of the electrode - skin contact area. A higher impedance usually indicates a poorer contact quality between the electrode and the skin, which will cause obstruction of the signal - transmission path, affecting the accuracy and transmission quality of the acquired signal. Therefore, this term is expressed in a negative - exponential form. When the impedance modulus is high, the signal - quality index Q(t) will decrease significantly, reminding the system that there may be a poor - contact problem. This design allows the system to identify problems when the contact state is not ideal, thereby providing real - time feedback to ensure that the signal quality is not reduced due to poor contact, thus enhancing the adaptability and feedback ability of the system. The integral of the conductive - coupling - agent density distribution ∫ Vω(x, t)dV is the second part of the signal quality index calculation formula, and its main function is to evaluate the distribution quality of the conductive coupling agent within the electrode coverage area. The density of the conductive coupling agent acts as a bridge at the contact interface, effectively reducing the resistance between the electrode and the skin, thereby improving the signal conduction efficiency. By integrating the density of the conductive coupling agent over the volume V, the overall distribution intensity of the conductive coupling agent within the entire electrode contact area can be obtained. If the distribution of the conductive coupling agent on the electrode surface is uneven or the density is insufficient, it will lead to an increase in contact resistance and discontinuous signal transmission, thereby reducing the clarity of the acquired signal. A higher density integral value indicates that the conductive coupling agent is sufficiently distributed and evenly covered, which helps to improve the signal conduction efficiency, thereby maintaining the stability and accuracy of signal acquisition. Through this integration operation, the system can evaluate the usage of the conductive coupling agent, ensure that the signal conduction path has a stable conductive ability, and reduce signal fluctuations and interference.

[0070] The third part of the formula further enhances the signal quality index's ability to capture signal characteristics. First, the second-order time derivative of the original electroencephalogram signal S(t) is the acceleration of the signal's change rate over time, reflecting the instantaneous fluctuation of the signal. Electroencephalogram signals usually contain high-frequency and low-frequency components and exhibit dynamic fluctuations over time. The second-order time derivative can capture the instantaneous mutations and drastic changes of the signal, especially in high-frequency noise and dynamic environments, and can effectively identify abnormal signal components. Introducing the second derivative into the formula enables the signal quality index to not only focus on the static quality of the signal but also respond in a timely manner when the signal changes drastically, thereby avoiding possible signal distortion or mutation problems during the acquisition process and ensuring the continuity and stability of the signal. In addition, the smoothing factor λ combined with the Laplacian operator further adjusts the spatial smoothness of the signal. The Laplacian operator is used to calculate the change rate of the signal in space, reflecting the fluctuation degree of the signal at different positions. By introducing the Laplacian operator, the system can monitor the spatial consistency of the signal. Especially when there are local fluctuations in the electrode and skin contact area, it can weaken the interference caused by local imbalance through spatial smoothing processing, thereby further enhancing the signal clarity. The addition of the smoothing factor λ enables the system to adjust the sensitivity to spatial changes, reduce the error caused by the unevenness of the contact interface, and ensure that the acquired electroencephalogram signal is smoother and more fluent. This smoothing process is particularly crucial in practical applications and can effectively cope with interference caused by factors such as slight head movement of the user or environmental changes, keeping the signal transmission of high quality.

[0071] Example 7: In step 2, according to the following formula, signal enhancement is performed based on the signal quality index to obtain an enhanced signal:

[0072]

[0073] Among them, S enhanced (t) represents the enhanced signal at time t; v is the time integration variable; ρ(x, v) is the representation of ρ(x, t) under the time integration variable; Z interface (v, ω) is the representation of Z interface (t, ω) under the time integration variable.

[0074] Specifically, the core of the formula is to directly multiply the original EEG signal S(t) by the signal quality index Q(t) to achieve the adaptive enhancement of the signal. The signal quality index Q(t) is obtained from the previous step and represents the overall quality of the currently acquired signal. It evaluates the transmission quality of the signal through impedance feedback, conductive coupling agent density, and dynamic adjustment of signal characteristics. A higher Q(t) value indicates better contact and conduction conditions for signal transmission, and higher signal integrity and accuracy. Through the direct product operation of S(t)·Q(t), the system can automatically adjust the enhancement effect according to the actual acquisition quality, avoiding ineffective or excessive enhancement of the signal under low-quality acquisition conditions. This method ensures the intelligence of the enhancement process, making the amplification degree of the signal precisely match the actual acquisition situation, enabling natural enhancement when the acquisition quality is high, and automatically reducing the enhancement amplitude when the contact is poor or the conduction is unstable to maintain the authenticity of the signal. The second part of the formula introduces the time integration of the impedance gradient and the conductive coupling agent density distribution to further adjust the enhancement effect of the signal. Here, represents the square term of the gradient of the interface impedance under the time variable v, reflecting the change rate and change amplitude of the impedance at the electrode-skin contact interface. The larger the impedance gradient, it usually indicates that there are significant impedance fluctuations at the electrode-skin contact interface during this time period, which may be caused by external interference or unstable contact. During the signal enhancement process, introducing the time integration of the square of the impedance gradient can suppress these fluctuations, making the enhanced signal remain stable in a changing contact environment and avoiding signal distortion caused by short-term fluctuations. This design ensures the smoothness and consistency of the enhanced signal, helps to obtain high-fidelity data during long-term acquisition, and is especially suitable for complex acquisition scenarios of EEG signals.

[0075] The introduction of the density ω(x, v) of the conductive coupling agent plays an important regulatory role in the signal enhancement process. The conductive coupling agent is the key medium for signal conduction, and the conductive path formed at the electrode-skin interface directly affects the signal transmission efficiency. By incorporating the density distribution into the integral term, the system can further optimize the enhancement effect according to the current distribution of the conductive coupling agent at the contact interface. If the density of the conductive coupling agent is low, the conduction effect may be poor, and the system will automatically reduce the enhancement effect to avoid excessive amplification of the signal and prevent distortion caused by uneven conduction. Conversely, when the conductive coupling agent is sufficiently distributed, the signal enhancement effect can be greater, ensuring the maximization of the signal enhancement degree under good conduction conditions and optimizing the signal transmission quality and acquisition accuracy. The time integral term in this formula is designed to provide a historical feedback mechanism for signal enhancement, enabling the signal enhancement effect to not only consider the current acquisition conditions but also reflect the cumulative effects of impedance and conductive coupling agent during the acquisition process. This cumulative integration method can gradually adjust the signal amplification effect during the enhancement process, thereby achieving optimized enhancement of signals under different acquisition conditions. The time integral also plays a role in smooth adjustment, avoiding sudden enhancements caused by instantaneous changes when enhancing the signal and maintaining the continuity of signal output. Finally, the enhanced signal S enhanced (t) is the combined result of the original signal S(t), the signal quality index Q(t), and the time integrals of the impedance gradient and the conductive coupling agent density. This enhanced signal not only retains the key information of the original EEG signal but also realizes the optimization of the signal through an intelligent enhancement mechanism. The product of the signal quality index in the formula ensures the self-adaptability of the enhancement effect, the suppression effect of the square term of the impedance gradient on interface fluctuations improves the signal stability, and the introduction of the conductive coupling agent density effectively prevents distortion caused by insufficient conduction.

[0076] Example 8: In step 3, the feature signal is obtained by extracting features from the enhanced signal through the following formula:

[0077]

[0078] where is the Hilbert transform operator; F(t) represents the feature signal at time t.

[0079] Specifically, the leading term of the feature extraction formula, that is, the gradient of the signal quality index It reflects the change rate of signal quality at different time points, thus capturing the subtle changes in the electrode-skin contact situation. The signal quality index Q(t) is a comprehensive evaluation of the interface impedance and the distribution of the conductive coupling agent, and its gradient value further reveals the instantaneous fluctuation of the signal quality. This kind of fluctuation is often closely related to the stability of the electrode-skin contact. During long-term use, the electrode will inevitably have uneven contact due to movement, sweating or small displacements. Therefore, the gradient of the quality index can respond to these dynamic changes in real time. Introducing this gradient value into the feature signal extraction can improve the dynamic adaptability of the feature signal, enabling the system to still maintain the ability to capture the true information of the signal under fluctuating contact conditions. This design ensures the sensitivity of the feature signal when the contact state changes, so that even under an ideal contact state, the system can extract signal features with high representativeness. The second part of the formula, further enhances the stability and smoothness of the feature extraction. In this term, the density gradient of the conductive coupling agent and the modulus value of the interface impedance |Z interface (t, ω)| are combined together and evaluated for the entire contact area through volume integration. The gradient of the conductive coupling agent density describes the uniformity of the distribution of the conductive coupling agent on the interface. If the density gradient is large, it means that the distribution of the conductive coupling agent is uneven, which may lead to blocked or unstable local signal conduction. The addition of the interface impedance modulus value as a balancing factor ensures a stronger inhibitory effect on the uneven density distribution in the case of high impedance. This design forms an automatic smoothing mechanism in the complex electrode contact environment by balancing the gradient of the conductive coupling agent distribution and the modulus value of the interface impedance, making the feature signal extraction smoother and reducing the fluctuations caused by the uneven distribution of the conductive coupling agent. The exponential form of this part also ensures the smoothness of the signal extraction, preventing the instability introduced by local density fluctuations, thereby further improving the clarity and stability of the feature signal. The last part of the formula is for the enhanced signal S enhanced(t) Perform the Hilbert transform H{·} to obtain the instantaneous phase and frequency information of the enhanced signal. The Hilbert transform is a commonly used tool in signal processing. By converting a real-valued signal into a complex form, the system can extract the instantaneous phase and frequency from it. These information are particularly important in EEG signals because they reflect the periodic and rhythmic characteristics of EEG activities. EEG signals often contain multiple frequency components, and different EEG frequency bands correspond to different brain activity states. The Hilbert transform can help the system accurately locate these frequency components in the time-frequency plane, enabling the feature signal to better represent the current EEG activity characteristics. By introducing the time-frequency characteristics of the enhanced signal into the feature signal extraction process, the system not only retains the amplitude information of the signal but also captures its frequency and phase characteristics, thus providing more comprehensive features during signal analysis. This is of great significance for the efficient acquisition of EEG signals because it ensures that the system can not only sense the intensity of the signal but also perform a detailed analysis of the signal's spectral distribution, helping to identify important patterns and regularities in EEG activities.

[0080] Example 9: In step 3, according to the following formula, perform dynamic noise suppression based on the feature signal to obtain the noise suppression result:

[0081]

[0082] where σ is the Gaussian noise variance; max(|Z interface (t, ω)|, Z0) represents taking the maximum value between |Z interface (t, ω)| and Z0; S denoised (t) is the noise suppression result at time t.

[0083] Specifically, the first part of the formula adjusts the intensity of noise suppression by the ratio of the square of the signal quality index Q(t) to the Gaussian noise variance σ 2 . Here, the signal quality index Q(t) provides an overall quality assessment of the current signal, and σ represents the standard deviation of the noise. The variance of the noise is obtained by squaring it, which is used to describe the intensity of the noise in the signal. When Q(t) is high, it means that the acquisition quality of the signal is relatively good, the suppression intensity is reduced, and more original signal information is retained; on the contrary, when Q(t) is low, the suppression intensity is increased, thereby enhancing the noise suppression effect. This adaptability enables the system to automatically adjust the strength of noise suppression, ensuring improved suppression effect under low signal quality conditions and retaining more signal details under high signal quality conditions, avoiding signal loss caused by over-suppression. The second part of the formula further strengthens the dynamics of noise suppression. This term acts on the enhanced signal S enhanced(t) is differentiated with respect to time from the maximum value of the interface impedance, aiming to capture the changing trend of the enhanced signal and adjust the smoothness of the signal according to the dynamic change of the interface impedance. Taking the maximum values of |Z interface (t, ω)| and the standard impedance Z0 as the denominator helps to stabilize the signal amplification ratio during the noise suppression process. The change of the interface impedance reflects the fluctuation of the contact condition between the electrode and the skin. Therefore, during the noise suppression process, a higher impedance means poor contact between the electrode and the skin, and the signal is easily interfered. While when the impedance is lower, the signal conduction quality is better. By normalizing the enhanced signal and combining the dynamic change of the interface impedance, the formula realizes the fine control of noise suppression. Combining these parts together, based on the characteristic signal F(t), the noise suppression formula can dynamically adapt under different acquisition conditions through the dual feedback of the quality index and the interface impedance, ensuring that the signal remains clear and stable. The characteristic signal F(t) itself is the result of refining the important components in the EEG signal. Therefore, in noise suppression, it plays the role of retaining the core signal, avoiding the loss of important information in the signal during the suppression process. By multiplying the characteristic signal with other regulatory terms, the system can filter out the noise components with higher frequencies and lower amplitudes while maintaining the main characteristics of the signal, realizing the effective distinction between the signal and the noise. In addition, the design of the dynamic noise suppression formula also ensures special attention to Gaussian noise. The Gaussian distribution characteristic of the noise is reflected through the adjustment of the variance σ in the formula, enabling the system to adaptively adjust according to different noise levels. This suppression method is particularly suitable for the acquisition process of EEG signals because EEG signals are often easily affected by various interferences such as background noise, muscle activity, and equipment noise, and Gaussian noise is typical among these interferences. By using the parameter of the Gaussian noise variance, the system can more precisely control the intensity of noise suppression, effectively reducing the influence of background noise on the signal, and thus enhancing the signal-to-noise ratio.

[0084] Example 10: In step 4, according to the following formula, signal reconstruction is performed based on the noise suppression result to obtain the reconstructed signal:

[0085]

[0086] where S final (t) is the reconstructed signal at time t; ||·||2 is the L2 norm.

[0087] Specifically, the first part of the formula and the noise suppression result S denoisedMultiply by (t) with the aim of balancing the amplification effect of the signal through the normalization adjustment of the signal quality index Q(t). Here, Q(t) is a dynamic signal quality evaluation parameter, reflecting the quality of the acquired signal at a specific time t, and max(Q(t)) is its maximum value, which is used for relative normalization of the current signal quality. Through this normalization adjustment, the formula can dynamically control the signal amplification effect. When the signal quality is high, the amplification factor increases, making the reconstructed signal clearer; while when the signal quality is low, the amplification factor decreases to prevent distortion caused by excessive amplification under poor contact or high-noise conditions. This adaptive amplification design ensures the flexibility of the reconstructed signal under different acquisition conditions, enabling the system to automatically adjust the amplification factor according to the current signal quality and ensuring the smoothness and consistency of the signal. The second part of the formula further introduces the density gradient of the conductive coupling agent, the characteristic signal F(t), and the enhanced signal S enhanced (t) information to achieve fine-tuning of signal reconstruction. The density gradient of the conductive coupling agent reflects the uniformity of the distribution of the conductive coupling agent at the electrode-skin interface, ensuring the stability of the conduction path. By incorporating the density gradient into the volume integral and combining with the weight λ, the formula can automatically adjust the smoothness of the reconstructed signal according to the uniformity of the conductive coupling agent distribution, thereby reducing signal fluctuations caused by uneven local density distribution. This design plays a balancing role especially in long-term monitoring of electroencephalogram signal acquisition, ensuring the continuity of the signal conduction path and improving the overall quality of the acquired signal. In addition, the characteristic signal F(t) and the enhanced signal S enhanced (t) L2 norms (i.e., the second norm) ||F(t)||2 and ||S enhanced (t)||2 are introduced into the formula. The second norm operation reflects the overall energy intensity of the signal by calculating the Euclidean length of the signal. During the reconstruction process, by adjusting the ratio of the second norms of these two signal characteristics, it can be ensured that the energy of the reconstructed signal is concentrated in the effective frequency band, thereby suppressing unnecessary noise components. The L2 norm ||F(t)||2 of the characteristic signal reflects the key information intensity in the signal characteristics. Combining with the L2 norm ||S enhanced (t)||2 of the enhanced signal can dynamically control the balance of the signal in different frequency bands and intensities, enabling the finally output reconstructed signal to retain important characteristic information while removing noise and invalid components.

[0088] The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and thus do not limit the scope of rights of the present disclosure. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the present disclosure shall fall within the scope of rights of the present disclosure.

Claims

1. A bioelectric signal acquisition electrode, characterized in that, Comprising: A conductive hollow catheter, an airbag structure, and a flexible buffer head. The buffer head and the airbag structure are separately arranged at both ends of the hollow catheter. A gap structure is provided at one end of the buffer head away from the hollow catheter. A threaded structure is provided on the outer surface of the hollow catheter, so that the acquisition electrode can be screwed into or out of a matching electrical signal processing device during use. A conductive coupling agent is suitable for being stored in the hollow structure of the hollow catheter. By squeezing the airbag structure, the conductive coupling agent flows out from the gap structure of the buffer head. The airbag structure is set as an automatically adjustable pneumatic bladder. The airbag structure includes a pneumatic bladder and a micro-valve. The micro-valve is used to regulate the internal air pressure of the pneumatic bladder to control the outflow amount of the conductive coupling agent. The buffer head is set as an eight-shaped claw structure. An airbag fixing seat is connected between the airbag structure and the hollow catheter. The micro-valve includes an electric control switch. Electric connection contacts are provided on the airbag fixing seat. The electric control switch is electrically connected to the electric connection contacts. The electric control switch is used to automatically control the outflow of the conductive coupling agent.

2. The bioelectric signal acquisition electrode according to claim 1, wherein: The conductive coupling agent is conductive paste; the material of the hollow catheter is selected as medical-grade 304 stainless steel.

3. An efficient electroencephalogram signal acquisition method for the bioelectric signal acquisition electrode according to any one of claims 1 to 2, characterized in that, The method includes: Step 1: Based on ion diffusion and capacitance effect, perform dynamic impedance modeling of the electrode-tissue interface to obtain an interface impedance function; based on the impedance function, calculate the density distribution of the conductive coupling agent. Step 2: According to the impedance function and the density distribution of the conductive coupling agent, calculate a signal quality index; perform signal enhancement according to the signal quality index to obtain an enhanced signal. Step 3: Extract features from the enhanced signal to obtain a feature signal; perform dynamic noise suppression according to the feature signal to obtain a noise suppression result. Step 4: Perform signal reconstruction according to the noise suppression result to obtain a reconstructed signal, completing the acquisition of electroencephalogram signals.

4. The high-efficiency EEG signal acquisition method according to claim 3, wherein In Step 1, based on ion diffusion and capacitance effect, perform dynamic impedance modeling of the electrode-tissue interface through the following formula to obtain an interface impedance function: ； Among them, is the interfacial impedance function, representing the impedance of the electrode-tissue interface at time and angular frequency ; is the charge transfer resistance, representing the resistance of electron transfer between the electrode and the tissue; is the angular frequency, representing the frequency of the alternating current signal; is the time constant, used to describe the time characteristics of the charge transfer process. The larger the time constant, the slower the speed of charge passing through the interface; is the phase factor; is the ion diffusion coefficient, representing the diffusion rate of the th ion on the interface; is the Laplacian operator of the ion concentration, representing the spatial distribution gradient of the concentration of the th ion on the electrode surface; ; is the ion diffusion influence factor, used to adjust the influence degree of the ion concentration gradient on the impedance; is the double-layer capacitance, representing the capacitance value of the double layer formed at the electrode-tissue interface; is the imaginary symbol; is the number of ion species; is the spatial position coordinate.

5. The high-efficiency EEG signal acquisition method according to claim 4, wherein Calculate the density distribution of the conductive coupling agent based on the impedance function through the following formula: ； in, is the density distribution of the conductive coupling agent, expressed in spatial coordinates and time The density of the conductive coupling agent under To represent the coordinates of the spatial position and time Conductive coupling agent density under ; is the initial density of the conductive coupling agent; is the modulus of the interface impedance function; is the standard impedance value; is the diffusion coefficient of the conductive coupling agent; is the dielectric constant of vacuum; is the relative dielectric constant; Represents the spatial coordinates and time The local electric field strength; is the Laplace operator.

6. The high-efficiency EEG signal acquisition method according to claim 5, wherein In Step 2, calculate the signal quality index according to the impedance function and the density distribution of the conductive coupling agent through the following formula: ； Among them, represents the signal quality index at time is the electrode coverage volume; is time when the original electroencephalogram signal; is the smoothing factor.

7. The high-efficiency EEG signal acquisition method according to claim 6, wherein In Step 2, perform signal enhancement according to the signal quality index through the following formula to obtain an enhanced signal: ； in, Indicates time The enhanced signal when is the time-integrated variable; for Representation under time-integrated variables; for Representation under time-integrated variables.

8. The high-efficiency EEG signal acquisition method according to claim 7, wherein In Step 3, extract features from the enhanced signal through the following formula to obtain a feature signal: ； Among them, is the Hilbert transform operator; represents time when the characteristic signal; In Step 3, perform dynamic noise suppression according to the feature signal through the following formula to obtain a noise suppression result: ； Among them, is the Gaussian noise variance; denotes taking and the maximum value of the two; is the noise suppression result at time ; In Step 4, perform signal reconstruction according to the noise suppression result through the following formula to obtain a reconstructed signal: ； wherein, is the reconstruction signal at time ; is the L2 norm.

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