Bio-electricity signal acquisition electrode and efficient electroencephalogram signal acquisition method

Abstract

The invention discloses a bio-electricity signal acquisition electrode and an efficient electroencephalogram signal acquisition method, and relates to the technical field of signal processing. The method comprises the following steps: step 1, carrying out electrode-tissue interface dynamic impedance modeling based on ion diffusion and a capacitance effect; calculating density distribution of the conductive coupling agent based on the impedance function; 2, calculating a signal quality index; performing signal enhancement according to the signal quality index to obtain an enhanced signal; 3, performing feature extraction on the enhanced signal to obtain a feature signal; performing dynamic noise suppression according to the characteristic signal to obtain a noise suppression result; and 4, performing signal reconstruction according to a noise suppression result to obtain a reconstructed signal, and completing electroencephalogram signal acquisition. According to the electrode design, the contact quality is improved, the distribution of the conductive coupling agent is more uniform, and long-time stable monitoring is ensured. Meanwhile, the definition and fidelity of the signal are effectively improved through adaptive noise suppression and a signal reconstruction mechanism.

Description

Technical Field

[0001] The present disclosure relates to but is not limited to the field of bioelectric signal acquisition and processing technology, and in particular to a bioelectric signal acquisition electrode and an efficient EEG signal acquisition method. Background Art

[0002] In the prior art, EEG signal acquisition technology has been widely used in medical diagnosis, neuroscience research, and intelligent human-computer interaction. EEG signals are electrical activity data collected from the surface of the scalp through electrodes. These data can reflect the state of neural activity in the brain and are an important basis for assessing the health of the nervous system, studying brain wave characteristics, and even developing brain-computer interfaces. With the continuous increase in application demand, the need to collect high-quality and stable EEG signals has become more urgent, especially in long-term monitoring and dynamic acquisition environments, where signal stability and accuracy are particularly important. However, the existing technology faces many challenges in the process of EEG signal acquisition, especially in the reuse of electrodes, stable signal transmission, noise suppression, etc. There are still significant problems.

[0003] First, most of the existing EEG signal acquisition devices use wet electrodes, and their working principle is to rely on coupling agents such as conductive gel to enhance the contact quality between the electrode and the skin to obtain a clear signal. However, the use of wet electrodes has obvious defects. The conductive gel will gradually dry up over time, resulting in a decrease in the contact quality between the electrode and the skin, thereby deteriorating the signal quality and making it difficult to meet the needs 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. In order to overcome the shortcomings of wet electrodes, some studies and commercial applications have introduced dry electrode technology to try to collect EEG signals under gel-free conditions. However, the contact quality of dry electrodes is poor and is easily affected by factors such as oil and sweat on the scalp surface, resulting in unstable signal conduction, which in turn affects the quality of the collected signal. Secondly, signal conduction and quality stability have always been key issues in EEG signal acquisition. In the prior art, due to the high impedance of the scalp surface, poor or uneven contact between the electrode and the skin can easily lead to excessive contact resistance, thereby affecting signal transmission. This problem is more prominent in dynamic environments. For example, when the patient moves, changes head posture or sweats, the electrode contact quality can easily change, causing EEG signals to fluctuate or even distort. To this end, some technical solutions have attempted to improve the contact quality between electrodes and skin by improving electrode materials and designing flexible structures, but these measures have not fundamentally solved the signal instability caused by contact resistance fluctuations. In addition, in a dynamic environment, friction and displacement between the electrode and the skin can also cause electrical noise interference, further reducing signal clarity.

[0004] In the process of EEG signal acquisition, noise interference is another important factor affecting signal quality. The amplitude of EEG signals is generally at the microvolt level, which is very weak and easily affected by various factors such as external electromagnetic interference, myoelectric noise, and equipment noise. Existing technologies often suppress noise through methods such as hardware shielding, low-pass filtering, and digital signal processing, but these methods are difficult to completely filter out noise in complex environments, and while suppressing noise, they often weaken the effective components of the signal, affecting the overall clarity and fidelity of the signal. In addition, traditional noise suppression methods are usually difficult to adapt to dynamically changing noise environments, resulting in poor suppression effects during long-term or dynamic monitoring. Summary of the invention

[0005] The present invention provides a bioelectric signal acquisition electrode and an efficient method for acquiring brain electrical signals.

[0006] In order to solve the above problems, the technical solution of the present invention is achieved as follows:

[0007] A bioelectric signal collection electrode comprises: a conductive hollow catheter, an airbag structure and a flexible buffer head, wherein the buffer head and the airbag structure are separately arranged at two 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 collection electrode can be screwed into or out of an adapted electrical signal processing device when in use; the hollow structure of the hollow catheter is suitable for storing a conductive coupling agent, and the conductive coupling agent flows out from the gap structure of the buffer head by squeezing the airbag structure.

[0008] Furthermore, the conductive coupling agent is conductive paste; and the hollow catheter material is medical grade 304 stainless steel.

[0009] Furthermore, the airbag structure is configured as an automatically adjustable air pressure bag, and the airbag structure includes an air pressure bag and a micro air valve, and the micro air valve is used to regulate the internal air pressure of the air pressure bag to achieve control of the outflow of the conductive coupling agent.

[0010] Furthermore, the buffer head is configured as an eight-shaped claw-shaped structure; an airbag fixing seat is connected between the airbag structure and the hollow catheter, the micro air valve includes an electrically controlled switch, an electrical contact is provided on the airbag fixing seat, the electrically controlled switch is electrically connected to the electrical contact, and the electrically controlled switch is used to automatically control the outflow of the conductive coupling agent.

[0011] A method for efficiently collecting electroencephalogram (EEG) signals using a bioelectric signal collection electrode, characterized in that the method comprises:

[0012] Step 1: Based on ion diffusion and capacitance effects, the electrode-tissue interface dynamic impedance modeling is performed to obtain the interface impedance function; based on the impedance function, the density distribution of the conductive coupling agent is calculated;

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

[0014] Step 3: Extract features of 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: Reconstruct the signal according to the noise suppression result to obtain the reconstructed signal and complete the EEG signal acquisition.

[0016] Furthermore, in step 1, the electrode-tissue interface dynamic impedance modeling is performed based on ion diffusion and capacitance effects to obtain the interface impedance function through the following formula:

[0017]

[0018] Among them, Z interface (t, ω) is the interface impedance function, which represents the impedance of the electrode-tissue interface at time t and angular frequency ω; R ct is the charge transfer resistance, which represents the resistance to electron transfer between the electrode and the tissue; ω is the angular frequency, which represents the frequency of the AC signal; τ is the time constant, which is 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, which indicates the diffusion rate of the i-th ion on the interface; is the Laplace operator of ion concentration, which represents the concentration c of the i-th ion on the electrode surface i (x, t) is the distribution gradient in space; β is the ion diffusion influencing factor, which is used to adjust the influence of ion concentration gradient on impedance; C dl is the double layer capacitance, which indicates the capacitance value of the double layer formed at the electrode-tissue interface; j is the imaginary number symbol; N is the number of ion species; and x is the spatial position coordinate.

[0019] Furthermore, the density distribution of the conductive coupling agent is calculated based on the impedance function by the following formula:

[0020]

[0021] Wherein, ρ(x, t) is the density distribution of the conductive coupling agent, which represents 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 conductive coupling agent; |Z interface (t, ω)| is the modulus 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 dielectric constant; ∈ r is the relative dielectric constant; E(x, t) represents the local electric field intensity at the spatial position coordinate x and time t; is the Laplace operator.

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

[0023]

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

[0025] Furthermore, in step 2, the signal is enhanced according to the signal quality index by the following formula to obtain an enhanced signal:

[0026]

[0027] Among them, S enhanced (t) represents the enhanced signal at time t; v is the time-integrated variable; ρ(x, v) is the representation of ρ(x, t) under the time-integrated variable; Z interface (v, ω) is Z interface Representation of (t, ω) in terms of time-integrated variables.

[0028] Furthermore, in step 3, the feature extraction of the enhanced signal is performed by the following formula to obtain the feature signal:

[0029]

[0030] in, is the Hilbert transform operator; F(t) represents the characteristic signal at time t.

[0031] Furthermore, in step 3, dynamic noise suppression is performed according to the characteristic signal by the following formula to obtain a noise suppression result:

[0032]

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

[0034] Furthermore, in step 4, the signal is reconstructed according to the noise suppression result by the following formula to obtain a 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 efficient EEG signal 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 the hollow structure with the injection method of the conductive coupling agent, and by adjusting the distribution of the conductive coupling agent, ensures 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 reduces the maintenance cost. Compared with the traditional wet electrode, the electrode of the present invention can still maintain a good contact effect in a dry environment, so that a stable signal can be obtained without using a conductive gel, which is particularly important in dynamic and portable EEG acquisition devices. Secondly, the dynamic impedance modeling technology proposed in the present invention significantly improves the stability and accuracy of the signal. The interface impedance of the traditional electrode and skin contact will fluctuate significantly at different times and environments, while the present invention constructs a dynamic impedance model through ion diffusion and capacitance effect, 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 fluctuations. Especially in dynamic environments, the modeling method can effectively identify and compensate for impedance changes caused by slight movements of the head, 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 to further improve the clarity and fidelity of the signal. Traditional noise suppression methods are usually difficult to adapt to dynamic noise environments, while the present invention adjusts the intensity of noise suppression in real time through 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 the 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 Gaussian noise variance σ\sigmaσ is introduced into the noise suppression formula to ensure special suppression of Gaussian distribution noise, which can significantly reduce the impact of common noise sources such as electromagnetic interference and muscle noise on EEG signals. This dynamic suppression method is particularly suitable for long-term monitoring, and can adapt to changes in noise levels at different acquisition stages, so that the signal-to-noise ratio of the acquired signal is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1A structural stereogram of a bioelectric signal collection electrode provided by an embodiment of the present invention;

[0039] Figure 2 A structural cross-sectional view of a bioelectric signal collection electrode provided by an embodiment of the present invention;

[0040] Figure 3 A structural decomposition diagram of a bioelectric signal collection electrode provided by an embodiment of the present invention;

[0041] Figure 4 A schematic diagram of a method flow of a method for efficiently collecting EEG signals using a bioelectric signal collection electrode provided in an embodiment of the present invention. DETAILED DESCRIPTION

[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 is further described in detail below in conjunction with 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] Example 1, reference Figure 1 , Figure 2 , Figure 3 A bioelectric signal acquisition electrode 100, comprising: a hollow stainless steel hollow catheter 110, an airbag structure 120 and a buffer head 130 made of silicone, wherein the buffer head 130 and the airbag structure 120 are respectively disposed at the two ends of the hollow catheter 110, and a gap structure is disposed at one end of the buffer head 130 away from the hollow catheter 110. A threaded structure is disposed on the outer surface of the hollow catheter 110, so that the acquisition electrode 100 can be screwed into or out of an adapted electrical signal processing device during use and assembly; a conductive coupling agent is injected into the hollow structure of the hollow catheter 110, and the conductive coupling agent flows out of the gap structure of the buffer head 130 by squeezing the airbag structure 120, so as to penetrate into the scalp through the user's hair. A gap structure is disposed at the tail of the buffer head 130, so that the conductive coupling agent flows out of the gap and provides a buffer for contact with the skin; the acquisition electrode 100 is assembled on an electroencephalogram cap.

[0044] In this embodiment, the conductive coupling agent is a conductive paste, and the hollow catheter 110 material is medical grade 304 stainless steel. The hollow stainless steel hollow catheter 110 is used as the main body of the electrode. This choice is based on the conductivity, corrosion resistance and structural strength of the stainless steel material. The conductivity of stainless steel ensures that the electrode can stably collect EEG signals, and its corrosion resistance extends the service life of the electrode, laying a solid foundation for reusability. The design of the hollow structure not only reduces the weight of the electrode, but also provides space for the filling of the conductive coupling agent, which facilitates the conductive medium to directly reach the skin surface during the signal acquisition process. By adding a threaded structure to the outer surface of the hollow catheter 110, the adjustability and connection stability of the electrode installed on the EEG cap can be ensured, and poor contact caused by accidental movement or position displacement can be avoided, thereby reducing noise interference during the acquisition process and improving the accuracy of signal acquisition. The conductive coupling agent filled in the hollow catheter 110 plays an important role in the signal acquisition process. Unlike traditional electrodes that need to directly contact the skin, the present invention improves the quality of signal acquisition to a higher level through the effect of the conductive coupling agent. Conductive coupling agent is a conductive medium between the electrode and the skin, which can effectively reduce the contact resistance and thus optimize the signal conduction efficiency. Since the scalp itself has a large impedance and the hair layer easily blocks the direct contact between the electrode and the skin, it is difficult to obtain a stable signal 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 controls the outflow 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 of the outflow of the conductive coupling agent. The user can squeeze the airbag as needed so that the outflow of the conductive coupling agent adapts to the needs of different scalp parts, thereby ensuring the consistency and stability of the collected signal.

[0045] The silicone buffer head 130 at the front end of the electrode is also an important part of the design. Traditional electrodes often cause greater pressure during wearing due to direct contact with the skin, resulting in discomfort after long-term wearing, and even affecting the stability of the signal. The present invention, by setting a silicone buffer head 130 at the front end of the electrode, uses the elastic properties of silicone to buffer the direct pressure between the electrode and the skin, thereby improving the comfort of wearing. A gap structure is also designed at the tail of the silicone buffer head 130, so that the conductive coupling agent can flow out through the gap and slowly penetrate into the skin surface. The design of this gap structure ensures that the conductive coupling agent can be evenly and slowly transmitted to the skin surface, avoiding the problem 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, so that the electrode can maintain a stable signal output during long-term EEG signal acquisition. The overall design of the present invention optimizes the structure at multiple levels, so that the electrode not only has high conductivity and signal conduction efficiency, but also has wearing comfort. Traditional electrodes often cause signal fluctuations due to poor contact or uneven pressure distribution during long-term EEG acquisition, and the present invention effectively solves these problems by designing 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 the stable output of the signal, 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. Since the electrode of the present invention adopts this multi-level design, the performance of the electrode is fully optimized, which can not only improve the quality of signal acquisition, but also ensure the comfort of the user during long-term monitoring. In addition, the electrode structure design of the present invention makes it highly reusable. The selection of the stainless steel hollow catheter 110 ensures the corrosion resistance and oxidation resistance of the electrode, so that the electrode maintains good conductivity during multiple uses. The conductive coupling agent can be removed by a simple cleaning step after use, and the hollow catheter 110 and the silicone buffer head 130 can also be treated by disinfection and other methods for reuse. This design not only reduces resource waste, but also reduces the cost of use, which meets the needs of environmental protection. In general, the present invention provides an electrode device with a reasonable structure and excellent performance, which can provide efficient and stable signal conduction in EEG acquisition, while taking into account the user's wearing experience and the reusability of the electrode. In practical applications, the 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 methods and effectively improving the user's wearing comfort.

[0046] Embodiment 2: In this embodiment, the conductive coupling agent is a conductive paste; the hollow catheter 110 is made of medical grade 304 stainless steel; the airbag structure 120 is an automatically adjustable air bag, and the internal air pressure is regulated by a micro air valve 121 arranged on the air bag, and the air valve releases the pressure through an electrically controlled switch to automatically control the outflow of the conductive coupling agent. Specifically, the airbag structure 120 includes an air bag 122 and a micro air valve 121, and the micro air valve 121 is used to regulate the internal air pressure of the air bag 122 to control the outflow of the conductive coupling agent. The buffer head 130 is set as an eight-shaped claw structure; an airbag fixing seat 140 is connected between the airbag structure 120 and the hollow catheter 110, and the micro air valve 121 includes an electrically controlled switch, and an electrical contact is provided on the airbag fixing seat 140, and the electrically controlled switch is electrically connected to the electrical contact, and the electrically controlled 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 conductivity attenuation after repeated use. Since the stainless steel material itself has extremely high corrosion resistance and oxidation resistance, the electrode can maintain stable conductivity 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 in long-term use, which is crucial for the signal acquisition accuracy in long-term monitoring tasks. In order to achieve efficient adaptation with EEG signal acquisition, the design of the electrode not only takes into account conductivity, but also through precise structural regulation, the conductive coupling agent can contact the scalp in an optimal way, thereby improving signal conduction efficiency.

[0048] In order 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. This conductive medium plays a vital role in the electrode structure. In the design of the present invention, the conductive coupling agent is deliberately filled in the hollow part of the electrode and controlled by a special airbag system. This airbag system can not only accurately adjust the outflow of the conductive coupling agent, but also can be regulated at any time as needed during use, so that the conductive coupling agent can evenly penetrate the scalp surface, thereby effectively improving the stability and quality of the signal. The conductive coupling agent has a dual advantage in signal transmission. On the one hand, it reduces the contact resistance between the electrode and the scalp, ensuring that the signal can be transmitted more efficiently; on the other hand, it forms a continuous conductive path, reducing the signal fluctuation problem caused by the change of 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 comfort of wearing, but also provides flexible buffering in the process of the electrode contacting the scalp. Unlike hard materials, silicone has good flexibility and deformation adaptability, and can be evenly distributed under pressure, thereby alleviating the discomfort caused by long-term wearing of the electrode. The silicone buffer head 130 is also provided with a slit structure, which can guide the conductive coupling agent to flow from the hollow catheter 110 to the scalp surface and slowly release it in the slit to ensure that the conductive coupling agent covers the contact area. This slit release design not only avoids the problem of excessive outflow of the coupling agent, but also effectively ensures the signal conduction quality of the electrode when it contacts the skin. This design is crucial for the long-term stability and efficiency of EEG signal acquisition, especially in dynamic scenes, it can significantly reduce signal fluctuations, making the collected EEG data more reliable and accurate.

[0049] Example 3, reference Figure 4 , a method for efficiently collecting electroencephalogram signals using a bioelectric signal collecting electrode, the method comprising:

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

[0051] At the moment the electrode contacts the skin, the ions in the conductive coupling agent will penetrate into the microstructures on the skin surface, such as sweat glands, pores, and skin wrinkles. This diffusion process causes the ions to form a dynamic distribution layer on the skin surface, which in turn produces 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 has a direct impact on 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, thereby producing a sensitive response to small charge changes during signal acquisition. The capacitance effect in this process is the key to achieving efficient acquisition of EEG signals, because it enables the electrode to more sensitively capture weak bioelectric signals on the scalp surface. As the ions continue to diffuse to the skin surface, the interface capacitance will gradually increase, allowing the electrode to maintain a stable electrical response capability under small changes in contact area. Through the dynamic impedance model, the system can monitor and adjust the distribution of ions and changes in capacitance in real time, thereby 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 determine the distribution density of the conductive coupling agent by analyzing the change of interface impedance. As a key medium for signal conduction, the distribution density of the conductive coupling agent directly affects the signal conduction path and conductivity stability. In the process of electrode contacting the skin, the conductive coupling agent acts as a bridge between the electrode and the skin, allowing the electrical signal to be transmitted smoothly. However, due to the unevenness of the skin surface structure and the influence of factors such as hair and oil, the distribution density of the conductive coupling agent at different contact sites will vary, resulting in local impedance fluctuations. In this case, the dynamic impedance model can monitor these impedance fluctuations in real time and automatically correct them based on the density distribution of the conductive coupling agent to ensure the stability and accuracy of signal acquisition. This real-time adjustment mechanism allows the conductive coupling agent to optimize its distribution at any time according to the dynamic changes of the electrode-tissue interface, thereby maintaining the high efficiency of signal conduction under complex conditions. It is worth noting that the dynamic impedance model not only takes into account the distribution density of the conductive coupling agent, but also constructs a complete impedance change mapping mechanism through the combined effect of the dynamic characteristics of ion diffusion and the capacitance effect. The interaction between the conductivity change brought about by ion diffusion and the impedance regulation brought about by the capacitance effect provides the system with the ability to fully monitor changes in the electrode-tissue interface. This design is particularly important in long-term signal acquisition. Since EEG signal acquisition usually takes several hours or even days, slight fluctuations in interface impedance may affect the stability of the signal, and the traditional static impedance model cannot meet this requirement. By introducing a dynamic impedance model, the present invention can automatically adapt to interface impedance fluctuations caused by movement, changes in environmental humidity, etc., thereby maintaining high-quality signal output throughout the acquisition process. This design greatly enhances the adaptability of the system, allowing 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 density distribution 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. Through real-time evaluation of these parameters, 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 is to adaptively adjust the contact parameters of the electrode and the distribution density of the conductive coupling agent to optimize the signal acquisition path, thereby producing a clearer and more stable signal at the contact interface. Signal enhancement is not just a simple amplification of signal strength, but also 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. In the process of signal enhancement, the system will also optimize 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 interface between the electrode and the skin, reducing the signal fluctuation problem caused by poor contact or insufficient conductivity. This adaptive enhancement method is not only suitable for 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 collected signal meets expectations in terms of clarity and accuracy, so that the stable transmission of EEG signals can be maintained 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 signal quality, the system can identify external interference factors that may affect the signal, such as scalp oil, sweat, ambient temperature, etc., which often have an adverse effect on the stability of the signal 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, thus laying a solid 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, and can ensure the validity of data whether it is long-term static monitoring or short-term dynamic acquisition.

[0055] Step 3: Extract features of 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 completing feature extraction, the system enters the dynamic noise suppression phase. EEG signals are easily affected by various interference factors during the acquisition process, such as power supply noise, muscle activity and other external interferences, which will introduce different degrees of random noise into the signal, thereby affecting the accuracy and availability of the signal. Dynamic noise suppression can efficiently identify and suppress non-target signal components by real-time monitoring and processing of the extracted feature signals. Unlike traditional static noise suppression methods, dynamic noise suppression can be adaptively adjusted according to changes in the signal acquisition environment to ensure efficient noise suppression effects in 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 time when the noise occurs, so that the collected EEG signals maintain a high degree of clarity and stability. In order to achieve more effective dynamic noise suppression, the system will simultaneously analyze the time-frequency characteristics of the signal during the feature extraction process and establish a signal feature model based on frequency and time. This model can distinguish the different frequency components of noise and target signals, so as to accurately suppress the noise components through frequency band filtering and other methods. For example, power supply noise usually fluctuates within a specific frequency range, and interference such as muscle activity also has its frequency characteristics. By establishing a frequency discrimination model, the system can effectively filter out these non-target signals. In this process, the frequency components and amplitude characteristics of the signal are accurately captured and separated, so that the real EEG signal can be clearly presented from the noise, ensuring the high resolution of the signal. Another important aspect of the dynamic noise suppression process is its adaptive ability, which enables the system to automatically adjust the noise suppression parameters according to the changes in the environment of the collected signal. The collection environment of EEG signals may change due to changes in the user's movements, postures or external environment. Static noise suppression methods often appear 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 moves slightly, the impedance change of 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 foundation for the next step of signal reconstruction. This multi-level signal optimization method not only greatly improves the quality of EEG signal acquisition, but also enhances the robustness of the system, enabling it to obtain clear and accurate EEG signals in complex and changing environments. The combination of feature extraction and dynamic noise suppression enables the system to always maintain accurate signal recognition and interference suppression capabilities during the processing process, ultimately achieving efficient acquisition of EEG signals.

[0057] Step 4: Reconstruct the signal according to the noise suppression result to obtain the reconstructed signal and complete the EEG signal acquisition.

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

[0059]

[0060] Among them, Z interface (t, ω) is the interface impedance function, which represents the impedance of the electrode-tissue interface at time t and angular frequency ω; R ct is the charge transfer resistance, which represents the resistance to electron transfer between the electrode and the tissue; ω is the angular frequency, which represents the frequency of the AC signal; τ is the time constant, which is 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, which indicates the diffusion rate of the i-th ion on the interface; is the Laplace operator of ion concentration, which represents the concentration c of the i-th ion on the electrode surface i (x, t) is the distribution gradient in space; β is the ion diffusion influencing factor, which is used to adjust the influence of ion concentration gradient on impedance; C dl is the double layer capacitance, which indicates the capacitance value of the double layer formed at the electrode-tissue interface; j is the imaginary number symbol; N is the number of ion species; and x is the spatial position coordinate.

[0061] Specifically, the design of the formula introduces the charge transfer resistance R ct The ratio of the time constant τ This part reveals the rate and difficulty of charge transfer from the electrode to the tissue surface. Charge transfer resistance R c t is a key factor in the electrochemical reaction at the electrode-tissue interface. It represents the resistance to charge transfer at the interface between the electrode and the skin, and reflects the difficulty encountered by electrons when passing through the interface. The greater the resistance, the greater the restriction on the flow of electrons at the interface, which may lead to signal attenuation and decreased acquisition accuracy. Correspondingly, the time constant τ determines the accumulation and flow rate of charge at the interface. When the time constant is small, the charge can pass through the interface more quickly, which is crucial for rapidly changing high-frequency EEG signals; at a larger τ value, the charge transfer speed slows down, which is more suitable for the acquisition of low-frequency stable signals, making the electrode widely adaptable to signals of different frequencies. 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 in different signal frequency ranges. Next, the influence of ion diffusion is measured through The expression further enriches the dynamic characteristics of the interface impedance. idescribes the diffusion rate of different ions on the interface, while the Laplace operator It characterizes the spatial distribution gradient of ion concentration. Ion diffusion is crucial for 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, which leads to differences in local impedance. This spatial distribution characteristic of ion diffusion enables the formula to dynamically capture the behavior of different ions at the interface and ensure the consistency and stability of signal acquisition under multi-ion conditions. By adjusting the β parameter, the formula can also control the weight of the influence 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 need to maintain uniform conductivity for a long time. Double layer capacitance C dl The addition of adds a capacitance effect to the formula, which is inversely proportional to the frequency component jω, reflecting the support of the double-layer capacitor for signal conduction under low-frequency conditions. The double-layer capacitor is a thin-layer capacitor structure on the interface, which is formed by the charge separation between the ions adsorbed on the electrode surface and the skin. This capacitance effect enables the interface to increase charge retention in low-frequency signal acquisition, thereby enhancing the stability of the signal. In the actual acquisition of EEG signals, low-frequency signals are usually susceptible to interference from impedance changes due to their low frequency, but the existence of double-layer capacitors provides additional support for signal conduction, allowing the signal to maintain a certain clarity at low frequencies. At the same time, in the formula The term further reflects the impact of time changes on the concentrations of different ions. By incorporating the temporal changes in the concentrations of multiple ions into the model, it ensures that the fluctuations in impedance can still be accurately reflected in dynamic environments (such as user movement, ambient temperature changes, etc.).

[0062] Embodiment 5: The density distribution of the conductive coupling agent is calculated based on the impedance function by the following formula:

[0063]

[0064] Wherein, ρ(x, t) is the density distribution of the conductive coupling agent, which represents 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 conductive coupling agent; |Z interface (t, ω)| is the modulus 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 dielectric constant; ∈ r is the relative dielectric constant; E(x, t) represents the local electric field intensity at the spatial position coordinate x and time t; is the Laplace operator.

[0065] Specifically, the core part of the formula consists of two modules: the first is the impedance feedback control mechanism, which The density of the conductive coupling agent is adjusted. interface (t, ω)| represents the modulus of the interface impedance function, which is used to monitor the contact between the electrode and the skin interface in real time, while Z0 is a standard impedance value, which is used to normalize the impedance modulus. The introduction of this part of the impedance control mechanism is intended 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; when the impedance is small, the distribution of the conductive coupling agent will be appropriately increased to ensure stable signal transmission. This design optimizes the distribution of the conductive coupling agent through impedance feedback, which helps to reduce 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 and electric field influence of the conductive coupling agent, and uses the Laplace operator Acting on the density change rate and spatial diffusion, the distribution regulation of the conductive coupling agent is further refined. The density change of the conductive coupling agent at the previous moment is captured to ensure the continuity and stability of the density distribution in time and avoid signal fluctuations caused by mutations. The diffusion behavior of the conductive coupling agent is determined by the diffusion coefficient D g and the Laplacian operator Description. Diffusion coefficient D g Control the diffusion rate of the conductive coupling agent in space, so as to dynamically balance the density of the conductive coupling agent in different areas. The diffusion of the conductive coupling agent not only helps to form a uniform distribution on the electrode surface and reduce the local impedance fluctuation on the contact interface, but also ensures the stability of signal acquisition during long-term monitoring. g The conductive coupling agent diffuses quickly to accommodate a wide signal collection area, while a smaller diffusion coefficient helps to concentrate the distribution of the conductive coupling agent to achieve high efficiency of signal conduction in a specific area. It is described by reflecting the regulating effect of the local electric field on the density of the conductive coupling agent. Here we introduce the vacuum dielectric constant ∈ 0 and the relative dielectric constant ∈ r Taking into account the electrical characteristics of the interface environment, the square electric field E 2(x, t) reflects the nonlinear 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 areas. 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 areas with strong local electric fields, thereby achieving signal enhancement in the key area where the electrode contacts the skin. Laplace operator The combined 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 can also be uniformly adjusted in space to form a dynamic and stable density distribution. Through the comprehensive regulation of the above items, the impedance feedback, diffusion coefficient and electric field effect in the formula together constitute the density distribution model of the conductive coupling agent, so that the conductive coupling agent at the electrode-skin contact interface can 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] Embodiment 6: In step 2, the signal quality index is calculated according to the impedance function and the density distribution of the conductive coupling agent by the following formula:

[0067]

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

[0069] Specifically, the first part of the signal quality indicator formula is It is used to measure the influence of electrode-skin interface impedance. Here, the interface impedance function modulus |Z interface (t, ω)| reflects the impedance of the contact area between the electrode and the skin. Higher impedance usually indicates poor contact quality between the electrode and the skin, which will lead to obstruction of the signal transmission path, affecting the accuracy of the collected signal and the transmission quality. Therefore, this term is expressed in the form of a negative exponential. When the impedance modulus is high, the signal quality index Q(t) will drop 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, thereby improving the adaptability and feedback capabilities 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 in 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 in the volume V, the overall distribution intensity of the conductive coupling agent in the entire electrode contact area can be obtained. If the conductive coupling agent is unevenly distributed on the electrode surface or the density is insufficient, it will lead to increased contact resistance and discontinuous signal transmission, thereby reducing the clarity of the collected signal. A higher density integral value indicates that the conductive coupling agent is fully 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 use of the conductive coupling agent, ensure that the signal conduction path has stable conductivity, and reduce signal fluctuations and interference.

[0070] The third part of the formula This further improves the signal quality index’s ability to capture signal features. First, the second-order time derivative of the original EEG signal S(t) It is the acceleration of the rate of change of the signal in time, reflecting the instantaneous fluctuation of the signal. EEG signals usually contain high-frequency and low-frequency components, and show dynamic fluctuations in 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. The introduction of the second-order derivative in the formula allows the signal quality index to not only focus on the static quality of the signal, but also respond in time when the signal changes drastically, thereby avoiding signal distortion or mutation problems that may occur during the acquisition process, and ensuring the continuity and stability of the signal. In addition, the smoothing factor λ is combined with the Laplace operator Further adjust the spatial smoothness of the signal. The Laplace operator is used to calculate the rate of change of the signal in space, reflecting the degree of fluctuation of the signal at different positions. By introducing the Laplace operator, the system can monitor the consistency of the signal in space, especially when local fluctuations occur in the contact area between the electrode and the skin, and can reduce the interference caused by local imbalance through spatial smoothing, thereby further improving the clarity of the signal. The addition of the smoothing factor λ enables the system to adjust its sensitivity to spatial changes, reduce the errors caused by the unevenness of the contact interface, and ensure that the collected EEG signals are smoother and smoother. This smoothing process is particularly critical in practical applications, and can effectively deal with interference caused by factors such as slight movements of the user's head or changes in the environment, so that the signal maintains high-quality transmission.

[0071] Embodiment 7: In step 2, the signal is enhanced according to the signal quality index by the following formula to obtain an enhanced signal:

[0072]

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

[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 adaptive signal enhancement. The signal quality index Q(t) is obtained from the previous step and represents the overall quality of the current acquisition signal. It evaluates the signal transmission quality through impedance feedback, conductive coupling agent density and dynamic adjustment of signal characteristics. A higher Q(t) value indicates that the contact and conduction conditions of signal transmission are better, and the signal integrity and accuracy are higher. 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 invalid or excessive signal enhancement under low-quality acquisition conditions. This method ensures the intelligence of the enhancement process, so that the degree of signal amplification accurately matches the actual acquisition situation, and can achieve natural enhancement when the acquisition quality is high, and automatically reduce 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 The time integral of the impedance gradient and the density distribution of the conductive coupling agent is introduced to further adjust the signal enhancement effect. Here, It represents the squared gradient of the interface impedance under the time variable v, reflecting the rate and amplitude of change of the impedance at the electrode-skin contact interface. The larger the impedance gradient, the more significant impedance fluctuations there are at the electrode-skin contact interface during this time period, which may be caused by external interference or contact instability. In the signal enhancement process, the time integral of the squared impedance gradient is introduced to suppress these fluctuations, so that the enhanced signal remains stable in a drastically changing contact environment, 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 in long-term acquisition, and is especially suitable for complex acquisition scenarios of EEG signals.

[0075] The introduction of the conductive coupling agent density ω(x, v) plays an important regulatory role in the signal enhancement process. The conductive coupling agent is a key medium for signal conduction. The conductive path it forms at the interface between the electrode and the skin directly affects the signal transmission efficiency. By incorporating the density distribution into the integral term, the system can further optimize the enhancement effect based on the distribution of the conductive coupling agent at the current 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 fully distributed, the signal enhancement effect can be greater, ensuring that the signal enhancement is maximized under good conduction conditions and optimizing the signal transmission quality and acquisition accuracy. The time integral term in this formula The design provides a historical feedback mechanism for signal enhancement, so that the signal enhancement effect not only considers the current acquisition conditions, but also reflects the cumulative effect 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 the signal under different acquisition conditions. Time integration also plays a role in smoothing adjustment, avoiding sudden enhancement caused by instantaneous changes when enhancing the signal, and maintaining the continuity of the signal output. Finally, the enhanced signal S enhanced (t) is the combined effect of the original signal S(t), the signal quality index Q(t), the impedance gradient, and the time integral result of the conductive coupling agent density. The enhanced signal not only retains the key information of the original EEG signal, but also optimizes the signal through an intelligent enhancement mechanism. The product of the signal quality index in the formula ensures the adaptability of the enhancement effect, the suppression of the interface fluctuation by the square term of the impedance gradient improves the stability of the signal, and the introduction of the conductive coupling agent density effectively prevents the distortion caused by insufficient conductivity.

[0076] Embodiment 8: In step 3, the enhanced signal is feature extracted by the following formula to obtain a feature signal:

[0077]

[0078] in, is the Hilbert transform operator; F(t) represents the characteristic signal at time t.

[0079] Specifically, the first term of the feature extraction formula, that is, the gradient of the signal quality index It reflects the rate of change of signal quality at different time points, thereby capturing subtle changes in the contact between the electrode and the skin. 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 fluctuations in signal quality. This fluctuation is often closely related to the stability of the electrode-skin contact. During long-term use, the electrode will inevitably cause uneven contact due to movement, sweating or slight displacement. 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, so that the system can still capture the true information of the signal when the contact conditions fluctuate. This design ensures the sensitivity of the feature signal when the contact state changes, so that even in non-ideal contact conditions, the system can extract highly representative signal features. The second part of the formula, The stability and smoothness of feature extraction are further enhanced. In this case, the density gradient of the conductive coupling agent and the modulus of interface impedance |Z interface (t, ω)| are combined together and evaluated over the entire contact area by volume integration. The gradient of the conductive coupling agent density describes the uniformity of the conductive coupling agent distribution on the interface. If the density gradient is large, it means that the conductive coupling agent is unevenly distributed, which may lead to local signal conduction obstruction or instability. The addition of the interface impedance modulus as a balancing factor ensures that the suppression of uneven density distribution is stronger when the impedance is high. This design forms an automatic smoothing mechanism in a complex electrode contact environment by balancing the gradient of the conductive coupling agent distribution and the interface impedance modulus, so that the characteristic signal extraction can be smoother and the fluctuations caused by the uneven distribution of the conductive coupling agent are reduced. The exponential form of this part also ensures the smoothness of the signal extraction and prevents the instability introduced by local density fluctuations, thereby further improving the clarity and stability of the characteristic signal. The last part of the formula is an enhancement of the signal S enhanced(t) Perform Hilbert transform H{·} to obtain the instantaneous phase and frequency information of the enhanced signal. Hilbert transform is a commonly used tool in signal processing. By converting real signals into complex forms, the system can extract instantaneous phase and frequency from them. This information is particularly important in EEG signals because they reflect the periodic and rhythmic characteristics of EEG activity. EEG signals often contain multiple frequency components, and different EEG frequency bands correspond to different brain activity states. Hilbert transform can help the system accurately locate these frequency components on the time-frequency plane, so that the characteristic signal can better characterize 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, thereby 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 perceive the strength of the signal, but also perform detailed analysis of the spectral distribution of the signal, helping to identify important patterns and laws in EEG activity.

[0080] Embodiment 9: In step 3, dynamic noise suppression is performed according to the characteristic signal by the following formula to obtain a noise suppression result:

[0081]

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

[0083] Specifically, the first part of the formula The square of the signal quality index Q(t) and the Gaussian noise variance σ 2 Here, the signal quality index Q(t) provides an overall quality assessment of the current signal, while σ represents the standard deviation of the noise. The square of the noise is used to obtain the variance of the noise, which is used to describe the strength of the noise in the signal. When Q(t) is high, it means that the signal acquisition quality 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 increases, thereby enhancing the noise suppression effect. This adaptability enables the system to automatically adjust the strength of noise suppression, ensuring that the suppression effect is improved under low signal quality conditions, while retaining more signal details under high signal quality conditions, avoiding signal loss caused by excessive suppression. The second part of the formula This further enhances the dynamics of noise suppression. enhanced(t) is derivatized with the maximum value of the interface impedance in time, in order 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. interface The maximum value of (t, ω)| and the standard impedance Z0 as the denominator helps to stabilize the signal amplification ratio during noise suppression. The change of interface impedance reflects the fluctuation of the contact conditions between the electrode and the skin. Therefore, in the process of noise suppression, if the impedance is high, it means that the electrode is not in good contact with the skin and the signal is easily disturbed, while when the impedance is low, the signal transmission quality is better. By normalizing the enhanced signal and combining it with the dynamic change of the interface impedance, the formula achieves fine control of noise suppression. Combining these parts together, the noise suppression formula can dynamically adapt to different acquisition conditions based on the characteristic signal F(t) through the dual feedback of quality indicators and interface impedance to ensure 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 and avoiding the loss of important information in the signal during the suppression process. By multiplying the characteristic signal with other control items, the system can filter out the noise components with higher frequency and lower amplitude while maintaining the main characteristics of the signal, thus achieving effective distinction between signal and noise. In addition, the design of the dynamic noise suppression formula also ensures special attention to Gaussian noise. The Gaussian distribution characteristics of noise are reflected in the formula through the adjustment of variance σ, which enables the system to be adaptively adjusted 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, equipment noise, etc., and Gaussian noise is typical among these interferences. By using the parameters of Gaussian noise variance, the system can more accurately control the intensity of noise suppression, effectively reduce the impact of background noise on the signal, and thus enhance the signal-to-noise ratio of the signal.

[0084] Embodiment 10: In step 4, the signal is reconstructed according to the noise suppression result by the following formula to obtain a reconstructed signal:

[0085]

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

[0087] Specifically, the first part of the formula The noise suppression result S denoised(t) is multiplied by the signal quality indicator Q(t) in order to balance the amplification effect of the signal through the normalization adjustment of the signal quality indicator Q(t). Here, Q(t) is a dynamic signal quality evaluation parameter that reflects the quality of the acquired signal at a specific time t, and max(Q(t)) is its maximum value, which is used to relatively normalize 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 to make the reconstructed signal clearer; when the signal quality is low, the amplification factor decreases to prevent distortion caused by over-amplification under poor contact or high noise conditions. This adaptive amplification design ensures the flexibility of the reconstructed signal under different acquisition conditions, allowing the system to automatically adjust the amplification factor according to the current signal quality to ensure the smoothness and consistency of the signal. The second part of the formula The conductive coupling agent density gradient, characteristic signal F(t) and enhanced signal S are further introduced. enhanced (t) to achieve fine adjustment of signal reconstruction. Density gradient of conductive coupling agent It reflects the uniformity of the distribution of the conductive coupling agent on the electrode-skin interface and ensures the stability of the conductive path. By incorporating the density gradient into the volume integral and combining 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 the signal fluctuation caused by the uneven local density distribution. This design plays a balancing role in the long-term monitoring of EEG 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 are introduced into the formula. enhanced (t)’s L2 norm (i.e., second norm) ||F(t)||2 and ||S enhanced (t)||2. The L2 norm operation reflects the overall energy intensity of the signal by calculating the Euclidean length of the signal. In the reconstruction process, by adjusting the ratio of the L2 norms of the two signal features, it can ensure that the energy of the reconstructed signal is concentrated in the effective frequency band, thereby suppressing unnecessary noise components. The L2 norm of the characteristic signal ||F(t)||2 reflects the key information strength in the signal feature, combined with the L2 norm of the enhanced signal ||S enhanced (t)||2, which can dynamically control the balance of signals in different frequency bands and intensities, so that the final output reconstructed signal retains important characteristic information while removing noise and invalid components.

[0088] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, but the scope of the present disclosure is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present disclosure shall be within the scope of the present disclosure.

Claims

1. A bioelectric signal collection electrode, characterized in that: include: A conductive hollow catheter, an airbag structure and a flexible buffer head, wherein the buffer head and the airbag structure are separately arranged at two 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 collection electrode can be screwed into or out of an adapted electrical signal processing device when in use; the hollow structure of the hollow catheter is suitable for storing a conductive coupling agent, and the conductive coupling agent flows out from the gap structure of the buffer head by squeezing the airbag structure.

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

3. The bioelectric signal acquisition electrode according to claim 1, characterized in that: The airbag structure is configured as an automatically adjustable airbag, and the airbag structure includes an airbag and a micro air valve, wherein the micro air valve is used to adjust the internal air pressure of the airbag to achieve control of the outflow of the conductive coupling agent.

4. The bioelectric signal acquisition electrode according to claim 3, characterized in that: The buffer head is configured as an eight-shaped claw-shaped structure; an airbag fixing seat is connected between the airbag structure and the hollow catheter, the micro air valve includes an electrically controlled switch, an electrical contact is provided on the airbag fixing seat, the electrically controlled switch is electrically connected to the electrical contact, and the electrically controlled switch is used to automatically control the outflow of the conductive coupling agent.

5. A method for efficiently collecting electroencephalogram signals using the bioelectric signal collection electrode according to any one of claims 1 to 4, characterized in that: The method comprises: Step 1: Based on ion diffusion and capacitance effects, the electrode-tissue interface dynamic impedance modeling is performed to obtain the interface impedance function; based on the impedance function, the density distribution of the conductive coupling agent is calculated; Step 2: Calculate the signal quality index according to the impedance function and the density distribution of the conductive coupling agent; perform signal enhancement according to the signal quality index to obtain an enhanced signal; Step 3: Extract features of the enhanced signal to obtain a feature signal; perform dynamic noise suppression based on the feature signal to obtain a noise suppression result; Step 4: Reconstruct the signal according to the noise suppression result to obtain the reconstructed signal and complete the EEG signal acquisition.

6. The method for efficiently collecting EEG signals according to claim 5, characterized in that: In step 1, the dynamic impedance modeling of the electrode-tissue interface is performed based on ion diffusion and capacitance effects, and the interface impedance function is obtained by the following formula: Among them, Z interface (t, ω) is the interface impedance function, which represents the impedance of the electrode-tissue interface at time t and angular frequency ω; R ct is the charge transfer resistance, which represents the resistance to electron transfer between the electrode and the tissue; ω is the angular frequency, which represents the frequency of the AC signal; τ is the time constant, which is 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, which indicates the diffusion rate of the i-th ion on the interface; is the Laplace operator of ion concentration, which represents the concentration c of the i-th ion on the electrode surface i (x, t) is the distribution gradient in space; β is the ion diffusion influencing factor, which is used to adjust the influence of ion concentration gradient on impedance; C dl is the double layer capacitance, which indicates the capacitance value of the double layer formed at the electrode-tissue interface; j is the imaginary number symbol; N is the number of ion species; and x is the spatial position coordinate.

7. The method for efficiently collecting EEG signals according to claim 6, characterized in that: The conductive coupling agent density distribution is calculated based on the impedance function using the following formula: Wherein, ρ(x, t) is the density distribution of the conductive coupling agent, which represents 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 conductive coupling agent; |Z interface (t, ω)| is the modulus of the interface impedance function; Z0 is the standard impedance value; D g is the diffusion coefficient of the conductive coupling agent; ∈0 is the vacuum dielectric constant; ∈ r is the relative dielectric constant; E(x, t) represents the local electric field intensity at the spatial position coordinate x and time t; is the Laplace operator.

8. The method for efficiently collecting EEG signals according to claim 7, characterized in that: In step 2, the signal quality index is calculated based on the impedance function and the density distribution of the conductive coupling agent using the following formula: Among them, Q(t) represents the signal quality index at time t; V is the electrode coverage volume; S(t) is the original EEG signal at time t; λ is the smoothing factor.

9. The method for efficiently collecting EEG signals according to claim 8, characterized in that: In step 2, the signal is enhanced according to the signal quality index by the following formula to obtain an enhanced signal: Among them, S enhanced (t) represents the enhanced signal at time t; v is the time-integrated variable; ρ(x, v) is the representation of ρ(x, t) under the time-integrated variable; Z interface (v, ω) is Z interface Representation of (t, ω) in terms of time-integrated variables.

10. The method for efficiently collecting EEG signals according to claim 9, characterized in that: In step 3, the enhanced signal is feature extracted using the following formula to obtain a feature signal: in, is the Hilbert transform operator; F(t) represents the characteristic signal at time t; In step 3, dynamic noise suppression is performed according to the characteristic signal using the following formula to obtain the noise suppression result: Where σ is the Gaussian noise variance; max(|Z interface (t, ω)|, Z0) means taking |Z interface The maximum value between (t, ω)| and Z0; S denoised (t) is the noise suppression result at time t; In step 4, the signal is reconstructed according to the noise suppression result by the following formula to obtain the reconstructed signal: Among them, S final (t) is the reconstructed signal at time t; ∥·∥2 is the L2 norm.

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