A neural detection method

Abstract

This invention discloses a neural detection method, relating to the field of bioelectrical signal detection technology. The neural detection method includes the following steps: acquiring a user's baseline health database; detecting the user's current skin conductance response signal within a preset time period and generating a current skin conductance response signal curve; obtaining the user's current physiological state based on the current skin conductance response signal curve; matching a baseline neural electrical signal curve corresponding to the current physiological state from the baseline health database; detecting the user's current neural electrical signal within a preset time period and generating a current neural electrical signal curve; comparing the current neural electrical signal curve with the baseline neural electrical signal curve to obtain the user's current neural state. This invention combines the user's current skin conductance response signal curve to determine the user's current physiological state and matches the corresponding baseline neural electrical signal curve based on the current physiological state, thereby improving the accuracy of the neural detection method in judging the user's detection results.

Description

Technical Field

[0001] This invention relates to the field of bioelectric signal detection technology, and in particular to a neural detection method. Background Technology

[0002] Neuroelectric signal detection can be applied to daily health monitoring for ordinary people, training and reaction assessment tests for athletes, fatigue assessment for equipment operators, and identification and intervention training for Alzheimer's disease and cognitive status in the elderly. Furthermore, it can lead to new testing items and functions beyond traditional tests, such as reaction tests, attention tests, and depression detection.

[0003] However, existing neural electrical signal detection technologies typically only detect one type of signal, neural electrical signal, and then make judgments based on the neural electrical signal. However, when performing neural electrical signal detection, the user's physiological state has a great influence on the neural electrical signal, resulting in large errors in the detection results under different physiological states, thus affecting the accuracy of the judgment of the user's detection results. Summary of the Invention

[0004] The main objective of this invention is to propose a neural detection method that aims to solve the problem that the detection results of neural electrical signals under different physiological states in the prior art have large errors, which affects the accuracy of the user's judgment of the detection results.

[0005] To achieve the above objectives, this invention proposes a neural detection method, comprising the following steps:

[0006] Obtain the user's baseline health database, wherein the baseline health database includes baseline neural electrical signal curves of the user under different physiological states;

[0007] The system detects the user's current skin conductance response signal within a preset time period and generates a current skin conductance response signal curve. The user's current physiological state is then obtained based on the current skin conductance response signal curve.

[0008] The baseline neural electrical signal curve corresponding to the current physiological state is matched from the baseline health database;

[0009] Detect the current neural electrical signal within a preset time period and generate the current neural electrical signal curve;

[0010] The current neural electrical signal curve is compared with the reference neural electrical signal curve to obtain the user's current neural state.

[0011] In one embodiment, the step of obtaining a user's baseline health database includes:

[0012] The system detects skin conductance response signals and nerve electrical signals within a preset time period corresponding to different times, environmental conditions, and physiological states, and generates a baseline skin conductance response signal curve.

[0013] A physiological state dataset is obtained based on the correspondence between the baseline skin conductance signal curve, the time, the environmental state, and the physiological state.

[0014] A baseline neural electrical signal curve is generated using the neural electrical signal, and a neural electrical data set is obtained based on the correspondence between the baseline neural electrical signal curve and the physiological state.

[0015] The baseline health database is obtained using the physiological state dataset and the neural electrophysiology dataset.

[0016] In one embodiment, the step of detecting skin conductance signals and nerve conductance signals corresponding to different times, environmental conditions, and physiological states further includes:

[0017] Based on the user's gender, age, height, and weight data, obtain the corresponding standard neural electrical signal curves;

[0018] The steps for generating a reference neural electrical signal curve from the neural electrical signal include:

[0019] The standard neural electrical signal curve is modified based on the neural electrical signal to generate the reference neural electrical signal curve.

[0020] In one embodiment, the step of detecting the user's current skin conductance response signal within a preset time period and generating a current skin signal curve, and obtaining the user's current physiological state based on the current skin conductance response signal curve includes:

[0021] The system detects the user's current skin conductance response signal based on the current time, current environmental conditions, and a preset time period, and generates the current skin conductance response signal curve.

[0022] Multiple physiological states corresponding to the current skin conductance signal curve are matched from the physiological state dataset;

[0023] Based on the current time and the current environmental state, a corresponding physiological state is selected and used as the current physiological state.

[0024] In one embodiment, the step of matching the benchmark neural electrical signal curve corresponding to the current physiological state from the benchmark health database includes:

[0025] The baseline neural electrical signal curve corresponding to the current physiological state is matched from the neural electrical data set.

[0026] In one embodiment, the step of matching multiple physiological states corresponding to the current skin conductance response signal curve from the physiological state dataset includes:

[0027] Based on the instantaneous fluctuations and rising / falling trends of the current skin conductance response signal curve, multiple reference skin conductance response signal curves are matched with the current skin conductance response signal curve to obtain multiple physiological states corresponding to the multiple reference skin conductance response signal curves.

[0028] In one embodiment, the neural detection method is implemented through a neural detection module, which includes:

[0029] case;

[0030] A detection electrode assembly is disposed on one side of the housing, and the detection electrode assembly includes a skin conductance electrode and a nerve conductance electrode.

[0031] The steps of detecting the user's current electrodermal response signal within a preset time and generating the current electrodermal response signal curve include:

[0032] The neural detection module is attached to the user's skin;

[0033] The skin conduction electrode is used to acquire bioelectrical signals on the user's skin within a preset time period, wherein the bioelectrical signals include skin conduction signals and nerve signals.

[0034] The current skin electrical response signal is selected by filtering the frequency and potential amplitude of the bioelectric signal;

[0035] The current skin conductance response signal curve is generated by using multiple current skin conductance response signals within a preset time period.

[0036] In one embodiment, the neural detection module further includes a circuit board, the detection electrode group is electrically connected to the circuit board, and a temperature sensor is also electrically connected to the circuit board, the temperature sensor being located on the side of the circuit board away from the detection electrode group;

[0037] The steps for detecting the current environmental status include:

[0038] The ambient temperature is obtained through the temperature sensor to obtain the current environmental state.

[0039] In one embodiment, the step of generating a current neural electrical signal curve by detecting the current neural electrical signal within a preset time period includes:

[0040] The bioelectrical signals on the user's skin are acquired within a preset time period using the neural electrical signal electrodes;

[0041] The current neural electrical signal is selected by filtering the frequency and potential amplitude of the bioelectrical signal;

[0042] The current neural electrical signal curve is generated by using multiple current neural electrical signals within a preset time period.

[0043] In one embodiment, the step of filtering the skin conductance signal based on the frequency and potential amplitude of the bioelectric signal includes:

[0044] The electrical signals with a potential amplitude of 0.1mV~10mV and a frequency of 0.01Hz~0.5Hz from the bioelectric signals are selected as the skin conductance response signals;

[0045] The step of filtering out the skin conductance response signal based on the frequency and potential amplitude of the bioelectric signal includes:

[0046] The electrical signals with a potential amplitude of 50mV~100mV and a frequency of 10Hz~1000Hz from the bioelectrical signals are selected as the neural electrical signals.

[0047] In the technical solution of this invention, a benchmark health database is created, containing benchmark neural electrical signal curves corresponding to different physiological states. The activity of sweat glands is determined by the autonomic nervous system, which comprises two main subunits: the parasympathetic nervous system and the sympathetic nervous system. The sweat glands of the skin are entirely controlled by the sympathetic nervous system, making them a good indicator of internal tension and pressure. Therefore, the user's physiological state can be obtained through the skin conductance response signal curve. Then, by matching the physiological state with the corresponding benchmark neural electrical signal curve and comparing the detection result with the benchmark neural electrical signal curve under the current physiological state, detection biases caused by different physiological states can be more accurately eliminated, thus accurately determining the user's current neural state even under different physiological conditions.

[0048] Therefore, this invention can not only realize the real-time detection of the user's neural electrical signals, but also determine the user's current physiological state by combining the user's current skin conductance response signal curve, and match the corresponding benchmark neural electrical signal curve based on the current physiological state, so as to ensure that the comparison basis of the neural detection method is targeted and consistent, thereby improving the accuracy and reliability of the neural detection method in judging the user's detection results. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a neural detection method provided in an embodiment of the present invention;

[0051] Figure 2 A detailed flowchart of step S100 of a neural detection method provided in an embodiment of the present invention;

[0052] Figure 3 A detailed flowchart of step S200 of the neural detection method provided in an embodiment of the present invention;

[0053] Figure 4 A detailed flowchart of step S400 of a neural detection method provided in an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the structure of a neural detection module provided in an embodiment of the present invention.

[0055] Explanation of icon numbers:

[0056] 100. Nerve detection module; 1. Detection electrode group; 11. Nerve electrical signal electrode; 12. Skin conductance electrode; 2. Circuit board; 3. Temperature sensor.

[0057] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0060] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0061] Existing neural electrical signal detection technologies typically detect only one type of signal, the neural electrical signal, and then make judgments based on it. However, the user's physiological state has a significant impact on the neural electrical signal during detection, resulting in large errors in the detection results under different physiological states, which in turn affects the accuracy of the judgment of the user's detection results.

[0062] To address the above problems, this invention proposes a neural detection method.

[0063] Please see Figure 1 The neural detection method of this embodiment includes the following steps:

[0064] S100: Obtain the user's baseline health database, wherein the baseline health database includes baseline neural electrical signal curves of the user under different physiological states;

[0065] Specifically, multiple sampling tests can be performed on users to collect neural electrical signal data under various physiological states such as resting, exercise, tension, and relaxation. Based on the collected neural electrical signal data, corresponding benchmark neural electrical signal curves can be generated. These curves can be classified and stored according to physiological states to form a user-individualized benchmark health database.

[0066] S200: Detect the current skin conductance response signal within a preset time period and generate a current skin conductance response signal curve, and obtain the user's current physiological state based on the current skin conductance response signal curve;

[0067] The skin conductance signal is caused by the regulation of sweat gland activity by the sympathetic nervous system. It can reflect the activity of the autonomic nervous system of users under different emotional and physiological states. By collecting the skin conductance signal of users in real time within a preset time, the current skin conductance signal curve of users is formed, and the current physiological state of users can be obtained through the curve.

[0068] S300: Match the baseline neural electrical signal curve corresponding to the current physiological state from the baseline health database;

[0069] After identifying the user's current physiological state, the benchmark neural electrical signal curve corresponding to that physiological state is retrieved from the benchmark health database and used as the reference object for subsequent comparisons.

[0070] S400: Detects the current neural electrical signal within a user-preset time and generates the current neural electrical signal curve;

[0071] S500: Compare the current neural electrical signal curve with the reference neural electrical signal curve to obtain the user's current neural state.

[0072] The current neural electrical signal curve is compared point by point with the benchmark neural electrical signal curve corresponding to the user's current physiological state selected in the benchmark health database, and the overall morphological analysis is performed. The user's current neural state is determined by difference calculation and matching algorithm, such as whether it is in abnormal fluctuation or whether there are signal characteristics that deviate from the normal range.

[0073] In the technical solution of this invention, a benchmark health database is created, containing benchmark neural electrical signal curves corresponding to different physiological states. The activity of sweat glands is determined by the autonomic nervous system, which comprises two main subunits: the parasympathetic nervous system and the sympathetic nervous system. The sweat glands of the skin are entirely controlled by the sympathetic nervous system, making them a good indicator of internal tension and pressure. Therefore, the user's physiological state can be obtained through the skin conductance response signal curve. Then, by matching the physiological state with the corresponding benchmark neural electrical signal curve and comparing the detection result with the benchmark neural electrical signal curve under the current physiological state, detection biases caused by different physiological states can be more accurately eliminated, thus accurately determining the user's current neural state even under different physiological conditions.

[0074] Therefore, this invention can not only realize real-time detection of user's neural electrical signals, but also determine the user's current physiological state by combining the user's current skin conductance response signal curve, and match the corresponding benchmark neural electrical signal curve based on the current physiological state, so as to ensure that the comparison basis of the neural detection method is targeted and consistent, thereby improving the accuracy and reliability of the neural detection method in judging the user's detection results.

[0075] Please see Figure 2 In one embodiment, step S100 includes:

[0076] S110: Detects skin conductance response signals and nerve electrical signals within a preset time period corresponding to different times, different environmental conditions and different physiological states, and generates a baseline skin conductance response signal curve;

[0077] It should be noted that, considering that the skin conductance response signal is closely related to environmental conditions (such as ambient temperature and humidity), detection time (such as when you first wake up in the morning, when you are most alert at noon, and when you are tired at night), and physiological state (such as stress, anxiety, drowsiness, and focus), it is necessary to detect skin conductance response signals and nerve electrical signals corresponding to different times, different environmental conditions, and different physiological states.

[0078] S120: Obtain a physiological state dataset based on the correspondence between the baseline skin conductance response signal curve, the time, the environmental state, and the physiological state;

[0079] The system performs multidimensional annotation on the collected baseline skin conductance response signal curves, establishes a correlation between them and the corresponding time, environmental conditions and physiological conditions, and stores them in the form of a dataset to form a complete physiological state dataset. This allows for accurate determination of the user's current physiological state during subsequent testing based on time, environmental conditions and skin conductance response signal curves.

[0080] S130: Generate a reference neural electrical signal curve using the neural electrical signal, and obtain a neural electrical data set based on the correspondence between the reference neural electrical signal curve and the physiological state;

[0081] By binding baseline neural electrical signal curves to corresponding physiological states, a neural electrical dataset is formed. It is ensured that each baseline neural electrical signal curve has a clear label corresponding to a physiological state.

[0082] S140: Obtain the baseline health database using the physiological state dataset and the neural electrophysiology dataset.

[0083] By matching the physiological state dataset and the neural electrical dataset twice, the physiological state matched by the skin conductance response signal curve can be accurately matched with the corresponding neural electrical signal curve, thereby improving the accuracy of the judgment of the user's neural detection results.

[0084] In one embodiment, the method further includes the following steps before step S110:

[0085] S109: Obtain the corresponding standard neural electrical signal curve based on the user's gender data, age data, height data, and weight data;

[0086] By using a health database or a large sample population model, and combining basic physiological parameters such as users' gender, age, height, and weight data, a standard neural electrical signal curve that is closest to the characteristics of the user group is obtained. This curve serves as the initial reference curve for generating personalized baseline data, thereby improving the accuracy and adaptability of subsequent baseline neural electrical signal curve generation.

[0087] The steps for generating a reference neural electrical signal curve from the neural electrical signal include:

[0088] S131: Correct the standard neural electrical signal curve based on the neural electrical signal to generate the reference neural electrical signal curve.

[0089] Using the user's actual collected neural electrical signals as the basis for correction, the standard neural electrical signal curve obtained in step S109 is corrected to better reflect the user's individual characteristics, thus ultimately generating a user-specific baseline neural electrical signal curve. This allows for the gradual personalization of the group-based standard neural electrical signal curve, ultimately resulting in a baseline neural electrical signal curve that accurately matches the user's own characteristics, improving the individualization and accuracy of the detection. Compared to directly generating the baseline neural electrical signal curve from neural electrical signals, the correction method based on the standard neural electrical signal curve can fully utilize the statistical advantages of the standard neural electrical signal curve obtained based on the user's gender, age, height, and weight data. This ensures that the initial curve has a more reasonable overall shape and reference range, avoiding the instability of the baseline neural electrical signal curve caused by accidental deviations in single or small amounts of neural electrical signal data collection. It also shortens the establishment cycle of the baseline health database.

[0090] Please see Figure 3 In one embodiment, step S200 includes:

[0091] S210: Detect the current skin conductance response signal of the user within the current time, current environmental state, and preset time, and generate the current skin conductance response signal curve;

[0092] Real-time acquisition of the user's skin conductance response signal at the moment of detection, obtaining the user's current skin conductance response signal curve, and recording the current time and current environmental conditions at the time of detection;

[0093] S220: Match multiple physiological states corresponding to the current skin conductance response signal curve from the physiological state dataset;

[0094] Understandably, the skin conductance response signal is affected by multiple factors, including time, environmental conditions, and physiological state. Therefore, the skin conductance response signal curve may correspond to multiple states at different times and in different environmental conditions. For example, a user may be in an excited state in a low-temperature environment due to the regulation of the sympathetic nervous system, while in a high-temperature environment, they may be in a normal state. Similarly, the skin conductance response signal curve may correspond to an excited state when a user has just woken up, while the same skin conductance response signal curve may correspond to a normal state when the user is in better spirits at noon. Therefore, a skin conductance response signal curve may match multiple physiological states.

[0095] S230: Select a corresponding physiological state based on the current time and the current environmental state and use it as the current physiological state.

[0096] By combining the user's current time and current environmental state, multiple candidate physiological states are filtered, and physiological states that do not match the current time and current environmental state are eliminated, thereby determining a unique current physiological state. By combining the current time and environmental state during the filtering stage to further lock in a unique physiological state, the accuracy and reliability of the user's current physiological state identification are ensured, thus laying a precise foundation for subsequent benchmark neural electrical signal curve matching and neural state judgment.

[0097] In one embodiment, step S300 includes:

[0098] S310: Match the baseline neural electrical signal curve corresponding to the current physiological state from the neural electrical data set.

[0099] Once the user's current physiological state is obtained, the system retrieves the baseline neural electrical signal curve corresponding to that physiological state from the neural electrical data set and uses it as the reference curve for subsequent comparisons. This ensures that the baseline neural electrical signal curve used is consistent with the user's current physiological state, thereby improving the accuracy of the comparison results.

[0100] In one embodiment, step S220 includes:

[0101] S2201: Based on the instantaneous fluctuations and rising / falling trends of the current skin conductance response signal curve, a plurality of reference skin conductance response signal curves that match the current skin conductance response signal curve are matched, so as to obtain a plurality of physiological states corresponding to the plurality of reference skin conductance response signal curves.

[0102] It should be noted that the instantaneous fluctuations of the current skin conductance response (SCR) signal curve can be used to determine whether a user is in a stimulated state, thereby detecting the intensity of the user's emotions. When a user is stimulated by external factors or experiences tension, the SCR signal will show obvious instantaneous fluctuations, thus identifying changes in the user's emotional intensity. Furthermore, the rising and falling trends of the SCR signal curve can reflect the user's engagement and stress levels. When a user is focused on performing a task, the SCR signal usually shows an increasing trend, while when the user is resting or relaxed, the SCR signal shows a decreasing trend. If the SCR signal continues to rise for a short period of time, it may indicate that the user is in a high-stress state, while a gradual decrease over a longer period of time indicates that the stress is gradually being released.

[0103] In addition, the skin conductance response signal exhibits typical diurnal variation characteristics: skin conductance levels are lower in the morning, peak at noon, gradually decrease in the evening, remain low during sleep or hypnosis, and rapidly increase upon awakening. Based on these characteristics, the rising and falling trends and level changes of the skin conductance response signal curve can be used to detect a user's level of arousal, thereby determining whether the user is in different physiological states such as sleep, wakefulness, or high concentration. Therefore, by matching instantaneous fluctuations and rising and falling trends, not only can precise corresponding physiological states be matched, but a comprehensive analysis can also be performed by combining the user's level of concentration, stress level, and level of arousal, thereby improving the refinement and accuracy of physiological state identification and providing richer pre-existing information for subsequent baseline neural electrical signal curve matching and neural state assessment.

[0104] In one embodiment, the neural detection method is implemented through a neural detection module 100, which includes:

[0105] case;

[0106] The detection electrode assembly 1 is disposed on one side of the housing and includes a skin electrical response electrode 12 and a nerve electrical signal electrode 11.

[0107] The steps of detecting the user's current electrodermal response signal within a preset time and generating the current electrodermal response signal curve include:

[0108] S2101: The neural detection module is attached to the user's skin;

[0109] Typically, the nerve detection module 100 can be attached to the arm or forehead. The specific attachment can be determined based on the area to be detected. For example, if the nerve reaction speed in the thigh needs to be detected, it can be attached to the thigh.

[0110] S2102: Obtain bioelectrical signals on the user's skin within a preset time period through the skin conduction electrode, wherein the bioelectrical signals include skin conduction signals and nerve electrical signals;

[0111] Understandably, the skin conductance electrode 12 cannot screen out skin conductance signals alone; it can only collect all biological signals and screen them through structures such as control boards or chips.

[0112] S2103: The current skin conductance response signal is selected by the frequency and potential amplitude of the bioelectric signal;

[0113] S2104: Generate the current skin conductance response signal curve using multiple current skin conductance response signals within a preset time period.

[0114] By using clear frequency and amplitude screening criteria, the skin conductance response signal can be accurately separated from the mixed bioelectrical signals, effectively avoiding the mixing of high-frequency neural electrical signals and external interference signals, thereby ensuring that the subsequently generated current skin conductance response signal curve truly reflects the changes in the user's physiological state.

[0115] In one embodiment, the neural detection module 100 module further includes a circuit board 2, the detection electrode group 1 is electrically connected to the circuit board 2, and a temperature sensor 3 is also electrically connected to the circuit board 2, the temperature sensor 3 being located on the side of the circuit board 2 away from the detection electrode group 1.

[0116] The steps for detecting the current environmental status include:

[0117] S2105: Obtain the ambient temperature through the temperature sensor to obtain the current environmental state.

[0118] Specifically, the temperature sensor 3 is located on the side of the circuit board 2 away from the detection electrode group 1 to reduce the direct influence of skin temperature. The temperature measured by the temperature sensor 3 is used as the current environmental state, which improves the accuracy of matching physiological state.

[0119] Please see Figure 4 In one embodiment, step S400 includes:

[0120] S410: Acquire the bioelectrical signal on the user's skin within a preset time period through the neural electrical signal electrode;

[0121] S420: The current neural electrical signal is selected based on the frequency and potential amplitude of the bioelectrical signal;

[0122] S430: Generate the current neural electrical signal curve using multiple current neural electrical signals within a preset time period.

[0123] By using clear frequency and amplitude screening criteria, neural electrical signals can be accurately separated from mixed bioelectrical signals, effectively avoiding the mixing of skin conductance response signals and external interference signals, thereby ensuring that the current neural electrical signal curve generated subsequently truly reflects the user's autonomic nervous activity.

[0124] In one embodiment, step S2103 includes:

[0125] S21031: Select the electrical signals with a potential amplitude of 0.1mV~10mV and a frequency of 0.01Hz~0.5Hz from the bioelectrical signals and use them as the skin conductance response signals;

[0126] The bioelectric signals collected within a preset time period are bandpass filtered to retain the slowly varying low-frequency signals (0.01 Hz to 0.5 Hz) and remove high-frequency components and abnormal signals with potential amplitudes exceeding the range of 0.1 mV to 10 mV.

[0127] Step S420 includes:

[0128] S4201: Select electrical signals with a potential amplitude of 50mV~100mV and a frequency of 10Hz~1000Hz from the bioelectrical signals and use them as the neural electrical signals.

[0129] The bioelectrical signals collected within a preset time period are subjected to high-pass or band-pass filtering to retain high-frequency components from 10 Hz to 1000 Hz, while removing low-frequency drift and interference signals with potential amplitudes exceeding the 50mV to 100mV range. By clearly defining the frequency and amplitude range, skin conductance response signals and neural electrical signals are effectively distinguished, avoiding mixed interference. This ensures that the generated skin conductance response signal curves and neural electrical signal curves can accurately reflect the user's physiological state changes and neural activity. It should be noted that the action potential pulse width of the neural electrical signal is approximately 1ms to 2ms, which can also be used as a reference parameter for screening neural electrical signals.

[0130] The above are merely optional embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made under the concept of the present invention using the contents of the specification and drawings of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method of neural detection, the method comprising: The method comprises the following steps: obtaining a baseline health database of the user, wherein the baseline health database comprises baseline neuroelectric signal curves of the user in different physiological states; detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin response signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve; matching the baseline neuroelectric signal curve corresponding to the current physiological state from the baseline health database; detecting a current neuroelectric signal of the user within a preset time and generating a current neuroelectric signal curve; comparing the current neuroelectric signal curve with the baseline neuroelectric signal curve to obtain a current neurostate of the user; The step of obtaining a baseline health database of the user comprises: detecting the galvanic skin response signal and the neuroelectric signal corresponding to different times, different environmental states and different physiological states within a preset time, and generating a baseline galvanic skin response signal curve; obtaining a physiological state data set according to the correspondence between the baseline galvanic skin response signal curve, the time, the environmental state and the physiological state; generating a baseline neuroelectric signal curve through the neuroelectric signal, and obtaining a neuroelectric data set according to the correspondence between the baseline neuroelectric signal curve and the physiological state; obtaining the baseline health database through the physiological state data set and the neuroelectric data set.

2. The nerve detection method of claim 1, wherein, The step of detecting the galvanic skin response signal and the neuroelectric signal corresponding to different times, different environmental states and different physiological states further comprises: obtaining a corresponding standard neuroelectric signal curve according to the gender data, age data, height data and weight data of the user; The step of generating a baseline neuroelectric signal curve through the neuroelectric signal comprises: correcting the standard neuroelectric signal curve according to the neuroelectric signal to generate the baseline neuroelectric signal curve.

3. The nerve detection method of claim 1, wherein, The step of detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises:

4. The nerve detection method of claim 3, wherein, detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises:

5. The nerve detection method of claim 3, wherein, detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting a current galvanic skin response signal of the user within a preset time and generating a current galvanic skin signal curve, and obtaining a current physiological state of the user according to the current galvanic skin response 6. The nerve detection method of claim 3, wherein, The nerve detection method is realized by a nerve detection module, and the nerve detection module comprises: a shell; a detection electrode group arranged on one side of the shell, and comprising a galvanic skin response electrode and a nerve electrical signal electrode; the step of detecting a current galvanic skin response signal of a user within a preset time and generating a current galvanic skin response signal curve comprises: attaching the nerve detection module to the skin of the user; obtaining bioelectric signals on the skin of the user within a preset time through the galvanic skin response electrode, wherein the bioelectric signals comprise a galvanic skin response signal and a nerve electrical signal; screening the current galvanic skin response signal through the frequency and potential amplitude of the bioelectric signals; generating the current galvanic skin response signal curve through a plurality of current galvanic skin response signals within a preset time.

7. The nerve detection method of claim 6, wherein, The nerve detection module further comprises a circuit board, the detection electrode group is electrically connected with the circuit board, and a temperature sensor is further electrically connected to the circuit board, and the temperature sensor is located on the side of the circuit board away from the detection electrode group; the step of detecting a current environmental state comprises: obtaining an environmental temperature through the temperature sensor to obtain the current environmental state.

8. The nerve detection method of claim 6, wherein, The step of detecting a current nerve electrical signal of a user within a preset time to generate a current nerve electrical signal curve comprises: obtaining the bioelectric signals on the skin of the user within a preset time through the nerve electrical signal electrode; screening the current nerve electrical signal through the frequency and potential amplitude of the bioelectric signals; generating the current nerve electrical signal curve through a plurality of current nerve electrical signals within a preset time.

9. The nerve detection method of claim 8, wherein, The step of screening the galvanic skin response signal through the frequency and potential amplitude of the bioelectric signals comprises: screening the electrical signal with a potential amplitude of 0.1 mV to 10 mV and a frequency of 0.01 Hz to 0.5 Hz in the bioelectric signals as the galvanic skin response signal; The step of screening the galvanic skin response signal through the frequency and potential amplitude of the bioelectric signals comprises: screening the electrical signal with a potential amplitude of 50 mV to 100 mV and a frequency of 10 Hz to 1000 Hz in the bioelectric signals as the nerve electrical signal.

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