Nerve detection method

Abstract

The invention discloses a nerve detection method, and relates to the technical field of bioelectricity signal detection, and the nerve detection method comprises the following steps: obtaining a reference health database of a user; detecting a current skin electric reaction signal of the user in a preset time, generating a current skin electric reaction signal curve, and acquiring a current physiological state of the user according to the current skin electric reaction signal curve; matching a reference electroneurographic signal curve corresponding to the current physiological state from a reference health database; detecting a current electroneurographic signal of the user within a preset time to generate a current electroneurographic signal curve; and comparing the current electroneurographic signal curve with the reference electroneurographic signal curve to obtain the current neural state of the user. The current physiological state of the user is determined by combining the current skin electric response signal curve of the user, and the corresponding reference electroneurographic signal curve is matched based on the current physiological state, so that the accuracy of judging the detection result of the user by the nerve detection method is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bioelectric signal detection, in particular to a neural detection method. BACKGROUND

[0002] Neural electrical signal detection can be applied to daily health monitoring of ordinary people, training and reaction force evaluation test of athletes, fatigue evaluation of device operators, or identification and intervention training of senile dementia and cognitive conditions of the elderly. And it can derive new detection items and functions outside traditional detection items, such as reaction force test, concentration test, and depression detection test.

[0003] However, the neural electrical signal detection of the prior art usually detects only one kind of neural electrical signal, and then judges based on the neural electrical signal. However, when detecting the neural electrical signal, the physiological state of the user has a great influence on the neural electrical signal, resulting in a large error in the detection result of the neural electrical signal under different physiological states, thereby affecting the accuracy of the detection result of the user. SUMMARY

[0004] The main purpose of the present application is to provide a neural detection method, which aims to solve the problem of large error in the detection result of the neural electrical signal under different physiological states in the prior art, which affects the accuracy of the detection result of the user.

[0005] To achieve the above purpose, the present application provides a neural detection method, comprising the following steps: obtaining a reference health database of a user, wherein the reference health database comprises reference neural electrical signal curves of the user under 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 reference neural electrical signal curve corresponding to the current physiological state from the reference health database; detecting a current neural electrical signal of the user within a preset time to generate a current neural electrical signal curve; comparing the current neural electrical signal curve with the reference neural electrical signal curve to obtain a current neural state of the user.

[0006] In an embodiment, the step of obtaining the reference health database of the user comprises: detecting the galvanic skin response signal and the neural electrical signal within a preset time corresponding to different times, different environmental states and different physiological states, and generating a reference galvanic skin response signal curve; obtaining a physiological state data set according to the corresponding relationship between the reference galvanic skin response signal curve, the time, the environmental state and the physiological state; generating a reference neuroelectric signal curve through the neuroelectric signal, and obtaining a neuroelectric data set according to the corresponding relationship between the reference neuroelectric signal curve and the physiological state; obtaining the reference health database through the physiological state data set and the neuroelectric data set.

[0007] In an embodiment, 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 reference neuroelectric signal curve through the neuroelectric signal comprises: correcting the standard neuroelectric signal curve according to the neuroelectric signal to generate the reference neuroelectric signal curve.

[0008] In an embodiment, the step of detecting the current galvanic skin response signal of the user within the preset time and generating a current galvanic signal curve, and obtaining the current physiological state of the user according to the current galvanic skin response signal curve comprises: detecting the current time, the current environmental state and the current galvanic skin response signal of the user within the preset time and generating the current galvanic skin response signal curve; matching a plurality of physiological states corresponding to the current galvanic skin response signal curve from the physiological state data set; selecting a corresponding physiological state according to the current time and the current environmental state and taking it as the current physiological state.

[0009] In an embodiment, the step of matching the reference neuroelectric signal curve corresponding to the current physiological state from the reference health database comprises: matching the reference neuroelectric signal curve corresponding to the current physiological state from the neuroelectric data set.

[0010] In an embodiment, the step of matching a plurality of physiological states corresponding to the current galvanic skin response signal curve from the physiological state data set comprises: matching a plurality of reference galvanic skin response signal curves corresponding to the current galvanic skin response signal curve according to the instantaneous fluctuation and rising and falling trend of the current galvanic skin response signal curve, so as to obtain a plurality of physiological states corresponding to a plurality of reference galvanic skin response signal curves.

[0011] In one embodiment, the nerve detection method is implemented by a nerve detection module, and the nerve detection module includes: case; A detection electrode group, the detection electrode group is arranged on one side of the housing, and the detection electrode group includes skin galvanic response electrodes and neural electrical signal electrodes; The step of detecting the user's current galvanic skin response signal within a preset time and generating the current galvanic skin response signal curve includes: Applying the nerve detection module to the user's skin; Acquiring bioelectrical signals on the user's skin within a preset time through the galvanic skin response electrodes, wherein the bioelectrical signals include galvanic skin response signals and neural electrical signals; Filtering the current galvanic skin response signal by the frequency and potential amplitude of the bioelectric signal; The current galvanic skin response signal curve is generated by using a plurality of the current galvanic skin response signals within a preset time.

[0012] In one embodiment, the nerve detection module further includes a circuit board, the detection electrode group is electrically connected to the circuit board, and the circuit board is also electrically connected to a temperature sensor, and the temperature sensor is located on a side of the circuit board away from the detection electrode group; The steps to detect the current environment status include: The ambient temperature is acquired by the temperature sensor to obtain the current ambient state.

[0013] In one embodiment, the step of detecting a current neural electrical signal within a preset time period of a user and generating a current neural electrical signal curve includes: Acquiring the bioelectrical signal on the user's skin within a preset time through the neural electrical signal electrode; Filtering the current neural electrical signal by the frequency and potential amplitude of the bioelectrical signal; The current neural electrical signal curve is generated by using a plurality of the current neural electrical signals within a preset time.

[0014] In one embodiment, the step of filtering out the galvanic skin response signal by the frequency and potential amplitude of the bioelectric signal includes: Filtering out the electrical signals with a potential amplitude of 0.1 mV to 10 mV and a frequency of 0.01 Hz to 0.5 Hz from the bioelectrical signals as the galvanic skin response signals; The step of filtering out the skin electrical response signal by the frequency and potential amplitude of the bioelectric signal comprises: Screen out the electrical signal with a potential amplitude of 50mV-100mV and a frequency of 10Hz-1000Hz in the biological electrical signal as the neural electrical signal.

[0015] In the technical solution of the present application, by creating a reference health database, the reference health database has reference neural electrical signal curves corresponding to different physiological states, the activity of sweat glands is determined by the autonomic nervous system, which includes two main subunits, the parasympathetic nervous system and the sympathetic nervous system. The sweat glands of the skin are completely controlled by the sympathetic nervous system, making it a good indicator of internal tension and stress. Therefore, the physiological state of the user can be obtained through the skin galvanic skin response signal curve, and then the corresponding reference neural electrical signal curve is matched through the physiological state. By comparing the detection result with the reference neural electrical signal curve under the current physiological state, the detection deviation caused by different physiological states of the user can be more accurately eliminated, so that the current neural state of the user can still be accurately judged under different physiological states.

[0016] Therefore, the present application can not only realize real-time detection of the neural electrical signal of the user, but also determine the current physiological state of the user by combining the current skin galvanic skin response signal curve, and match the corresponding reference 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 detection result of the neural detection method. BRIEF DESCRIPTION OF DRAWINGS In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 The flowchart of the neural detection method provided by an embodiment of the present application is shown in the figure. Figure 2 The detailed flowchart of step S100 of the neural detection method provided by an embodiment of the present application is shown in the figure. Figure 3 The detailed flowchart of step S200 of the neural detection method provided by an embodiment of the present application is shown in the figure. Figure 4 The detailed flowchart of step S400 of the neural detection method provided by an embodiment of the present application is shown in the figure. Figure 5 The structural schematic diagram of the neural detection module provided by an embodiment of the present application is shown in the figure.

[0018] Explanation of reference numerals: 100, nerve detection module; 1, detection electrode group; 11, nerve electrical signal electrode; 12, galvanic skin response electrode; 2, circuit board; 3, temperature sensor.

[0019] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments in combination with the accompanying drawings. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0021] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship, movement condition, etc. between the components in a certain posture, and if the certain posture changes, the directional indications also change accordingly.

[0022] In addition, if the embodiments of the present application involve descriptions of "first", "second", etc., the descriptions of "first", "second", etc. are only for description purposes, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first" and "second" can explicitly or implicitly include at least one of the features. In addition, "and / or" or "and / or" appearing throughout the text means that the three parallel schemes are included, for example, "A and / or B" includes A scheme, or B scheme, or A and B scheme. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person skilled in the art can realize it, and when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0023] The existing technology of nerve electrical signal detection usually detects only one kind of nerve electrical signal, and then judges based on the nerve electrical signal. However, when detecting the nerve electrical signal, the physiological state of the user has a great influence on the nerve electrical signal, resulting in a large error in the detection result of the nerve electrical signal under different physiological states, thereby affecting the accuracy of the detection result of the user.

[0024] To solve the above problems, the present application provides a nerve detection method.

[0025] Please refer to Figure 1 The nerve detection method of the present embodiment comprises the following steps: S100: Obtain a reference health database of the user, wherein the reference health database comprises reference neuroelectric signal curves of the user in different physiological states; Specifically, the user can be detected by multiple sampling, and neuroelectric signal data is collected in different physiological states such as resting state, exercise state, tension state, relaxation state, and the like. The corresponding reference neuroelectric signal curves are generated based on the collected neuroelectric signal data, and the curves are classified and stored according to the physiological state, thereby forming a user individualized reference health database. S200: Detect the current galvanic skin response signal of the user within a preset time and generate a current galvanic skin response signal curve, and obtain the current physiological state of the user according to the current galvanic skin response signal curve; The galvanic skin response signal is caused by the regulation of the sympathetic nervous system on the activity of sweat glands, and can reflect the autonomic nervous system activity of the user in different emotional states and physiological states. The current galvanic skin response signal curve of the user is formed by real-time collection of the galvanic skin response signal of the user within a preset time, and the current physiological state of the user is obtained through the curve. S300: Match the reference neuroelectric signal curve corresponding to the current physiological state from the reference health database; After identifying the current physiological state of the user, the reference neuroelectric signal curve corresponding to the physiological state is retrieved from the reference health database, and is used as a reference object for subsequent comparison. S400: Detect the current neuroelectric signal of the user within a preset time and generate a current neuroelectric signal curve; S500: Compare the current neuroelectric signal curve with the reference neuroelectric signal curve to obtain the current nervous state of the user.

[0026] The current neuroelectric signal curve is compared with the reference neuroelectric signal curve corresponding to the current physiological state of the user selected from the reference health database, and the nervous state of the user at this moment is determined through difference calculation and matching algorithm, such as whether it is in abnormal fluctuation, whether there is a signal feature deviating from the normal range, etc.

[0027] In the technical solution of the present application, by creating a benchmark health database, the benchmark health database has benchmark neuroelectric signal curves corresponding to different physiological states, the activity of sweat glands is determined by the autonomic nervous system, which includes two main subunits, the parasympathetic nervous system and the sympathetic nervous system. The sweat glands of the skin are completely controlled by the sympathetic nervous system, making it a good indicator of internal tension and stress. Therefore, the physiological state of the user can be obtained through the skin galvanic response signal curve, and then the corresponding benchmark neuroelectric signal curve is matched through the physiological state. By comparing the detection results with the benchmark neuroelectric signal curve under the current physiological state, the detection deviation caused by different physiological states of the user can be more accurately eliminated, so that the current neural state of the user can still be accurately judged under different physiological states.

[0028] Therefore, the present application can not only realize real-time detection of the neuroelectric signal of the user, but also determine the current physiological state of the user in combination with the current skin galvanic response signal curve of the user, and match the corresponding benchmark neuroelectric 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 detection result judgment of the neural detection method for the user. Please refer to Figure 2 In an embodiment, step S100 comprises: S110: detecting skin galvanic response signals and neuroelectric signals in a preset time corresponding to different times, different environmental states and different physiological states, and generating a benchmark skin galvanic response signal curve; It should be noted that the skin galvanic response signal is closely related to the environmental state (such as environmental temperature, environmental humidity), the detection time (such as the time just after waking up in the morning, the time when the spirit is best at noon, and the time when tired at night), and the physiological state (such as stress, anxiety, drowsiness, concentration, etc.), so it is necessary to detect the skin galvanic response signal and the neuroelectric signal corresponding to different times, different environmental states and different physiological states; S120: obtaining a physiological state data set according to the corresponding relationship of the benchmark skin galvanic response signal curve, the time, the environmental state and the physiological state; The system establishes an association relationship between the collected benchmark skin galvanic response signal curve and the corresponding time, environmental state and physiological state by multi-dimensional labeling, and stores it in the form of a data set to form a complete physiological state data set, so that the current physiological state of the user can be accurately judged through the time, environmental state and skin galvanic response signal curve during subsequent detection; S130: generating a benchmark neuroelectric signal curve through the neuroelectric signal, and obtaining a neuroelectric data set according to the corresponding relationship of the benchmark neuroelectric signal curve and the physiological state; binding the benchmark neuroelectric signal curve with the corresponding physiological state to form a neuroelectric data set. Ensure that each benchmark neuroelectric signal curve has a clear physiological state corresponding label; S140: obtaining the benchmark health database through the physiological state data set and the neuroelectric data set.

[0029] Through the two matches of the physiological state data set and the neuroelectric data set, the physiological state matched through the skin electric response signal curve can be accurately matched to the corresponding neuroelectric signal curve, thereby improving the accuracy of the user's neural detection result judgment.

[0030] In an embodiment, step S110 further includes: S109: obtaining the corresponding standard neuroelectric signal curve according to the user's gender data, age data, height data and weight data; Through the health database or the large sample population model, combined with the user's basic physiological parameters such as gender data, age data, height data and weight data, the standard neuroelectric signal curve closest to the user's group characteristics is obtained as the initial reference curve for the generation of personalized benchmark data, thereby improving the accuracy and adaptability of the subsequent benchmark neuroelectric signal curve generation; The step of generating a benchmark neuroelectric signal curve through the neuroelectric signal includes: S131: correcting the standard neuroelectric signal curve according to the neuroelectric signal to generate the benchmark neuroelectric signal curve.

[0031] Taking the user's real neuroelectric signal as the correction basis, the standard neuroelectric signal curve obtained in step S109 is corrected to make it closer to the individual characteristics of the user, thereby finally generating a benchmark neuroelectric signal curve exclusive to the user. Thus, the group standard neuroelectric signal curve can be gradually personalized, and finally a benchmark neuroelectric signal curve that accurately meets the user's own characteristics is obtained, improving the individualization and accuracy of detection. Compared with directly generating a benchmark neuroelectric signal curve from a neuroelectric signal, the correction on the standard neuroelectric signal curve can fully utilize the statistical advantage of the standard neuroelectric signal curve obtained based on the user's gender data, age data, height data and weight data, so that the initial curve has a reasonable overall shape and reference range, which can avoid the problem of unstable benchmark neuroelectric signal curve caused by accidental deviation of single or small amount of neuroelectric signal acquisition data, and also can shorten the establishment period of the benchmark health database.

[0032] Please refer to Figure 3 In an embodiment, step S200 includes: S210: detecting a current time, a current environment state and a current GSR signal of a user in a preset time and generating a current GSR signal curve of the user; The GSR signal of the user at the detection time is collected in real time to obtain a current GSR signal curve of the user, and the current time and the current environment state at the detection time are recorded; S220: matching a plurality of physiological states corresponding to the current GSR signal curve from the physiological state data set; It can be understood that the GSR signal is affected by time, environment state and physiological state, and therefore the GSR signal curve may correspond to a plurality of state conditions under different times and different environment states. For example, the user may be in an excited state due to the adjustment of the sympathetic nervous system in a low-temperature environment, and may be in a normal state in a high-temperature environment. For another example, the GSR signal curve of the user when waking up may correspond to an excited state, and the same GSR signal curve may correspond to a normal state when the user is in good spirits at noon. Therefore, one GSR signal curve may match a plurality of physiological states. S230: screening a corresponding physiological state according to the current time and the current environment state and taking the physiological state as the current physiological state.

[0033] The plurality of candidate physiological states are screened in combination with the current time and the current environment state of the user, and the physiological states that do not match the current time and the current environment state are eliminated, so as to determine a unique current physiological state. By combining the current time and the environment state in the screening stage to further lock the unique physiological state, the accuracy and reliability of the current physiological state recognition of the user are ensured, thereby laying an accurate foundation for subsequent reference neural electrical signal curve matching and neural state judgment.

[0034] In an embodiment, step S300 comprises: S310: matching the reference neural electrical signal curve corresponding to the current physiological state from the neural electrical data set.

[0035] After the current physiological state of the user is obtained, the system retrieves the reference neural electrical signal curve corresponding to the physiological state from the neural electrical data set, and takes the reference neural electrical signal curve as a reference curve for subsequent comparison, so as to ensure that the reference neural electrical signal curve used is consistent with the physiological state of the user at the moment, thereby improving the accuracy of the comparison result.

[0036] In an embodiment, step S220 comprises: S2201: According to the instantaneous fluctuation and the rising and falling trend of the current skin galvanic response signal curve, a plurality of reference skin galvanic response signal curves matched with the current skin galvanic response signal curve are matched to obtain a plurality of physiological states corresponding to the plurality of reference skin galvanic response signal curves.

[0037] It should be noted that the instantaneous fluctuation of the current skin galvanic response signal curve can be used to determine whether the user is in a stimulating state, thereby detecting the emotional intensity of the user. When the user is stimulated externally or has a nervous emotion, the skin galvanic response signal will have a significant instantaneous fluctuation, thereby identifying the change in emotional intensity of the user. Further, the rising and falling trend of the skin galvanic response signal curve can reflect the user's state of engagement and stress state. When the user is focused on performing a task, the skin galvanic response signal usually shows an increasing trend, while when the user is in a resting or relaxed state, the skin galvanic response signal shows a decreasing trend. If the skin galvanic response signal continues to rise in a short period of time, it may also indicate that the user is in a high stress state, while a gradual decrease in a longer period of time indicates that the stress is gradually released.

[0038] In addition, the skin galvanic response signal also has typical circadian variation characteristics: the skin galvanic level is low in the morning, reaches the highest at noon, gradually decreases at night, is at a lower level during sleep or hypnosis, and rapidly increases after awakening. Based on the above characteristics, the rising and falling trend and the level change of the skin galvanic response signal curve can be used to detect the user's degree of awakening, and further determine the different physiological states of the user in sleep, awakening or high concentration. Therefore, by matching the instantaneous fluctuation and the rising and falling trend, not only can the accurate corresponding physiological state be matched, but also the user's concentration, stress level and awakening degree can be comprehensively analyzed, thereby improving the refinement and accuracy of physiological state recognition, and providing more rich pre-information for subsequent reference neural electrical signal curve matching and neural state evaluation.

[0039] In an embodiment, the neural detection method is realized by a neural detection module 100, and the neural detection module 100 comprises: a shell; a detection electrode group 1, the detection electrode group 1 is arranged on one side of the shell, and the detection electrode group 1 comprises a skin galvanic response electrode 12 and a neural electrical signal electrode 11; The step of detecting the current skin galvanic response signal of the user within a preset time and generating the current skin galvanic response signal curve comprises: S2101: The neural detection module is attached to the skin of the user; Generally, the neural detection module 100 can be attached to the arm or the forehead, and can be attached according to the part to be detected, such as the thigh, etc. S2102: Obtain the bioelectric signal on the skin of the user within a preset time through the GSR electrode, wherein the bioelectric signal includes a GSR signal and a neural electric signal; It can be understood that the GSR electrode 12 cannot screen out the GSR signal alone, and can only collect all bioelectric signals and screen them through a control board or a chip and the like; S2103: Screen the current GSR signal through the frequency and potential amplitude of the bioelectric signal; S2104: Generate the current GSR signal curve through a plurality of current GSR signals within a preset time.

[0040] The GSR signal can be accurately separated from the mixed bioelectric signal through the explicit frequency and amplitude screening criteria, the mixing of high-frequency neural electric signals and external interference signals is effectively avoided, and thus it is ensured that the current GSR signal curve generated subsequently truly reflects the physiological state change of the user.

[0041] In an embodiment, the neural detection module 100 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 further electrically connected to the circuit board 2, and the temperature sensor 3 is located on a side of the circuit board 2 away from the detection electrode group 1; The step of detecting the current environment state includes: S2105: Obtain the environment temperature through the temperature sensor to obtain the current environment state.

[0042] 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 the skin temperature, the temperature measured through the temperature sensor 3 is taken as the current environment state, and the accuracy of the physiological state matching is improved.

[0043] Please refer to Figure 4 In an embodiment, the step S400 includes: S410: Obtain the bioelectric signal on the skin of the user within a preset time through the neural electric signal electrode; S420: Screen the current neural electric signal through the frequency and potential amplitude of the bioelectric signal; S430: Generate the current neural electric signal curve through a plurality of current neural electric signals within a preset time.

[0044] The neural electric signal can be accurately separated from the mixed bioelectric signal through the explicit frequency and amplitude screening criteria, the mixing of the GSR signal and external interference signals is effectively avoided, and thus it is ensured that the current neural electric signal curve generated subsequently truly reflects the autonomic nervous activity of the user.

[0045] In an embodiment, step S2103 comprises: S21031: screening out 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 from the bioelectric signal as the skin electrical response signal. The bioelectric signal collected within a preset time is subjected to band-pass filtering processing, and slowly varying signals with low frequencies of 0.01 Hz to 0.5 Hz are retained, and abnormal signals with high frequency components and potential amplitudes exceeding the range of 0.1 mV to 10 mV are eliminated.

[0046] Step S420 comprises: S4201: screening out the electrical signal with a potential amplitude of 50 mV to 100 mV and a frequency of 10 Hz to 1000 Hz from the bioelectric signal as the neural electrical signal.

[0047] The bioelectric signal collected within a preset time is subjected to high-pass or band-pass filtering processing, and high-frequency components with frequencies of 10 Hz to 1000 Hz are retained, and interference signals with low frequency drift and potential amplitudes exceeding the range of 50 mV to 100 mV are eliminated. By specifying the frequency and amplitude range, the skin electrical response signal and the neural electrical signal can be effectively distinguished to avoid mixed interference, so that the generated skin electrical response signal curve and neural electrical signal curve can truly reflect the physiological state change and neural activity state of the user. It should be noted that the action potential pulse width of the neural electrical signal is about 1 ms to 2 ms, which can also be used as a reference parameter for screening the neural electrical signal.

[0048] The above is only an optional embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made under the concept of the present application, or direct / indirect application in other related technical fields using the content of the present application specification and drawings is included in the patent protection scope of the present application.

Claims

1. A nerve detection method, characterized in that: The following steps are involved: Acquiring a baseline health database of the user, wherein the baseline health database includes baseline neural electrical signal curves under different physiological states of the user; Detecting the user's current galvanic skin response signal within a preset time and generating a current galvanic skin response signal curve, and obtaining the user's current physiological state based on the current galvanic skin response signal curve; Matching the reference neural electrical signal curve corresponding to the current physiological state from the reference health database; Detect the current neural electrical signal within the user's preset time and generate the current neural electrical signal curve; The current neural electrical signal curve is compared with the reference neural electrical signal curve to obtain the current neural state of the user.

2. The nerve detection method according to claim 1, wherein: The steps to obtain the user's baseline health database include: Detecting skin galvanic response signals and neural electrical signals within a preset time corresponding to different times, different environmental conditions, and different physiological conditions, and generating a baseline skin galvanic response signal curve; Obtaining a physiological state data set according to a correspondence between the reference skin electrical response signal curve, the time, the environmental state, and the physiological state; generating a reference neural electrical signal curve using the neural electrical signal, and obtaining a neural electrical data set based on a correspondence between the reference neural electrical signal curve and the physiological state; The baseline health database is obtained through the physiological state dataset and the neural electrical dataset.

3. The nerve detection method according to claim 2, wherein: Before the step of detecting skin electrical response signals and neural electrical signals corresponding to different times, different environmental states, and different physiological states, the following steps are also included: Obtain the corresponding standard neural electrical signal curve based on the user's gender data, age data, height data, and weight data; The step of generating a reference neural electrical signal curve using the neural electrical signal comprises: The standard neural electrical signal curve is corrected according to the neural electrical signal to generate the reference neural electrical signal curve.

4. The nerve detection method according to claim 2, wherein: The steps of detecting the current galvanic skin response signal of the user within a preset time and generating a current skin signal curve, and obtaining the current physiological state of the user according to the current galvanic skin response signal curve include: Detecting a current galvanic skin response signal of a user at a current time, a current environmental state, and a preset time and generating a current galvanic skin response signal curve; Matching a plurality of physiological states corresponding to the current galvanic skin response signal curve from the physiological state data set; A corresponding physiological state is selected according to the current time and the current environmental state and is used as the current physiological state.

5. The nerve detection method according to claim 4, characterized in that: The step of matching the reference neural electrical signal curve corresponding to the current physiological state from the reference health database includes: The reference neural electrical signal curve corresponding to the current physiological state is matched from the neural electrical data set.

6. The nerve detection method according to claim 4, characterized in that: The step of matching a plurality of physiological states corresponding to the current galvanic skin response signal curve from the physiological state data set includes: According to the instantaneous fluctuation and rising and falling trends of the current galvanic skin response signal curve, a plurality of reference galvanic skin response signal curves that match the current galvanic skin response signal curve are matched to obtain the plurality of physiological states corresponding to the plurality of reference galvanic skin response signal curves.

7. The nerve detection method according to claim 4, characterized in that: The nerve detection method is implemented by a nerve detection module, and the nerve detection module includes: case; A detection electrode group, the detection electrode group is arranged on one side of the housing, and the detection electrode group includes skin galvanic response electrodes and neural electrical signal electrodes; The step of detecting the user's current galvanic skin response signal within a preset time and generating the current galvanic skin response signal curve includes: Applying the nerve detection module to the user's skin; Acquiring bioelectrical signals on the user's skin within a preset time through the galvanic skin response electrodes, wherein the bioelectrical signals include galvanic skin response signals and neural electrical signals; Filtering the current galvanic skin response signal by the frequency and potential amplitude of the bioelectric signal; The current galvanic skin response signal curve is generated by using a plurality of the current galvanic skin response signals within a preset time.

8. The nerve detection method according to claim 7, wherein: The nerve detection module further includes a circuit board, the detection electrode group is electrically connected to the circuit board, and the circuit board is also electrically connected to a temperature sensor, and the temperature sensor is located on a side of the circuit board away from the detection electrode group; The steps to detect the current environment status include: The ambient temperature is acquired by the temperature sensor to obtain the current ambient state.

9. The nerve detection method according to claim 7, wherein: The steps of detecting the current neural electrical signal within the user's preset time and generating the current neural electrical signal curve include: Acquiring the bioelectrical signal on the user's skin within a preset time through the neural electrical signal electrode; Filtering the current neural electrical signal by the frequency and potential amplitude of the bioelectrical signal; The current neural electrical signal curve is generated by using a plurality of the current neural electrical signals within a preset time.

10. The nerve detection method according to claim 9, characterized in that: The step of filtering out the skin electrical response signal by the frequency and potential amplitude of the bioelectric signal comprises: Filtering out the electrical signals with a potential amplitude of 0.1 mV to 10 mV and a frequency of 0.01 Hz to 0.5 Hz from the bioelectrical signals as the galvanic skin response signals; The step of filtering out the skin electrical response signal by the frequency and potential amplitude of the bioelectric signal comprises: The electrical signals with a potential amplitude of 50mV to 100mV and a frequency of 10Hz to 1000Hz in the bioelectrical signals are screened out as the neural electrical signals.

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