A neural response data monitoring system and method based on electroencephalogram signals

Abstract

The present invention discloses a neural response data monitoring system and method based on electroencephalogram (EEG) signals. An acquisition device generates an audio signal and controls an audio output module based on the audio signal to output sound stimulation of a set stimulation duration to an object at set stimulation intervals, thereby forming multiple groups of stimulations. A sensor module synchronously acquires original EEG signals of the object of the set collection duration corresponding to each group of sound stimulations and uploads them to a host computer, wherein the set stimulation duration is less than the set collection duration and less than the set stimulation interval. The host computer receives the multiple groups of original EEG signals, performs average signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals, performs average processing on the multiple groups of preprocessed EEG signals to obtain auditory brainstem response waveform data including seven positive waves, namely, wave I to wave VII; based on the auditory brainstem response waveform data, peak delay data of wave I to wave VII is acquired; and based on the peak delay data of wave I to wave VII, the neural response data of the object is acquired.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) signal acquisition and processing, and in particular to a neural response data monitoring system and method based on EEG signals. Background Art

[0002] In some special scenarios where precise operations are required, it is necessary to collect the operator's EEG signals under sound stimulation, and process and analyze the EEG signals to obtain the operator's neural response data. There is currently a utility model patent with application number CN200720169680.5, entitled "An Otoacoustic Detection and Analysis Device". In this patent, the stimulation sound is sent by the first sound unit 2 or the second sound unit 3, and the auditory brainstem evoked potential (ABR) signal is received by the EEG signal receiving unit 12. It is connected to the A / D conversion unit 8 through the low-noise high common-mode amplifier 13, and then connected to the central control unit, and then transmitted to the computer through the USB interface unit. The computer then removes artifacts, adds windows and removes interference on the collected samples, and gives an analysis and judgment result. However, this patent does not provide a technology for optimizing the processing of auditory brainstem evoked potential signals. It can be seen that the existing technology lacks a technology for optimizing the processing of EEG signals under sound stimulation to obtain neural response data. Summary of the Invention

[0003] In view of the problems and shortcomings of the prior art, the present invention provides a neural response data monitoring system and method based on EEG signals.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] The present invention provides a neural response data monitoring system based on EEG signals, which is characterized in that it includes a host computer, an acquisition device, and a wearable component for being worn on the head of a subject, wherein the wearable component is provided with an audio output module and a sensor module;

[0006] The acquisition device is used to generate an audio signal and control the audio output module based on the audio signal to output a sound stimulus of a set stimulation duration to the subject every set stimulation interval, forming multiple groups of stimulations, synchronously collecting the original EEG signal of the subject of a set collection duration corresponding to each group of sound stimulations through the sensor module and uploading it to the host computer, and the set stimulation duration is less than the set collection duration and less than the set stimulation interval;

[0007] The host computer is used to receive multiple groups of original EEG signals, perform signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals, and perform mean processing on the multiple groups of preprocessed EEG signals to obtain auditory brainstem response waveform data, where the auditory brainstem response waveform data includes seven positive waves, namely wave I, wave II, wave III, wave IV, wave V, wave VI and wave VII;

[0008] The host computer is also used to obtain the peak delay data of waves I to VII based on the auditory brainstem response waveform data, and to obtain the subject's neural response data based on the peak delay data of waves I to VII.

[0009] Preferably, the host computer is used to receive multiple groups of original EEG signals, perform signal outlier removal and filtering preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals, and perform mean processing on the multiple groups of preprocessed EEG signals to obtain auditory brainstem response waveform data;

[0010] The host computer is also used to obtain the peak time t1-t7 of the wave I to wave VII based on the auditory brainstem response waveform data, taking the sound stimulation generation time as time 0, and taking the peak time t1-t7 as the peak delay data of wave I to wave VII, respectively, to obtain the reference peak delay data corresponding to wave I to wave VII respectively. Based on benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII, based on the benchmark peak delay data corresponding to waves I-VII The weight values ω1-ω7 and the peak time t1-t7 are used to calculate the neural response data τ of the object; wherein,

[0011]

[0012] Preferably, the host computer is also used to obtain the reference peak delay data corresponding to wave I to wave VII respectively. A historical database storing multiple historical auditory brainstem response waveform data of the same subject is constructed, and the peak time t1'-t7' of each current historical auditory brainstem response waveform data in the historical database is obtained. For any wave from wave I to wave VII, the deviation data in the peak time data set of the wave is filtered out based on the 3σ criterion, and the benchmark sample peak time data set of the wave is screened out. The mean of all samples in the benchmark sample peak time data set of the wave is used as the benchmark peak delay data of the wave, thereby obtaining the benchmark peak delay data corresponding to waves I to wave VII respectively.

[0013] The host computer is also used to obtain each auditory brainstem response waveform data of the subject and the corresponding peak times t1-t7 of the peaks of waves I to VII, and store them in the historical database corresponding to the subject.

[0014] Preferably, the host computer is also used to calculate the peak delay data based on the reference peak delay data. Construct weight values ω1-ω7 corresponding to waves I-VII respectively: based on the current benchmark peak delay data Based on the benchmark sample peak time data set, the corresponding variance value σ1-σ7 is obtained, and the weight value ω1-ω7 is calculated based on the inverse of the variance value σ1-σ7, that is, for any weight value ω i ,

[0015]

[0016] The present invention also provides a method for monitoring neural response data based on EEG signals, which is characterized in that it includes the following steps:

[0017] S1, the acquisition device generates an audio signal and controls the audio output module based on the audio signal to output a sound stimulus of a set stimulation duration to the subject at intervals of a set stimulation interval, forming multiple groups of stimulations, synchronously collecting the original EEG signal of the subject of a set collection duration corresponding to each group of sound stimulations through the sensor module and uploading it to the host computer, and setting the stimulation duration to be less than the set collection duration and less than the set stimulation interval;

[0018] S2. The host computer receives multiple groups of original EEG signals and performs signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals;

[0019] S3, the host computer performs mean processing on the multiple groups of pre-processed EEG signals to obtain auditory brainstem response waveform data, the auditory brainstem response waveform data including seven positive waves, namely wave I, wave II, wave III, wave IV, wave V, wave VI and wave VII;

[0020] S4. The host computer obtains the peak delay data of waves I to VII based on the auditory brainstem response waveform data, and obtains the neural response data of the subject based on the peak delay data of waves I to VII.

[0021] Preferably, in step S2, the host computer receives multiple groups of original EEG signals, and performs signal outlier removal and filtering preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals;

[0022] In step S4, the host computer obtains the peak time t1-t7 of the wave I-wave VII based on the auditory brainstem response waveform data, with the sound stimulation generation time as time 0, and uses the peak time t1-t7 as the peak delay data of wave I-wave VII, and obtains the reference peak delay data corresponding to wave I-wave VII respectively. Based on benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII, based on the benchmark peak delay data corresponding to waves I-VII The weight values ω1-ω7 and the peak time t1-t7 are used to calculate the neural response data τ of the object; wherein,

[0023]

[0024] Preferably, in step S4, the host computer obtains the reference peak delay data corresponding to wave I to wave VII respectively. A historical database storing multiple historical auditory brainstem response waveform data of the same subject is constructed, and the peak time t1'-t7' of each current historical auditory brainstem response waveform data in the historical database is obtained. For any wave from wave I to wave VII, the deviation data in the peak time data set of the wave is filtered out based on the 3σ criterion, and the benchmark sample peak time data set of the wave is screened out. The mean of all samples in the benchmark sample peak time data set of the wave is used as the benchmark peak delay data of the wave, thereby obtaining the benchmark peak delay data corresponding to waves I to wave VII respectively.

[0025] After the host computer acquires each auditory brainstem response waveform data of the subject and the corresponding peak times t1-t7 of the peaks of waves I to VII, the data will be stored in the historical database corresponding to the subject.

[0026] Preferably, in step S4, the host computer is based on the reference peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII respectively: based on the current benchmark peak delay data Based on the benchmark sample peak time data set, the corresponding variance value σ1-σ7 is obtained, and the weight value ω1-ω7 is calculated based on the inverse of the variance value σ1-σ7, that is, for any weight value ω i ,

[0027]

[0028] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0029] The positive progress effect of the present invention is:

[0030] The present invention can realize synchronous collection of the subject's EEG signals while sound stimulation is being loaded, optimize the EEG signals, and analyze and obtain the neural response data in the subject's current state. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1This is a principle block diagram of a neural response data monitoring system based on EEG signals according to a preferred embodiment of the present invention.

[0032] Figure 2 Schematic diagram of the stimulation and acquisition process of a preferred embodiment of the present invention.

[0033] Figure 3 Schematic diagram of the auditory brainstem response waveform according to a preferred embodiment of the present invention.

[0034] Figure 4 Schematic diagram of the data processing flow during the monitoring process of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0036] like Figure 1-4 As shown, this embodiment provides a neural response data monitoring system based on EEG signals, which includes a host computer, an acquisition device, and a wearable component for being worn on the head of a subject, wherein the wearable component is provided with an audio output module and a sensor module.

[0037] In this embodiment, sound stimulation can be loaded into the human body through the acquisition device. By real-time acquisition of the human body's EEG signals when receiving the sound stimulation and sending them to the host computer for processing, the neural response data of the human body in the current state can be analyzed and obtained.

[0038] Among them, the acquisition device includes a power supply interface, a power module, an audio generation module, an audio driver module, an audio interface, a synchronization module, a sensor interface, a signal reading module, a control module and a communication module.

[0039] The power module is used to connect to an external power supply through the power supply interface to power the acquisition device.

[0040] The control module is configured to send a trigger signal to the audio generation module, the audio generation module is configured to generate an audio signal upon receiving the trigger signal, the audio driver module is configured to receive the audio signal and generate an audio driver instruction containing the audio signal based on the audio signal, the control module is configured to control the audio driver module to output an audio driver instruction of a set stimulation duration via an audio interface at every set stimulation interval, thereby controlling the audio output module to output sound stimulation of a set stimulation duration to the subject at every set stimulation interval, thereby forming multiple groups of stimulations. The set stimulation duration is less than the set acquisition duration and less than the set stimulation interval.

[0041] The synchronization module is used to generate a synchronization signal based on the audio signal, and the control module is also used to generate an acquisition instruction for controlling the action of the signal reading module based on the synchronization signal. The signal reading module is used to synchronously read the original EEG signals of the object with a set acquisition time corresponding to each group of sound stimulations collected by the sensor module through the sensor interface, perform analog-to-digital conversion on each group of original EEG signals to generate original EEG signals in the form of digital signals and send them to the control module. The control module is also used to encode each group of original EEG signals to generate original data packets and send them to the host computer via the communication module.

[0042] Through the above, it is possible to better realize the synchronous collection of human brain electrical signals while sound stimulation is being loaded.

[0043] In this embodiment, the sensor module can be, for example, an ABR sensor, which can be used to collect brain electrical signals of a human body to obtain raw brain electrical signals. It is understandable that the raw brain electrical signals are analog signals with noise.

[0044] In this embodiment, the signal reading module can be implemented based on the ADS1299 chip. The signal reading module can convert the original EEG signal into digital form, thereby obtaining the EEG signal as a digital signal.

[0045] In this embodiment, an audio interface can be formed at the audio driving module and a sensor interface can be formed at the signal reading module, so that the audio output module and the sensor module at the wearable component can be conveniently connected to the acquisition device.

[0046] In this embodiment, the audio output module can be, for example, an earphone device integrated into the wearable component.

[0047] In this embodiment, a communication module can also be provided at the acquisition device, so that data interaction between the acquisition device and the host computer can be preferably realized in a wireless transmission manner.

[0048] In this embodiment, a power supply module can also be provided at the acquisition device. The power supply module is formed with a power supply interface for accessing an external power supply, thereby enabling better overall power supply to the acquisition device.

[0049] The host computer includes a decoding module, a pre-processing module, a processing module and a display module.

[0050] The decoding module is used to decode each original data packet after receiving it and then extract the original EEG signal.

[0051] The preprocessing module is used to perform signal preprocessing (such as signal outlier removal and filtering preprocessing) on each extracted original EEG signal to obtain the corresponding preprocessed EEG signal and transmit it to the processing module.

[0052] The processing module is used to perform mean processing on multiple groups of pre-processed EEG signals to obtain auditory brainstem response waveform data and control the display module to display the auditory brainstem response waveform data.

[0053] Seen in Figure 2 It can be understood that the auditory brainstem response waveform data reflects the situation of neurons activated from the outside to the inside in the process of sound stimulation entering the human ear canal. The auditory brainstem response waveform data usually includes 7 positive waves, which are represented by Roman numerals I to VII. These waveforms reflect different parts of the auditory conduction pathway, as follows.

[0054] Wave I: represents the action potential of the auditory nerve, located in the extracranial segment of the auditory nerve;

[0055] Wave II: originates from the cochlear nucleus;

[0056] Wave III: originates from the superior olivary nucleus of the inferior pons;

[0057] Wave IV: originates from the lateral superior colliculus nucleus;

[0058] Wave V: originates from the inferior colliculus of the midbrain;

[0059] Wave VI: originates from the medial geniculate body of the thalamus;

[0060] Wave VII: represents the potential activity of auditory radiation.

[0061] Seen in Figure 3 , obtaining EEG signals includes:

[0062] Based on the trigger signal, the audio generating module is controlled to generate an audio signal;

[0063] Based on the audio signal, a synchronization signal is generated through the synchronization module, and an audio driving instruction is generated through the audio driving module;

[0064] Based on the synchronization signal, the control module generates an acquisition instruction;

[0065] Based on the audio driver instructions, the audio output module outputs the preset sound;

[0066] Based on the acquisition instruction, the signal reading module acquires the EEG signal from the sensor module;

[0067] Based on the communication module, the collected EEG signals are sent to the host computer.

[0068] In the above process, EEG signals can be collected at a preset fixed period, so that the original data can be obtained better.

[0069] In one EEG signal acquisition cycle, the following actions can be included:

[0070] In this embodiment, after the trigger signal occurs, it represents the start of EEG acquisition in the current cycle. At the same time as the audio drive instruction is generated, the signal reading module also starts to read and save the collected original EEG signal; wherein, the duration of the audio drive instruction can be, for example, 0.1ms; wherein, the sampling time of the signal reading module can be, for example, 15ms.

[0071] In this embodiment, the signal reading module obtains the original EEG signal in the form of an analog signal with noise during the sampling period. After the sampling period ends, the signal reading module can perform digital-to-analog conversion on the original EEG signal to obtain the EEG signal in the form of a digital signal.

[0072] In this embodiment, after receiving the EEG signal, the control module can encode the EEG signal to generate an original data packet and send it to the host computer via the communication module. This process can be set to 150ms.

[0073] At this point, one collection cycle ends and the next collection cycle begins.

[0074] The processing module is also used to obtain the peak delay data of waves I to VII based on the auditory brainstem response waveform data, and to obtain the subject's neural response data based on the peak delay data of waves I to VII, which can better achieve the acquisition of the user's neural response data. Specifically, for the auditory brainstem response waveform data, the time when the sound stimulation is generated is taken as the 0 time, that is, the time when the audio drive instruction is generated is taken as the 0 time, and the peak time t1-t7 of the peak of waves I to wave VII is obtained, and the peak time t1-t7 is used as the peak delay data of waves I to wave VII respectively, and the reference peak delay data corresponding to waves I to wave VII are obtained respectively. Based on benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII, based on the benchmark peak delay data corresponding to waves I-VII The weight values ω1-ω7 and the peak time t1-t7 are used to calculate the neural response data τ of the object; wherein,

[0075]

[0076] Based on the above, the automatic calculation and acquisition of the neural response data τ can be achieved preferably.

[0077] Among them, obtain the benchmark peak delay data corresponding to wave I to wave VII respectively The specific implementation method is as follows: construct a historical database to store multiple historical auditory brainstem response waveform data of the same object, obtain the peak time t1'-t7' of each current historical auditory brainstem response waveform data in the historical database, and for any wave from wave I to wave VII, filter out the deviation data in the peak time data set of the wave based on the 3σ criterion, screen out the benchmark sample peak time data set of the wave, and use the mean of all samples in the benchmark sample peak time data set of the wave as the benchmark peak delay data of the wave, thereby obtaining the benchmark peak delay data corresponding to waves I to wave VII respectively.

[0078] As a further explanation, the processing module is also used to obtain each auditory brainstem response waveform data of the subject and the corresponding peak times t1-t7 of the peaks of waves I to VII, and store them in the historical database corresponding to the subject.

[0079] That is, in the historical database, 7 data sets corresponding to waves I to VII can be established, and each data set stores the corresponding peak time data.

[0080] After filtering out the deviation data in the wave I to wave VII data set based on the 3σ criterion, the benchmark sample peak time data set can be obtained.

[0081] Then, the data of the benchmark sample peak time data set is averaged to obtain the corresponding benchmark peak delay data.

[0082] Based on the above, the benchmark peak delay data can be analyzed in real time for each data collected from the same object. Update to make the data more meaningful.

[0083] It is understandable that when constructing the historical database, it can be classified according to population categories (such as gender, age, occupation, etc.). Each population category can have multiple object sets that meet the characteristics of the category, and each object set contains multiple historical data of the object.

[0084] In determining the baseline peak delay data When creating a data source, you can use objects, categories, or the entire data set as the data source according to your needs.

[0085] Among them, based on the benchmark peak delay data The specific implementation method of constructing the weight values ω1-ω7 corresponding to waves I-VII is as follows: based on the current benchmark peak delay data Based on the benchmark sample peak time data set, the corresponding variance value σ1-σ7 is obtained, and the weight value ω1-ω7 is calculated based on the inverse of the variance value σ1-σ7, that is, for any weight value ω i ,

[0086]

[0087] It can be understood that the variance represents the degree of discreteness of the data. Based on the above, the peak moments with greater discreteness of the data in waves I to VII can be assigned smaller weights, so that the neural response data τ can better reflect the actual level.

[0088] In addition, based on the above, real-time self-adjustment of the weight values ω1-ω7 can be achieved, avoiding defects such as lack of flexibility caused by human experience settings.

[0089] Seen in Figure 1 In actual application, a neural response data monitoring system of this embodiment, after the audio generation module generates an audio signal, on the one hand, the audio signal will be passed to the audio driving module to generate an audio driving instruction for sending to the audio output module; on the other hand, based on the generation of the audio signal, a synchronization signal for sending to the control module will be generated through the synchronization module, and then an acquisition instruction for sending to the signal reading module will be generated at the control module.

[0090] The sensor module can collect the original EEG signals of the human body in real time when external sound stimulation is applied. The original EEG signals are analog signals with noise.

[0091] Afterwards, the signal reading module performs analog-to-digital conversion on the original EEG signal to generate an EEG signal in the form of a digital signal and sends it to the control module.

[0092] Afterwards, the control module encodes the EEG signal to generate an original data packet and sends it to the host computer via the communication module.

[0093] After receiving the original data packet, the host computer will decode it through the decoding module to extract the EEG signal, and perform filtering processing on the extracted EEG signal through the preprocessing module to remove noise; the EEG signal after noise removal can be optimized through the processing module to obtain parameters such as auditory brainstem response waveform data and neural response data. At the same time, the obtained auditory brainstem response waveform data can also be displayed on the display module of the host computer to facilitate manual analysis and confirmation.

[0094] This embodiment also provides a method for monitoring neural response data based on EEG signals, which includes the following steps:

[0095] S1. The acquisition device generates an audio signal and controls the audio output module based on the audio signal to output a sound stimulus of a set stimulation duration to the object every set stimulation interval, forming multiple groups of stimulations. The original EEG signal of the object of a set collection duration corresponding to each group of sound stimulations is synchronously collected through the sensor module and uploaded to the host computer. The set stimulation duration is less than the set collection duration and less than the set stimulation interval.

[0096] S2. The host computer receives multiple groups of original EEG signals and performs signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals.

[0097] In step S2, the host computer receives multiple groups of original EEG signals, and performs signal outlier removal and filtering preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals.

[0098] S3. The host computer performs mean processing on multiple groups of pre-processed EEG signals to obtain auditory brainstem response waveform data. The auditory brainstem response waveform data includes seven positive waves, namely wave I, wave II, wave III, wave IV, wave V, wave VI and wave VII.

[0099] S4. The host computer obtains the peak delay data of waves I to VII based on the auditory brainstem response waveform data, and obtains the neural response data of the subject based on the peak delay data of waves I to VII.

[0100] In step S4, the host computer uses the moment of sound stimulation as the 0th moment to obtain the peak moments t1-t7 of the peaks of waves I to VII for the auditory brainstem response waveform data, and uses the peak moments t1-t7 as the peak delay data of waves I to VII, and obtains the reference peak delay data corresponding to waves I to VII respectively. Based on benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII, based on the benchmark peak delay data corresponding to waves I-VII The weight values ω1-ω7 and the peak time t1-t7 are used to calculate the neural response data τ of the object; wherein,

[0101]

[0102] The host computer obtains the benchmark peak delay data corresponding to wave I to wave VII A historical database storing multiple historical auditory brainstem response waveform data of the same subject is constructed, and the peak time t1'-t7' of each current historical auditory brainstem response waveform data in the historical database is obtained. For any wave from wave I to wave VII, the deviation data in the peak time data set of the wave is filtered out based on the 3σ criterion, and the benchmark sample peak time data set of the wave is screened out. The mean of all samples in the benchmark sample peak time data set of the wave is used as the benchmark peak delay data of the wave, thereby obtaining the benchmark peak delay data corresponding to waves I to wave VII respectively.

[0103] After the host computer obtains each auditory brainstem response waveform data of the subject and the corresponding peak times t1-t7 of the peaks of waves I to VII, they will be stored in the historical database corresponding to the subject.

[0104] The host computer is based on the benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII respectively: based on the current benchmark peak delay data Based on the benchmark sample peak time data set, the corresponding variance value σ1-σ7 is obtained, and the weight value ω1-ω7 is calculated based on the inverse of the variance value σ1-σ7, that is, for any weight value ω i ,

[0105]

[0106] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A neural response data monitoring system based on EEG signals, characterized in that: The system includes a host computer, a collection device, and a wearable component for being worn on the subject's head, wherein the wearable component is provided with an audio output module and a sensor module; The acquisition device is used to generate an audio signal and control the audio output module based on the audio signal to output a sound stimulus of a set stimulation duration to the subject every set stimulation interval, forming multiple groups of stimulations, synchronously collecting the original EEG signal of the subject of a set collection duration corresponding to each group of sound stimulations through the sensor module and uploading it to the host computer, and the set stimulation duration is less than the set collection duration and less than the set stimulation interval; The host computer is used to receive multiple groups of original EEG signals, perform signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals, and perform mean processing on the multiple groups of preprocessed EEG signals to obtain auditory brainstem response waveform data, where the auditory brainstem response waveform data includes seven positive waves, namely wave I, wave II, wave III, wave IV, wave V, wave VI and wave VII; The host computer is further used to obtain peak delay data of waves I to VII based on the auditory brainstem response waveform data, and to obtain neural response data of the subject based on the peak delay data of waves I to VII; The host computer is used to receive multiple groups of original EEG signals, perform signal outlier removal and filtering preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals, and perform mean processing on the multiple groups of preprocessed EEG signals to obtain auditory brainstem response waveform data; The host computer is also used to obtain the peak time t1-t7 of the wave I to wave VII based on the auditory brainstem response waveform data, taking the sound stimulation generation time as time 0, and taking the peak time t1-t7 as the peak delay data of wave I to wave VII, respectively, to obtain the reference peak delay data corresponding to wave I to wave VII respectively. Based on benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII, based on the benchmark peak delay data corresponding to waves I-VII The weight values ω1-ω7 and the peak time t1-t7 are used to calculate the neural response data τ of the object; wherein, 2. The neural response data monitoring system based on EEG signals according to claim 1, characterized in that: The host computer is also used to obtain the reference peak delay data corresponding to wave I to wave VII respectively A historical database storing multiple historical auditory brainstem response waveform data of the same subject is constructed, and the peak time t1'-t7' of each current historical auditory brainstem response waveform data in the historical database is obtained. For any wave from wave I to wave VII, the deviation data in the peak time data set of any wave is filtered out based on the 3σ criterion, and the benchmark sample peak time data set of any wave is screened out. The mean of all samples in the benchmark sample peak time data set of any wave is used as the benchmark peak delay data of any wave, thereby obtaining the benchmark peak delay data corresponding to waves I to wave VII respectively. The host computer is also used to obtain each auditory brainstem response waveform data of the subject and the corresponding peak times t1-t7 of the peaks of waves I to VII, and store them in the historical database corresponding to the subject.

3. The neural response data monitoring system based on EEG signals according to claim 2, characterized in that: The host computer is also used to Construct weight values ω1-ω7 corresponding to waves I-VII respectively: based on the current benchmark peak delay data Based on the benchmark sample peak time data set, the corresponding variance value σ1-σ7 is obtained, and the weight value ω1-ω7 is calculated based on the inverse of the variance value σ1-σ7, that is, for any weight value ω i , 4. The neural response data monitoring system based on EEG signals according to claim 1, characterized in that: The acquisition device includes a power supply interface, a power module, an audio generation module, an audio driver module, an audio interface, a synchronization module, a sensor interface, a signal reading module, a control module and a communication module; The power supply module is used to connect to an external power supply through the power supply interface to supply power to the acquisition device; The control module is used to send a trigger signal to the audio generation module, the audio generation module is used to generate an audio signal after receiving the trigger signal, the audio driving module is used to receive the audio signal and generate an audio driving instruction containing the audio signal based on the audio signal, the control module is used to control the audio driving module to output the audio driving instruction of the set stimulation duration through the audio interface at every set stimulation interval, thereby controlling the audio output module to output the sound stimulation of the set stimulation duration to the subject at every set stimulation interval, thereby forming multiple groups of stimulation; The synchronization module is used to generate a synchronization signal based on the audio signal, and the control module is also used to generate an acquisition instruction for controlling the action of the signal reading module based on the synchronization signal. The signal reading module is used to synchronously read the original EEG signals of the object with a set acquisition time corresponding to each group of sound stimulations collected by the sensor module through the sensor interface, perform analog-to-digital conversion on each group of original EEG signals to generate original EEG signals in the form of digital signals and send them to the control module. The control module is also used to encode each group of original EEG signals to generate original data packets and send them to the host computer via the communication module.

5. The neural response data monitoring system based on EEG signals according to claim 4, characterized in that: The host computer includes a decoding module, a pre-processing module, a processing module and a display module; The decoding module is used to decode each original data packet after receiving it and then extract the original EEG signal; The preprocessing module is used to perform signal preprocessing on each extracted original EEG signal to obtain a corresponding preprocessed EEG signal and transmit it to the processing module; The processing module is used to perform mean processing on multiple groups of pre-processed EEG signals to obtain auditory brainstem response waveform data and control the display module to display the auditory brainstem response waveform data; The processing module is further used to obtain the peak delay data of waves I to VII based on the auditory brainstem response waveform data, and to obtain the neural response data of the subject based on the peak delay data of waves I to VII.

6. A method for monitoring neural response data based on EEG signals, characterized in that: It includes the following steps: S1, the acquisition device generates an audio signal and controls the audio output module based on the audio signal to output a sound stimulus of a set stimulation duration to the subject at intervals of a set stimulation interval, forming multiple groups of stimulations, synchronously collecting the original EEG signal of the subject of a set collection duration corresponding to each group of sound stimulations through the sensor module and uploading it to the host computer, and setting the stimulation duration to be less than the set collection duration and less than the set stimulation interval; S2. The host computer receives multiple groups of original EEG signals and performs signal preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals; S3, the host computer performs mean processing on the multiple groups of pre-processed EEG signals to obtain auditory brainstem response waveform data, the auditory brainstem response waveform data including seven positive waves, namely wave I, wave II, wave III, wave IV, wave V, wave VI and wave VII; S4. The host computer obtains peak delay data of waves I to VII based on the auditory brainstem response waveform data, and obtains neural response data of the subject based on the peak delay data of waves I to VII; In step S2, the host computer receives multiple groups of original EEG signals, and performs signal outlier removal and filtering preprocessing on each group of original EEG signals to obtain corresponding preprocessed EEG signals; In step S4, the host computer obtains the peak time t1-t7 of the wave I-wave VII based on the auditory brainstem response waveform data, with the sound stimulation generation time as time 0, and uses the peak time t1-t7 as the peak delay data of wave I-wave VII, and obtains the reference peak delay data corresponding to wave I-wave VII respectively. Based on benchmark peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII, based on the benchmark peak delay data corresponding to waves I-VII The weight values ω1-ω7 and the peak time t1-t7 are used to calculate the neural response data τ of the object; wherein, 7. The method for monitoring neural response data based on EEG signals according to claim 6, wherein: In step S4, the host computer obtains the reference peak delay data corresponding to wave I to wave VII respectively A historical database storing multiple historical auditory brainstem response waveform data of the same subject is constructed, and the peak time t1'-t7' of each current historical auditory brainstem response waveform data in the historical database is obtained. For any wave from wave I to wave VII, the deviation data in the peak time data set of any wave is filtered out based on the 3σ criterion, and the benchmark sample peak time data set of any wave is screened out. The mean of all samples in the benchmark sample peak time data set of any wave is used as the benchmark peak delay data of any wave, thereby obtaining the benchmark peak delay data corresponding to waves I to wave VII respectively. After the host computer acquires each auditory brainstem response waveform data of the subject and the corresponding peak times t1-t7 of the peaks of waves I to VII, they are stored in the historical database corresponding to the subject.

8. The method for monitoring neural response data based on EEG signals according to claim 7, wherein: In step S4, the host computer uses the reference peak delay data Construct weight values ω1-ω7 corresponding to waves I-VII respectively: based on the current benchmark peak delay data Based on the benchmark sample peak time data set, the corresponding variance value σ1-σ7 is obtained, and the weight value ω1-ω7 is calculated based on the inverse of the variance value σ1-σ7, that is, for any weight value ω i ,

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