Environmental pollutant neurotoxicity detection method and system based on neuroelectrophysiology, storage medium and terminal

Abstract

The invention discloses an environmental pollutant neurotoxicity detection method and system based on neuroelectrophysiology, a storage medium and a terminal, and belongs to the field of environmental pollutant detection.The method mainly comprises the steps that S1, subjects are screened, specifically, two groups of subjects are selected, one group is people known to be exposed to specific environmental pollutants, and the other group is people known to be exposed to specific environmental pollutants; the other group is healthy people not exposed to the specific environmental pollutants; s2, collecting electroencephalogram signals: collecting the electroencephalogram signals of the two groups of subjects by using electroencephalogram equipment; s3, the collected electroencephalogram signals are preprocessed; s4, feature extraction: extracting related neural electrophysiological features from the preprocessed electroencephalogram signals; s5, statistical analysis: comparing the neural electrophysiological feature difference between the two groups of subjects by adopting a statistical method, and identifying the neural electrophysiological change related to pollutant exposure; s6, neurotoxicity evaluation: according to the neuroelectrophysiological change, evaluating the neurotoxicity effect of the environmental pollutants, and establishing a relation model between pollutant exposure and the neuroelectrophysiological change. According to the method, the neurotoxicity of the pollutants can be quickly and accurately evaluated by monitoring the influence of the pollutants on the electrical activity of nerve cells.

Description

Technical Field

[0001] The present invention relates to the field of environmental pollutant detection, and in particular to a method, system, storage medium and terminal for detecting the neurotoxicity of environmental pollutants based on neuroelectrophysiology. Background Art

[0002] With the acceleration of industrialization and urbanization, various environmental pollutants enter the ecosystem through air, water and soil. Some of these pollutants have been proven to be toxic to the nervous system. Such environmental pollutants are also called neurotoxic pollutants, which pose a serious threat to human health and the ecological environment. In the field of environmental science and public health, accurate assessment of the neurotoxicity of environmental pollutants is an important task. Traditional pollutant detection methods mainly rely on chemical analysis. Although these methods can identify and quantify pollutants, they are often unable to directly evaluate the potential effects of pollutants on organisms, especially the human nervous system.

[0003] As an important means of studying the function of the nervous system, neuroelectrophysiology has been widely used in neuroscience research and clinical diagnosis. By monitoring the electrical activity of nerve cells, the response of the nervous system to external stimuli can be intuitively reflected. Applying neuroelectrophysiology technology to the toxicity detection of environmental pollutants can provide a new way to evaluate the neurotoxicity of pollutants. Existing studies have shown that certain environmental pollutants can cause changes in neuroelectrophysiology, which may be related to the neurotoxicity of pollutants.

[0004] However, there is currently a lack of a standardized, neuroelectrophysiology-based method for detecting the neurotoxicity of environmental pollutants. Existing detection methods are either too dependent on animal experiments or cannot accurately reflect the effects of pollutants on the nervous system. Therefore, it is particularly important to develop a detection method that can directly evaluate the neurotoxicity of pollutants. Summary of the invention

[0005] The purpose of the present invention is to overcome the problems existing in the prior art and to provide a method, system, storage medium and terminal for detecting the neurotoxicity of environmental pollutants based on neuroelectrophysiology, aiming to evaluate the toxicity of pollutants by monitoring their effects on neural electrical activity.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] In a first aspect, a method for detecting the neurotoxicity of environmental pollutants based on neuroelectrophysiology is provided, comprising the following steps:

[0008] S1. Subject screening: Two groups of subjects were selected, one group was a population known to be exposed to specific environmental pollutants, and the other group was a healthy population not exposed to the specific environmental pollutants;

[0009] S2. EEG signal collection: EEG signals of two groups of subjects were collected using EEG equipment;

[0010] S3, preprocessing the collected EEG signals;

[0011] S4, feature extraction: extract relevant neurophysiological features from the preprocessed EEG signals;

[0012] S5. Statistical analysis: Statistical methods were used to compare the differences in neuroelectrophysiological characteristics between the two groups of subjects and to identify neuroelectrophysiological changes associated with pollutant exposure;

[0013] S6. Neurotoxicity assessment: Evaluate the neurotoxic effects of environmental pollutants based on neuroelectrophysiological changes, and establish a relationship model between pollutant exposure and neuroelectrophysiological changes.

[0014] In some embodiments, in step S1, the subjects are preliminarily screened using a questionnaire and a psychological assessment scale.

[0015] In some embodiments, the step S2 uses an electroencephalogram device to collect electroencephalogram signals from two groups of subjects, including:

[0016] Under a standardized environment, the subjects' EEG activity was recorded at rest and during various neuropsychological tasks.

[0017] In some embodiments, the neuropsychological task includes an emotional picture stimulation task, which requires the subject to respond within a set time after the picture is displayed.

[0018] Preferably, the set time is 2000ms.

[0019] In some embodiments, the step S3 pre-processes the collected EEG signal, including:

[0020] The collected EEG signals are filtered, artifacts are removed and signal amplified.

[0021] In some embodiments, the neuroelectrophysiological characteristics include power spectral density, event-related potentials, neural coherence, and neural synchrony.

[0022] In a second aspect, a neurophysiological-based environmental pollutant neurotoxicity detection system is provided, comprising:

[0023] A subject screening module, used to select two groups of subjects, one group is a population known to be exposed to specific environmental pollutants, and the other group is a healthy population not exposed to the specific environmental pollutants;

[0024] An EEG signal acquisition module is used to acquire EEG signals from two groups of subjects using an EEG device;

[0025] A data preprocessing module is used to preprocess the collected EEG signals;

[0026] A feature extraction module is used to extract relevant neuro-electrophysiological features from the preprocessed EEG signals;

[0027] Statistical analysis module, used to compare the differences in neuroelectrophysiological characteristics between the two groups of subjects using statistical methods and identify neuroelectrophysiological changes related to pollutant exposure;

[0028] The neurotoxicity assessment module is used to evaluate the neurotoxic effects of environmental pollutants based on neuroelectrophysiological changes and to establish a relationship model between pollutant exposure and neuroelectrophysiological changes.

[0029] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the methods for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology is implemented.

[0030] In a fourth aspect, a terminal is provided, comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and when the processor executes the computer instructions, any one of the methods for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology is executed.

[0031] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) The present invention quickly and accurately evaluates the neurotoxicity of pollutants by monitoring the effects of pollutants on the electrical activity of nerve cells. As a non-invasive, high-time resolution detection method, the advantage of electroencephalography in detecting the effects of environmental pollution on the nervous system is that it can directly reflect the electrophysiological activity of the brain and capture changes in neural signals related to environmental pollution. In addition, the accuracy of detecting environmental pollution hazards can be improved through the joint interactive analysis of multiple brain regions of the electroencephalogram. This method can provide a scientific basis for the risk assessment and control of environmental pollutants, which is of great significance for protecting human health and the ecological environment.

[0034] (2) The present invention has direct relevance. Specifically, by directly comparing the EEG changes of polluted people with those of unpolluted people, the impact of environmental pollutants on the human nervous system can be intuitively reflected.

[0035] (3) The present invention has the advantage of early detection. Specifically, it can detect brain electrophysiological changes caused by pollutants at an early stage and provide early warning for neurotoxicity caused by environmental pollution.

[0036] (4) The present invention can provide health risk assessment. Specifically, it provides a new tool for assessing the health risks of polluted populations, which is helpful for public health decision-making and the formulation of environmental pollution intervention measures.

[0037] (5) It provides a scientific basis for the study of neurotoxicity of environmental pollutants and helps to deepen our understanding of the mechanism of action of pollutants on the nervous system.

[0038] (6) The present invention has potential for clinical application. Specifically, the present method is expected to be applied in clinical and epidemiological research to provide diagnostic and therapeutic support for neurological diseases related to environmental pollution.

[0039] (7) The present invention can promote environmental protection. By revealing the direct impact of pollutants on human health, the present invention helps to promote the formulation and implementation of environmental protection policies, reduce pollutant emissions, and protect public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to an embodiment of the present invention;

[0041] Figure 2 The process of subject screening and EEG signal acquisition shown in the embodiment of the present invention;

[0042] Figure 3 The flowchart of the EEG recording and emotional picture stimulation task shown in the embodiment of the present invention;

[0043] Figure 4 A comparison diagram of brain wave band changes shown in an embodiment of the present invention;

[0044] Figure 5 A comparison diagram of temporal lobe and frontal lobe brain area activities shown in an embodiment of the present invention;

[0045] Figure 6 A diagram showing changes in brain region correlations during positive and negative picture stimulation tasks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0047] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present application for the above-mentioned problems below should all be the contributions made by the inventor to the present application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0048] In view of the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows:

[0049] In an exemplary embodiment, a method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology is provided, referring to Figure 1 , the method comprises the following steps:

[0050] S1. Subject screening: Two groups of subjects were selected, one group was the population known to be exposed to specific environmental pollutants, i.e. the experimental group / exposure group, and the other group was the healthy population not exposed to the specific environmental pollutants, i.e. the control group;

[0051] S2. EEG signal collection: EEG signals of two groups of subjects were collected using EEG equipment;

[0052] S3, preprocessing the collected EEG signals;

[0053] S4, feature extraction: extract relevant neurophysiological features from the preprocessed EEG signals;

[0054] S5. Statistical analysis: Statistical methods were used to compare the differences in neuroelectrophysiological characteristics between the two groups of subjects and to identify neuroelectrophysiological changes associated with pollutant exposure;

[0055] S6. Neurotoxicity assessment: Evaluate the neurotoxic effects of environmental pollutants based on neuroelectrophysiological changes, and establish a relationship model between pollutant exposure and neuroelectrophysiological changes.

[0056] Specifically, the method is based on the principle of neuroelectrophysiology, that is, the response of the nervous system to external stimuli can be monitored by changes in EEG signals. Environmental pollutants may interfere with normal neural signal conduction, resulting in abnormal EEG activity. By analyzing these abnormalities, we can evaluate the neurotoxicity of pollutants. In addition, the present invention is also based on the principle of psychophysiology, that is, emotional stimulation can cause specific EEG reactions, which can be used as indicators for evaluating the functional state of the nervous system. By directly monitoring the changes in EEG activity of subjects under specific emotional stimulation, the problem that traditional chemical analysis methods cannot directly evaluate the effects of pollutants on the nervous system is solved. By comparing the EEG characteristics of the experimental group and the control group, the present invention can intuitively reflect the effects of environmental pollutants on the human nervous system and provide early warning for neurotoxicity caused by environmental pollution. In addition, the present method reduces the dependence on experimental animals and conforms to the development trend of ethical and sustainable research.

[0057] Further, Figure 2 The process of subject screening and EEG signal collection. Subject screening: Two groups of subjects were selected, one group living in an area with severe environmental pollution (exposed group) and the other group living in an area with a good environment (control group). Questionnaires and psychological assessment scales (such as CES-D and STAI) were used to screen the subjects to ensure that they met the experimental requirements, such as age, health status, and psychological state. EEG signal collection: An electroencephalogram (EEG) device was installed on each subject, ensuring that the electrodes were correctly placed at the predetermined position on the head, and the subjects were kept in a resting state to record basic EEG activity.

[0058] Figure 3 This is a flowchart for EEG recording and emotional picture stimulation tasks. Specifically, the emotional picture stimulation task is designed, including the display of positive and negative emotional pictures. The figure describes the specific steps of the experiment, including the time period of picture retention and picture appearance, and the rapid selection task that the subjects are required to perform after the picture appears. This is a task design used to stimulate the subject's emotional response and record EEG activity. The task requires the subject to respond within 2000ms after the picture appears, such as pressing the "K" or "L" key on the keyboard. While the picture is displayed, the changes in the subject's EEG signals are recorded, especially in the time period after the emotional stimulation.

[0059] Furthermore, the collected EEG signals are first subjected to preprocessing steps such as filtering, artifact removal, and signal amplification to ensure the quality and comparability of the data. Then, the preprocessed EEG signals are subjected to feature extraction, including extracting neurophysiological features (also known as EEG features) such as power spectral density, event-related potentials (ERPs), neural coherence, and neural synchronization. Among them, feature extraction of EEG signals is a routine and key step in electroencephalogram (EEG) analysis, which involves extracting features from raw EEG data that are helpful for subsequent analysis and classification. There are many methods for feature extraction, including but not limited to the following:

[0060] 1. Time domain analysis: This is the most intuitive feature extraction method, which usually involves calculating the waveform characteristics of the EEG signal, such as peak value, mean, variance, standard deviation, waveform width, etc. These statistical indicators reflect the changes in signal strength and help identify EEG activity patterns.

[0061] 2. Frequency domain analysis: EEG signals are converted from the time domain to the frequency domain through methods such as Fourier transform (FT), power spectral density (PSD), and band power (BP) to identify the relationship between different frequency components in the signal. For example, changes in the activity of alpha waves (8-14Hz) and beta waves (14-30Hz) can reflect different states of the brain.

[0062] 3. Wavelet transform: As a time-frequency domain analysis tool, wavelet transform can accurately locate the characteristics of EEG signals at different time and frequency scales, and is suitable for detecting frequency changes occurring at a specific time.

[0063] 4. Common Spatial Pattern (CSP): This is a method commonly used in the field of brain-computer interface (BCI) to extract features that can maximize the distinction between different brain states.

[0064] 5. Deep learning technology: In recent years, deep learning methods, such as convolutional neural networks (CNN), have been used to automatically extract features of EEG signals and have achieved remarkable results in fields such as BCI. For example, EEGNet is a compact CNN designed specifically for feature extraction of EEG signals.

[0065] 6. Nonlinear feature extraction: including entropy correlation analysis, complex network analysis, etc. These methods can reveal the nonlinear and dynamic characteristics of EEG signals.

[0066] 7. Multidimensional feature extraction: Combine features from the time domain, frequency domain, time-frequency domain, and spatial domain to obtain a more comprehensive signal description.

[0067] The above feature extraction methods can be used alone or in combination to improve the accuracy and efficiency of EEG signal analysis. In practical applications, the selection of appropriate feature extraction methods depends on the specific research objectives and the available data types. With the development of technology, new analysis methods and algorithms continue to emerge, further promoting the development of the field of EEG signal processing.

[0068] Furthermore, statistical analysis was performed and statistical methods were used to compare the differences in EEG characteristics between the exposure group and the control group to identify neuroelectrophysiological changes associated with pollutant exposure.

[0069] Furthermore, neurotoxicity assessment was conducted to evaluate the neurotoxic effects of environmental pollutants based on the changes in EEG characteristics, and a relationship model between pollutant exposure and EEG changes was established. Figure 4 The figure shows the changes in EEG bands. This figure shows the changes in EEG topography in different EEG bands (Delta, Theta, Alpha, Beta, Low-gamma) in the exposure group and the control group in the positive and negative emotional picture stimulation tasks. The changes in color depth in the picture represent the strength of the EEG signal, reflecting the impact of environmental pollutants on EEG activity.

[0070] Further, Figure 5 This is a comparison of the activities of the temporal lobe and frontal lobe brain regions. This figure shows in detail the time-frequency changes in the EEG activities of the temporal lobe and frontal lobe brain regions in the positive and negative emotional picture stimulation tasks. The changes in the depth of the color in the picture represent the strength of the EEG signal, which is used to compare the differences in activity in specific brain regions between the exposure group and the control group.

[0071] Figure 6 This is a graph showing the changes in brain region correlation in positive and negative picture stimulation tasks. This graph shows the changes in correlation between the frontal and temporal lobe brain regions in positive and negative emotional picture stimulation tasks. The curves in the graph represent the changes in coherence between brain regions at different frequencies, revealing the effects of pollutant exposure on the interaction between brain regions. The accuracy of environmental pollution hazard detection can be improved through multi-brain region joint interaction analysis of EEG, for the following reasons:

[0072] 1. Multi-brain region information integration: Different brain regions are responsible for different functions. By analyzing EEG signals from multiple brain regions, more comprehensive brain activity information can be obtained. This integration can reveal the interactions between different brain regions, thereby more accurately detecting and evaluating the impact of environmental pollution on brain function.

[0073] 2. Improve the spatiotemporal resolution of signals: Multi-brain region analysis can improve the spatiotemporal resolution of EEG signals, helping to identify changes in activity in specific brain regions under pollution exposure, which is crucial for understanding how environmental pollutants affect brain function.

[0074] 3. Enhanced feature recognition capabilities: Joint analysis of multiple brain regions can enhance the ability to recognize brain activity patterns, which is very important for distinguishing changes in brain activity caused by environmental pollution from changes caused by other factors.

[0075] 4. Improve the performance of machine learning models: Combining EEG data from multiple brain regions can train more accurate machine learning models to identify and predict the impact of environmental pollution on individual health. For example, by using deep learning technology, a large amount of EEG data from multiple brain regions can be processed and analyzed, thereby improving the accuracy of detecting environmental hazards.

[0076] 5. Complexity of the effects of environmental factors on the brain: Environmental pollution may affect the brain through multiple pathways, including causing inflammatory responses, oxidative stress, and changes in neurotransmitters. Multi-brain EEG analysis helps to reveal these complex mechanisms, thereby providing more accurate information for the neurotoxicity assessment of environmental pollution.

[0077] 6. Assessment of psychological and emotional responses: Environmental pollution not only affects physiological functions, but may also affect the psychological and emotional state of individuals. Multi-brain EEG analysis can assess the impact of environmental factors on emotions and cognitive functions, which is crucial for a comprehensive understanding of the hazards of environmental pollution.

[0078] In summary, multi-brain region EEG joint interaction analysis can help improve the accuracy of environmental pollution hazard detection by providing more comprehensive brain activity information, which is of great significance for formulating effective prevention and intervention measures.

[0079] In another exemplary embodiment, based on the same inventive concept as the method, a neurophysiological-based environmental pollutant neurotoxicity detection system is provided, comprising:

[0080] A subject screening module, used to select two groups of subjects, one group is a population known to be exposed to specific environmental pollutants, and the other group is a healthy population not exposed to the specific environmental pollutants;

[0081] An EEG signal acquisition module is used to acquire EEG signals from two groups of subjects using an EEG device;

[0082] A data preprocessing module is used to preprocess the collected EEG signals;

[0083] A feature extraction module is used to extract relevant neuro-electrophysiological features from the preprocessed EEG signals;

[0084] Statistical analysis module, used to compare the differences in neuroelectrophysiological characteristics between the two groups of subjects using statistical methods and identify neuroelectrophysiological changes related to pollutant exposure;

[0085] The neurotoxicity assessment module is used to evaluate the neurotoxic effects of environmental pollutants based on neuroelectrophysiological changes and to establish a relationship model between pollutant exposure and neuroelectrophysiological changes.

[0086] It should be noted that the system implements the same functions as the steps in the method, which will not be described in detail here.

[0087] In another exemplary embodiment, based on the same inventive concept as the method, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology provided in an embodiment of the present invention is implemented. Based on such an understanding, the technical solution of this embodiment is essentially or partly contributed to the prior art or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0088] In another exemplary embodiment, based on the same inventive concept as the method, a terminal is provided, including a memory and a processor, wherein the memory stores computer instructions executable on the processor, and when the processor executes the computer instructions, a method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology provided in an embodiment of the present invention is executed.

[0089] The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0090] Embodiments of the subject matter and functional operations described in this specification may be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver device for execution by the data processing device.

[0091] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuits, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits), and the apparatus can also be implemented as special purpose logic circuits.

[0092] Processors suitable for executing computer programs include, for example, general and / or special microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more large-capacity storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to this large-capacity storage device to receive data from it or to transmit data to it, or both. However, the computer does not necessarily have such a device. In addition, the computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, just to name a few.

[0093] It should be understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0094] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for detecting the neurotoxicity of environmental pollutants based on neuroelectrophysiology, characterized in that: The following steps are involved: S1. Subject screening: Two groups of subjects were selected, one group was a population known to be exposed to specific environmental pollutants, and the other group was a healthy population not exposed to the specific environmental pollutants; S2. EEG signal collection: EEG signals of two groups of subjects were collected using EEG equipment; S3, preprocessing the collected EEG signals; S4, feature extraction: extract relevant neurophysiological features from the preprocessed EEG signals; S5. Statistical analysis: Statistical methods were used to compare the differences in neuroelectrophysiological characteristics between the two groups of subjects and to identify neuroelectrophysiological changes associated with pollutant exposure; S6. Neurotoxicity assessment: Evaluate the neurotoxic effects of environmental pollutants based on neuroelectrophysiological changes, and establish a relationship model between pollutant exposure and neuroelectrophysiological changes.

2. The method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to claim 1, characterized in that: In step S1, the subjects are preliminarily screened using a questionnaire and a psychological assessment scale.

3. The method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to claim 1, characterized in that: In step S2, the electroencephalogram (EEG) equipment is used to collect EEG signals from two groups of subjects, including: Under a standardized environment, the subjects' EEG activity was recorded at rest and during various neuropsychological tasks.

4. The method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to claim 3, characterized in that: The neuropsychological task includes an emotional picture stimulation task, which requires the subject to respond within a set time after the picture is displayed.

5. The method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to claim 4, characterized in that: The set time is 2000ms.

6. The method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to claim 1, characterized in that: The step S3 pre-processes the collected EEG signal, including: The collected EEG signals are filtered, artifacts are removed and signal amplified.

7. The method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology according to claim 1, characterized in that: The neurophysiological characteristics include power spectral density, event-related potentials, neural coherence and neural synchronization.

8. A neurophysiological-based environmental pollutant neurotoxicity detection system, characterized in that: include: A subject screening module, used to select two groups of subjects, one group is a population known to be exposed to specific environmental pollutants, and the other group is a healthy population not exposed to the specific environmental pollutants; An EEG signal acquisition module is used to acquire EEG signals from two groups of subjects using an EEG device; A data preprocessing module is used to preprocess the collected EEG signals; A feature extraction module is used to extract relevant neuro-electrophysiological features from the preprocessed EEG signals; Statistical analysis module, used to compare the differences in neuroelectrophysiological characteristics between the two groups of subjects using statistical methods and identify neuroelectrophysiological changes related to pollutant exposure; The neurotoxicity assessment module is used to evaluate the neurotoxic effects of environmental pollutants based on neuroelectrophysiological changes and to establish a relationship model between pollutant exposure and neuroelectrophysiological changes.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for detecting neurotoxicity of environmental pollutants based on neuroelectrophysiology as described in any one of claims 1 to 7 is implemented.

10. A terminal comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, characterized in that: When the processor runs the computer instructions, it executes the neurotoxicity detection method of environmental pollutants based on neuroelectrophysiology as described in any one of claims 1-7.

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