A neural conduction bundle-based electroencephalogram transmission speed feature analysis method, system and electronic device

Abstract

The application provides a neural conduction bundle-based electroencephalogram transmission speed feature analysis method and system and an electronic device. According to the neural conduction bundle, the electroencephalogram collection sites of the deep brain and the cerebral cortex are selected, the defects of the traditional non-invasive electroencephalogram signal based on the skull surface distribution according to the international 10-20 system and the single node detection of the invasive electrode are compensated, and the joint collection of the neural conduction bundle multi-node electroencephalogram signal is realized. By calculating the transmission speed difference of the target electroencephalogram on the neural conduction bundle and the non-neural conduction bundle, the influence of the neural conduction bundle state on the electroencephalogram transmission speed can be more accurately reflected. The application breaks through the bottleneck that the prior art cannot comprehensively analyze the neural conduction bundle-related electroencephalogram transmission speed, can objectively and accurately identify the neural conduction bundle-related electroencephalogram transmission feature, and can be widely applied to personal rehabilitation effect evaluation, remote health monitoring and the like.

Description

Technical Field

[0001] This invention relates to the field of neuroelectrophysiology or brain-computer interface technology, and in particular to an analysis method, system and electronic device based on the characteristics of brain electrical transmission velocity of nerve conduction tracts. Background Technology

[0002] Electroencephalography (EEG) signals are the sum of synchronized postsynaptic potentials of a large number of neurons in the cerebral cortex. EEG conduction occurs through several pathways: action potentials are transmitted along axons to form neural tracts, and classical chemical synaptic transmission at their terminals, which forms the basis for precise and directional information processing. In addition, there are non-neural tract transmission pathways, including volume conduction and field effects, neuromodulation diffusion, and cortical diffusion of abnormal discharges. Neural tracts are bundles of axons with similar functions and largely consistent origins and endpoints. They are responsible for transmitting electrical signals from one group of neurons to another at a distance, and for planning, initiating, and precisely controlling motor commands. Neural tracts typically include cortical central neurons, midbrain synaptic neurons, and spinal cord peripheral neurons, participating in the transmission of motor and sensory signals under normal conditions. Non-neural tract transmission is involved in the transmission of abnormal EEG signals, such as during epileptic seizures. Conventional cortical electroencephalography (EEG) is a synchronous rhythmic activity of a large cluster of neurons in the cerebral cortex, primarily involving electrical signals from central neurons in the cortex. For specific deep brain EEG, invasive surgical techniques such as electrode implantation and / or brain-computer interfaces can detect EEG signals in specific regions. Therefore, through the aforementioned non-invasive and invasive methods, EEG signals from different deep brain and / or cortical nodes of neural conduction tracts can be acquired. Currently, non-invasive EEG signal acquisition sites are placed on the scalp according to the international 10-20 system standardization, while invasive EEG signal acquisition sites are placed at pre-defined deep abnormality points on the computer. Single traditional methods cannot simultaneously acquire EEG signals from key nodes in neural circuits in a unified spatiotemporal context, cannot analyze the transmission speed of EEG signals in neural and non-neural conduction tracts, and are difficult to assess the impact of neural conduction tract status on EEG signal transmission speed; they also lack information on the transmission characteristics of EEG signals related to neural conduction tracts. Currently, there are no reported methods or systems for analyzing the transmission speed characteristics of EEG signals related to neural conduction tracts. Summary of the Invention

[0003] To address the problems existing in the prior art, the present invention aims to provide a method, system, and electronic device for analyzing the transmission velocity characteristics of electroencephalogram (EEG) signals based on neural conduction tracts. Based on electrode implantation and brain-computer interface technology, it proposes implanting detection electrodes at key nodes of the neural conduction tract, avoiding the limitations of traditional international 10-20 system standards and single brain parenchyma detection point standards, thus overcoming the inability to analyze the transmission velocity characteristics of EEG signals in both neural and non-neural conduction tracts. Furthermore, by introducing EEG velocity factors from non-neural conduction tracts, it can more accurately reflect the propagation characteristics of EEG velocity in the neural conduction tracts, providing a new approach for identifying EEG velocity characteristics related to neural conduction tracts.

[0004] To achieve the above objectives, the present invention provides the following solution: An analysis method based on the characteristics of brain electrical transmission velocity in neural conduction tracts includes the following steps: S1. Obtain the electroencephalogram (EEG) signal characteristics of the deep brain and cerebral cortex at different locations on the neural conduction tract through electrode implantation or brain-computer interface. S2. Based on the characteristics of the EEG signal, acquire the target EEG waveform, and acquire the spatial distance and time interval between the target EEG waveform at different EEG acquisition sites; S3. Calculate the transmission speed of the target EEG in the nerve conduction tract and / or in the non-nerve conduction tract based on the spatial distance and time interval. S4. Based on the transmission velocity on the nerve conduction tracts and the transmission velocity on the non-nerve conduction tracts, generate a joint model of EEG velocity transmission characteristics, and use the joint model to analyze the EEG transmission velocity characteristics.

[0005] Preferably, S1 includes: The EEG acquisition sites in the deep brain and cerebral cortex are determined based on the neural conduction tracts to be tested. Depending on the location of the EEG acquisition, the characteristics of deep brain EEG signals can be obtained by implanting invasive electrodes or brain-computer interfaces into the brain parenchyma. Depending on the location of the EEG acquisition, the characteristics of the EEG signal in the cerebral cortex are obtained by placing non-invasive electrodes or brain-computer interfaces outside the brain parenchyma.

[0006] Preferably, S2 includes: Based on the characteristics of the electroencephalogram (EEG) signals, target EEG waveforms are identified in different deep brain and cortical EEG acquisition sites. The spatial distance is obtained based on the spatial location of the target EEG waveform at the EEG acquisition site. The time interval is obtained based on the time point of the target EEG waveform at the EEG acquisition site.

[0007] Preferably, S3 includes: Calculate the EEG transmission velocity along the nerve conduction tract based on the spatial distance Ltract and time interval Ttract of the target EEG waveform at the EEG acquisition site along the nerve conduction tract: Vbundle = Lbundle / Tbundle

[0008] Preferably, S3 further includes: Based on the spatial distance Lnon-tract and time interval Tnon-tract of the target EEG waveform at the EEG acquisition site on the non-neural conduction tract, calculate the EEG transmission velocity on the non-neural conduction tract: Vnon-beam = Lnon-beam / Tnon-be

[0009] Preferably, S4 includes: generating relative change features based on the EEG transmission velocity Vbundle on the neural conduction tract and the EEG transmission velocity Vnon-bundle on the non-neural conduction tract. M = Vbeam / Vnon-beam M = Vnon-beam / Vbeam Where M represents the relative change in the electroencephalographic transmission velocity of the neural conduction tract compared to the non-conduction tract.

[0010] Preferably, S4 further includes: generating absolute change features based on the EEG transmission velocity Vbundle on the neural conduction tract and the EEG transmission velocity Vnon-bundle on the non-neural conduction tract. N = Vnon-beam - Vbeam N = Vbundle - Vnon-bundle Wherein, N represents the absolute change in the EEG transmission velocity of the nerve conduction tract compared to the non-conduction tract.

[0011] Preferably, in S1, the deep brain regions and cerebral cortex at different locations on the nerve conduction tract include: upstream and downstream nodes or nodes with mutual regulatory relationships in a conduction network composed of different types of neurons in the brain connected by synapses.

[0012] The present invention also provides an analysis system for the transmission velocity of electroencephalograms based on nerve conduction tracts, the system being used to implement the above method, comprising: The acquisition module is used to acquire the electroencephalogram (EEG) signal characteristics of the deep brain and cerebral cortex at different locations along the nerve conduction tract via electrode implantation or brain-computer interface. The processing module is used to acquire the target EEG waveform based on the characteristics of the EEG signal, and to acquire the spatial distance and time interval between the target EEG waveform at different EEG acquisition sites; The calculation module is used to calculate the transmission speed of the target EEG in the neural conduction tract and / or in the non-neural conduction tract based on the spatial distance and time interval; The generation module is used to generate a joint model of EEG velocity transmission characteristics based on the transmission velocity on the neural conduction tracts and the transmission velocity on the non-neural conduction tracts, and to analyze the EEG transmission velocity characteristics using the joint model.

[0013] This embodiment also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements each step of the above-described analysis method.

[0014] The beneficial effects of this invention are as follows: This invention determines the EEG acquisition sites in the deep brain and / or cerebral cortex based on the target neural conduction tract, acquires EEG signal characteristics of different deep brain and / or cerebral cortex regions within a specific range of the target neural conduction tract through electrode implantation and / or brain-computer interface, determines the target EEG waveform, and obtains the relative spatial and temporal characteristics of the target EEG waveform in different deep brain or cerebral cortex EEG features. Based on the relative spatial and temporal information, and according to the differences in the transmission path along the neural conduction tract, the transmission velocity of the target EEG is calculated, generating a joint analysis model of EEG velocity transmission characteristics, thereby achieving more accurate characteristic analysis of EEG velocity related to neural conduction tracts.

[0015] This invention selects EEG acquisition sites in the deep brain and cerebral cortex based on neural conduction tracts. This overcomes the limitations of previous non-invasive EEG signal placement methods, which followed the international 10-20 system standard and were based on the spatial distribution of the skull surface, neglecting neural conduction tract nodes. Since deep brain electrodes and brain-computer interface electrodes are implanted at single nodes, they cannot jointly analyze the transmission speed of EEG signals along neural and non-neural conduction tracts, making it difficult to assess the impact of neural conduction tract status on EEG signal transmission speed. Therefore, this invention proposes selecting EEG acquisition sites in the deep brain and cerebral cortex based on neural conduction tract distribution, which more accurately reflects the state of neural conduction tracts than traditional EEG methods.

[0016] Existing EEG analysis systems lack methods for real-time detection and analysis of multiple nodes along neural conduction tracts, making it difficult to calculate and analyze EEG signal transmission velocity characteristics and thus hindering the assessment of neural circuit states. This invention analyzes the EEG signal transmission velocity characteristics of different locations in the deep brain and cerebral cortex based on target waveforms, and incorporates spatiotemporal differences in the location of the EEG acquisition site within the neural conduction tract, resulting in a more accurate representation of EEG transmission velocity characteristics related to the neural conduction tract. This invention integrates EEG transmission velocity characteristics related to the neural conduction tract, providing more information on the influence of neural circuit states on EEG transmission velocity compared to traditional EEG detection methods.

[0017] This invention overcomes the bottleneck of existing technologies in comprehensively analyzing the transmission velocity of EEG related to nerve conduction tracts, and achieves precise analysis of the characteristics of EEG transmission velocity related to nerve conduction tracts. It can objectively and accurately identify the transmission characteristics of EEG related to nerve conduction tracts and can be applied to scenarios such as personal rehabilitation effect assessment and remote health monitoring. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the brainwave transmission velocity characteristic analysis method, system, and electronic device based on nerve conduction tracts according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an electronic device according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the analysis of EEG transmission velocity characteristics based on nerve conduction tracts according to an embodiment of the present invention. Figure 4 These are CT and MRI localization images of the deep brain electrode and corresponding cortical electrode for implantation according to an embodiment of the present invention; Figure 5 This is an electroencephalogram (EEG) recorded by an implanted deep striatum electrode, according to an embodiment of the present invention. Figure 6 This is an electroencephalogram of the cerebral cortex of a patient with essential tremor, recorded by cortical electrodes according to an embodiment of the present invention. Figure 7 This is an example of the analysis process of the transmission time of EEG signals in the nerve conduction bundle of patients with essential tremor according to an embodiment of the present invention; Figure 8 This is an example of the analysis process of non-neural conduction tract EEG signal transmission time in patients with essential tremor according to an embodiment of the present invention; Figure 9 This is an example of the process for analyzing the transmission distance of electroencephalogram (EEG) signals in patients with essential tremor according to an embodiment of the present invention. Figure 10 This is a statistical analysis diagram illustrating the differentiation between Parkinson's disease and essential tremor based on the EEG transmission velocity characteristics of nerve conduction tracts, according to an embodiment of the present invention. Figure 11 ROC analysis diagram for distinguishing Parkinson's disease and essential tremor based on EEG transmission velocity characteristics of nerve conduction tracts in an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached figures: 300 - Analysis system for EEG characteristics of neural circuits; 301 - Communication interface; 302 - Memory; 303 - Processor; 304 - Display. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1 like Figure 1 As shown in the figure, this embodiment discloses a method for analyzing the characteristics of brain electrical transmission velocity based on nerve conduction tracts, the steps of which include: S1. By implanting electrodes or using a brain-computer interface, the characteristics of electroencephalogram (EEG) signals from different locations in the deep brain and cerebral cortex along the neural conduction tract are obtained.

[0024] Electroencephalogram (EEG) signal characteristics of the deep brain and cerebral cortex at different locations along the neural conduction tracts are obtained through electrode implantation or brain-computer interface. The neural conduction tract is a conduction network formed by synaptic connections between different types of neurons in the brain. In this embodiment, the deep brain and cerebral cortex refer to the upstream and downstream or interactive regulatory relationships of the EEG signal transmission of the neural conduction tracts.

[0025] Deep brain electroencephalogram (EEG) signals are acquired by implanting invasive electrodes or EEG interfaces into the brain parenchyma to obtain EEG signal characteristics at different locations of nerve conduction tracts. Cortical EEG signals are acquired by placing non-invasive electrodes or EEG interfaces outside the brain parenchyma to obtain EEG signal characteristics at different locations of nerve conduction tracts.

[0026] S2. Based on the characteristics of the EEG signal, acquire the target EEG waveform, and acquire the spatial distance and time interval between different EEG acquisition sites of the target EEG waveform.

[0027] The target waveforms are obtained from different EEG acquisition sites, such as deep brain regions and the cerebral cortex, and are similar in morphology and frequency, as well as in time. In this embodiment, the characteristics of the EEG signal are its amplitude, duration, and frequency. Spatial distance refers to the spatial distance of the target waveform at the EEG acquisition site; time interval refers to the temporal interval between the target waveforms at the EEG acquisition site.

[0028] S3. Calculate the transmission speed of the target EEG in the neural conduction tract and / or in the non-neural conduction tract based on the spatial distance and time interval.

[0029] Obtaining the transmission velocity of the target EEG in the neural conduction tract includes: V 束 = L 束 / T 束 ; Among them, V 束 L represents the speed of EEG signal transmission along the nerve conduction tract, primarily reflecting the influence of the condition of the nerve conduction tract on the speed of EEG signal transmission. 束 The spatial distance of the target waveform at the EEG acquisition site; T 束 The time interval between the target waveform at the EEG acquisition site.

[0030] Furthermore, calculating the transmission velocity of the target EEG in non-neural conduction pathways includes: V 非束 = L 非束 / T 非束 ; Among them, V 非束 L represents the external EEG transmission velocity, primarily reflecting the influence of external conditions on the speed of EEG signal transmission. 非束 The spatial distance of the target waveform at the EEG acquisition site; T 非束 The time interval between the target waveform at the EEG acquisition site.

[0031] S4. Based on the transmission velocity on the nerve conduction tracts and the transmission velocity on the non-nerve conduction tracts, generate a joint model of EEG velocity transmission characteristics, and use the joint model to analyze the EEG transmission velocity characteristics.

[0032] A joint model for generating EEG velocity transmission features: M = Vbeam / Vnon-beam or Vnon-beam / Vbeam; N = Vnon-beam-Vbeam or Vbeam-Vnon-beam Wherein, Vbundle represents the EEG transmission velocity in the nerve conduction tract, Vnonbundle represents the EEG transmission velocity in the non-nerve conduction tract, M represents the relative change in EEG transmission velocity between the nerve conduction tract and the non-conducting tract, and N represents the absolute change in EEG transmission velocity between the nerve conduction tract and the non-conducting tract.

[0033] Among them, M and N can identify the influence of neural conduction tracts on the transmission of EEG signals compared to non-neural conduction tracts.

[0034] This embodiment analyzes the characteristics of EEG transmission velocity related to nerve conduction tracts, selects EEG detection nodes through neural circuit information, and extracts the intervention characteristics of nerve conduction tracts on EEG transmission, providing a reference for the evaluation and intervention of nerve conduction tracts.

[0035] Example 2 This embodiment also provides an analysis system based on the electroencephalogram (EEG) transmission velocity characteristics of nerve conduction tracts, including: The acquisition module is used to acquire the electroencephalogram (EEG) signal characteristics of the deep brain and cerebral cortex at different locations along the nerve conduction tract via electrode implantation or brain-computer interface. The processing module is used to acquire the target EEG waveform based on the characteristics of the EEG signal, and to acquire the spatial distance and time interval between the target EEG waveform at different EEG acquisition sites; The calculation module is used to calculate the transmission speed of the target EEG in the neural conduction tract and / or in the non-neural conduction tract based on the spatial distance and time interval; The generation module is used to generate a joint model of EEG velocity transmission characteristics based on the transmission velocity on the neural conduction tracts and the transmission velocity on the non-neural conduction tracts, and to analyze the EEG transmission velocity characteristics using the joint model.

[0036] Example 3 The present invention also provides an electronic device, such as Figure 2 As shown, the neural circuit EEG feature analysis system 300 includes a communication interface 301, a processor 303, a memory 302, a display 304, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the analysis method of one embodiment.

[0037] Specifically, the communication interface 301 may include an interface for communicating with other devices. The interface may be a wired interface, a wireless interface, or a combination thereof. The communication interface 301 is used to receive image measurement information from a peripheral CT or MRI device. The processor may be a central processing unit (CPU), and the memory may include random-access memory (RAM) or non-volatile memory.

[0038] Example 4 The following is combined Figure 1-3 The specific implementation process of the present invention will be described.

[0039] Step 1: This embodiment uses a GESYTEC 3000I CT and MRI machine manufactured by General Electric Company (GE). The CT window includes bone windows and soft tissue windows, etc.; it collects imaging images of human cranial brain tissue to obtain the spatial positions of deep electrodes on nerve conduction tracts and cerebral cortex electrodes, such as... Figure 4 As shown.

[0040] Step 2: Select patients with essential tremor who have undergone deep brain electrode implantation, based on... Figure 4 The CT and MRI images of the head shown clearly indicate that the deep electrode implantation site is the bilateral ventral intermediate nucleus of the thalamus.

[0041] Step 3: Spatial location of different nodes on the ventral intermediate nucleus-cortical loop of the thalamus. Based on the criteria of upstream and downstream conduction or mutual influence on the nerve conduction tracts, the location for cortical EEG acquisition on the conduction tract is determined to be the frontal Fp1, i.e., the cortical electrode acquired by the Fp1-AV channel; and the location for cortical EEG acquisition on the non-conduction tract is the frontal Fp3, i.e., the cortical electrode acquired by the Fp3-AV channel.

[0042] Step 4: Collect EEG signals recorded by the implanted deep thalamic ventral intermediate nucleus electrode, such as... Figure 5 As shown.

[0043] Step 5: Acquire raw, uncorrected 64-channel electrocorticography (EEG) using cortical electrodes; such as... Figure 6 As shown.

[0044] Step 6: Based on the conclusion of Step 3, select the Fp1-AV channel to collect cortical EEG data from the neural conduction tract, and select the Fp1-AV channel and the ventral thalamic nucleus channel to collect waveforms of simultaneous discharge in the EEG. According to the definition of EEG signal characteristics, classify them as EEG characteristics of two nodes on the ventral thalamic nucleus-cortical conduction tract, and determine the target EEG waveform; measure the time interval between the target EEG nodes as the time interval of the target waveform on the neural conduction tract, such as... Figure 7 As shown.

[0045] Step 7: For the non-neural conduction tracts of the cerebral cortex, select the Fp3-AV channel to acquire the EEG data. Based on the determined target EEG waveform, measure the time interval between nodes of the target EEG as the time interval of the target waveform on the non-neural conduction tract. Figure 8 As shown.

[0046] Step 8, according to Figure 4 The cranial MRI image information shown measures the distance from the deep electrode in the ventral intermediate nucleus of the thalamus to the electrode in the Fp1-AV channel on the neural conduction tract that acquires cortical data, i.e., T. 束 In the example, the measurement time was 24ms; and the distance from the deep electrode in the ventral intermediate nucleus of the thalamus to the electrode in the Fp3-AV channel on the neural conduction tract, i.e., T... 非束 In the example, it is 52ms; such as Figure 9 As shown.

[0047] Step 9: Select target waveforms from different EEG acquisition sites, such as deep brain regions and the cerebral cortex, that are similar in morphology and frequency, and close in time; calculate the transmission velocity of the target EEG in the neural conduction tract, i.e., V.束 =L 束 / T 束 =825mm / 24ms=34.38m / s; and the transmission velocity in non-neural conduction pathways, i.e., V 非束 =L 非束 / T 非束 =1131mm / 52ms=21.75m / s; Step 10: Using this method, collect cortical EEG data from multiple detection points and the EEG transmission velocity V between the brain and the ventral intermediate nucleus of the thalamus. 束 and V 非束 M = V 束 / V 非束 = 34.38 / 21.75 = 1.58, where M represents the relative change in EEG transmission velocity between neural conduction tracts and non-conduction tracts. The distribution characteristics and comparative results of M values ​​in Parkinson's disease and essential tremor patients are shown in [reference needed]. Figure 10 The ROC curves for differentiating the two diseases are shown in [reference needed]. Figure 11 .

[0048] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An analytical method for analyzing the transmission velocity characteristics of brain electrical signals based on nerve conduction tracts, characterized in that, include: S1. Obtain the electroencephalogram (EEG) signal characteristics of the deep brain and cerebral cortex at different locations on the neural conduction tract through electrode implantation or brain-computer interface. S2. Based on the characteristics of the EEG signal, acquire the target EEG waveform, and acquire the spatial distance and time interval between the target EEG waveform at different EEG acquisition sites; S3. Calculate the transmission speed of the target EEG in the nerve conduction tract and / or in the non-nerve conduction tract based on the spatial distance and time interval. S4. Based on the transmission velocity on the nerve conduction tracts and the transmission velocity on the non-nerve conduction tracts, generate a joint model of EEG velocity transmission characteristics, and use the joint model to analyze the EEG transmission velocity characteristics.

2. The analysis method for EEG transmission velocity characteristics based on nerve conduction tracts according to claim 1, characterized in that, S1 includes: The EEG acquisition sites in the deep brain and cerebral cortex are determined based on the neural conduction tracts to be tested. Depending on the location of the EEG acquisition, the characteristics of deep brain EEG signals can be obtained by implanting invasive electrodes or brain-computer interfaces into the brain parenchyma. Depending on the location of the EEG acquisition, the characteristics of the EEG signal in the cerebral cortex are obtained by placing non-invasive electrodes or brain-computer interfaces outside the brain parenchyma.

3. The analysis method for EEG transmission velocity characteristics based on nerve conduction tracts according to claim 1, characterized in that, S2 includes: Based on the characteristics of the electroencephalogram (EEG) signals, target EEG waveforms are identified in different deep brain and cortical EEG acquisition sites. The spatial distance is obtained based on the spatial location of the target EEG waveform at the EEG acquisition site. The time interval is obtained based on the time point of the target EEG waveform at the EEG acquisition site.

4. The analysis method for EEG transmission velocity characteristics based on nerve conduction tracts according to claim 1, characterized in that, S3 includes: Calculate the EEG transmission velocity along the nerve conduction tract based on the spatial distance Ltract and time interval Ttract of the target EEG waveform at the EEG acquisition site along the nerve conduction tract: Vbundle = Lbundle / Tbundle 5. The analysis method for EEG transmission velocity characteristics based on nerve conduction tracts according to claim 4, characterized in that, S3 further includes: Based on the spatial distance Lnon-tract and time interval Tnon-tract of the target EEG waveform at the EEG acquisition site on the non-neural conduction tract, calculate the EEG transmission velocity on the non-neural conduction tract: Vnon-beam = Lnon-beam / Tnon-beam 6. The analysis method for EEG transmission velocity characteristics based on nerve conduction tracts according to claim 5, characterized in that, S4 includes: generating relative change features based on the EEG transmission velocity Vbundle on the neural conduction tract and the EEG transmission velocity Vnon-bundle on the non-neural conduction tract. M = Vbeam / Vnon-beam M = Vnon-beam / Vbeam Where M represents the relative change in the electroencephalographic transmission velocity of the neural conduction tract compared to the non-conduction tract.

7. The analysis method for EEG transmission velocity characteristics based on nerve conduction tracts according to claim 5, characterized in that, S4 further includes: generating absolute change features based on the EEG transmission velocity Vbundle on the neural conduction tract and the EEG transmission velocity Vnon-bundle on the non-neural conduction tract. N = Vnon-beam - Vbeam N = Vbundle - Vnon-bundle Wherein, N represents the absolute change in the EEG transmission velocity of the nerve conduction tract compared to the non-conduction tract.

8. The method for analyzing the characteristics of brain electrical transmission velocity based on nerve conduction tracts according to claim 1, characterized in that, In S1, the deep brain and cerebral cortex at different locations on the nerve conduction tract include: upstream and downstream nodes or nodes with mutual regulatory relationships in the conduction network formed by different types of neurons in the brain through synaptic connections.

9. An analysis system for brainwave transmission velocity based on neural conduction tracts, said system being used to implement the method according to any one of claims 1-8, characterized in that, include: The acquisition module is used to acquire the electroencephalogram (EEG) signal characteristics of the deep brain and cerebral cortex at different locations along the nerve conduction tract via electrode implantation or brain-computer interface. The processing module is used to acquire the target EEG waveform based on the characteristics of the EEG signal, and to acquire the spatial distance and time interval between the target EEG waveform at different EEG acquisition sites; The calculation module is used to calculate the transmission speed of the target EEG in the neural conduction tract and / or in the non-neural conduction tract based on the spatial distance and time interval; The generation module is used to generate a joint model of EEG velocity transmission characteristics based on the transmission velocity on the neural conduction tracts and the transmission velocity on the non-neural conduction tracts, and to analyze the EEG transmission velocity characteristics using the joint model.

10. An electronic device, comprising: A communication interface, a processor, a memory, a display, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements each step of the analysis method according to any one of claims 1-8.

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