A method and system for analyzing neural circuit brain electroencephalogram transmission characteristics

Abstract

This invention relates to a method and system for analyzing the electroencephalogram (EEG) transmission characteristics of neural circuits. The method includes: determining the EEG acquisition sites in the deep brain and cerebral cortex based on the neural circuit to be tested; acquiring EEG signal characteristics in and near different deep brain and cerebral cortex regions along the neural circuit to be tested via electrode implantation or brain-computer interface; determining the target waveform; analyzing the relative signal changes of the target waveform in different deep brain and cerebral cortex regions; and constructing a correction model for EEG signal characteristics in the cerebral cortex and different deep brain regions based on the relative signal characteristic change information. This invention provides the characteristics of the target EEG signal after processing through neural circuit transmission, enabling more accurate identification of EEG signals related to neural circuits, and providing new schemes and methods for EEG signal identification.

Description

Technical Field

[0001] This invention relates to the field of neuroelectrophysiology or brain-computer interface technology, and in particular to a method and system for analyzing the characteristics of brain electrical transmission in neural circuits. Background Technology

[0002] Electroencephalography (EEG) signals are the macroscopic manifestation of the collective activity of neural circuits in the cerebral cortex. EEG generates electrical signals as neural circuits operate and transmit them through these circuits. The synchronized activity of these neural circuits forms a recordable macroscopic EEG signal; this synchronization is primarily achieved through rhythmic pacing of specific circuits, such as the thalamic-cortical circuit, and precise modulation of cortical interneurons, such as GABAergic inhibitory neuronal networks. After processing by different neural circuits, EEG signals exhibit processing characteristics specific to these neural circuits, such as signal amplification, signal suppression, clutter filtering, and the induction of new waveforms.

[0003] Traditional non-invasive EEG acquisition methods, such as the scalp electrodes used in conventional EEG, record superimposed potential differences appearing in the cortex on the scalp, forming a conventional cortical EEG waveform. For specific deep brain EEG signals, invasive surgical methods are now used, namely electrode implantation and brain-computer interfaces to precisely detect specific regions deep in the brain and acquire corresponding EEG signals. Therefore, through the above-mentioned non-invasive and invasive approaches, EEG signals from different deep brain and cortical nodes of neural circuits can be obtained.

[0004] Existing non-invasive EEG signal acquisition sites are placed on the scalp according to the international 10-20 system standard, while invasive EEG signal acquisition sites are placed at pre-defined deep abnormality locations on the CT scan site. Therefore, traditional methods fail to achieve simultaneous joint acquisition of EEG information from key nodes in the neural circuit at a unified spatiotemporal scale, making it impossible to analyze the spatiotemporal characteristics of EEG signal transmission and synchronization within the neural circuit, and difficult to analyze the interrelationships of EEG signals in different deep brain regions and the cerebral cortex within the neural circuit; they also fail to provide information on the processing characteristics of EEG signals related to the brain circuit. Therefore, this invention, based on electrode implantation and brain-computer interface technology, proposes implanting detection electrodes at the locations of key nodes in the neural circuit, avoiding the neglect of the characteristics of electrical signal circuit transmission and synchronization by the traditional international 10-20 system standard and the CT scan site standard. Secondly, by introducing unified spatiotemporal information from different EEG acquisition sites, it can increase the spatiotemporal characteristics of EEG signal propagation and changes within the neural circuit, providing a new approach for more accurate identification of brain circuit-related EEG signals. Currently, there are no reports on methods or systems for analyzing the transmission characteristics of EEG signals related to neural circuits. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for analyzing the characteristics of neural circuit electroencephalographic transmission, so as to solve the problems existing in the prior art.

[0006] To achieve the above objectives, the present invention provides the following solution: A method for analyzing the electroencephalographic transmission characteristics of neural circuits, comprising: The EEG acquisition sites for the deep brain and cerebral cortex are determined based on the neural circuits to be tested. Electroencephalogram (EEG) signal characteristics of different deep brain and cerebral cortex layers in and around the neural circuit under test are obtained through electrode implantation or brain-computer interface. Determine the target waveform and analyze the relative signal changes of the target waveform in different deep brain regions and cerebral cortex detections; Based on the relative signal change information, a correction model for the characteristics of electroencephalogram (EEG) signals in different cerebral cortex and deep brain is constructed.

[0007] Optionally, the electroencephalogram (EEG) signals from the deep brain and cerebral cortex represent the upstream and downstream or interactive regulatory relationships of neural circuit transmission.

[0008] Optionally, the EEG acquisition sites in the deep brain and cerebral cortex are located within the target area of ​​a specific neural circuit.

[0009] Optionally, the EEG signal characteristics of different deep brain regions and cerebral cortexes are spatially located within the target range of a specific neural circuit, temporally located within the target interval, and morphologically have the same peak-to-valley ratio.

[0010] Optionally, acquiring the electroencephalographic signal features of the deep brain and cerebral cortex includes: The brain electrical signal characteristics of the deep brain are obtained by implanting invasive electrodes or EEG interfaces into the brain parenchyma, while the brain electrical signal characteristics of the cerebral cortex are obtained by placing non-invasive electrodes or EEG interfaces outside the brain parenchyma.

[0011] Optionally, the EEG signal information features include: amplitude, duration, and frequency of the EEG signal features.

[0012] Optionally, the relative signal changes of the target waveform in different deep brain regions and cerebral cortex detections are analyzed as follows: R = C1 / C2; or R = C1 - C2; or R = C2 / C1; or R = C2 - C1; or other correction modes; Where R is the relative signal characteristic ratio, which reflects the EEG transmission characteristics of the neural circuit, including signal amplification, signal suppression, filtering of clutter and induction of new waveforms; C1 is the EEG signal characteristics of the target waveform obtained from deep brain electrodes or brain-computer interfaces, and C2 is the EEG signal characteristics of the target waveform obtained from cortical electrodes or other neural circuit nodes; other correction parameters can be added.

[0013] To achieve the above objectives, the present invention also provides an analysis system for the transmission characteristics of neural circuit EEG, comprising: an acquisition module, a communication module, and a processing module; The acquisition module is used to determine the EEG acquisition sites in the deep brain and cerebral cortex based on the neural circuit to be tested; and to acquire the EEG signal characteristics of different deep brain and cerebral cortex areas in and around the neural circuit to be tested through electrode implantation or brain-computer interface. The communication module is used to transmit the collected electroencephalogram (EEG) signal features from the deep brain and cerebral cortex to the processing module; The processing module is used to determine the target waveform, analyze the relative signal change information of the target waveform in different deep brain and cerebral cortex detections, and construct a correction model of EEG signal characteristics in different cerebral cortex and deep brain based on the relative signal change information.

[0014] Optionally, the processing module includes: a proposed identification unit, a signal strength acquisition unit, and a construction unit; The proposed identification unit is used to select the target waveform; The signal intensity acquisition unit is used to acquire the relative signal characteristic changes of the target waveform in the cerebral cortex and deep EEG, respectively; The construction unit constructs correction models for different cortical and deep brain electroencephalogram (EEG) signal characteristics based on the relative signal feature changes.

[0015] To achieve the above objectives, the present invention also provides an electronic device, the device comprising: a processor and a memory storing computer program instructions; wherein the processor, when executing the computer program instructions, implements the method for analyzing the brain electrical transmission characteristics of neural circuits.

[0016] The beneficial effects of this invention are as follows: Compared with the prior art, the present invention has the following advantages and technical effects: This invention provides a method for analyzing the EEG transmission characteristics of neural circuits, comprising: first, determining the EEG acquisition sites in the deep brain and cerebral cortex based on the neural circuit to be tested; second, acquiring EEG signal characteristics in different deep brain and cerebral cortex regions on or near the neural circuit to be tested via electrode implantation or brain-computer interface; third, determining the target waveform and analyzing the relative signal changes of the target waveform in different deep brain and cerebral cortex regions; further, based on the relative signal characteristic change information, constructing a correction model for EEG signal characteristics in different cerebral cortex and deep brain regions, extracting the characteristics of EEG signals processed by neural circuit transmission, and achieving more accurate neural circuit-related EEG signal recognition technology.

[0017] This invention selects EEG acquisition sites in the deep brain and cerebral cortex based on neural circuits, filling the technological gap of previous methods that lacked joint acquisition of EEG signals at key nodes along neural circuits. Previous non-invasive EEG signal placement, following the international 10-20 system standard, was based on the spatial distribution of the skull surface and did not consider neural circuit nodes. Furthermore, the implantation points of deep brain electrodes and brain-computer interfaces are located at the brain region being tested, representing single nodes and failing to reflect the state of neural circuits or the changes before and after EEG processing. In reality, neural electrical signals primarily propagate along neural circuits, undergoing processing such as signal amplification, signal suppression, clutter filtering, and the induction of new waveforms during transmission—processes not present in previous methods. Therefore, this invention proposes selecting EEG acquisition sites in the deep brain and cerebral cortex based on neural circuits, providing a more accurate reflection of the state of neural circuits than traditional EEG signal acquisition methods.

[0018] Previous EEG analysis systems lacked methods for real-time detection and analysis of multiple nodes along neural circuits, making it difficult to accurately reflect the state of these circuits. This invention analyzes relative signal changes in different deep brain regions and the cerebral cortex based on target waveforms, thus revealing the EEG transmission characteristics between key nodes along neural circuits. This invention extracts the EEG signal change characteristics along neural circuits, adding information reflecting the influence of neural circuits on the formation, transmission, and interference of EEG signals compared to traditional EEG detection methods.

[0019] This invention overcomes the bottleneck of existing technologies that cannot comprehensively analyze the EEG transmission characteristics of neural circuits, and achieves accurate extraction of EEG characteristics of neural circuits. It can objectively and accurately identify EEG characteristics related to neural circuits and can be applied to scenarios such as personal rehabilitation effect assessment and remote health monitoring. Attached Figure Description

[0020] 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.

[0021] Figure 1 This is a schematic diagram of a method for analyzing the electroencephalographic transmission characteristics of neural circuits according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the device according to an embodiment of the present invention; Figure 3 This is a flowchart of a method for analyzing the electroencephalographic transmission characteristics of neural circuits according to an embodiment of the present invention; Figure 4 These are CT and MRI localization images of the deep brain electrodes and corresponding cortical electrodes used in the modeling example of this invention. Figure 5This is an electroencephalogram (EEG) recorded using implanted deep striatal electrodes in a modeling example of an embodiment of the present invention. Figure 6 The electroencephalogram of the brain of a patient with essential tremor recorded by pre-corrected cortical electrodes in a modeling example of this invention; Figure 7 This is a cortical electroencephalogram of a patient with essential tremor corrected for deep brain electroencephalography in a modeling example of an embodiment of the present invention; Figure 8 This is a pre-correction electroencephalogram (EEG) of a Parkinson's disease patient recorded with cortical electrodes in a modeling example of this invention. Figure 9 This is a deep EEG-corrected electroencephalogram of a Parkinson's disease patient in a modeling example of an embodiment of the present invention. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0024] like Figure 1 , 3 As shown, this embodiment provides a method for analyzing the EEG transmission characteristics of neural circuits, including: S1. Identify the EEG acquisition sites in the deep brain and cerebral cortex based on neural circuits; The neural circuit refers to the upstream and downstream or interactive regulatory relationship of EEG signals in the deep brain and cerebral cortex.

[0025] S2. Electroencephalogram (EEG) signal characteristics of different deep brain regions or cerebral cortexes in and around neural circuits obtained through electrode implantation or brain-computer interface. Among them, deep brain and cerebral cortex EEG signals are acquired by implanting invasive electrodes or EEG interfaces into the brain parenchyma, while cerebral cortex EEG signals are acquired by placing non-invasive electrodes or EEG interfaces outside the brain parenchyma.

[0026] S3. Determine the target waveform; The target waveform refers to EEG signals from different deep brain regions and the cerebral cortex, which are spatially located within the target range of a specific neural circuit, and have the same peak-to-valley ratio in terms of time within the target interval.

[0027] S4. Acquire the signal changes of the target waveform in different deep brain and cortical EEG detections; Among them, the characteristics of EEG signals include the amplitude, duration, and frequency of EEG signals.

[0028] S5. Further, the relative signal characteristic changes of the target waveform in the deep brain and cortical EEG features are as follows: R = C1 / C2; or R = C1 - C2; or R = C2 / C1; or R = C2 - C1; or other correction modes Wherein, R is the relative signal characteristic ratio, which reflects the characteristics of EEG transmission in neural circuits, including signal amplification, signal suppression, filtering of clutter, and induction of new waveforms; C1 is the EEG signal characteristics of the target waveform obtained from deep brain electrodes or brain-computer interfaces, and C2 is the EEG signal characteristics of the target waveform obtained from cortical electrodes or brain-computer interfaces; other correction parameters can be added.

[0029] Based on the relative signal feature change information, a correction model for the electroencephalogram (EEG) signal features of the cerebral cortex and different deep brain regions is constructed.

[0030] This embodiment analyzes the changes in electroencephalogram (EEG) signals in the deep brain and cerebral cortex along neural circuits, selects EEG detection nodes based on neural circuit information, and extracts the characteristics of EEG transmission along neural circuits, providing a reference for the assessment and intervention of neural circuits.

[0031] Based on the same inventive concept, this embodiment also provides an analysis system for the electroencephalographic transmission characteristics of neural circuits, including: The target waveform is selected by the identification unit. The signal intensity acquisition unit is used to acquire the relative signal characteristic changes of the target waveform in the cerebral cortex and deep EEG, respectively; The construction unit constructs a correction model based on the relative signal characteristic changes; A model of changes in the characteristics of electroencephalogram (EEG) signals in the cerebral cortex and deep brain regions, synthesized from a single unit.

[0032] The neural circuit EEG transmission characteristic analysis system provided in this embodiment has all the advantages of the neural circuit EEG transmission characteristic analysis method provided in Embodiment 1.

[0033] Furthermore, the computer program for the method is stored in a memory, such as... Figure 2 The diagram shown is one of the structural schematics of the device. This data collection device includes: an acquisition module, a communication module, a processing module, and a memory. Optionally, the data collection device also includes a display, a power supply, and other communication interfaces (not shown).

[0034] The processor can be a central processing unit (CPU), and the memory may include random-access memory (RAM) or non-volatile memory.

[0035] Figure 2 The data collection device shown may take the form of a smart terminal, wearable device, etc.

[0036] Figure 2 The data collection device shown may not include an EEG signal collection device, which can be replaced by a communication interface for communicating with other devices. The interface can be a wired interface, a wireless interface, or a combination thereof. The communication interface is used to receive the user's adversarial pattern characteristics and EMG signal writing data sent by the peripheral data collection device. The functionality of other components remains unchanged.

[0037] The following is combined Figure 3 The specific implementation process of this application will be explained.

[0038] 1. The GESYTEC 3000I CT and MRI equipment, manufactured by General Electric Company (GE), was used. The CT windows included bone windows and soft tissue windows. Imaging images of the human head and brain tissue were collected to obtain the spatial positioning of deep electrodes and cortical electrodes, such as... Figure 4 .

[0039] 2. Furthermore, the CT and MRI image information obtained using the above method is transmitted to the memory via the communication interface, such as... Figure 2 The diagram shows the structure of the analysis system. The device includes a communication interface 304, a processor 303, a memory 302, a data acquisition module 301, a display, and a power supply. Specifically, the communication interface 304 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 304 is used to receive image measurement information from external CT or MRI equipment.

[0040] 3. The processor 303 may be a central processing unit (CPU), and the memory 302 may include random-access memory (RAM) or non-volatile memory.

[0041] 4. Figure 2 The device shown may take the form of a smart terminal, wearable device, etc.

[0042] 5. Memory 302 is used to store programs and calibration formulas, and can also store user reference values, processed data and historical data such as final imaging.

[0043] 6. Processor 303 is used to call the program stored in memory to execute the neural circuit EEG transmission characteristic process.

[0044] 7. Display screen, used to show the user's analysis results.

[0045] 8. The specific implementation process of this application will be described below with reference to the accompanying drawings.

[0046] 9. 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.

[0047] 10. According to Figure 1 The spatial locations of different nodes on the ventral intermediate nucleus-cortical loop of the thalamus are shown. Based on the criteria of upstream and downstream propagation or mutual influence in neural loops, the site for cortical EEG acquisition is determined to be the frontal region, i.e., the cortical electrodes acquired by the Fp1-AV channel.

[0048] 11. Collect electroencephalogram (EEG) signals recorded by electrodes implanted in the deep thalamus ventral intermediate nucleus, such as... Figure 5 As shown.

[0049] 12. Acquire raw, uncorrected 64-channel electrocorticography (EEG) using cortical electrodes, such as... Figure 6 As shown.

[0050] 13. Based on the conclusion of step 10, select the Fp1-AV channel to collect cortical EEG data, and select the Fp1-AV channel and the ventral thalamic nucleus channel to collect waveforms of simultaneous discharges in the EEG. According to the principle of spatiotemporal consistency, classify them as EEG characteristics of two nodes on the ventral thalamic nucleus-cortical loop; and apply the previously summarized correction formula R=C Fp1-AV / C 丘脑腹中间核 The cortical EEG was corrected to obtain the cortical EEG of patients with essential tremor after deep EEG correction.

[0051] 14. In this category, the method can be used to analyze the EEG transmission characteristics of neural circuits in patients with Parkinson's disease who have undergone deep brain electrode implantation. 15. Similar to this, raw, uncorrected 64-channel electrocorticography (EEG) is acquired using cortical electrodes, such as... Figure 8 As shown.

[0052] 16. Based on the principle of spatiotemporal consistency, EEG analysis was performed on two nodes along the subthalamic-cortical loop; the correction formula R=C was applied based on previous findings. Fp1-AV / C丘脑底核 The cortical EEG was corrected to obtain a cortical EEG of a Parkinson's disease patient corrected for deep brain electroencephalography (DBE); such as Figure 9 As shown.

[0053] This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0054] This embodiment also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0055] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for analyzing the electroencephalographic transmission characteristics of neural circuits, characterized in that, include: The EEG acquisition sites for the deep brain and cerebral cortex are determined based on the neural circuits to be tested. Electroencephalogram (EEG) signal characteristics of different deep brain and cerebral cortex layers in and around the neural circuit under test are obtained through electrode implantation or brain-computer interface. Determine the target waveform and analyze the relative signal changes of the target waveform in different deep brain regions and cerebral cortex detections; Based on the relative signal change information, a correction model for the characteristics of electroencephalogram (EEG) signals in different cerebral cortex and deep brain is constructed.

2. The method for analyzing the EEG transmission characteristics of neural circuits according to claim 1, characterized in that, Electroencephalogram (EEG) signals in the deep brain and cerebral cortex represent the upstream and downstream or interactive regulatory relationships of neural circuit transmission.

3. The method for analyzing the EEG transmission characteristics of neural circuits according to claim 1, characterized in that, The EEG acquisition sites in the deep brain and cerebral cortex are located within the target area of ​​a specific neural circuit.

4. The method for analyzing the EEG transmission characteristics of neural circuits according to claim 1, characterized in that, The EEG signal characteristics of different deep brain regions and cerebral cortex are spatially located within the target range of specific neural circuits, temporally located within the target interval, and morphologically have the same peak-to-valley ratio.

5. The method for analyzing the EEG transmission characteristics of neural circuits according to claim 1, characterized in that, Obtaining the electroencephalographic signal characteristics of the deep brain and cerebral cortex includes: The brain electrical signal characteristics of the deep brain are obtained by implanting invasive electrodes or EEG interfaces into the brain parenchyma, while the brain electrical signal characteristics of the cerebral cortex are obtained by placing non-invasive electrodes or EEG interfaces outside the brain parenchyma.

6. The method for analyzing the EEG transmission characteristics of neural circuits according to claim 1, characterized in that, The characteristics of the EEG signal information include: amplitude, duration, and frequency of the EEG signal characteristics.

7. The method for analyzing the EEG transmission characteristics of neural circuits according to claim 1, characterized in that, The relative signal changes of the target waveform in different deep brain regions and cerebral cortex detections are analyzed as follows: R = C1 / C2; or R = C1 - C2; or R = C2 / C1; or R = C2 - C1; or other correction modes; Where R is the relative signal characteristic ratio, which reflects the EEG transmission characteristics of the neural circuit, including signal amplification, signal suppression, filtering of clutter and induction of new waveforms; C1 is the EEG signal characteristics of the target waveform obtained from deep brain electrodes or brain-computer interfaces, and C2 is the EEG signal characteristics of the target waveform obtained from cortical electrodes or other neural circuit nodes; other correction parameters can be added.

8. A system for analyzing the electroencephalographic transmission characteristics of neural circuits, characterized in that, The method for analyzing the EEG transmission characteristics of neural circuits as described in any one of claims 1-7 includes: an acquisition module, a communication module, and a processing module; The acquisition module is used to determine the EEG acquisition sites in the deep brain and cerebral cortex based on the neural circuit to be tested; and to acquire the EEG signal characteristics of different deep brain and cerebral cortex areas in and around the neural circuit to be tested through electrode implantation or brain-computer interface. The communication module is used to transmit the collected electroencephalogram (EEG) signal features from the deep brain and cerebral cortex to the processing module; The processing module is used to determine the target waveform, analyze the relative signal change information of the target waveform in different deep brain and cerebral cortex detections, and construct a correction model of EEG signal characteristics in different cerebral cortex and deep brain based on the relative signal change information.

9. The analysis system for the EEG transmission characteristics of neural circuits according to claim 8, characterized in that, The processing module includes: a preliminary identification unit, a signal strength acquisition unit, and a construction unit; The proposed identification unit is used to select the target waveform; The signal intensity acquisition unit is used to acquire the relative signal characteristic changes of the target waveform in the cerebral cortex and deep EEG, respectively; The construction unit constructs correction models for different cortical and deep brain electroencephalogram (EEG) signal characteristics based on the relative signal feature changes.

10. An electronic device, characterized in that, The device includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the method for analyzing the brain electrical transmission characteristics of neural circuits.

Images