Cerebral cortex activation rule analysis method and related product
By preprocessing and grouping the EEG signals of unilateral and bilateral limb motor imagery, the problem of unknown activation mechanism of the cerebral cortex in bilateral training was solved, achieving efficient and accurate analysis results and guiding the theoretical application of bilateral training.
Patent Information
- Application Number
- CN202511584353.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-31
- Publication Date
- 2026-01-23
AI Technical Summary
The lack of research into the activation mechanism of the cerebral cortex under bilateral training conditions in existing technologies has resulted in a lack of theoretical guidance for the practical application of bilateral training, making it difficult to promote it systematically.
By acquiring EEG signals of unilateral and bilateral limb motor imagery, and after preprocessing, the signals were grouped according to the type of motor action. Event-related spectrum perturbation, power spectral density, and brain functional network analysis were performed in parallel, including power frequency filtering, 0.1-45Hz bandpass filtering, common average reference space filtering, baseline correction, and ICA to remove electrooculography and electromyography artifacts. The ERSP difference and power spectral density were calculated, and the activation patterns of the cerebral cortex were analyzed.
It achieves efficient analysis of brain activity under different motor imagery conditions, improves analysis speed and accuracy, provides a reliable basis for identifying neural differences between unilateral and bilateral motor imagery, and guides the theoretical application of bilateral training.
Smart Images
Figure CN121370189A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of biological signal processing technology, specifically to a method for analyzing the activation patterns of the cerebral cortex and related products. Background Technology
[0002] Currently, there are two main rehabilitation approaches for patients with unilateral muscle spasticity after surgery: unilateral training rehabilitation and bilateral training rehabilitation. Unilateral training rehabilitation involves the patient imagining or actually controlling the movement of the affected limb. This method is highly effective for patients with unilateral muscle spasticity in the early stages of rehabilitation. However, as the rehabilitation process progresses, the imbalance between the left and right limbs will gradually become apparent, and this approach requires the affected limb to have a certain level of motor ability. Furthermore, research indicates that some patients have poor motor imagery abilities in the brain regions corresponding to the affected limb, making them insufficient for motor imagery rehabilitation. In response, some researchers have proposed freeing the healthy limb, allowing both the healthy and affected limbs to move simultaneously—this is the bilateral training rehabilitation method. Numerous studies have shown that bilateral training is highly effective for the rehabilitation of patients with unilateral muscle spasticity. However, the effects vary depending on the specific bilateral training paradigm. Moreover, the lack of research into the activation mechanisms of the cerebral cortex under bilateral training conditions means that bilateral training still lacks theoretical guidance in practical application, hindering its systematic promotion. Summary of the Invention
[0003] The technical problem to be solved by the present invention is to provide a method and related products for analyzing the activation patterns of the cerebral cortex, which addresses the shortcomings of the prior art. This method is used to solve the technical problem that the lack of research on the activation mechanism of the cerebral cortex under bilateral training conditions has resulted in a lack of theoretical guidance for the practical application of bilateral training.
[0004] The objective of this invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for analyzing the activation patterns of the cerebral cortex, comprising: EEG signals corresponding to unilateral and bilateral limb motor imagery were acquired separately; the EEG signals were preprocessed. The preprocessed EEG signals were grouped according to the type of motor action, and the activation patterns of the cerebral cortex were analyzed in parallel to obtain the corresponding analysis results. The analysis of activation patterns of brain functional networks includes event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of preprocessed EEG signals; the types of motor movements include bilateral and unilateral movements.
[0005] As a further improvement of the present invention, the preprocessing of the EEG signal includes: The acquired EEG signals were sequentially subjected to power frequency filtering, 0.1-45Hz bandpass filtering to extract motion-related rhythms, common-average reference space filtering, baseline correction, and ICA to remove electrooculography and electromyography artifacts.
[0006] As a further improvement of the present invention, the step of performing event-related spectrum perturbation and difference analysis includes: Calculate the event correlation spectrum perturbation value of the preprocessed EEG signal, and then... ERSP calculates the ERSP difference between two motor imagery actions in a single channel to characterize the differences in the time-frequency characteristics of EEG signals.
[0007] As a further improvement of the present invention, the event-related spectrum perturbation value ERSP is:
[0008] The ERSP is:
[0009] In the formula, In frequency f ,time t Event-related spectrum perturbation values at the location, The number of times the same imagined movement is repeatedly collected. For the first k In each test, the EEG signal was at a frequency f ,time t The Fourier transform result at the point, The difference in total ERSP energy between the first type of motor imagery and the second type of motor imagery within a specific time-frequency interval. Imagine the movement of the first type of motion at a certain frequency. f ,time t The ERSP value at that location, Imagine the movement in the second type of motion at a certain frequency. f ,time t The ERSP value at that location.
[0010] As a further improvement of the present invention, the power spectral density analysis includes: The power spectral density function is used to calculate the power spectral density of the preprocessed EEG signal under different electrode channels and different motion imagination actions. The energy distribution of the EEG signal in different frequency bands is analyzed based on the power spectral density value.
[0011] As a further improvement to the present invention, the power spectral density function is:
[0012] In the formula, For EEG signals at frequency ν The power spectral density function at that point, For EEG signals within a time window T The Fourier transform result within, This represents the length of the observation time window for the EEG signal.
[0013] As a further improvement of the present invention, the brain functional network analysis includes: The electrode locations corresponding to the preprocessed EEG signals are used as nodes of the brain functional network. The edges of the nodes are calculated, including node connectivity coefficients, node degree, and average degree. The parameters of nodes and edges are used to characterize the correlation between cerebral cortex regions. The node connection coefficient is:
[0014] In the formula, To represent the first k In the first experimental trial, the... i The connection probability of an electrode node to other nodes. ε Distance threshold , These are the time window parameters. The total number of nodes. The EEG signal feature value of the i-th node in the k-th trial is given. Let be the EEG signal feature value of the j-th node in the k-th trial.
[0015] Secondly, the present invention provides a system for analyzing the activation patterns of the cerebral cortex, comprising: The signal acquisition module is used to acquire EEG signals corresponding to unilateral limb motor imagery and bilateral limb motor imagery, respectively. The preprocessing module is used to preprocess the EEG signal; The computational analysis module is used to group the preprocessed EEG signals according to the type of motor action, perform parallel analysis of the activation patterns of the cerebral cortex, and obtain the corresponding analysis results. The analysis of activation patterns of brain functional networks includes event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of preprocessed EEG signals; the types of motor movements include bilateral and unilateral movements.
[0016] Thirdly, the present invention provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the above-described method for analyzing the activation patterns of the cerebral cortex.
[0017] Fourthly, the present invention provides a computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for performing the above-described method for analyzing the activation patterns of the cerebral cortex.
[0018] The beneficial effects of this invention are as follows: This invention provides a method for analyzing the activation patterns of the cerebral cortex. By acquiring EEG signals corresponding to unilateral and bilateral limb motor imagery respectively, and preprocessing the EEG signals, the preprocessed EEG signals are then grouped into bilateral and unilateral movements according to the type of motor action. The activation patterns of the cerebral cortex are analyzed in parallel. The activation pattern analysis includes event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis. This grouped parallel processing method enables efficient analysis of brain activity under different motor imagery conditions, effectively improving the analysis speed and accuracy, avoiding the delay problem of traditional serial processing, and providing a reliable basis for identifying neural differences between unilateral and bilateral motor imagery. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is a schematic diagram of the method for analyzing the activation patterns of the cerebral cortex in an embodiment of the present invention; Figure 2 This is an implementation flowchart of an embodiment of the present invention; Figure 3 This is a diagram of the 128 conductive electrode partitions in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a computer device in an embodiment of the present invention. Detailed Implementation
[0021] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.
[0022] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. The described embodiments are only some embodiments of the present invention, and not all embodiments.
[0023] Example 1 like Figure 1 As shown, this embodiment provides a method for analyzing the activation patterns of the cerebral cortex. Addressing the shortcomings of current unilateral training and rehabilitation methods, while the mechanism of action of bilateral motor imagery training remains unclear, this embodiment proposes a method for analyzing the activation patterns of the cerebral cortex during unilateral and bilateral motor imagery. A set of comparative experiments on unilateral and bilateral motor imagery with seven different movements was designed. Through energy spectrum analysis, brain functional network, and source localization analysis of the data, the activation patterns of the prefrontal cortex during bilateral training were summarized. This activation pattern analysis method includes the following steps: acquiring EEG (electroencephalogram) signals corresponding to unilateral and bilateral limb motor imagery respectively; preprocessing the EEG signals; grouping the preprocessed EEG signals according to the type of motor movement, and performing parallel analysis of the activation patterns of the cerebral cortex to obtain the corresponding analysis results.
[0024] The analysis included activation patterns of brain functional networks, such as event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of preprocessed EEG signals; the types of motor movements included bilateral and unilateral movements.
[0025] This embodiment acquires EEG signals corresponding to unilateral and bilateral limb motor imagery respectively, providing raw data for subsequent analysis of cortical activation patterns under corresponding motor imagery scenarios. The EEG signals are then preprocessed to eliminate interference components and ensure signal quality. Subsequently, the preprocessed EEG signals are grouped according to the type of motor action (including bilateral and unilateral actions), and cortical activation pattern analysis is conducted in parallel. This analysis specifically includes event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of the preprocessed EEG signals. Through targeted data acquisition, signal preprocessing, and multi-dimensional analysis in parallel grouping, the activity characteristics of the cerebral cortex under different types of motor imagery are accurately captured. This results in a technical effect that effectively obtains cortical activation pattern analysis results corresponding to unilateral and bilateral training motor imagery, ensuring the relevance and reliability of the analysis results.
[0026] It is worth noting that unilateral and bilateral movements include lifting heavy objects with both hands, wringing towels with both hands, washing hair with both hands, turning doors with the left and right hands respectively, and grasping with the left and right hands respectively.
[0027] The EEG signal preprocessing further includes: sequentially performing power frequency filtering, 0.1-45Hz bandpass filtering to extract motion-related rhythms, common-mean-reference space filtering, baseline correction, and ICA to remove EEG and EMG artifacts. First, power frequency filtering eliminates interference signals in the power frequency band. Then, 0.1-45Hz bandpass filtering accurately extracts motion-related rhythmic components. Subsequently, common-mean-reference space filtering optimizes the spatial distribution characteristics of the signal, baseline correction unifies the signal baseline level, and finally, ICA removes EEG and EMG artifacts. This series of orderly and targeted processing operations effectively reduces various interferences and artifacts in the EEG signal, preserves key motion-related rhythmic information, and improves the quality of the EEG signal, providing a reliable data foundation for subsequent analysis based on this EEG signal.
[0028] Furthermore, event-related spectrum perturbation and difference analysis are performed, including: Calculate the event correlation spectrum perturbation value of the preprocessed EEG signal, and then... ERSP calculates the ERSP difference between two motor imagery actions in a single channel to characterize the differences in the time-frequency characteristics of EEG signals.
[0029] The event-related spectrum perturbation value ERSP is:
[0030] The ERSP is:
[0031] In the formula, In frequency f ,time t Event-related spectrum perturbation values at the location, The number of times the same imagined movement is repeatedly collected. For the first k In each test, the EEG signal was at a frequency f ,time t The Fourier transform result at the point, The difference in total ERSP energy between the first type of motor imagery and the second type of motor imagery within a specific time-frequency interval. Imagine the movement of the first type of motion at a certain frequency. f ,time t The ERSP value at that location, Imagine the movement in the second type of motion at a certain frequency. f ,time tThe ERSP value at the location. In this embodiment, the basic information of the time-frequency characteristics of the EEG signal is obtained by using the event-related spectrum perturbation value. Then, the difference in ERSP values between the two motor imagery actions is calculated to quantify the difference in time-frequency characteristics between the two, thereby producing the technical effect of accurately capturing the difference in time-frequency characteristics of the EEG signals corresponding to the two motor imagery actions in a single channel and providing reliable time-frequency difference data support for subsequent analysis.
[0032] The power spectral density analysis further includes: using the power spectral density function to calculate the power spectral density of the preprocessed EEG signal under different electrode channels and different motion imagination actions, and analyzing the energy distribution of the EEG signal in different frequency bands based on the power spectral density value.
[0033] The power spectral density function is:
[0034] In the formula, For EEG signals at frequency ν The power spectral density function at that point, For EEG signals within a time window T The Fourier transform result within, This represents the length of the observation time window for the EEG signal.
[0035] Brain functional network analysis further includes: using the electrode locations corresponding to the preprocessed EEG signals as nodes of the brain functional network, calculating the edges of the nodes, where each edge includes node connectivity coefficients, node degree, and average degree; and characterizing the correlation between cerebral cortical regions through the parameters of nodes and edges; the node connectivity coefficients are:
[0036] In the formula, To represent the first k In the first experimental trial, the... i The connection probability of an electrode node to other nodes. ε Distance threshold , These are the time window parameters. The total number of nodes. The EEG signal feature value of the i-th node in the k-th trial is given. Let be the EEG signal feature value of the j-th node in the k-th trial.
[0037] The number of nodes adjacent to node i. A higher degree for a node indicates greater importance within the network. The degree of node i can be defined using the following formula:
[0038] in, For elements of a binary network, the number of nodes is also called the network size, N. A value of 1 indicates that node i and node j are not connected, while a value of 1 indicates that i and j are connected. The average degree of a node is defined as the average degree of all N nodes, as shown in the following formula:
[0039] Brain functional network results showed that during weightlifting imagery, the left side showed significantly higher connectivity, while during the hair-washing motion, the left and right anterior motor cortexes also showed significantly higher connectivity. This indicates that during proximal limb activity, the healthy anterior region compensates for some of the function of the affected side. Unlike the motor cortex rhythm, which is mainly related to movement, during bilateral motor imagery training, the activation of the anterior frontotemporal cortex is mainly related to communication and coordination between different motor areas. When the affected side's motor imagery ability is insufficient and unable to complete motor imagery classification and recognition, bilateral motor imagery training, which collects motor imagery information from the healthy side and simultaneously collects activation information from the anterior frontotemporal lobe as features, is an effective approach. Furthermore, because the activation between various brain nodes is stronger and information flow is more active during bilateral motor imagery training, the brain receives more comprehensive training, which is more beneficial for stroke patient rehabilitation. The cerebral cortex regions in this embodiment are as follows: Figure 3 As shown in the figure, RF represents the prefrontal lobe region, LF represents the left frontal lobe region, RF represents the right frontal lobe region, LT represents the left temporal lobe region, LC represents the left central region, RC represents the right central region, RT represents the right temporal lobe region, LP represents the left parietal region, RP represents the right parietal region, O represents the diagnostic region, and M represents the central motor region. The LF region and the RF region each contain 8 electrodes.
[0040] Example 2 This embodiment provides a specific implementation method for analyzing the activation patterns of the cerebral cortex. The specific steps are as follows.
[0041] Step 1, as follows Figure 2 As shown, to explore the activation patterns and dynamics of the cerebral cortex in patients with unilateral muscle spasticity under different bilateral and unilateral training methods, a comparative experiment of unilateral and bilateral motor imagery with seven different actions was designed. The seven motor imagery actions included: lifting a heavy object with both hands, wringing a towel with both hands, washing hair with both hands, twisting a door with the left and right hands respectively, and grasping with the left and right hands respectively. At the start of the experiment, participants had two seconds to concentrate and wait for the images of the motor imagery actions to appear. The seven images appeared randomly, with each image appearing 50 times, each lasting 4 seconds. After each action was completed, participants rested for 8 seconds. Data was collected from 8 patients with unilateral muscle spasticity, with 3 sets of data collected from each participant. Therefore, each participant had a total of 7*50*3=1050 data points. The participants' ages ranged from 51 to 72 years old.
[0042] This embodiment provides seven types of image stimuli for motor imagery, among which lifting weights, wringing, and washing hair are bilateral motor imagery. During the experiment, the patient sits 1 meter away from the computer desktop and imagines the corresponding action when seeing the seven images. These actions correspond to some actions in daily life to avoid data quality degradation due to the patient's unfamiliarity with them. During the experiment, a dedicated person records the subject's state and filters out data quality degradation caused by shaking or lack of concentration.
[0043] Step two: The acquisition device used was a 128-electrode amplifier from Neuroscan, with a sampling frequency of 500Hz. The synchronization device used Eprime 2.0 acquisition software and a synchronization core. To avoid interference from electrooculogram (EOG) artifacts, EOG signals were acquired simultaneously with EEG signals. This facilitates the recording and removal of EOG artifacts during subsequent ICA processing. In this embodiment, four EOG electrodes (VU, VD, HL, and HR) were used to record EOG signals. Before electrode placement, the corresponding skin areas were cleaned with Nuprep scrub to remove dead skin and keratin, reducing resistance. The electrodes were bowl-shaped AgCl electrodes, filled with conductive paste to improve conductivity. Electrode placement generally followed a 10-20 system, with the first pair of electrodes spaced 10% of the total length and the remaining electrodes spaced 20% of the total length.
[0044] Step 3 involves preprocessing the acquired EEG signals, including power frequency filtering, extraction of motion-related cortical rhythms from 0.1-45Hz, common-average reference, baseline removal, and ICA analysis to remove interference from electrooculography (EOG) and electromyography (EMG). Power frequency refers to 50Hz, and 0.1-45Hz includes delta rhythms (0.1-4Hz), alpha rhythms (8-12Hz), theta waves (4-8Hz), and beta rhythms (14-30Hz), all of which are motion-related. Independent component analysis (ICA) is a widely used artifact removal method in EEG signal processing, extensively used for EOG and EMG artifact removal. ICA is performed using the ICA tool within the eeglab toolbox in MATLAB. The common-average reference method effectively performs spatial filtering, reducing correlation between leads. Its calculation formula is:
[0045] In the formula, For the first j The result after centering the samples For the first i Original sample values ( i It is the sample sequence number, with values ranging from 1 to... n ), This represents the total number of samples, i.e., the number of original data points used in the calculation.
[0046] Step 4: Perform Event Correlation Spectrum Perturbation (ERSP) analysis on the preprocessed EEG signal to calculate the ERSP difference of a certain channel under the two actions.
[0047] The event-related spectrum perturbation value ERSP is:
[0048] The ERSP is:
[0049] In the formula, In frequency f ,time t Event-related spectrum perturbation values at the location, The number of times the same imagined movement is repeatedly collected. For the first k In each test, the EEG signal was at a frequency f ,time t The Fourier transform result at the point, The difference in total ERSP energy between the first type of motor imagery and the second type of motor imagery within a specific time-frequency interval. Imagine the movement of the first type of motion at a certain frequency. f ,time t The ERSP value at that location, Imagine the movement in the second type of motion at a certain frequency. f ,time t The ERSP value at that location.
[0050] During the calculation process, all topographic maps were obtained by subtracting the ERSP values from the current action state and the resting state. Because 128 electrodes were considered too numerous and insufficient for fully illustrating the phenomena in brain topographic maps, these electrodes were partitioned into eight regions, such as... Figure 3 As shown, it is mainly divided into 10 regions, including the prefrontal cortex, central motor cortex, left motor cortex, right motor cortex, occipital cortex, and limbic region. The analysis focuses on the activation of the prefrontal and temporal lobes and the left and right motor cortex during motor imagery. For each region... The ERSP index was calculated, and the results are shown in Table 1.
[0051] Table 1 Seven types of actions ERSP index (unit: dB)
[0052] As shown in Table 1, bilateral training significantly enhanced activation in the anterior region compared to unilateral training. Specifically, the LF, PF, and RF regions, representing the prefrontal cortex, showed significantly higher activation rates during lifting and twisting exercises. The ERSP index, while in the case of unilateral training ERSP did not improve significantly.
[0053] Step 5: The power spectral density function was used to calculate and analyze the power spectral density comparison of different movements in different channels of the preprocessed EEG signal. In the α segment (8-16Hz), μ segment (10-12Hz), and β segment (14-28Hz) of the motor-related cortical rhythms, bilateral proximal limb movements showed a significant power increase compared to other movements. To quantify this phenomenon, the energy generated by different motor images in different channels within the 8-30Hz range was compared. When hand motor images were involved, the peak frequency was to the left, closer to the μ rhythm, consistent with the conclusion that hand motor images are related to the μ rhythm, indirectly proving the effectiveness of the power spectral density method.
[0054] This is a method for quantitatively analyzing the energy distribution of different frequency bands of electroencephalogram (EEG) signals, assuming... If it is a function that can undergo Fourier transform, then ,because It is a real function. Generally, it is a complex function, and they satisfy the Barceval formula:
[0055] The left end indicates The total energy, the integrand in the right-hand integral is called the energy spectral density, which is a non-negative real number representing the energy per unit frequency. The power spectral density function can be written as:
[0056] In the formula, For EEG signals at frequency ν The power spectral density function at that point, For EEG signals within a time window T The Fourier transform result within, This represents the length of the observation time window for the EEG signal.
[0057] Step six involves calculating the nodes and edges of the brain functional network for the preprocessed EEG signal. The construction of the brain network mathematically represents the complex neural network through nodes and edges, enabling mathematical analysis. Generally, nodes can represent the location of the EEG signal or a region of the brain, while edges represent the connections between nodes. Bilateral training of the brain functional network analysis primarily focuses on the correlation between electrodes in different regions. The formula for the connection coefficient between the two channels is shown below:
[0058] In the formula: This means that the distance between the two electrodes is less than ε probability ω1 and ω2 This represents the time window. The degree represents the number of edges connected to a node, or the number of nodes adjacent to that node. A higher degree indicates a more important node in the network. The degree of node i can be defined using the following formula:
[0059] in, For elements of a binary network, the number of nodes is also called the network size, N. A value of 1 indicates that node i and node j are not connected, while a value of 1 indicates that i and j are connected. The average degree of a node is defined as the average degree of all N nodes, as shown in the following formula:
[0060] Brain functional network results showed that during weightlifting imagery, the left-side region exhibited significantly higher connectivity, while during hair-washing imagery, the left and right anterior motor cortex regions also showed significantly higher connectivity. This indicates that during proximal limb activity, the healthy anterior region compensates for some of the function of the affected side. Unlike the motor cortex rhythm, which is primarily related to movement, during bilateral motor imagery training, activation of the anterior frontotemporal cortex is mainly related to communication and coordination between different motor areas. When the affected side's motor imagery ability is insufficient and unable to complete motor imagery classification and recognition, bilateral motor imagery training, which collects motor imagery information from the healthy side and simultaneously collects activation information from the anterior frontotemporal lobe region as features, is an effective approach. Furthermore, because the activation between various brain nodes is stronger and information flow is more active during bilateral motor imagery training, the brain receives more comprehensive exercise, which is more beneficial for stroke patient rehabilitation.
[0061] Example 3 Based on the methods for analyzing the activation patterns of the cerebral cortex in Examples 1 and 2, this example provides a system for analyzing the activation patterns of the cerebral cortex. The system includes: The signal acquisition module is used to acquire EEG signals corresponding to unilateral limb motor imagery and bilateral limb motor imagery, respectively. The preprocessing module is used to preprocess the EEG signal; The computational analysis module is used to group the preprocessed EEG signals according to the type of motor action, perform parallel analysis of the activation patterns of the cerebral cortex, and obtain the corresponding analysis results. Activation patterns of brain functional networks were analyzed, including event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of preprocessed EEG signals; motor movement types included bilateral and unilateral movements.
[0062] Example 4 In another embodiment of the present invention, a computer-readable storage medium is provided as a storage component within a terminal device, the function of which is to store programs and data. It should be noted that the computer-readable storage medium here encompasses not only the built-in storage components of the terminal device but also extended storage components supported by the device. Essentially, it is a tangible medium capable of containing or storing programs that can be invoked by or in conjunction with an instruction execution system, device, or apparatus. This storage medium provides storage areas for the terminal's operating system and stores one or more instructions suitable for processor loading and execution, which can constitute one or more computer programs containing program code.
[0063] Specifically, examples of computer-readable storage media (a non-exclusive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable optical disc read-only memory, optical storage devices, magnetic storage devices, or any reasonable combination of the above types.
[0064] The storage medium may also include data signals propagated as part of a baseband portion or a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any reasonable combination of both. Furthermore, computer-readable storage medium may also refer to other readable media besides conventional readable storage media, capable of sending, propagating, or transmitting programs for use or operation by an instruction execution system, apparatus, or device. Program code on the storage medium can be transmitted via any suitable medium, including but not limited to wireless, wired, optical fiber, or any reasonable combination thereof.
[0065] The program code used to implement the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C. The execution modes of the program code include: running entirely on the user's computing device, running partially on the user's device as a standalone software package, running partially in a distributed manner on both the user's device and a remote computing device, or running entirely on a remote computing device or server. When a remote computing device is involved, the device can be connected to the user's computing device via any type of network such as a local area network (LAN) or a wide area network (WAN), or connected to an external computing device via the Internet through an Internet service provider.
[0066] The processor is capable of loading and executing one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for analyzing the activation patterns of the cerebral cortex described in Example 1.
[0067] Example 5 Figure 4 This is a schematic diagram of a computer device provided according to an embodiment of the present invention.
[0068] Please see Figure 4 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the method for analyzing the activation patterns of the cerebral cortex in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the computational system constituting the method for analyzing the activation patterns of the cerebral cortex in this embodiment. To avoid repetition, these details are not elaborated here.
[0069] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 4 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.
[0070] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.
[0071] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.
[0072] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.
[0073] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (Read-Only Memory). Memory includes ROM, magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0074] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
Claims
1. A method for analyzing the activation patterns of the cerebral cortex, characterized in that, include: EEG signals corresponding to unilateral and bilateral limb motor imagery were acquired respectively. The EEG signal is preprocessed; The preprocessed EEG signals were grouped according to the type of motor action, and the activation patterns of the cerebral cortex were analyzed in parallel to obtain the corresponding analysis results. The analysis of activation patterns of brain functional networks includes event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of preprocessed EEG signals; the types of motor movements include bilateral and unilateral movements.
2. The method for analyzing the activation patterns of the cerebral cortex according to claim 1, characterized in that, The preprocessing of the EEG signal includes: The acquired EEG signals were sequentially subjected to power frequency filtering, 0.1-45Hz bandpass filtering to extract motion-related rhythms, common-average reference space filtering, baseline correction, and ICA to remove electrooculography and electromyography artifacts.
3. The method for analyzing the activation patterns of the cerebral cortex according to claim 2, characterized in that, The event-related spectrum perturbation and difference analysis includes: Calculate the event correlation spectrum perturbation value of the preprocessed EEG signal, and then... ERSP calculates the ERSP difference between two motor imagery actions in a single channel to characterize the differences in the time-frequency characteristics of EEG signals.
4. The method for analyzing the activation patterns of the cerebral cortex according to claim 3, characterized in that, The event-related spectrum perturbation value ERSP is: The ERSP is: In the formula, In frequency f ,time t Event-related spectrum perturbation values at the location, The number of times the same imagined movement is repeatedly collected. For the first k In each test, the EEG signal was at a frequency f ,time t The Fourier transform result at the point, The difference in total ERSP energy between the first type of motor imagery and the second type of motor imagery within a specific time-frequency interval. Imagine the movement of the first type of motion at a certain frequency. f ,time t The ERSP value at that location, Imagine the movement in the second type of motion at a certain frequency. f ,time t The ERSP value at that location.
5. The method for analyzing the activation patterns of the cerebral cortex according to claim 2, characterized in that, The power spectral density analysis includes: The power spectral density function is used to calculate the power spectral density of the preprocessed EEG signal under different electrode channels and different motion imagination actions. The energy distribution of the EEG signal in different frequency bands is analyzed based on the power spectral density value.
6. The method for analyzing the activation patterns of the cerebral cortex according to claim 5, characterized in that, The power spectral density function is: In the formula, For EEG signals at frequency ν The power spectral density function at that point, For EEG signals within a time window T The Fourier transform result within, This represents the length of the observation time window for the EEG signal.
7. The method for analyzing the activation patterns of the cerebral cortex according to claim 2, characterized in that, The brain functional network analysis includes: The electrode locations corresponding to the preprocessed EEG signals are used as nodes of the brain functional network. The edges of the nodes are calculated, including node connectivity coefficients, node degree, and average degree. The parameters of nodes and edges are used to characterize the correlation between cerebral cortex regions. The node connection coefficient is: In the formula, To represent the first k In the first experimental trial, the... i The connection probability of an electrode node to other nodes. ε Distance threshold , These are the time window parameters. The total number of nodes. The EEG signal feature value of the i-th node in the k-th trial is given. Let be the EEG signal feature value of the j-th node in the k-th trial.
8. A system for analyzing the activation patterns of the cerebral cortex, characterized in that, include: The signal acquisition module is used to acquire EEG signals corresponding to unilateral limb motor imagery and bilateral limb motor imagery, respectively. The preprocessing module is used to preprocess the EEG signal; The computational analysis module is used to group the preprocessed EEG signals according to the type of motor action, perform parallel analysis of the activation patterns of the cerebral cortex, and obtain the corresponding analysis results. The analysis of activation patterns of brain functional networks includes event-related spectrum perturbation and difference analysis, power spectral density analysis, and brain functional network analysis of preprocessed EEG signals; the types of motor movements include bilateral and unilateral movements.
9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method for analyzing the activation patterns of the cerebral cortex as described in any one of claims 1 to 7.
10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the steps in the method for analyzing the activation patterns of the cerebral cortex according to any one of claims 1 to 7.