Pain relief system based on brain neural oscillation regulation

Abstract

The application provides an analgesia system based on brain nerve oscillation regulation, relates to the technical field of pain regulation, and first establishes a brain signal standardization information base of healthy people; for each pain patient, the brain nerve signal index of the pain patient is compared with a standard normal range obtained from the brain signal standardization information base of healthy people to determine abnormal electroencephalogram nerve indexes of each pain patient; then, according to the pain specificity of each pain patient, the correlation between the behavior indexes and the abnormal electroencephalogram nerve indexes of the pain patient is analyzed to determine potential regulation target points; the regulation target points to be intervened and regulation modes are selected from the potential regulation target points; different regulation modes are selected according to actual requirements to regulate the pain of the pain patient; and then, the pain regulation effect is evaluated, so that the effect of relieving pain is achieved, the analgesia system has the characteristics of safety, economy and easy operation, and is expected to be applied to the treatment of clinical pain.

Description

Technical Field

[0001] This invention relates to the technical field of pain regulation, and more specifically, to an analgesic system based on brain neural oscillation regulation. Background Technology

[0002] Neural oscillations are rhythmic or repetitive electrical activities of neurons, widely present in the cerebral cortex, hippocampus, subcortical structures, and sensory organs. Numerous studies have shown that neural oscillations of different frequencies exist in the human brain, reflecting periodic changes in the excitability levels of neuronal clusters. These different forms of oscillation play important roles in the brain's processing, transmission, and integration of perceptual information, memory consolidation, and higher cognitive activities such as attention, decision-making, and behavior.

[0003] Pain, as a complex perception, is a comprehensive phenomenon resulting from the dynamic interaction between sensory and situational processes (including cognition, emotion, and motivation). Existing research indicates that pain information processing involves the synchronization of neural oscillations across multiple brain regions and frequency bands, and the development of clinical pain is associated with abnormalities in neural oscillation activity. For example, pain induces an increase in gamma (30-100Hz) frequency band neural oscillations and a decrease in alpha (8-13Hz) frequency band neural oscillations, participating in bottom-up and top-down information processing, respectively, and is related to stimulus intensity and pain perception encoding. Chronic pain manifests as abnormalities in spontaneous neural oscillation activity, such as a decrease in alpha neural oscillations and an increase in gamma neural oscillations, and is related to the patient's self-reported pain intensity. Therefore, modulating these neural oscillation activities related to pain encoding may lead to effective pain relief.

[0004] Neurofeedback and transcranial alternating current stimulation (TCC), as non-invasive brain neuromodulation techniques, have become cutting-edge research topics in neuroscience and rehabilitation, and are considered promising new methods for pain management. Neurofeedback utilizes the principle of operant conditioning, providing real-time feedback on an individual's brain's neural activity through visual and auditory feedback. This allows the individual to understand their own psychological and physiological state and, through self-awareness, enhance or weaken neural activity in relevant cortical areas, thereby achieving self-regulation of internal psychological and physiological changes and optimizing their physical and mental state. Neurofeedback helps individuals achieve autonomous regulation of brain function by providing real-time feedback on brain neural activity, offering advantages such as safety, non-invasiveness, and no side effects. However, current research indicates significant inter-individual variability in the effectiveness of neurofeedback, with non-responsive individuals (those unable to successfully regulate their own brain neural activity) accounting for 20%-50%. Combining neurofeedback with other auxiliary methods can help improve its effectiveness.

[0005] Transcranial alternating current (TAC) stimulation is a novel non-invasive neuromodulation technique that interacts with spontaneous brain oscillations through alternating current at specific frequencies, specifically modulating the amplitude and intensity of these oscillations to regulate perception and behavior. Existing technologies disclose methods and systems for modulating brain oscillations, using transcranial electrical stimulation devices and employing one or more non-invasive stimuli, alone or in combination, to increase, decrease, or otherwise modulate neural oscillations, spontaneously generated rhythmic and / or repetitive electrical activity, and respond to stimulation of central nervous tissue to achieve pain control. However, the modulation mode of non-invasive stimulation is relatively singular, resulting in a low overall analgesic effect. Furthermore, pain patients exhibit specific changes in their brain oscillation patterns, necessitating the design of corresponding brain oscillation modulation strategies to maximize analgesic effects. Summary of the Invention

[0006] To address the problem of limited and ineffective traditional neuromodulation methods for pain relief, this invention proposes a pain relief system based on brain neural oscillation modulation. This system integrates neurofeedback technology and transcranial alternating current stimulation technology to specifically modulate individualized neural oscillation indicators in pain patients, thereby maximizing the analgesic effect.

[0007] To solve the above problems, the technical solution adopted in this application is as follows:

[0008] An analgesic system based on brain neural oscillation modulation, the system comprising:

[0009] The module for constructing a standardized information database of brain signals in healthy individuals is used to extract brain nerve signal indicators from the electroencephalogram (EEG) signals of healthy individuals, determine the standard normal range of brain nerve signal indicators, and form a standardized information database of brain nerve signals in healthy individuals.

[0010] The module for extracting abnormal brain nerve signal indicators in pain patients is used to extract brain nerve signal indicators from the electroencephalogram (EEG) signals of pain patients. The module compares the brain nerve signal indicators of pain patients with the standard normal range. If the indicators exceed the normal range, the brain nerve signal indicators of pain patients are considered abnormal.

[0011] The pain potential regulation target extraction module is used to collect the electroencephalogram (EEG) signals of pain patients, obtain the behavioral indicators of pain patients, analyze the correlation between the behavioral indicators and abnormal brain nerve signal indicators, and determine the potential regulation targets of pain patients based on the correlation.

[0012] The pain regulation module is used to select regulatory targets from potential regulatory targets and choose different regulation modes according to actual needs to regulate pain in patients.

[0013] The regulation effect evaluation module is used to evaluate the pain regulation effect.

[0014] Preferably, the process of forming a standardized information database of brain signals of healthy people is as follows: First, select healthy people and take them as the general norm group. Divide the general norm group into several levels according to region, age and gender. Randomly select several healthy individuals from each level to form the norm group of that level. Create an information database for each level of norm group. The information database includes gender, age and psychological state.

[0015] Then, resting-state, eye-closed EEG signals were collected from individuals in each level of the norm group in stages and multiple times. The collected EEG signals were preprocessed, and neurological signal indices were extracted, including spectral energy characteristics, functional connectivity characteristics, and brain network characteristics. The neurological signal indices of each level were used as norms, and the negative standard deviation of the corresponding data was taken as -3σ. j Sum of positive standard deviation +3σ j Composition interval (-3σ) j +3σ j ), the interval (-3σ j +3σ j ) serves as the standard normal range for brain nerve signal indicators, where j represents the order of brain nerve signal indicators, thus forming a standardized information database of brain nerve signals for healthy individuals.

[0016] Through the above-mentioned technical means, a standardized information database of brain nerve signals of healthy people has been formed, which can be used as a standard for comparison of subsequent pain patients and is more conducive to extracting the regulatory targets to be intervened.

[0017] Preferably, the module for extracting abnormal brain nerve signal indicators of pain patients collects resting-state closed-eye EEG signals of pain patients in stages and multiple times when extracting EEG signals of pain patients, preprocesses the collected EEG signals, and extracts brain nerve signal indicators of EEG signals, including: spectral energy characteristics, functional connectivity characteristics and brain network characteristics, as brain nerve signal indicators of pain patients.

[0018] Compare the brain nerve signal indicators of patients with pain with the standard normal range. If they exceed the standard normal range, the brain nerve signal indicators of patients with pain are considered abnormal.

[0019] Preferably, in the pain potential regulation target extraction module, the resting-state closed-eye electroencephalogram (EEG) signals of the pain patient are collected in stages and multiple times, and the current pain status score of the pain patient is reported. The pain status score is used as a behavioral indicator. The pain status includes: "non-pain" status, "mild pain" status and "severe pain" status.

[0020] The correlation between behavioral indicators and abnormal brain nerve signal indicators is analyzed to obtain the correlation coefficient, and the absolute value of the correlation coefficient is used as an indicator of the degree of correlation.

[0021] By setting a correlation threshold, behavioral indicators whose absolute values ​​of correlation coefficients are greater than the correlation threshold can be used as potential regulatory targets for pain patients.

[0022] By using the above-mentioned technical means, the differences in brain nerve oscillation indicators between pain patients and healthy people are compared, and brain nerve oscillation indicators that are sensitive to individual pain states are identified, thereby extracting brain nerve oscillation modulation targets for specific pain patients.

[0023] Preferably, the pain regulation module includes a regulation target selection module, a regulation mode selection module, and a regulation execution module. The regulation target selection module selects regulation targets to be intervened from potential regulation targets, compares the changes in abnormal brain nerve signal indicators of pain patients under different pain states, and uses the abnormal brain nerve signal indicators that change with the pain state as brain nerve oscillation indicators sensitive to the degree of pain. The regulation mode selection module is used to select a regulation mode. The regulation execution module performs regulation intervention on the regulation target to be intervened according to the regulation mode.

[0024] Preferably, when the regulatory target selection module selects a regulatory target to be intervened from potential regulatory targets, it sorts the potential regulatory targets by correlation coefficient from high to low, and selects the potential regulatory target with the highest correlation coefficient as the regulatory target.

[0025] Preferably, when the control target selection module selects a control target to be intervened from potential control targets, it selects the control target to be intervened from potential control targets according to the user's own needs.

[0026] Preferably, the control mode selected by the control mode selection module is a control mode that integrates neurofeedback training and transcranial alternating current stimulation.

[0027] Preferably, when the regulation execution module performs regulation intervention, it includes the following intervention steps:

[0028] S1. Determine the regulatory target, feedback type, and total training duration to be intervened, initialize the training difficulty, and begin neurofeedback training;

[0029] S2. Based on the feedback type, analyze the changes of the regulatory target points to be intervened in the trainee in real time online, and feed back the changes of the regulatory target points to be intervened to the trainee, paying attention to the neural oscillatory activity response of the trainee's cerebral cortex.

[0030] S3. Determine whether the trainee can consciously enhance or reduce the neural oscillation activity of the cerebral cortex. If so, give positive feedback, record the training duration of this positive feedback, and proceed to step S4; otherwise, proceed to step S4.

[0031] S4. Determine if the total training time has been reached. If yes, end the neurofeedback training and proceed to step S5; otherwise, increase the training difficulty and return to step S2.

[0032] S5. Calculate the total training time when the training difficulty is higher than the set training difficulty threshold and positive feedback is given. Determine the training efficiency of neurofeedback training based on the total training time and proceed to step S6.

[0033] S6. Determine if the training efficiency is lower than the training efficiency threshold. If so, initiate transcranial alternating current stimulation to assist neurofeedback training and continue the regulatory intervention until the training efficiency reaches the training efficiency threshold. Then switch to neurofeedback training and return to step S4. Otherwise, the regulatory intervention ends.

[0034] By combining the above-mentioned techniques with endogenous neurofeedback training and exogenous transcranial alternating current stimulation, abnormal brain nerve oscillation patterns in patients with pain can be intervened and regulated, thereby achieving the effect of relieving pain. This approach is safe, economical, and easy to operate, and is expected to be applied to the clinical treatment of pain.

[0035] Preferably, the feedback type includes visual, auditory, or tactile.

[0036] The formula for calculating the training efficiency of the neurofeedback training described in step S5 is as follows:

[0037]

[0038] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0039] This invention proposes an analgesia system based on brain neural oscillation modulation, comprising a module for constructing a standardized information database of brain signals from healthy individuals, a module for extracting abnormal brain neural signal indicators from pain patients, a module for extracting potential pain modulation targets, a pain modulation module, and a module for evaluating the modulation effect. First, a standardized information database of brain signals from healthy individuals is established. For each pain patient, their brain neural signal indicators are compared with the standard normal range obtained from the standardized information database of brain signals from healthy individuals to identify abnormal brain electrical neural indicators for each pain patient. Then, based on the pain specificity of each pain patient, the correlation between the patient's behavioral indicators and the abnormal brain electrical neural indicators is analyzed to identify potential modulation targets. From these potential targets, modulation targets and modulation modes are selected for intervention. Different modulation modes are selected according to actual needs to modulate the pain in pain patients. Finally, the pain modulation effect is evaluated to achieve pain relief. This system is safe, economical, and easy to operate, and is expected to be applied to the clinical treatment of pain. Attached Figure Description

[0040] Figure 1 This diagram illustrates the basic structural composition of the analgesic system based on brain neural oscillation modulation proposed in Embodiment 1 of the present invention.

[0041] Figure 2 A diagram illustrating the composition of the resting-state brain nerve signal indices proposed in Embodiment 1 of the present invention;

[0042] Figure 3 This is a flowchart illustrating the process of the control execution module performing control intervention as proposed in Embodiment 3 of the present invention.

[0043] Figure 4 This is a schematic diagram illustrating the neural feedback training scenario proposed in Embodiment 3 of the present invention. Detailed Implementation

[0044] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0045] To better illustrate this embodiment, some parts of the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions;

[0046] It is understandable to those skilled in the art that some well-known details may be omitted from the accompanying drawings.

[0047] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments;

[0048] The positional relationships depicted in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0049] Example 1

[0050] like Figure 1 As shown, this embodiment proposes an analgesic system based on brain neural oscillation modulation. See [link to documentation]. Figure 1 The system includes:

[0051] Module 1 for constructing a standardized information database of brain signals in healthy individuals is used to extract brain nerve signal indicators from brain electrical signals of healthy individuals, determine the standard normal range of brain nerve signal indicators, and form a standardized information database of brain nerve signals in healthy individuals.

[0052] The module 2 for extracting abnormal brain nerve signal indicators in pain patients is used to extract brain nerve signal indicators from the electroencephalogram (EEG) signals of pain patients. The module compares the brain nerve signal indicators of pain patients with the standard normal range. If the indicators exceed the normal range, the brain nerve signal indicators of pain patients are considered abnormal brain nerve signal indicators.

[0053] The pain potential regulation target extraction module 3 is used to collect the electroencephalogram (EEG) signals of pain patients, obtain the behavioral indicators of pain patients, analyze the correlation between the behavioral indicators and abnormal brain nerve signal indicators, and determine the potential regulation targets of pain patients based on the correlation.

[0054] The pain regulation module 4 is used to select regulation targets to be intervened from potential regulation targets, and select different regulation modes according to actual needs to regulate pain in patients.

[0055] The regulation effect evaluation module 5 is used to evaluate the pain regulation effect.

[0056] In the healthy population brain signal standardization information database construction module 1, the process of forming the healthy population brain signal standardization information database is as follows: First, a healthy population is selected and taken as the general norm group. The general norm group is divided into several levels according to region, age and gender. Several healthy individuals are randomly selected from each level. For example, in this embodiment, 2,000 people are selected to form the norm group of this level. For each level of norm group, an information database is created. The information database includes gender, age and psychological state.

[0057] Then, resting-state, eye-closed EEG signals were collected from individuals in each level of the norm group in stages and multiple times. In practice, a specialized EEG acquisition device was used to collect 5 minutes of resting-state, eye-closed EEG signals. The collected EEG signals underwent preprocessing, such as signal filtering, bad segment removal, and independent principal component analysis. Then, spectral analysis, coherence analysis, and cross-band coupling analysis were used to extract neurological signal indices from the EEG signals, including spectral energy characteristics, functional connectivity characteristics, and brain network characteristics. The composition diagram of these resting-state neurological signal indices is shown below. Figure 2 As shown, in Figure 2In the study, spectral energy features include peak frequency and power spectrum; functional connectivity features include phase-based and amplitude-based functional connectivity; and brain network features include local and global features. Local features include degree and clustering coefficients, while global features include global clustering coefficients, global efficiency, and small-world properties. The study uses 2000 neuronal signal indicators at each level (such as peak frequency of α-oscillations, power spectrum, and phase- and amplitude-based functional connectivity parameters distributed normally) as norms, and takes the negative standard deviation (-3σ) of the corresponding data. j Sum of positive standard deviation +3σ j Composition interval (-3σ) j +3σ j ), the interval (-3σ j +3σ j ) serves as the standard normal range for brain nerve signal indicators, where j represents the order of brain nerve signal indicators, thus forming a standardized information database of brain nerve signals for healthy individuals.

[0058] In psychological statistics, outliers are defined as values ​​in a set of measurements that deviate from the mean by more than three standard deviations. Here, we take the negative standard deviation of each neural oscillation, -3σ. j With positive standard deviation +3σ j Composition interval (-3σ) j +3σ j This is used to set the normal range for brain nerve signal indicators; anything outside the normal range is considered abnormal.

[0059] The module for extracting abnormal brain nerve signal indicators in pain patients collects resting-state, closed-eye EEG signals from pain patients in stages and multiple times. It preprocesses the collected EEG signals and extracts brain nerve signal indicators, including spectral energy characteristics, functional connectivity characteristics, and brain network characteristics, as brain nerve signal indicators for pain patients.

[0060] Compare the brain nerve signal indicators of patients with pain with the standard normal range. If they exceed the standard normal range, the brain nerve signal indicators of patients with pain are considered abnormal.

[0061] In the pain potential regulation target extraction module, the resting-state closed-eye electroencephalogram (EEG) signals of pain patients are collected in stages and multiple times to obtain information on changes in pain state. The pain state includes: "non-pain" state, "mild pain" state and "severe pain" state. The information on changes in pain state is used as a behavioral indicator.

[0062] The correlation between behavioral indicators and abnormal brain nerve signal indicators is analyzed to obtain the correlation coefficient, and the absolute value of the correlation coefficient is used as an indicator of the degree of correlation.

[0063] By setting a correlation threshold, behavioral indicators whose absolute values ​​of correlation coefficients are greater than the correlation threshold can be used as potential regulatory targets for pain patients.

[0064] In practice, questionnaires were administered to pain patients to collect their basic information, pain status, and psychological state, including age, gender, type of illness, location of pain, duration of pain, severity of pain, degree of pain interference, sleep quality, anxiety, and depression. Under different pain states, an electroencephalogram (EEG) acquisition system was used to collect resting-state, closed-eye EEG signals from pain patients multiple times in stages, and the current pain status score of each patient was reported. This pain status score was used as a behavioral indicator; here, pain patients rate themselves based on their own feelings against a set score range, such as the Visual Analogue Scale (VAS) for pain intensity or the Numerical Rating Scale (NRS).

[0065] Based on multiple EEG and pain behavior data, global and local features of EEG signals are extracted. Correlation analysis is performed on the pain state behavior indicators extracted from individuals at multiple time points and the corresponding EEG signal features to obtain correlation coefficients. The absolute value of the correlation coefficients is used as an indicator of the degree of correlation, thereby determining the EEG indicators most relevant to the patient's pain perception.

[0066] Simultaneously, the user's brain nerve signals are analyzed in the EEG processing module to obtain brain nerve oscillation characteristic values. The user's brain indicator information is compared with a database of nerve oscillation information from healthy individuals to identify brain nerve oscillation characteristics with discrepancies: when the brain nerve characteristic results of the pain patient at different time periods with eyes closed and at rest exceed the range (-3σ)... j +3σ j When the neural oscillation index of a pain patient exceeds the set normal range, it needs to be adjusted. It can be used as a potential modifiable neural oscillation index for pain patients who are significantly different from healthy individuals and are sensitive to pain.

[0067] Ultimately, one or more brain oscillation indicators that are specific to normal individuals and highly correlated with the pain state of the pain patients themselves were selected as potential regulatory targets.

[0068] Example 2

[0069] In this embodiment, the pain regulation module 4 includes a regulation target selection module 401, a regulation mode selection module 402, and a regulation execution module 403. The regulation target selection module 401 selects the regulation target to be intervened from potential regulation targets, compares the changes in the brain nerve oscillation patterns of abnormal brain nerve signal indicators of pain patients under different pain states, and uses the abnormal brain nerve signal indicators that change with the pain state as brain nerve oscillation indicators sensitive to the degree of pain. The regulation mode selection module 402 is used to select the regulation mode. The regulation execution module 403 performs regulation intervention on the regulation target to be intervened according to the regulation mode.

[0070] When selecting a regulatory target from potential regulatory targets, the regulatory target selection module 401 sorts the potential regulatory targets by their correlation coefficients from high to low. The regulatory target selection module 402 selects the potential regulatory target with the highest correlation coefficient as the regulatory target. Alternatively, the user can select a regulatory target from the potential regulatory targets according to their own needs. This can be done by regulating a single potential regulatory target (e.g., increasing the α-neural oscillation energy of the sensorimotor cortex) or by simultaneously regulating multiple potential regulatory targets (e.g., increasing the α-neural oscillation energy of the sensorimotor cortex and decreasing the γ-neural oscillation energy of the prefrontal cortex).

[0071] Example 3

[0072] In this embodiment, the control mode selected by the control mode selection module 401 is a control mode that integrates neurofeedback training and transcranial alternating current stimulation.

[0073] like Figure 3 As shown, when the regulation execution module performs regulation intervention, it includes the following intervention steps:

[0074] S1. Determine the target point to be intervened, the feedback type, and the total training duration; initialize the training difficulty; and begin neurofeedback training. In this embodiment, the feedback type includes visual, auditory, or tactile feedback. For example, if the feedback type is visual, see [link to documentation]. Figure 4 The diagram shown illustrates the overall components required for neurofeedback training. Step S2 is then executed.

[0075] S2. Based on the feedback type, analyze the changes in the regulatory targets to be intervened on the trainee in real time online, and feed back the changes in the regulatory targets to be intervened on the trainee, paying attention to the neural oscillatory activity response of the trainee's cerebral cortex; such as Figure 4 As shown, the trainee is in a neurofeedback training center, watching images displayed on the screen and receiving real-time feedback. Changes in different training characteristics are analyzed online in real time, and EEG brain signals are recorded. This means that the changes in this indicator are fed back to the individual in real time through visual or auditory feedback via online real-time analysis. The training goal perceived by the individual is to regulate the changes in the feedback signal by changing their own brain activity.

[0076] S3. Determine if the trainee consciously increases or decreases neural oscillations in the cerebral cortex. If so, provide positive feedback, record the duration of the positive feedback, and proceed to step S4; otherwise, proceed to step S4. Positive feedback here can be a clearer visual image, a moving visual target, or an increased volume of auditory feedback, etc.

[0077] S4. Determine if the total training time has been reached. If yes, end the neurofeedback training and proceed to step S5; otherwise, increase the training difficulty and return to step S2.

[0078] S5. Calculate the total training time when the training difficulty is higher than the set training difficulty threshold and positive feedback is given. Determine the training efficiency of neurofeedback training based on the total training time and proceed to step S6.

[0079] S6. Determine if the training efficiency is lower than the training efficiency threshold. If so, initiate transcranial alternating current stimulation to assist neurofeedback training and continue the regulatory intervention until the training efficiency reaches the training efficiency threshold. Then switch to neurofeedback training and return to step S4. Otherwise, the regulatory intervention ends.

[0080] In practice, when setting transcranial alternating current parameters, the system will provide users with a control plan based on the brain nerve index characteristics extracted from the user's personalized parameter assessment module. Users can set personalized stimulation parameters according to their needs, such as: ① stimulation target: nerve oscillations at specific locations and frequency bands; ② stimulation intensity; ③ stimulation duration, etc.

[0081] Pain patients autonomously regulate pain neuromuscular oscillation indicators based on auditory or visual feedback. The training efficiency (the degree to which the regulation goal is achieved) is systematically evaluated at regular intervals. In this embodiment, training efficiency is defined as the percentage of successful regulation within 3 minutes of neurofeedback training, using the following formula:

[0082]

[0083] The total number of feedback sessions refers to the number of times the neurofeedback training module provides real-time feedback within 3 minutes. If the efficiency is lower than the set training efficiency (e.g., 70%), it indicates that the patient cannot effectively regulate the neural oscillation index based on the feedback. In this case, the transcranial alternating current stimulation system is activated to assist neurofeedback training, thereby optimizing the regulation effect. Once the brain regulation index reaches the set threshold, the stimulation is turned off, and neurofeedback training continues. The regulation ends when the preset duration (e.g., 20 minutes) is reached.

[0084] Finally, in the regulation effect evaluation module 105, the user's 5-minute resting-state EEG with eyes closed is collected, and the signal is analyzed and processed to obtain feature values, namely the personalized brain nerve signal indicators obtained in the pain marker extraction module; the audio acquisition module uses a machine to collect the patient's voice signal, converts the acquired audio information into text information, and matches it with audio information with a set score (0-10 points) to obtain the user's current pain evaluation; the questionnaire measurement module measures the user's anxiety and depression emotional state, and the three are combined to present the regulation effect to the user in a visual UI.

[0085] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. An analgesic system based on brain neural oscillation modulation, characterized in that, The system includes: The module for constructing a standardized information database of brain signals in healthy individuals is used to extract brain nerve signal indicators from the electroencephalogram (EEG) signals of healthy individuals, determine the standard normal range of brain nerve signal indicators, and form a standardized information database of brain nerve signals in healthy individuals. The module for extracting abnormal brain nerve signal indicators in pain patients is used to extract brain nerve signal indicators from the electroencephalogram (EEG) signals of pain patients. The module compares the brain nerve signal indicators of pain patients with the standard normal range. If the indicators exceed the normal range, the brain nerve signal indicators of pain patients are considered abnormal. The pain potential regulation target extraction module is used to collect the electroencephalogram (EEG) signals of pain patients, obtain the behavioral indicators of pain patients, analyze the correlation between the behavioral indicators and abnormal brain nerve signal indicators, and determine the potential regulation targets of pain patients based on the correlation. The pain control module is used to select control targets from potential control targets and choose different control modes according to actual needs to control pain in patients. The pain control module includes a control target selection module, a control mode selection module, and a control execution module. The modulation mode selection module selects a modulation mode that integrates neurofeedback training and transcranial alternating current stimulation; when the modulation execution module performs the modulation intervention, it includes the following intervention steps: S1. Determine the regulatory target, feedback type, and total training duration to be intervened, initialize the training difficulty, and begin neurofeedback training; S2. Based on the feedback type, analyze the changes of the regulatory target points to be intervened in the trainee in real time online, and feed back the changes of the regulatory target points to be intervened to the trainee, paying attention to the neural oscillatory activity response of the trainee's cerebral cortex. S3. Determine whether the trainee can consciously enhance or reduce the neural oscillation activity of the cerebral cortex. If yes, provide positive feedback, record the training duration of this positive feedback, and proceed to step S4; otherwise, proceed to step S4. S4. Determine if the total training time has been reached. If yes, end the neurofeedback training and proceed to step S5; otherwise, increase the training difficulty and return to step S2. S5. Calculate the total training time when the training difficulty is higher than the set training difficulty threshold and positive feedback is given. Determine the training efficiency of neurofeedback training based on the total training time and proceed to step S6. S6. Determine if the training efficiency is lower than the training efficiency threshold. If so, initiate transcranial alternating current stimulation to assist neurofeedback training and continue the regulatory intervention until the training efficiency reaches the training efficiency threshold. Then switch to neurofeedback training and return to step S4. Otherwise, the regulatory intervention ends. The regulation effect evaluation module is used to evaluate the pain regulation effect.

2. The analgesic system based on brain neural oscillation modulation according to claim 1, characterized in that, The process of forming a standardized database of brain signals from healthy individuals is as follows: First, a healthy population is selected as the overall norm group. The overall norm group is then divided into several levels according to region, age, and gender. Several healthy individuals are randomly selected from each level to form the norm group for that level. An information database is created for each level of the norm group, and the information database includes gender, age, and psychological state. Then, resting-state, eye-closed EEG signals were collected from individuals in each level of the norm group in stages and multiple times. The collected EEG signals were preprocessed, and neurological signal indices were extracted, including spectral energy characteristics, functional connectivity characteristics, and brain network characteristics. The neurological signal indices of each level were used as norms, and the negative standard deviation of the corresponding data was taken as -3σ. j Sum of positive standard deviation +3σ j Composition interval (-3σ) j +3σ j ), the interval (-3σ j +3σ j (j) serves as the standard normal range for brain nerve signal indicators, where j represents the order of brain nerve signal indicators, thus forming a standardized information database of brain nerve signals for healthy individuals.

3. The analgesic system based on brain neural oscillation modulation according to claim 2, characterized in that, The module for extracting abnormal brain nerve signal indicators in pain patients collects resting-state, closed-eye EEG signals from pain patients in stages and multiple times when extracting EEG signals. It preprocesses the collected EEG signals and extracts brain nerve signal indicators from the EEG signals, including: spectral energy characteristics, functional connectivity characteristics, and brain network characteristics, as brain nerve signal indicators for pain patients. Compare the brain nerve signal indicators of patients with pain with the standard normal range. If they exceed the standard normal range, the brain nerve signal indicators of patients with pain are considered abnormal.

4. The analgesic system based on brain neural oscillation modulation according to claim 3, characterized in that, In the pain potential regulation target extraction module, the resting-state closed-eye electroencephalogram (EEG) signals of pain patients are collected in stages and multiple times, and the current pain status score of the pain patients is reported. The pain status score is used as a behavioral indicator. The pain status includes: "non-pain" status, "mild pain" status and "severe pain" status. The correlation between behavioral indicators and abnormal brain nerve signal indicators is analyzed to obtain the correlation coefficient, and the absolute value of the correlation coefficient is used as an indicator of the degree of correlation. By setting a correlation threshold, behavioral indicators whose absolute values ​​of correlation coefficients are greater than the correlation threshold can be used as potential regulatory targets for pain patients.

5. The analgesic system based on brain neural oscillation modulation according to claim 4, characterized in that, The target selection module selects the target to be intervened from potential targets, compares the changes in abnormal brain nerve signal indicators of pain patients under different pain states, and uses the abnormal brain nerve signal indicators that change with the pain state as brain nerve oscillation indicators that are sensitive to the degree of pain. The regulation mode selection module is used to select the regulation mode. The regulation execution module performs regulation intervention on the target to be intervened according to the regulation mode.

6. The analgesic system based on brain neural oscillation modulation according to claim 5, characterized in that, When the regulatory target selection module selects a regulatory target to be intervened from potential regulatory targets, it sorts the potential regulatory targets by correlation coefficient from high to low, and selects the potential regulatory target with the highest correlation coefficient as the regulatory target.

7. The analgesic system based on brain neural oscillation modulation according to claim 5, characterized in that, When the control target selection module selects a control target to be intervened from potential control targets, it selects the control target to be intervened from potential control targets according to the user's own needs.

8. The analgesic system based on brain neural oscillation modulation according to claim 1, characterized in that, Feedback types include visual, auditory, or tactile; The formula for calculating the training efficiency of the neurofeedback training described in step S5 is as follows: 。

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