Self-adaptive closed-loop neural feedback system for improving anxiety based on individual specificity and implementation method

Abstract

The invention discloses a self-adaptive closed-loop neural feedback system for improving anxiety based on individual specificity and an implementation method, and the system comprises a data collection and processing module, an open-loop control module and a self-adaptive negative feedback module, a complete treatment closed loop from signal acquisition and processing to open-loop control to adaptive negative feedback to signal acquisition and processing is realized; an individual fuzzy controller unit is combined with a traditional electroencephalogram neural feedback framework, the anxiety state of an individual is monitored and adjusted in real time, the task difficulty is accurately changed, and therefore the optimal anxiety intervention effect is rapidly achieved. According to the method, the self-adjusting ability of each individual within a period of time is reflected through iterative evaluation of the anxiety state of the individual, and the task difficulty is adjusted according to different individual characteristics, so that the individual is stimulated to play a higher potential; neural feedback training is carried out by applying the self-adaptive task difficulty, individualized and accurate multi-course nerve regulation training can be made in an auxiliary mode, and the anxiety emotion of an individual is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mental health treatment, and particularly to an adaptive closed-loop neurofeedback system and implementation method for improving anxiety based on individual specificity. Background Art

[0002] Traditional treatment methods for anxiety mainly rely on drug treatment and psychotherapy. Drug treatment is a traditional way to treat anxiety. Commonly used drugs include antidepressants and anti-anxiety drugs, which can help relieve anxiety symptoms and improve the quality of life of patients. Although drug treatment can quickly relieve anxiety symptoms, it usually does not address the root cause of anxiety and may be accompanied by side effects. In addition, once drug treatment is stopped, the symptoms may reappear, indicating the need for a more lasting solution.

[0003] In the field of mental health treatment, neurofeedback therapy is gradually showing its unique advantages. With the continuous progress of psychotherapy methods, more and more anxiety therapies have begun to combine psychotherapy to guide subjects to relieve anxiety. In recent years, as an emerging treatment method, neurofeedback has shown potential in immediately regulating anxiety emotions because it can adjust feedback parameters in real time to adapt to individual brain wave changes. Research shows that neurofeedback can help individuals learn how to regulate their emotional responses by monitoring and providing feedback on brain activity. This immediate self-regulation ability is particularly important for relieving acute anxiety symptoms because it provides a rapid response mechanism that enables individuals to quickly adjust their emotional states when facing stress or threats. At the same time, neurofeedback can affect multiple brain regions and neural networks simultaneously, potentially providing a more comprehensive therapeutic effect in regulating emotions and behaviors. In neurofeedback treatment for relieving anxiety, selecting appropriate neurofeedback indicators is crucial. Recent survey results have reported the correlation between the alpha / theta ratio in the parietal cortex (PZ) and anxiety emotions.

[0004] Although traditional electroencephalogram neurofeedback therapy has been proven beneficial for treating various diseases, the regulation processes and means used for the same group of people are relatively similar, lacking personalized treatment plans. Individuals of different age groups and educational levels vary greatly in their adaptability to neurofeedback and learning ability. Therefore, in order to perform neurofeedback regulation in a personalized manner, the neurofeedback system needs to be adaptively designed to match different subjects. Although there is evidence that adaptive task difficulty adjustment is of great significance for neurofeedback training, there is currently little research at home and abroad on personalized and precise adjustment of task difficulty for different subjects. Most teams are still using a few fixed adaptive change coefficients and modifying the adaptive coefficients based on how many seconds of data in the overall regulation process have feedback values below the threshold. This type of adaptive regulation has the problem that the changing adaptive coefficients are too fixed and the change intervals are too long, unable to achieve real-time change and precise regulation. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide an adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity, aiming to address the differences among different anxiety individuals, thereby ensuring effective neurofeedback training for different anxiety individuals.

[0006] Technical Solution: An adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity includes a data acquisition and processing module, an open-loop control module, and an adaptive negative feedback module; the data acquisition and processing module is connected to the open-loop control module, the open-loop control module is connected to the adaptive negative feedback module, and the adaptive negative feedback module is connected to the data acquisition and processing module;

[0007] The data acquisition and processing module encapsulates the resting-state data and sends it to the open-loop control module. At the same time, after preprocessing the individual's electroencephalogram task-state data, extracting feedback features, and evaluating the anxiety state, it sends the anxiety state evaluation result to the adaptive negative feedback module;

[0008] After obtaining the electroencephalogram data from the data acquisition and processing module, the open-loop control module obtains personalized parameters and the anxiety state evaluation result; the adaptive negative feedback module controls the adjustment of the neurofeedback task difficulty, conducts neurofeedback training, realizes the adaptive change of the visual task difficulty, and finally displays and feeds it back to the subject.

[0009] Further, the data acquisition and processing module includes a resting-state data acquisition unit, a task-state data acquisition unit, a first data preprocessing unit, a first feedback feature extraction unit, and an anxiety state evaluation unit. The resting-state data acquisition unit is connected to the second data preprocessing unit in the open-loop control module, and the anxiety state evaluation unit is connected to the individual fuzzy controller unit in the adaptive negative feedback module;

[0010] The open-loop control module includes a second data preprocessing unit, a baseline threshold calculation unit, a second feedback feature extraction unit, and a personalized parameter configuration unit. The personalized parameter configuration unit is connected to the neural feedback training unit in the adaptive negative feedback module;

[0011] The adaptive negative feedback module includes an individual fuzzy controller unit, a task difficulty adjustment unit, and a neural feedback training unit;

[0012] Among them, in the data acquisition and processing module, the resting-state data acquisition unit acquires the resting-state EEG signals and encapsulates and sends them to the open-loop control module, and the anxiety state evaluation unit evaluates the current feedback features of the subject and then sends them to the adaptive negative feedback module;

[0013] After the open-loop control module obtains the EEG data from the data acquisition and processing module, it performs real-time data preprocessing on the resting-state EEG data, and then decodes it through the baseline threshold calculation unit and the second feedback feature extraction unit to obtain the baseline threshold as the personalized initial task difficulty, and calculates the feedback features to obtain the personalized self-regulation ability, obtaining the personalized initial task difficulty and self-regulation ability; and iteratively updates the initial parameters of each individual to the adaptive negative feedback module;

[0014] The personalized parameters generated by the open-loop control module are encapsulated by the neural feedback training unit, and at the same time cooperate with the individual fuzzy controller unit to control the task difficulty, and finally display and feedback to the subject.

[0015] A method for implementing an adaptive closed-loop neural feedback system for improving anxiety based on individual specificity. The subject conducts neural feedback training through any one of the above adaptive closed-loop neural feedback systems, and the steps are as follows:

[0016] S1. Select healthy subjects to be included in the group and collect EEG signals;

[0017] S2. Preprocess the collected EEG signals;

[0018] S3. Extract feedback features from the processed EEG signals, calculate the baseline threshold, and evaluate the current anxiety state of the subject according to the extracted feature values;

[0019] S4. The adaptive negative feedback module outputs the task difficulty that needs to be adjusted according to the control rules, and combines the initialized personality parameters to obtain the corrected personalized task difficulty;

[0020] S5. The subject relieves the anxiety state through the neural feedback training unit by using the neural feedback training method according to the corrected personalized task difficulty.

[0021] Furthermore, the feedback features are calculated by the following formula:

[0022]

[0023] Among them, D represents the extracted feedback feature, A represents the neurofeedback experiment feature, and B represents the energy value of the baseline resting-state EEG signal; the smaller the D value, the lighter the anxiety state of the subject, and vice versa, the heavier the anxiety state of the subject.

[0024] The calculation of the energy value of the baseline resting-state EEG signal is as follows: The sliding window method is adopted. By collecting the resting-state EEG data of the individual after preprocessing, the signal index is calculated every 3 seconds with a step size of 1 second to obtain 118 consecutive neurofeedback experiment features, and the average value of the 118 neurofeedback experiment features is calculated.

[0025] Furthermore, the difference e and the difference change rate ec between the current anxiety state feature value of the subject and the target value are used as the inputs of the adaptive negative feedback module, and the task difficulty change amount is used as the output of the adaptive negative feedback module. The adaptive negative feedback module outputs the task difficulty that needs to be adjusted according to the control rule, and combines the initial personalized parameters to obtain the corrected personalized task difficulty.

[0026] The method for adjusting the task difficulty includes the following steps:

[0027] Sb1. When the individual fuzzy controller unit starts to work, the baseline threshold calculated by the open-loop control module is used as the initial task difficulty, and the feedback feature is used as the visual initial index of the current anxiety state of the individual.

[0028] Sb2. Based on the relationship between the input and the output, the adaptive negative feedback module generates the control rule through fuzzy logic.

[0029] Sb3. The individual fuzzy controller unit continuously monitors the difference between the output and the target value and feeds the difference information back to the task difficulty adjustment unit; if the real-time feedback feature value continuously exceeds the target value, it indicates that the current task difficulty is too difficult for the subject and the difficulty needs to be reduced; on the contrary, if the feedback feature value of the subject is always lower than the target value, it indicates that the subject has adapted to the current task difficulty and the task difficulty needs to be appropriately increased to provide a higher feedback feature value.

[0030] Furthermore, the neurofeedback training method needs to be continuously carried out 4 to 10 times of feedback training; among them, a single feedback training includes the following steps:

[0031] Sc1. Record the brain activity of the participant in the natural resting state before training. The EEG signal at this stage is set as the reference point and used as the benchmark value of the resting-state feedback signal energy.

[0032] Sc2 guides participants to understand the entire process of the experiment and encourages them to develop 3 to 5 possible neurofeedback strategies; during the practice phase, participants identify 1 to 2 strategies that are most suitable for themselves;

[0033] Sc3 conducts formal feedback training: participants will use the strategies that are most suitable for themselves to conduct three groups of formal neurofeedback training; each group of training consists of three scenarios: rest, regulation, and calculation; each scenario is 40 seconds, and the three scenarios are repeated four times in sequence;

[0034] In the rest scenario, participants try to stay as relaxed as possible to achieve a resting state;

[0035] In the regulation scenario, through visual feedback, participants need to try to reduce the height of the feedback signal feature histogram;

[0036] The calculation scenario interferes with the previous regulation state through a math task;

[0037] Sc4 conducts transfer training: similar to the formal feedback training process, but in the regulation scenario, visual feedback is no longer provided; participants are required to self-regulate according to the strategies that are most suitable for themselves to verify whether participants can apply the learned regulation skills to situations without visual feedback;

[0038] Sc5 measures the resting-state brain activity of participants again after completing the neurofeedback training. If the energy of the feedback signal is higher than or equal to the baseline value of the resting-state feedback signal energy, it indicates that the training effect is not good and the anxiety state of the subject has not been alleviated; if the energy of the feedback signal is lower than the baseline value of the resting-state feedback signal energy, it indicates that the anxiety state of the subject has been alleviated to some extent.

[0039] Compared with the prior art, the remarkable effects of the present invention are as follows:

[0040] 1. In the adaptive closed-loop neurofeedback system of the present invention, an individual fuzzy controller unit is combined with a traditional electroencephalogram neurofeedback framework. To adapt to individual differences, the initial personalized parameters of each subject are adjusted separately; in addition, through the individual fuzzy controller unit in the adaptive negative feedback module, the task difficulty can be accurately changed in real time, and the anxiety state of the subject can be monitored and adjusted in real time, so as to achieve the best intervention effect; moreover, the system of the present invention also has reliable wearability and universality, and can be applied to the daily lives of different individuals to improve the anxiety emotions of users;

[0041] 2. The adaptive closed-loop neurofeedback system of the present invention includes a data acquisition and processing module, an open-loop control module, and an adaptive negative feedback module, realizing a complete treatment closed-loop from signal acquisition and processing - open-loop control - adaptive negative feedback - signal acquisition and processing, and can adjust the task difficulty according to different individual characteristics, thereby stimulating them to exert higher potential;

[0042] 3. In the implementation method of the present invention, by iteratively evaluating the anxiety state of each subject, the self-regulation ability of the subject over a period of time is reflected. Through the method of applying real-time adjustment of task difficulty for neurofeedback training, it can help different individuals quickly and effectively find the most suitable anxiety regulation strategy for themselves, so as to adjust the anxiety state of the individual to the optimal target threshold of the individual; and by gradually reducing the oscillation error and the error change rate to the expected range, it can cope with the differences between different individuals and provide the same high-level feedback regulation for each subject; compared with the traditional treatment method, the training method of the present invention has a lower cost and stronger ability to adapt to individual differences. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic structural diagram of the system of the present invention;

[0044] Figure 2 It is a flowchart of the implementation method of the system of the present invention applied to neurofeedback training;

[0045] Figure 3 It is a flowchart of the neurofeedback training of the present invention;

[0046] Figure 4 It is a schematic diagram of the change of task difficulty in a specific embodiment of the system of the present invention applied to neurofeedback training;

[0047] Figure 5 It is a schematic diagram of the training result in a specific embodiment of the system of the present invention applied to neurofeedback training;

[0048] In the figure, 1 - data acquisition and processing module; 2 - open-loop control module; 3 - adaptive negative feedback module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings of the specification and the specific embodiments.

[0050] The present invention provides an adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity, as Figure 1 shown, including a data acquisition and processing module 1, an open-loop control module 2 and an adaptive negative feedback module 3; the data acquisition and processing module 1 is connected to the open-loop control module 2, the open-loop control module 2 is connected to the adaptive negative feedback module 3, and the adaptive negative feedback module 3 is connected to the data acquisition and processing module 1, forming a closed-loop system. Using this closed-loop system can assist in formulating personalized and precise neurofeedback regulation training to improve the anxiety mood of individuals.

[0051] The data acquisition and processing module 1 includes a resting-state data acquisition unit, a task-state data acquisition unit, a first data preprocessing unit, a first feedback feature extraction unit, and an anxiety state assessment unit. The resting-state data acquisition unit is connected to the second data preprocessing unit in the open-loop control module 2, and the anxiety state assessment unit is connected to the individual fuzzy controller unit in the adaptive negative feedback module 3;

[0052] The open-loop control module 2 includes a second data preprocessing unit, a baseline threshold calculation unit, a second feedback feature extraction unit, and a personalized parameter configuration unit. The personalized parameter configuration unit is connected to the neurofeedback training unit in the adaptive negative feedback module 3;

[0053] The adaptive negative feedback module 3 includes an individual fuzzy controller unit, a task difficulty adjustment unit, and a neurofeedback training unit;

[0054] Among them, in the data acquisition and processing module 1, the resting-state data acquisition unit acquires the resting-state EEG signals and encapsulates and sends them to the open-loop control module 2. The anxiety state assessment unit sends the evaluated current feedback features of the subject to the adaptive negative feedback module 3; at the same time, after the data acquisition and processing module 1 preprocesses the individual's EEG task-state data, extracts the feedback features, and assesses the anxiety state, it sends the anxiety state assessment result to the adaptive negative feedback module 3.

[0055] After the open-loop control module 2 obtains the EEG data from the data acquisition and processing module 1, it performs real-time data preprocessing on the resting-state EEG data, and then decodes it through the baseline threshold calculation unit and the second feedback feature extraction unit, so as to obtain the baseline threshold as the personalized initial task difficulty, calculate the feedback features to obtain the personalized self-regulation ability, obtain the personalized initial task difficulty and self-regulation ability, thereby obtaining personalized parameters, and iteratively update the initial parameters of each individual to the adaptive negative feedback module 3.

[0056] The personalized parameters generated by the open-loop control module 2 are encapsulated by the neurofeedback training unit in the adaptive negative feedback module 3, and at the same time, cooperate with the individual fuzzy controller unit to control the task difficulty, and finally display and feedback to the subject.

[0057] The adaptive negative feedback module 3 controls the adjustment of the neurofeedback task difficulty and conducts neurofeedback training; after the open-loop control module 2 obtains the EEG data from the data acquisition and processing module 1, the generated personalized parameters and the anxiety state assessment result are adaptively changed to the visual task difficulty through the individual fuzzy controller unit, and the individual accordingly maintains or adjusts his own regulation strategy.

[0058] As Figure 2 shown, it is a flowchart of the implementation method of an adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity. The steps of the application to the neurofeedback training implementation method are as follows:

[0059] Step 1, data acquisition;

[0060] Select healthy subjects to be included in the group, and use the Neuvo electroencephalograph to collect 64-channel electroencephalogram (EEG) signals.

[0061] Step 2, data processing;

[0062] Preprocess the collected EEG signals by means of rereferencing, filtering, and removing eye movement artifacts.

[0063] The preprocessing of the EEG signals is as follows: First, band-pass filtering of the signals is performed from 1 to 100 Hz, and a notch filter (49 - 51 Hz) is used to eliminate the interference of the 50 Hz power frequency; then, the rereferencing of the EEG signals is performed using the common average reference strategy.

[0064] Step 3, extract feedback features from the processed EEG signals, calculate the baseline threshold, and evaluate the anxiety state of the subjects;

[0065] The neurofeedback experiment feature (ATR) is defined as: the average value of the alpha / theta ratio of the EEG PZ electrode for 4 cycles of single regulation. And the feedback feature extracted in this embodiment is the difference between ATR and the energy of the baseline resting-state EEG signal, divided by the energy value of the baseline resting-state EEG signal; it can be calculated by the following formula:

[0066]

[0067] Among them, D represents the extracted feedback feature, A represents the neurofeedback experiment feature, and B represents the energy value of the baseline resting-state EEG signal.

[0068] For the preprocessed EEG signals, through formula (1), extract the characteristic information of the EEG signals, so as to determine the personalized anxiety state. If the value of D is smaller, it indicates that the anxiety state of the subject is lighter, and vice versa, it indicates that the anxiety state of the subject is heavier.

[0069] The calculation of the energy value of the baseline resting-state EEG signal includes the following steps:

[0070] Mainly use the sliding window method. By collecting the resting-state EEG data of the individual after preprocessing, calculate the signal index every 3 seconds with a step size of 1 second, obtain 118 consecutive feedback signal features (i.e., ATR), and calculate the average value of the 118 ATRs to obtain the energy value B of the baseline resting-state EEG signal, that is, the baseline threshold.

[0071] Step 4, the adaptive negative feedback module outputs the task difficulty to be adjusted according to the control rules, and combines the initialized personality parameters to obtain the corrected personalized task difficulty;

[0072] Use the baseline threshold obtained in Step 3 as the initial personalized task difficulty.

[0073] Take the difference e between the current anxiety state characteristic value of the subject and the target value and the difference change rate ec as the input of the adaptive negative feedback module, and the task difficulty change amount as the output of the adaptive negative feedback module. The adaptive negative feedback module outputs the task difficulty that needs to be adjusted according to the control rule, and combines the initial personalized parameters to obtain the corrected personalized task difficulty.

[0074] The method for adjusting the task difficulty includes the following steps:

[0075] Step b1, initialization of the adaptive negative feedback module: When the individual fuzzy controller unit starts to work, use the baseline threshold calculated by the open-loop control module as the initial task difficulty, and use the feedback feature as the visual initial index of the individual's current anxiety state.

[0076] Step b3, generation of the control rule: The adaptive negative feedback module generates the control rule R through fuzzy logic as shown in the following formula:

[0077]

[0078] This control rule is based on the relationship between the input (such as 0.1 times the error e and the error change rate ec) and the output (task difficulty change amount), and uses the difference e between the current anxiety state characteristic value of the subject and the target value as the performance index to evaluate the effect of the control rule.

[0079] Step b4, task difficulty change: The individual fuzzy controller unit continuously monitors the difference between the output and the target value, and feeds this difference information back to the task difficulty adjustment unit. If the real-time feedback characteristic value continuously exceeds the target value, it indicates that the current task difficulty is too difficult for the subject and the difficulty needs to be reduced. On the contrary, if the feedback characteristic value of the subject is always lower than the target value, it indicates that the subject has adapted to the current task difficulty and the task difficulty needs to be appropriately increased to provide a higher feedback characteristic value; continuously iterate the above steps to achieve precise control of the individual.

[0080] Step Five, the subject relieves the anxiety state through the neurofeedback training unit according to the corrected personalized task difficulty by using the neurofeedback training method;

[0081] Furthermore, the neurofeedback training method needs to be continuously carried out 4 to 10 times of feedback training; as Figure 3 shown, a single feedback training includes but is not limited to the following steps:

[0082] Step c1, Baseline resting-state EEG measurement: First, record the brain activity of the participants in their natural resting state before training. The EEG signals at this stage are set as the reference point, that is, the baseline value of the energy of the resting-state feedback signal.

[0083] Step c2, Practice stage: In this session, guide the participants to understand the entire process of the experiment and encourage them to develop 3 to 5 possible neurofeedback strategies. Through the practice stage, the participants can identify 1 to 2 strategies that are most suitable for themselves for use in the subsequent formal feedback training.

[0084] Step c3, Formal feedback training stage: The participants will use the most suitable strategy for themselves to conduct three groups of formal neurofeedback training. The goal of the training is to achieve neuromodulation by reducing the energy of the feedback signal. Each group of training consists of three scenarios: rest, regulation, and calculation. In the rest scenario, the participants try to stay as relaxed as possible to reach the resting state; in the regulation scenario, through visual feedback, the participants need to try to reduce the height of the characteristic histogram of the feedback signal; the calculation scenario interferes with the previous regulation state through a simple mathematical calculation task. Each scenario is 40 seconds, and the three scenarios are repeated in sequence four times.

[0085] Step c4, Transfer training stage: This stage is similar to the formal feedback training stage, but in the regulation scenario, no visual feedback is provided. This requires the participants to self-regulate according to the most suitable strategy for themselves to verify whether the participants can apply the learned regulation skills to the situation without visual feedback, so as to test whether the participants' regulation ability has been established and can be transferred to ordinary life scenarios.

[0086] Step c5, Post-training resting-state EEG measurement: Finally, measure the resting-state brain activity of the participants again after completing the neurofeedback training to evaluate the impact of the training on the anxiety state of the subjects. Compared with the baseline value of the energy of the resting-state feedback signal, if the energy of the feedback signal is higher than or equal to the baseline value of the energy of the resting-state feedback signal, it means that the training effect is not good and the anxiety state of the subjects has not been alleviated; if the energy of the feedback signal is lower than the baseline value of the energy of the resting-state feedback signal, it means that the anxiety state of the subjects has been alleviated.

[0087] Example 1

[0088] In this example, healthy subjects without a history of mental illness were selected for the adaptive neurofeedback pre-experiment in the practice stage to make a preliminary estimate and adjustment of the real-time change of task difficulty under individual fuzzy control. The change of task difficulty is as Figure 4 shown, and it includes the following steps:

[0089] Step A1, Baseline Resting-State EEG Measurement: First, record the brain activity of the participants in their natural resting state before training. The EEG signals at this stage are set as the reference point, i.e., the baseline value of the energy of the resting-state feedback signal;

[0090] Step A2, Practice Phase: In this session, guide the participants to understand the entire experimental process and encourage them to develop 3 to 5 possible neurofeedback strategies. Through the practice phase, the participants can identify 1 to 2 strategies that are most suitable for themselves.

[0091] In the specific implementation case of this example, the anxiety state evaluation values obtained by the data acquisition and processing module, that is, the difference e between the anxiety feedback characteristics of the participants and the target value and the error change rate ec are both multiplied by the parameter 0.1 and then input into the individual fuzzy controller unit to change the task difficulty; through the adaptive neurofeedback regulation of the practice group, the task difficulty of the subjects changes in real time as Figure 4 shown. It can be seen that after the neurofeedback training of a practice group, the task difficulty of the subjects gradually increases and tends to be stable, indicating that the designed individual fuzzy controller unit can calculate the self-regulation ability of the subjects in real time, so as to accurately obtain the task difficulty suitable for individuals, providing a personalized and accurate neurofeedback regulation method for individuals.

[0092] Example 2

[0093] Apply the individual-specific adaptive closed-loop system to the complete neurofeedback training scenario, select healthy subjects without a history of mental illness, and conduct neurofeedback training to improve anxiety. The training results are as Figure 5 shown; the neurofeedback training described in this example includes the following steps:

[0094] Step B1, Baseline Resting-State EEG Measurement: First, record the brain activity of the participants in their natural resting state before training. The EEG signals at this stage are set as the reference point, i.e., the baseline value of the energy of the resting-state feedback signal;

[0095] Step B2, Practice Phase: In this session, guide the participants to understand the entire experimental process and encourage them to develop 3 to 5 possible neurofeedback strategies. Through the practice phase, the participants can identify 1 to 2 strategies that are most suitable for themselves for use in the subsequent formal feedback training;

[0096] Step B3, formal feedback training stage: The participants will use the strategy that suits them best to conduct three sets of formal neurofeedback training. The goal of the training is to achieve neuromodulation by reducing the energy of the feedback signal. Each set of training consists of three scenarios: rest, regulation, and calculation. In the rest scenario, the participants try to stay as relaxed as possible to reach the resting state; in the regulation scenario, through visual feedback, the participants need to try to reduce the height of the characteristic histogram of the feedback signal; the calculation scenario interferes with the previous regulation state through a mathematical task. Each scenario is 40 seconds, and the three scenarios are repeated four times in sequence;

[0097] Step B4, transfer training stage: This stage is similar to the formal feedback training stage in process, but in the regulation scenario, visual feedback is no longer provided. This requires the participants to self-regulate according to the strategy that suits them best to verify whether the participants can apply the learned regulation skills to the situation without visual feedback, so as to test whether the participants' regulation ability has been established and can be transferred to ordinary life scenarios;

[0098] Step B5, post-training resting-state EEG measurement: Finally, the resting-state brain activity of the participants after completing the neurofeedback training is measured again to evaluate the impact of the training on brain activity.

[0099] In the specific implementation case of this example, through a single course of treatment, the energy difference of the characteristic signals of the subjects is as Figure 5 shown. It can be seen that after a course of adaptive neurofeedback training, the energy of the characteristic signals related to the subjects' anxiety emotions has decreased, and the corresponding emotion regulation ability can still be maintained in the transfer training stage, and the low energy of the relevant signals can be maintained. The results show that the adaptive neurofeedback system designed by the present invention can well guide individuals to regulate anxiety emotions. Applying adaptive task difficulty for neurofeedback treatment can assist in formulating individualized and precise multi-course neuromodulation training to improve individuals' anxiety emotions, and provides a universal adaptive neuromodulation system for different individuals.

[0100] In summary, an adaptive closed-loop neurofeedback system based on individual specificity proposed by the present invention embeds an individual fuzzy controller unit into the traditional electroencephalogram neurofeedback framework to change the task difficulty of the neurofeedback training in real time, so as to provide personalized and precise neurofeedback regulation for individuals; applying neurofeedback treatment with real-time changing task difficulty can formulate an individualized adaptive neuromodulation training method to help different individuals quickly and effectively find the most suitable anxiety regulation strategy for themselves and apply it to daily life to achieve the purpose of generally improving the anxiety emotions of different individuals.

Claims

1. An adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity, characterized in that, It includes a data acquisition and processing module, an open-loop control module, and an adaptive negative feedback module; the data acquisition and processing module is connected to the open-loop control module, the open-loop control module is connected to the adaptive negative feedback module, and the adaptive negative feedback module is connected to the data acquisition and processing module; The data acquisition and processing module encapsulates the resting-state data and sends it to the open-loop control module. At the same time, after preprocessing the individual's EEG task-state data, extracting feedback features, and evaluating the anxiety state, it sends the anxiety state evaluation result to the adaptive negative feedback module; After obtaining the EEG data from the data acquisition and processing module, the open-loop control module obtains personalized parameters and the anxiety state evaluation result; the adaptive negative feedback module controls the adjustment of the neurofeedback task difficulty, conducts neurofeedback training, realizes the adaptive change of the visual task difficulty, and finally displays the feedback to the subject.

2. The adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity according to claim 1, wherein The data acquisition and processing module includes a resting-state data acquisition unit, a task-state data acquisition unit, a first data preprocessing unit, a first feedback feature extraction unit, and an anxiety state evaluation unit. The resting-state data acquisition unit is connected to the second data preprocessing unit in the open-loop control module, and the anxiety state evaluation unit is connected to the individual fuzzy controller unit in the adaptive negative feedback module; The open-loop control module includes a second data preprocessing unit, a baseline threshold calculation unit, a second feedback feature extraction unit, and a personalized parameter configuration unit. The personalized parameter configuration unit is connected to the neurofeedback training unit in the adaptive negative feedback module; The adaptive negative feedback module includes an individual fuzzy controller unit, a task difficulty adjustment unit, and a neurofeedback training unit; Among them, in the data acquisition and processing module, the resting-state data acquisition unit acquires the resting-state EEG signal, encapsulates it, and sends it to the open-loop control module. The anxiety state evaluation unit evaluates the current feedback features of the subject and sends them to the adaptive negative feedback module; After obtaining the EEG data from the data acquisition and processing module, the open-loop control module performs real-time data preprocessing on the resting-state EEG data, and then decodes it through the baseline threshold calculation unit and the second feedback feature extraction unit to obtain the baseline threshold as the personalized initial task difficulty, and calculates the feedback features to obtain the personalized self-regulation ability, obtaining the personalized initial task difficulty and self-regulation ability; and iteratively updates the initial parameters of each individual to the adaptive negative feedback module; The personalized parameters generated by the open-loop control module are encapsulated by the neurofeedback training unit, and at the same time, cooperate with the individual fuzzy controller unit to control the task difficulty, and finally display the feedback to the subject.

3. A method for implementing an adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity, characterized in that, The subject conducts neurofeedback training through the adaptive closed-loop neurofeedback system according to any one of claims 1-2, and the steps are as follows: S1, Select healthy subjects to be included in the group and collect EEG signals; S2, Preprocess the collected EEG signals; S3, Extract feedback features from the processed EEG signals, calculate the baseline threshold, and evaluate the current anxiety state of the subject according to the extracted feature values; S4, The adaptive negative feedback module outputs the task difficulty that needs to be adjusted according to the control rules, and combines the initialized personality parameters to obtain the corrected personalized task difficulty; S5. The subject relieves the anxiety state according to the corrected personalized task difficulty through the neurofeedback training unit using the neurofeedback training method.

4. The implementation method of the adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity according to claim 3, wherein, The feedback feature is calculated by the following formula: where D represents the extracted feedback feature, A represents the neurofeedback experiment feature, and B represents the energy value of the baseline resting-state EEG signal; the smaller the D value, the lighter the anxiety state of the subject, and vice versa. The calculation of the energy value of the baseline resting-state EEG signal is as follows: Using the sliding window method, by collecting the resting-state EEG data of the individual after preprocessing, the signal index is calculated every 3 seconds with a step size of 1 second to obtain 118 consecutive neurofeedback experiment features, and the average value of the 118 neurofeedback experiment features is calculated.

5. The implementation method of the adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity according to claim 3, characterized in that, The difference e and the difference change rate ec between the current anxiety state feature value of the subject and the target value are used as the input of the adaptive negative feedback module, and the task difficulty change amount is used as the output of the adaptive negative feedback module. The adaptive negative feedback module outputs the task difficulty that needs to be adjusted according to the control rule, and combines it with the initial personalized parameter to obtain the corrected personalized task difficulty. The method for adjusting the task difficulty includes the following steps: Sb1. When the individual fuzzy controller unit starts to work, the baseline threshold calculated by the open-loop control module is used as the initial task difficulty, and the feedback feature is used as the visual initial index of the current anxiety state of the individual. Sb2. Based on the relationship between the input and the output, the adaptive negative feedback module generates a control rule through fuzzy logic. Sb3. The individual fuzzy controller unit continuously monitors the difference between the output and the target value and feeds the difference information back to the task difficulty adjustment unit; if the real-time feedback feature value continuously exceeds the target value, it indicates that the current task difficulty is too difficult for the subject and the difficulty needs to be reduced; on the contrary, if the feedback feature value of the subject is always lower than the target value, it indicates that the subject has adapted to the current task difficulty and the task difficulty needs to be appropriately increased to provide a higher feedback feature value.

6. The implementation method of the adaptive closed-loop neurofeedback system for improving anxiety based on individual specificity according to claim 3, characterized in that, The neurofeedback training method needs to be continuously carried out 4 to 10 times of feedback training; among them, a single feedback training includes the following steps: Sc1. Record the brain activity of the participant in the natural resting state before receiving training. The EEG signal in this stage is set as the reference point and used as the benchmark value of the resting-state feedback signal energy. Sc2. Guide the participant to understand the entire process of the experiment and encourage the participant to develop 3 to 5 possible neurofeedback strategies; through the practice stage, the participant identifies 1 to 2 strategies that are most suitable for himself / herself. Sc3. Conduct formal feedback training: The participant will use the most suitable strategy for himself / herself to conduct three groups of formal neurofeedback training; each group of training consists of three scenarios: rest, regulation, and calculation; each scenario is 40 seconds, and the three scenarios are repeated four times in sequence. In the rest scenario, the participant tries to stay as relaxed as possible to reach the resting state. In the regulation scenario, through visual feedback, the participant needs to try to reduce the height of the feedback signal feature histogram. The calculation scenario interferes with the previous regulation state through a math task. Sc4, conduct transfer training: Similar to the formal feedback training process, but in the regulation scenario, visual feedback is no longer provided; participants are required to self-regulate according to the strategy that best suits them to verify whether they can apply the learned regulation skills to situations without visual feedback; Sc5, measure the resting-state brain activity of the participants again after completing the neurofeedback training. If the energy of the feedback signal is higher than or equal to the baseline value of the resting-state feedback signal energy, it indicates that the training effect is not good and the anxiety state of the subject has not been alleviated; if the energy of the feedback signal is lower than the baseline value of the resting-state feedback signal energy, it indicates that the anxiety state of the subject has been alleviated to some extent.

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