Closed-loop megatherium intervention method based on EEG and hybrid augmented reality

Abstract

The invention discloses an EEG (electroencephalogram) and hybrid augmented reality-based closed-loop metrorphism intervention method, which comprises the following steps of: constructing a closed-loop regulation and control framework integrating physiological signal acquisition, metrorphism state evaluation and visual feedback regulation to realize dynamic perception and guidance of an individual metrorphism state; the method comprises the following steps: acquiring electroencephalogram signals and related physiological information of a subject, carrying out multi-modal evaluation on a current metrorphism level, mapping an evaluation result into visual stimulation in a virtual reality environment, and realizing real-time linkage adjustment from a metrorphism state to perception feedback; and meanwhile, a feedback strategy is updated in combination with subjective evaluation information, so that the adaptability of the system to individual state changes is enhanced. By combining real-time neural feedback with immersive perception regulation, the method can continuously guide a subject to regulate attention and emotional states in the training process, so that a stable and extensible closed-loop metrorphic intervention mechanism is constructed, and an effective technical scheme is provided for psychological intervention, behavior regulation and remote health application.

Description

Technical Field

[0001] This invention belongs to the field of biosignal processing and psychological intervention technology, specifically relating to a closed-loop mindfulness intervention method based on EEG and mixed extended reality. Background Technology

[0002] To improve individual mental health, researchers have proposed various intervention approaches, primarily including pharmacological treatment, physical or somatic interventions, and behavioral and psychological interventions. Drug therapy typically alleviates anxiety or depressive symptoms by modulating neurotransmitter systems, but may be accompanied by adverse reactions such as gastrointestinal discomfort and hormonal changes, thus affecting long-term adherence. Physical or neuromodulation methods (such as transcranial magnetic stimulation and transcranial direct current stimulation) can work by modulating cortical excitability and neural activity patterns, but they usually rely on specialized equipment and professional operators, resulting in high costs and limitations in terms of comfort and scalability. In contrast, behavioral and psychological interventions offer advantages such as non-invasiveness, high safety, and long-term implementation, and are gradually becoming an important research direction in the field of mental health.

[0003] Among numerous behavioral intervention methods, mindfulness training has received widespread attention in recent years. Mindfulness is generally defined as a mental state of consciously focusing on the present moment without judgment, and is typically cultivated through methods such as seated meditation, walking meditation, or mindful actions. Numerous studies have shown that mindfulness training can effectively improve attentional control, emotion regulation, and mental flexibility, and has a positive effect on alleviating anxiety, depression, and stress-related symptoms, contributing to improved overall mental health. Furthermore, mindfulness training has been shown to improve cognitive functions, such as attention maintenance and executive control. Its mechanism of action is closely related to plasticity changes in brain regions and networks such as the anterior cingulate cortex, insula, frontolimbic system, and default mode network.

[0004] Currently, mainstream mindfulness interventions are typically conducted in the form of structured courses, such as Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT). These courses guide participants to gradually cultivate mindfulness abilities through systematic teaching content and training programs lasting several weeks. However, such interventions often rely on professional instructors, fixed schedules, and specific training environments, placing high demands on participants' time commitment and adherence to specific conditions. Furthermore, traditional mindfulness training often lacks objective quantitative assessment methods for an individual's current mindfulness state, making it difficult to reflect training effectiveness in a timely manner and to dynamically adjust according to changes in individual states. This, to some extent, limits its flexibility and personalization.

[0005] With the development of virtual reality (VR) technology, immersive VR is gradually being introduced into the field of psychological intervention. VR technology can construct a multi-sensory immersive environment, improving user engagement and focus, and reducing dependence on physical location and time conditions, thereby enhancing the flexibility and accessibility of the intervention process. By introducing physiological signal monitoring methods, such as heart rate, heart rate variability, skin conductance, and respiratory signals, an individual's physiological state can be assessed in real time, providing objective evidence for the intervention process. Furthermore, electroencephalography (EEG), as a non-invasive, high-temporal-resolution method of acquiring neural signals, can reflect brain activity characteristics related to attention, emotion regulation, and cognitive processing. Introducing it into mindfulness training helps to achieve a more refined portrayal of an individual's internal state.

[0006] Existing research indicates that combining physiological signal feedback with immersive virtual reality can construct adaptive training systems, enabling the system to dynamically adjust feedback content based on individual state changes, thereby enhancing training immersion and engagement. However, current methods primarily focus on unidirectional or offline analysis, lacking a complete closed-loop control mechanism, making it difficult to continuously and in real-time guide individuals into or maintain a mindfulness state during training. Furthermore, existing methods still have shortcomings in modeling individual differences and long-term use, making it difficult to achieve stable, personalized training results without continuous human intervention. Summary of the Invention

[0007] In view of the above, the present invention provides a closed-loop mindfulness intervention method based on EEG and mixed extended reality. By collecting and analyzing the user's EEG and related physiological signals in real time, a feedback mechanism reflecting the mindfulness state is constructed, and the feedback results are presented in a visual form in a virtual reality scene, thereby forming a closed-loop regulation process of perception-analysis-feedback. This method can provide users with a personalized and dynamically adjusted mindfulness training experience without continuous human guidance, which helps to improve the participation, continuity and practical application value of training.

[0008] A closed-loop mindfulness intervention method based on EEG and hybrid extended reality includes the following steps: (1) Set up a mindfulness training scenario, perform a three-dimensional model of the scenario in an immersive virtual reality environment, and preset interactive elements for feedback adjustment; (2) During mindfulness training, the subject’s dual-channel EEG signals and heart rate signals were collected, and the subject was guided to rate subjective indicators including arousal and pleasure at regular intervals. (3) The collected signals are analyzed and decoded in real time, the mindfulness level of the subjects is assessed based on the analysis results, and corresponding real-time feedback is generated accordingly; (4) Collect relevant questionnaire information from the subjects before and after the end of a single mindfulness intervention; (5) The effectiveness of mindfulness intervention is comprehensively evaluated based on questionnaire data and physiological signals (including EEG and heart rate) obtained during multiple interventions.

[0009] Furthermore, in step (1), the mindfulness training scenario is modeled using 3D modeling software to construct an immersive virtual reality scenario. The virtual reality scenario is a simulation scenario with a certain degree of realism but not completely equivalent to the real environment. The simulation scenario is equipped with adjustable interactive elements. The interactive elements are used to listen to external interface signals and update their parameters accordingly. When a control signal is received from the analysis module or feedback module, its visual attributes undergo changes that can be perceived by the human eye, thereby participating in the closed-loop adjustment process as a feedback carrier.

[0010] Furthermore, the mindfulness training process in step (2) is implemented through voice-guided instruction. The guiding words are used to prompt the subject to consciously focus on specific elements in the virtual reality scene or their current subjective experience. At the same time, the subject's electroencephalogram (EEG) signals are collected by electrodes embedded in the immersive virtual reality headset, and the heart rate signals are collected simultaneously. The collected signal data is then stored in real time for subsequent analysis and processing. In addition, predetermined time nodes or discontinuities are set in the guiding words to guide the subject to subjectively rate the arousal and pleasure levels of the current stage in order to obtain the corresponding subjective rating information.

[0011] Furthermore, the analysis and decoding process in step (3) includes spectral analysis processing of the acquired dual-channel EEG signal with a sampling rate of 250Hz. Specifically, firstly, a bandpass filter is used to limit the frequency range of the EEG signal to 0~40Hz; then, the relative energy characteristics of the θ band (4~8Hz) and α band (8~13Hz) of the signal are extracted by fast Fourier transform; then, the relative energy characteristics of these frequency bands are fused and analyzed with the synchronously acquired heart rate data and the subject's subjective rating information to evaluate the subject's real-time mindfulness level, and generate corresponding positive or negative feedback signals according to the preset feedback update strategy.

[0012] Furthermore, the feedback update strategy comprehensively adopts subjective evaluation indicators and objective physiological indicators, and regulates the subject's state through a combination of positive and negative feedback. When the change in the monitored indicators exceeds a preset upward threshold, or reaches the historical best level since the start of mindfulness training, the system triggers positive feedback; when the change in the monitored indicators is lower than a preset downward threshold, the system triggers negative feedback. The positive and negative feedback are presented through visual changes in the virtual reality scene, thereby forming visual stimuli that can be perceived by the subject: in the positive feedback state, the campfire in the virtual scene appears enhanced, enlarged, or brighter; in the negative feedback state, the intensity or size of the campfire in the virtual scene is weakened.

[0013] Furthermore, the relevant questionnaire information collected in step (4) includes the Mindfulness Five Factors Scale, the Emotion Regulation Difficulty Scale, and the System Satisfaction Scale, and the mindfulness intervention effect is comprehensively evaluated based on the scores of each scale; wherein, the Mindfulness Five Factors Scale score is used to characterize the individual's ability level in awareness, acceptance, and non-judgmental attention to the present experience, and the higher the score, the higher the individual's mindfulness characteristics and emotion regulation ability; the Emotion Regulation Difficulty Scale score is used to reflect the degree of difficulty experienced by the individual in the process of emotion regulation; the System Satisfaction Scale score is used to reflect the subject's subjective satisfaction with the intervention method and the usability level of the system.

[0014] Furthermore, the comprehensive evaluation of the mindfulness intervention effect in step (5) includes two methods: pre- and post-intervention evaluation for a single intervention and pre- and post-intervention evaluation for a long-term intervention. The pre- and post-intervention evaluation for a single intervention is used to analyze the changes in the state of the subject before and after a mindfulness training session. By comparing the questionnaire data and EEG signals collected before and after the intervention, the immediate impact of a single intervention on the subject's mindfulness level and related physiological state is evaluated. The EEG signals are analyzed based on data within a preset time period before and after the intervention. The pre- and post-intervention evaluation for a long-term intervention is used to analyze the cumulative effect of multiple mindfulness training sessions. By comparing the questionnaire data and physiological signals at the initial stage of the intervention with those after several interventions, the overall trend of the subject's mindfulness level and emotional regulation state during the training process is evaluated.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the aforementioned closed-loop mindfulness intervention method based on EEG and mixed extended reality.

[0016] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned closed-loop mindfulness intervention method based on EEG and hybrid extended reality.

[0017] Based on the above technical solution, this invention constructs a closed-loop mindfulness intervention method by combining real-time EEG feedback mechanisms with immersive virtual reality perception and regulation, achieving dynamic perception and control of the mindfulness state of subjects. This method integrates subjective evaluation information with objective physiological signals to continuously update and provide feedback on the training process, making the intervention process real-time, adaptive, and interactive, thereby improving the controllability and stability of the mindfulness training process. By introducing an immersive virtual reality environment as a feedback carrier, feedback information is presented in an intuitive perceptual form, which helps to enhance user participation and training immersion. The technical solution of this invention has the characteristics of clear structure, flexible implementation, and easy expansion, and can be adapted to different hardware conditions and application scenarios. It can be used in the fields of psychological intervention, behavior regulation, and related remote health services, and has good application prospects. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the closed-loop mindfulness intervention method based on EEG and hybrid extended reality of the present invention.

[0019] Figure 2 This is a schematic diagram of the overall framework of the closed-loop mindfulness intervention method based on EEG and hybrid extended reality of the present invention.

[0020] Figure 3 This is a box plot used in the verification example of this invention to analyze and statistically analyze the five-factor mindfulness scale of the subjects. Figure 3 In the table, (a) corresponds to the total score of the Mindfulness Five Factors Scale, (b) corresponds to the score of the non-response dimension of the scale, (c) corresponds to the score of the observation dimension, (d) corresponds to the score of the non-judgment dimension, (e) corresponds to the score of the description dimension, and (f) corresponds to the score of the conscious action dimension.

[0021] Figure 4 This is a line graph showing the change in the subject's emotional difficulty scale over the number of tests in a verification example of this invention. Figure 4 (a) corresponds to the total score of the Emotional Difficulty Scale, (b) corresponds to the score of the dimension of lack of emotional clarity of the scale, (c) corresponds to the score of the dimension of difficulty in goal-oriented behavioral input, (d) corresponds to the score of the dimension of non-acceptance of emotional reactions, (e) corresponds to the score of the dimension of difficulty in describing impulsive behavior, and (f) corresponds to the score of the dimension of limited access to effective emotion regulation strategies.

[0022] Figure 5 This is a box plot showing the spectral analysis of the collected EEG data of the subjects in a verification example of this invention. Figure 5 In the middle, (a) corresponds to the energy difference in the α band, and (b) corresponds to the energy difference in the θ band. Detailed Implementation

[0023] To describe the present invention in more detail, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1 and Figure 2 As shown, the closed-loop mindfulness intervention method based on EEG and hybrid extended reality of this invention has the following specific steps: (1) Set up a mindfulness training scenario, perform three-dimensional modeling of the scenario in an immersive virtual reality environment, and preset interactive elements that can be used for feedback adjustment.

[0025] In this embodiment, the mindfulness training scenario is modeled using 3D modeling software to construct an immersive virtual reality scenario. The virtual reality scenario is a simulation scenario with a certain degree of realism but not completely equivalent to the real environment. Adjustable interactive elements are set in the scenario. The adjustable interactive elements update their parameters by listening to external interface signals. When they receive control signals from the decoding module or feedback module, their visual attributes undergo changes that are perceptible to the human eye, thus forming a closed-loop adjustment process as a feedback carrier.

[0026] Based on this, the modeling program for mindfulness training scenarios also includes a communication monitoring module, which is used to continuously monitor external data input within the virtual reality system through a preset interface. This communication monitoring module can receive and parse signals from external terminals by writing program code, and drive the update of corresponding scene parameters based on the parsing results.

[0027] (2) During mindfulness training, the subject’s dual-channel EEG signals and heart rate signals were collected, and the subject was guided to rate subjective indicators such as arousal and pleasure at regular intervals.

[0028] In this implementation, during mindfulness training, the subject's electroencephalogram (EEG) signals are collected by electrodes embedded in an immersive virtual reality headset, and heart rate signals are collected simultaneously. The subject is prompted by voice to consciously direct their attention to specific elements in the virtual scene, and at preset time intervals, the subject is prompted to subjectively rate their arousal and pleasure levels at the current stage. The physiological signals and subjective rating data are saved in real time for subsequent analysis.

[0029] (3) The collected neural and physiological signals are analyzed and decoded in real time, and the mindfulness level is assessed based on the analysis results, and real-time feedback is given to the subjects.

[0030] The analysis and decoding process includes performing spectral analysis on the collected electroencephalogram (EEG) signals with two channels and a sampling rate of 250 Hz. Specifically: First, preprocess the original EEG signals by using a band-pass filter to limit the frequency range of the signals to 0 - 40 Hz to remove low-frequency drift and high-frequency noise interference; subsequently, perform a fast Fourier transform on the filtered time-domain signals to convert them from the time domain to the frequency domain, and its transformation form can be expressed as:

[0031] where: x ( n ) represents the discrete-time EEG signals, N is the number of sampling points, X ( k ) is the complex spectral representation of the corresponding frequency component. Calculate the power spectral density or amplitude spectrum according to the Fourier transform results, and integrate the spectral energy within the predefined frequency band range to obtain the energy characteristics of the corresponding frequency band.

[0032] According to the EEG rhythm characteristics related to mindfulness, extract the energy of the θ frequency band of 4 - 8 Hz and the energy of the α frequency band of 8 - 13 Hz, and by normalizing the energy of each frequency band, obtain the relative energy characteristics, which are used to characterize the proportion of different frequency bands in the overall EEG activity. The relative energy can be expressed as the ratio of the energy of the target frequency band to the total energy of 0 - 40 Hz.

[0033] On this basis, fuse and analyze the frequency band energy characteristics with the simultaneously collected heart rate data and the subjective scoring information given by the subject during the training process. Through the preset mapping rules or update strategies, comprehensively evaluate the current mindfulness level, and accordingly generate corresponding positive or negative feedback signals. The generated feedback signals are used to drive the changes of adjustable elements in the virtual reality scene, thus realizing the closed-loop adjustment process based on the EEG spectral characteristics.

[0034] In the terminal device for analyzing EEG signals, by configuring the network address and communication port corresponding to the target virtual reality device, use the communication method based on sockets (Socket) to send the decoded mindfulness-related control information to the virtual reality system, thus realizing cross-terminal data interaction and linkage control between the EEG analysis end and the immersive virtual reality device. Through the above method, real-time communication between the EEG signal analysis module and the virtual reality scene is achieved, enabling the analysis results of the mindfulness state to act on the scene feedback process synchronously, further supporting the realization of the closed-loop adjustment mechanism.

[0035] (4)Before and after the end of a single mindfulness intervention, collect the relevant questionnaire information of the subject.

[0036] This implementation method collects data from the Mindfulness Five Factors Scale, the Emotion Regulation Difficulty Scale, and the System Satisfaction Scale, and evaluates the effectiveness of mindfulness intervention based on the scale scores. The Mindfulness Five Factors Scale score is used to characterize the degree to which an individual focuses on the present experience in an aware, accepting, and non-judgmental manner; the Emotion Regulation Difficulty Scale score is used to reflect the degree of difficulty experienced by an individual in the process of emotion regulation; and the System Satisfaction Scale score is used to characterize the subjective satisfaction and usability of this intervention method.

[0037] (5) Based on the questionnaire data and physiological signals obtained during multiple interventions, the effectiveness of mindfulness intervention is comprehensively evaluated.

[0038] The comprehensive assessment includes two methods: pre- and post-intervention assessment for a single intervention and pre- and post-intervention assessment for long-term intervention. Pre- and post-intervention assessment for a single intervention analyzes changes in the subject's state before and after a single mindfulness training session. This is achieved by comparing questionnaire data and EEG signals collected before and after the intervention to assess the immediate impact of a single intervention on the subject's mindfulness level and related physiological state. EEG signals can be analyzed based on data collected within a pre-defined time period before and after the intervention. Pre- and post-intervention assessment for long-term intervention analyzes the cumulative effects of multiple mindfulness training sessions. This is achieved by comparing questionnaire data and physiological signals from the initial stage of the intervention with data collected after several interventions to assess the overall trend of changes in the subject's mindfulness level and emotional regulation state throughout the training process.

[0039] This implementation method was tested and evaluated in a total of 66 subjects, including 34 subjects in the closed-loop control group and 32 subjects in the control group (traditional headband training). The effectiveness of the method of the present invention was evaluated by comparing and analyzing questionnaires and physiological indicators before and after the intervention.

[0040] In terms of subjective questionnaire assessment, we used the five-factor mindfulness scale to analyze changes in the subjects' mindfulness levels, such as... Figure 3 The results show that, compared with the control group, the closed-loop regulation group exhibited a more stable and greater improvement in overall mindfulness levels after intervention, particularly in dimensions such as descriptive ability, conscious action, and non-reactive behavior. This indicates that a closed-loop feedback mechanism driven by real-time physiological signals helps promote an individual's awareness and regulation of their inner experiences.

[0041] Regarding questionnaires related to emotion regulation, such as Figure 4 As shown, the control group showed some improvement in the early stages of the intervention, but subsequently rebounded, indicating that its effect on reducing the difficulty of emotion regulation was limited in duration. In contrast, the closed-loop control group showed a gradual decline throughout the intervention period, indicating that this method helps to continuously reduce the degree of difficulty experienced by individuals in the process of emotion regulation.

[0042] In terms of physiological indicators, by comparing the differences in physiological indicators between the later and earlier stages of a single mindfulness session, i.e., the relative energy changes in the α and θ frequency bands, this indicator can reflect an individual's mindfulness level to some extent. For example... Figure 5 The results show that the relative energy difference in the closed-loop group in the above frequency bands is significantly higher than that in the headband group, indicating that the mindfulness-related physiological responses in the closed-loop group are more prominent in the later stages of mindfulness, suggesting that the closed-loop intervention method has a more significant effect on improving mindfulness.

[0043] The results above show that the closed-loop mindfulness intervention method based on immersive virtual reality of this invention, by combining multimodal physiological signals with dynamic scene feedback, can enhance the sense of participation, immersion and interactivity in the training process. As a result, it shows better results than traditional headband training methods in terms of improving mindfulness level, improving emotion regulation ability and overall training experience. This indicates that the closed-loop regulation mechanism has high application value and promotion potential in mindfulness intervention systems.

[0044] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. It will be apparent to those skilled in the art that various modifications can be made to the above embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. A closed-loop mindfulness intervention method based on EEG and mixed extended reality, characterized in that, Includes the following steps: (1) Set up a mindfulness training scenario, perform a three-dimensional model of the scenario in an immersive virtual reality environment, and preset interactive elements for feedback adjustment; (2) During mindfulness training, the subject’s dual-channel EEG signals and heart rate signals were collected, and the subject was guided to rate subjective indicators including arousal and pleasure at regular intervals. (3) The collected signals are analyzed and decoded in real time, the mindfulness level of the subjects is assessed based on the analysis results, and corresponding real-time feedback is generated accordingly; (4) Collect relevant questionnaire information from the subjects before and after the end of a single mindfulness intervention; (5) Based on the questionnaire data and physiological signals obtained during multiple interventions, the effectiveness of mindfulness intervention is comprehensively evaluated.

2. The closed-loop mindfulness intervention method based on EEG and mixed extended reality according to claim 1, characterized in that: In step (1), the mindfulness training scenario is modeled using 3D modeling software to construct an immersive virtual reality scenario. The virtual reality scenario is a simulation scenario with a certain degree of realism but not completely equivalent to the real environment. The simulation scenario is equipped with adjustable interactive elements. The interactive elements are used to listen to external interface signals and update their parameters accordingly. When a control signal is received from the analysis module or feedback module, its visual attributes undergo changes that can be perceived by the human eye, thereby participating in the closed-loop adjustment process as a feedback carrier.

3. The closed-loop mindfulness intervention method based on EEG and mixed extended reality according to claim 1, characterized in that: The mindfulness training process in step (2) is implemented through voice-guided instruction. The guiding words are used to prompt the subject to consciously focus on specific elements in the virtual reality scene or their current subjective experience. At the same time, the subject's electroencephalogram (EEG) signals are collected by electrodes embedded in the immersive virtual reality headset, and the heart rate signals are collected simultaneously. The collected signal data is then stored in real time for subsequent analysis and processing. In addition, predetermined time nodes or discontinuities are set in the guiding words to guide the subject to subjectively rate the arousal and pleasure levels of the current stage in order to obtain the corresponding subjective rating information.

4. The closed-loop mindfulness intervention method based on EEG and mixed extended reality according to claim 1, characterized in that: The analysis and decoding process in step (3) includes spectral analysis of the acquired dual-channel EEG signal with a sampling rate of 250Hz. Specifically, firstly, a bandpass filter is used to limit the frequency range of the EEG signal to 0~40Hz; then, the relative energy characteristics of the θ band of 4~8Hz and the α band of 8~13Hz are extracted by fast Fourier transform. The relative energy characteristics of these frequency bands are then fused and analyzed with the synchronously collected heart rate data and the subject's subjective rating information to assess the subject's real-time mindfulness level and generate corresponding positive or negative feedback signals based on a preset feedback update strategy.

5. The closed-loop mindfulness intervention method based on EEG and mixed extended reality according to claim 4, characterized in that: The feedback update strategy comprehensively adopts subjective evaluation indicators and objective physiological indicators, and regulates the subject's state through a combination of positive and negative feedback. When the change in the monitored indicators exceeds a preset upward threshold, or reaches the best historical level since the start of mindfulness training, the system triggers positive feedback; when the change in the monitored indicators is lower than a preset downward threshold, the system triggers negative feedback. The positive and negative feedback are presented through visual changes in the virtual reality scene, thereby forming visual stimuli that can be perceived by the subject: in the positive feedback state, the campfire in the virtual scene presents an enhanced, enlarged, or brighter visual effect. In a negative feedback state, the intensity or size of the campfire in the virtual scene decreases.

6. The closed-loop mindfulness intervention method based on EEG and mixed extended reality according to claim 1, characterized in that: The relevant questionnaire information collected in step (4) includes the Mindfulness Five Factors Scale, the Emotion Regulation Difficulty Scale, and the System Satisfaction Scale. The effectiveness of the mindfulness intervention is comprehensively evaluated based on the scores of each scale. The Mindfulness Five Factors Scale score is used to characterize an individual's ability to be aware of, accept, and non-judgmentally focus on the present experience. The higher the score, the higher the level of mindfulness characteristics and emotion regulation ability of the individual. The Emotion Regulation Difficulty Scale score is used to reflect the degree of difficulty experienced by the individual in the process of emotion regulation. The System Satisfaction Scale score is used to reflect the subject's subjective satisfaction with the intervention method and the usability of the system.

7. The closed-loop mindfulness intervention method based on EEG and hybrid extended reality according to claim 1, characterized in that: The comprehensive evaluation of the mindfulness intervention effect in step (5) includes two methods: pre- and post-intervention evaluation for a single intervention and pre- and post-intervention evaluation for a long-term intervention. The pre- and post-intervention evaluation for a single intervention is used to analyze the changes in the state of the subject before and after a mindfulness training session. By comparing the questionnaire data and EEG signals collected before and after the intervention, the immediate impact of a single intervention on the subject's mindfulness level and related physiological state is evaluated. The EEG signals are analyzed based on data within a preset time period before and after the intervention. The pre- and post-intervention evaluation for a long-term intervention is used to analyze the cumulative effect of multiple mindfulness training sessions. By comparing the questionnaire data and physiological signals at the initial stage of the intervention with those after several interventions, the overall trend of the subject's mindfulness level and emotional regulation state during the training process is evaluated.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: the processor is configured to execute the computer program to implement the closed-loop mindfulness intervention method based on EEG and hybrid extended reality as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that: when the computer program is executed by a processor, it implements the closed-loop mindfulness intervention method based on EEG and hybrid extended reality as described in any one of claims 1 to 7.

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