Personnel state adjustment training method based on brain-computer interface and virtual reality, edge computing device and medium

Abstract

The invention provides a personnel state adjustment training method based on a brain-computer interface and virtual reality, an edge computing device and a medium, and relates to the technical field of human factor engineering.The method comprises the steps that an initial virtual environment is presented in the view field of a user; acquiring an electroencephalogram signal when the user is immersed in the initial virtual environment; identifying a personnel state when the user is immersed in the initial virtual environment based on the electroencephalogram signal to obtain a personnel state identification result; adjusting stimulation factors of the initial virtual environment according to the personnel state recognition result, wherein the stimulation factors are used for representing factors causing user state fluctuation; determining the change condition of the electroencephalogram signal of the user; when the change condition of the electroencephalogram signal meets the condition of personnel state improvement, presenting a virtual interaction scene in the view field of the user; identifying a personnel state when the user is immersed in the virtual interaction scene based on the electroencephalogram signal of the user; and when the personnel state of the user reaches the target state or the training duration reaches the ending duration, ending the training.

Description

Technical Field

[0001] This application relates to the field of human factors engineering technology, and in particular to a human state regulation training method, edge computing device and medium based on brain-computer interface and virtual reality. Background Technology

[0002] The incidence of mental illnesses such as depression and anxiety is rising globally, and traditional treatments such as medication and psychological counseling have limitations. In recent years, virtual reality technology, due to its immersive and interactive features, has shown broad application prospects in the field of psychotherapy.

[0003] However, most existing VR (Virtual Reality) therapy systems use preset fixed scenes and lack feedback mechanisms for patients' real-time emotions and physiological states, making it difficult to achieve personalized treatment effects. Summary of the Invention

[0004] This application provides a method, device, edge computing device, electronic device, and computer-readable storage medium for human state regulation training based on brain-computer interface and virtual reality, which helps to adjust the virtual reality environment according to the EEG signals fed back by the user in real time, thereby achieving positive intervention and improvement of human state.

[0005] Firstly, this application provides a method for training and regulating human state based on brain-computer interfaces and virtual reality, including:

[0006] The initial virtual environment is presented within the user's field of vision;

[0007] Acquire the user's brainwave signals when the user is immersed in the initial virtual environment;

[0008] Based on EEG signal recognition, the user's state when immersed in the initial virtual environment is obtained, and the user state recognition result is obtained.

[0009] The stimuli of the initial virtual environment are adjusted based on the results of personnel status recognition. These stimuli are used to characterize the factors that cause fluctuations in user status.

[0010] Determine changes in the user's electroencephalogram (EEG) signals;

[0011] When the changes in brainwave signals meet the conditions for improving a person's condition, a virtual interactive scene is presented within the user's field of vision.

[0012] Based on the user's brainwave signals, the system identifies the user's state when immersed in a virtual interactive scene.

[0013] Training ends when the user's status reaches the target status or the training duration reaches the end time.

[0014] In one possible implementation, the user's state includes emotional state. The user's state when immersed in the initial virtual environment is identified based on electroencephalogram (EEG) signals, including:

[0015] The EEG signal is preprocessed and frequency domain features are extracted. The frequency domain features include power variation information in different frequency bands and frontal asymmetry in the same frequency band. Different frequency bands include alpha waves, beta waves, theta waves, and gamma waves.

[0016] Based on frequency domain features, identify the user's emotional state when immersed in the initial virtual environment; and / or,

[0017] Before presenting the virtual interactive scene within the user's field of vision, the method further includes: presenting a virtual transition scene within the user's field of vision, wherein the virtual transition scene includes using virtual characters to have simple interactions with the user or guiding the user into the virtual interactive scene.

[0018] In one possible implementation, the user's state recognition result includes an emotional state recognition result, which is anxiety or depression. The emotional state of the user when immersed in the initial virtual environment is identified based on frequency domain features, including:

[0019] Compared to baseline EEG data, if the power of beta and gamma waves increases while the power of alpha waves decreases, the emotional state is identified as anxiety. Baseline EEG data consists of EEG signals collected when the user is immersed in a neutral, quiet virtual environment; or

[0020] Compared with baseline EEG data, if the power of alpha and theta waves increases, the frontal asymmetry of alpha waves increases, and the overall power change of the EEG signal is small, then the emotional state identification result is depression.

[0021] In one possible implementation, the person's state recognition result includes emotional state recognition result, which is anxiety or depression. When the changes in the electroencephalogram (EEG) signal meet the conditions for improving the person's state, a virtual interactive scene is presented within the user's field of vision, including:

[0022] When the emotion state identification result is anxiety

[0023] If the alpha wave power ratio increases and the beta wave power remains below the baseline EEG data within a preset time period, the first virtual interactive scene will be presented in the user's field of vision. The baseline EEG data is the EEG signal collected when the user is immersed in a neutral and quiet virtual scene.

[0024] If the power ratio of beta waves to alpha waves in the first virtual interactive scenario is within a preset range and the user's subjective feedback is good, then a second virtual interactive scenario will be presented within the user's field of vision. The interactivity of the second virtual interactive scenario is stronger than that of the first virtual interactive scenario.

[0025] One possible implementation method also includes:

[0026] When the user enters the first virtual interactive scene to interact...

[0027] If the β wave power briefly increases, adjust the stimulus factors in the first virtual interactive scenario; or

[0028] If the beta wave power remains higher than the baseline EEG data and the user's heart rate increases rapidly, exit the first virtual interactive scene and switch to a relaxed, natural scene. Switch back to the first virtual interactive scene after the user's emotions have recovered.

[0029] In one possible implementation, the person's state recognition result includes emotional state recognition result, which is anxiety or depression. When the changes in the electroencephalogram (EEG) signal meet the conditions for improving the person's state, a virtual interactive scene is presented within the user's field of vision, including:

[0030] When the emotion state identification result is depression

[0031] If the alpha wave power decreases, the beta wave power increases, and the frontal asymmetry of the alpha wave decreases within a preset time, then a third virtual interactive scene will be presented in the user's field of vision.

[0032] If the power ratio of beta waves to alpha waves in the third virtual interactive scenario is within a preset range and the user's subjective feedback is good, then a fourth virtual interactive scenario will be presented in the user's field of vision. The interactivity of the fourth virtual interactive scenario is stronger than that of the third virtual interactive scenario.

[0033] In one possible implementation, the person's state includes their emotional state, and the person's state recognition result includes the emotional state recognition result, which is anxiety or depression. The stimuli include auditory and visual factors. Visual factors include scene, color, and brightness, while auditory factors include music, sound effects, and voice guidance.

[0034] The stimuli of the initial virtual environment are adjusted based on the personnel status recognition results, including:

[0035] When the emotional state is identified as anxiety, the scene is switched to a quiet, natural environment, and / or the brightness and color saturation of the scene are reduced, and / or soothing natural sounds or soft music are played, and / or voice prompts guiding the user through relaxation exercises or focusing attention are played; or

[0036] When the emotional state recognition result is depression, add positive interactive elements to the scene. Positive interactive elements are used to represent elements that promote active user interaction, and / or increase the brightness and color saturation of the scene, and / or add soothing and pleasant music, and / or play voice prompts that guide the user into positive thinking.

[0037] Secondly, this application provides a human state regulation and training device based on brain-computer interface and virtual reality, comprising:

[0038] The first presentation module is used to present the initial virtual environment within the user's field of vision;

[0039] The acquisition module is used to acquire the user's brainwave signals when the user is immersed in the initial virtual environment;

[0040] The first recognition module is used to identify the user's state when immersed in the initial virtual environment based on EEG signals, and to obtain the user state recognition result.

[0041] The adjustment module is used to adjust the stimuli of the initial virtual environment based on the personnel status recognition results. The stimuli are used to characterize the factors that cause fluctuations in the user's status.

[0042] The determination module is used to determine changes in the user's electroencephalogram (EEG) signals;

[0043] The second presentation module is used to present a virtual interactive scene within the user's field of vision when the changes in the EEG signal meet the conditions for improving the person's condition.

[0044] The second recognition module is used to identify the user's state when the user is immersed in a virtual interactive scene based on the user's brainwave signals.

[0045] The training end module terminates training when the user's status reaches the target status or the training duration reaches the end time.

[0046] Thirdly, this application provides an edge computing device, including: a processor and a memory, wherein the memory is used to store computer programs; and the processor is used to run the computer programs to implement the human state regulation training method based on brain-computer interface and virtual reality as described in the first aspect.

[0047] In one possible implementation, the edge computing device includes at least one of the following: a wearable display module for displaying virtual scenes; and an EEG signal acquisition module for acquiring EEG signals.

[0048] Fourthly, this application provides an electronic device, including: a processor and a memory, wherein the memory is used to store a computer program; and the processor is used to run the computer program to implement the human state regulation training method based on brain-computer interface and virtual reality as described in the first aspect.

[0049] Fifthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to implement the human state regulation training method based on brain-computer interface and virtual reality as described in the first aspect.

[0050] The beneficial effects of this application are as follows:

[0051] This application provides a training method for regulating human state based on brain-computer interface and virtual reality. The method involves presenting an initial virtual environment within the user's field of vision; acquiring the user's electroencephalogram (EEG) signals while immersed in the initial virtual environment; identifying the user's state based on the EEG signals; adjusting the stimuli of the initial virtual environment according to the state recognition results, where the stimuli represent factors causing fluctuations in the user's state; determining the changes in the user's EEG signals; presenting a virtual interactive scene within the user's field of vision when the changes in the EEG signals meet the conditions for improving the user's state; identifying the user's state while immersed in the virtual interactive scene based on the user's EEG signals; and ending the training when the user's state reaches the target state or the training duration reaches its end. By collecting the user's EEG signals and identifying the user's state based on these signals, the stimuli in the virtual reality environment are adjusted according to the state recognition results to achieve human state regulation training. Furthermore, by monitoring changes in the user's state and providing real-time feedback on the user's EEG signals, a closed-loop regulation of the user's state is achieved, which helps in the positive intervention and improvement of the user's state. Attached Figure Description

[0052] Figure 1 A flowchart illustrating the human state regulation training method based on brain-computer interface and virtual reality provided in this application embodiment;

[0053] Figure 2 A schematic diagram of the structure of a human state adjustment training device based on brain-computer interface and virtual reality provided in an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of the structure of an edge computing device provided in an embodiment of this application;

[0055] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0056] In this embodiment of the application, unless otherwise stated, the character " / " indicates that the preceding and following objects are in an OR relationship. For example, A / B can represent A or B. "AND / OR" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A AND / OR B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0057] It should be noted that the terms "first" and "second" used in the embodiments of this application are used only for distinguishing descriptive purposes and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor should they be construed as indicating or implying order.

[0058] In the embodiments of this application, "at least one" refers to one or more items, and "more than one" refers to two or more items. Furthermore, "at least one of the following" or similar expressions refer to any combination of these items, which may include any combination of a single item or a plurality of items. For example, at least one of A, B, or C can represent: A, B, C, A and B, A and C, B and C, or A, B, and C. Each of A, B, and C can be an element itself or a set containing one or more elements.

[0059] In this application, terms such as "exemplary," "in some embodiments," and "in another embodiment" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner.

[0060] In the embodiments of this application, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. Similarly, in the embodiments of this application, "communication" and "transmission" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. For example, transmission can include sending and / or receiving, and can be a noun or a verb.

[0061] In the embodiments of this application, the term "equal to" can be used in conjunction with "greater than" to apply to technical solutions employing the condition of "greater than", and can also be used in conjunction with "less than" to apply to technical solutions employing the condition of "less than". It should be noted that when "equal to" is used with "greater than", it cannot be used with "less than"; and when "equal to" is used with "less than", it cannot be used with "greater than".

[0062] Most existing VR therapy systems use preset fixed scenes and lack a feedback mechanism for patients' real-time emotions and physiological states, making it difficult to achieve personalized treatment results.

[0063] Based on the above problems, this application proposes a training method for adjusting the state of personnel based on brain-computer interface and virtual reality, which helps to adjust the virtual reality environment according to the EEG signals fed back by the user in real time, thereby realizing positive intervention and improvement of the state of personnel.

[0064] Figure 1 The flowchart of the human state regulation training method based on brain-computer interface and virtual reality provided in the embodiments of this application specifically includes the following steps:

[0065] Step S11: Present the initial virtual environment within the user's field of vision.

[0066] Specifically, an immersive virtual reality environment is built using virtual reality technology (such as the Unity engine). An initial virtual environment is created, which is the virtual environment when the user begins training and is presented within the user's field of vision. The user enters the initial virtual environment wearing a wireless EEG (Electroencephalogram) headset and a VR headset. The EEG device collects the user's brain signals in the initial virtual environment and transmits the data to the signal processing module in real time.

[0067] Step S12: Obtain the EEG signals of the user when they are immersed in the initial virtual environment.

[0068] Specifically, EEG devices are used to collect the electroencephalogram (EEG) signals of the user when they are immersed in the initial virtual environment.

[0069] In an optional embodiment, before acquiring the EEG signals of the user when immersed in the initial virtual environment, the method provided in this application further includes: acquiring the EEG signals of the user when immersed in a neutral and quiet virtual scene to obtain baseline EEG data.

[0070] Specifically, before entering the VR system, users wear an EEG brain-computer interface device and a VR headset. The system first performs calibration and baseline signal acquisition: a neutral and quiet virtual scene (such as a quiet room or a solid-color background) is presented in the VR environment for 1-2 minutes, allowing the user to relax and breathe. During this time, the EEG device records the user's brainwaves in a resting state, using the power of each frequency band as individual baseline data. This allows the system to obtain the user's brainwave characteristics in a relaxed state, i.e., obtain baseline EEG data, which serves as a reference value for subsequent user state recognition.

[0071] Step S13: Based on EEG signals, identify the user's state when immersed in the initial virtual environment to obtain the user state identification result.

[0072] In this application, the state of a person includes emotional state, cognitive load state, stress state, fatigue state, etc. The state of a person can be identified through electroencephalogram (EEG) signals; for example, EEG signals can be used to identify a user's emotional state (which may include anxiety, relaxation, depression, etc.) or the user's fatigue state (which may include alertness, mild fatigue, moderate fatigue, severe fatigue, etc.).

[0073] Optionally, the personnel status recognition results include emotional status recognition results, which are anxiety or depression.

[0074] The following uses the example of a person's emotional state to illustrate the method provided in this application.

[0075] In this step, the user's state when immersed in the initial virtual environment is identified based on the EEG signal, including: preprocessing the EEG signal and extracting frequency domain features, and identifying the user's emotional state when immersed in the initial virtual environment based on the frequency domain features.

[0076] Specifically, the signal processing module preprocesses the received EEG signals. Optionally, preprocessing includes filtering and artifact removal. Filtering may include: high-pass filtering to remove low-frequency noise, such as DC offset; low-pass filtering to remove high-frequency noise, such as electromagnetic interference; and band-pass filtering to retain signals within a specific frequency range, such as 1-45Hz, to include the main frequency bands of the EEG signals (delta, theta, alpha, beta, and gamma waves). Artifact removal may include: identifying and removing eye-movement artifacts using independent component analysis (ICA); removing electromyographic artifacts through filtering or ICA; and removing power line noise (50Hz or 60Hz) through notch filtering, etc.

[0077] Frequency domain features are extracted from the preprocessed EEG signals. Optionally, these features include power variation information across different frequency bands and frontal lobe asymmetry within the same frequency band. Different frequency bands include alpha waves, beta waves, theta waves, and gamma waves. Alpha waves (8–12 Hz) are associated with relaxation and attention levels; beta waves (13–30 Hz) are associated with anxiety and tension levels; theta waves (4–8 Hz) are associated with depressive mood and drowsiness levels; and gamma waves (31–100 Hz) are typically associated with higher cognitive functions. Asymmetry refers to the power difference between the left and right hemispheres within the same frequency band. This asymmetry can provide important information about the functional differences between the left and right hemispheres, helping to understand an individual's emotional state, cognitive function, and potential neuropsychological problems. Frontal lobe asymmetry within the same frequency band refers to the difference in electrophysiological activity between the left and right frontal lobes at specific frequency bands (such as alpha and beta waves), resulting in power asymmetry between the left and right frontal lobes at that specific frequency band.

[0078] Next, machine learning algorithms (such as support vector machines or deep neural networks) can be used to identify the user's emotional state based on frequency domain features, obtaining the emotional state identification result. Optionally, the emotional state identification result is anxiety or depression. The emotion identification logic includes: compared with baseline EEG data (baseline EEG data is EEG signals collected when the user is immersed in a neutral and quiet virtual scene), if the power of β waves and γ waves increases and the power of α waves decreases, it indicates that the brain is in a state of high arousal due to tension and anxiety, and the emotional state identification result is anxiety; or compared with baseline EEG data, if the power of α waves and θ waves increases, and the frontal asymmetry of α waves increases (e.g., the power of α waves on the left is higher than that on the right, suggesting relative inhibition of the left frontal lobe), and the overall power change of the EEG signal is small, it may indicate that the user is depressed and has decreased attention, which is consistent with the characteristics of a depressive state, and the emotional state identification result is depression. Optionally, when the power ratio of the user's β waves to α waves is within a preset range (e.g., 0-0.5), the machine learning algorithm identifies it as a relaxed state.

[0079] Based on the frequency domain features extracted from EEG signals, a pre-trained machine learning algorithm model categorizes the user's current emotional state into "relaxed," "anxious," or "depressed," and outputs the emotion recognition results in real time. When the user's EEG signals change, the system can promptly and accurately detect changes in the user's emotions.

[0080] Step S14: Adjust the stimuli of the initial virtual environment based on the personnel status recognition results.

[0081] Optionally, the stimuli of the initial virtual environment can be adjusted based on the emotional state recognition results. Here, the stimuli are used to characterize factors that cause fluctuations in the user's emotions.

[0082] Optionally, the emotion state recognition result is anxiety or depression, and the stimuli include auditory and visual factors. Visual factors include scene, color, and brightness, while auditory factors include music, sound effects, and voice guidance. The stimuli of the initial virtual environment are then adjusted based on the emotion state recognition result, including:

[0083] When the emotional state is identified as anxiety, the scene is switched to a quiet, natural environment, and / or the brightness and color saturation of the scene are reduced, and / or soothing natural sounds or soft music are played, and / or voice prompts guiding the user through relaxation exercises or focusing attention are played; or

[0084] When the emotional state recognition result is depression, positive interactive elements are added to the scene. Positive interactive elements are used to represent elements that promote active user interaction. These may include friendly virtual objects, such as virtual pets or friendly virtual characters, and / or increase the brightness and color saturation of the scene, and / or add soothing and pleasant music, and / or play voice prompts that guide users into positive thinking.

[0085] In this embodiment, one or more of the following adjustments are made to the initial virtual environment based on emotion recognition results: scene, color and brightness, sound effects (or music), and voice guidance. Specifically, scene changes include: automatically switching to a tranquil natural environment when anxious; automatically adding positive interactive elements, such as virtual pets or friendly virtual objects, when depressed. Color and brightness adjustments include: reducing light brightness for anxious users to alleviate visual stimulation; slightly increasing ambient brightness for depressed users to stimulate attention and positive emotions. Sound effect adjustments include: playing soothing natural sounds or light music when anxious; appropriately adding gentle and pleasant music to enhance interest when depressed. Voice guidance includes: playing virtual objects or voice prompts based on the emotional state, such as guiding the user to perform deep breathing, relaxation exercises, or concentration activities.

[0086] Step S15: Determine the changes in the user's EEG signals.

[0087] Optionally, by adjusting the stimuli of the initial virtual environment based on the emotional state recognition results, the user's emotional state in the initial virtual environment can be improved, reducing the degree of anxiety or depression. The user's electroencephalogram (EEG) signals are continuously collected using an EEG device to determine changes in these signals.

[0088] Step S16: When the changes in the EEG signal meet the conditions for improving the person's condition, a virtual interactive scene is presented within the user's field of vision.

[0089] Specifically, when a user's mood improves (e.g., a decrease in anxiety or depression), as evidenced by changes in brainwave signals that meet the conditions for improved emotional state, the user can proceed to the next stage (the interaction stage). At this point, the VR system presents a virtual interactive scene within the user's field of vision.

[0090] This application can intuitively display the user's emotional changes by monitoring changes in the user's electroencephalogram (EEG) signals. When the changes in the EEG signals meet the conditions for emotional improvement, the user is driven into an interactive scenario.

[0091] In one embodiment, when the changes in the EEG signal meet the conditions for improved emotional state, the user is driven into an interactive scene, including: when the emotional state recognition result is anxiety, if the alpha wave power ratio increases and the beta wave power remains below the baseline EEG data (the baseline EEG data is the EEG signal collected when the user is immersed in a neutral and quiet virtual scene) within a preset time period (such as 3 minutes, 5 minutes, 10 minutes, etc.), then a first virtual interactive scene is presented in the user's field of vision; if the power ratio of beta waves to alpha waves in the first virtual interactive scene is within a preset range and the user's subjective feedback is good, then a second virtual interactive scene is presented in the user's field of vision, and the interactivity of the second virtual interactive scene is stronger than that of the first virtual interactive scene.

[0092] In this embodiment, when the user's initial emotion recognition result in the virtual environment is anxiety, the changes in the user's EEG signals are monitored by adjusting the stimuli of the initial virtual environment. If, after adjustment, the user's EEG signals show an increase in the proportion of alpha wave power and a β wave power remaining below the baseline EEG data within a preset time period (e.g., 3 minutes, 5 minutes, 10 minutes), it indicates that the user has entered a relatively relaxed state, the user's anxiety has been alleviated, and the relaxed state lasts for a period of time. By monitoring the user's anxiety relief over a period of time, the randomness of emotion changes is eliminated.

[0093] After the user's anxiety subsides, a first virtual interactive scene is presented within the user's field of vision, prompting the user to enter this scene. This first virtual interactive scene can be a simple social dialogue exercise, simulating a safe and relaxed conversational environment where the user can engage in simple conversations with virtual objects. Users can also interact with the first virtual interactive scene using controllers and head-mounted displays. For example, users can interact with virtual objects in the scene through controller buttons and motion tracking functions. The head-mounted display can also track the user's head position and orientation, ensuring the virtual environment adjusts its perspective in real time based on head movements, providing an immersive visual experience to enhance user immersion and engagement. Simultaneously, the system continuously monitors brainwave signals and the user's emotional regulation.

[0094] When the power ratio of beta waves to alpha waves in the first virtual interactive scenario is within a preset range (e.g., 0-0.5) and the user's subjective feedback is positive, it indicates that the user does not exhibit abnormal anxiety characteristics and is in a relatively relaxed state. The user can then participate in the next level of interactive challenge. The system switches the scenario to a second virtual interactive scenario, which is more interactive than the first. For example, the second virtual interactive scenario can make the dialogue between the user and virtual objects more complex, or introduce a second virtual object into the dialogue. For instance, simple dialogues are suitable for rapid interaction and basic tasks, typically with short and direct answers. Complex dialogues are suitable for scenarios requiring deeper interaction, capable of handling complex language input and providing detailed answers or guidance. In practical applications, the first and second virtual interactive scenarios can be adjusted according to the training task.

[0095] This application monitors the user's emotional regulation. When the anxiety is relieved, the user is driven into the interaction stage. The complexity of the virtual interaction scenario is gradually increased. From relaxation training in the initial virtual environment to virtual interaction in the virtual interaction scenario, the user's ability to regulate emotions in a more realistic situation is gradually improved.

[0096] When users enter a virtual interactive scenario, they may experience anxiety, which can affect their interaction performance. If the user's anxiety is mild, it can be alleviated by slightly adjusting the stimuli in the virtual environment. However, if the user's anxiety is severe, the virtual scenario needs to be adjusted promptly to prevent discomfort during training.

[0097] Based on this, this application further proposes that: when a user enters the first virtual interactive scene, if the β wave power rises briefly, the stimulation factors of the first virtual interactive scene should be adjusted; or if the β wave power remains higher than the baseline and the user's heart rate rises faster, the user should exit the first virtual interactive scene and switch to a relaxing natural scene, and then switch back to the first virtual interactive scene after the user's emotions have recovered.

[0098] Specifically, when a user enters the first virtual interactive scenario and exhibits mild signs of anxiety, such as a brief increase in beta wave power, the system does not immediately exit the training scenario (i.e., the first virtual interactive scenario). Instead, it helps the user buffer their emotions by slightly adjusting the environment. This could be by reducing the brightness of the scene, slowing down the pace of the dialogue, lowering the background noise, or having a virtual instructor insert a reassuring verbal prompt. In this way, the user practices under controllable pressure, thus coping with anxiety.

[0099] When a user's anxiety level remains persistently high, such as a sustained increase in beta wave power above baseline and a rapid rise in heart rate (the system can use other physiological signals, such as heart rate, to help determine the anxiety state), the system determines that the user may be overly tense and needs immediate adjustment of the scenario intensity. For example, the current social situation (i.e., the first virtual interaction scenario) can be temporarily interrupted, and the environment can be switched back to the most relaxing natural scenario for a moment to allow the user's emotions to recover before attempting to switch back to the first virtual interaction scenario. In addition, voice prompts can reassure the user: "Don't be nervous, let's relax for a bit."

[0100] This application continuously monitors changes in the user's electroencephalogram (EEG) signals during virtual interactive scenarios, which helps to provide timely feedback on possible emotional changes in the user during virtual interactive scenarios and avoids excessive discomfort for the user during training.

[0101] In another embodiment, when the changes in the EEG signal meet the conditions for improved emotional state, presenting a virtual interactive scene within the user's field of vision may further include: when the emotional state recognition result is depression, if the alpha wave power decreases, the beta wave power increases, and the frontal asymmetry of the alpha wave decreases within a preset time period, then a third virtual interactive scene is presented within the user's field of vision; if the power ratio of the beta wave to the alpha wave in the third virtual interactive scene is within a preset range, and the user's subjective feedback is good, then a fourth virtual interactive scene is presented within the user's field of vision, wherein the interactivity of the fourth virtual interactive scene is stronger than that of the third virtual interactive scene.

[0102] In this embodiment, when the user's initial emotion recognition result in the virtual environment is depression, the changes in the user's EEG signals are monitored by adjusting the stimuli of the initial virtual environment. If, within a preset time period (e.g., 3 minutes, 5 minutes, 10 minutes, etc.), the alpha wave power decreases, the beta wave power increases, and the frontal asymmetry of the alpha wave decreases, it indicates that the user's depressive mood has been alleviated and their concentration has improved. By monitoring the alleviation of the user's depressive state over a period of time, the randomness of mood changes is eliminated.

[0103] After the user's depressive state is alleviated, a third virtual interactive scene is presented within the user's field of vision, which can drive the user into this scene. This third virtual interactive scene can be a simple interaction between the user and a virtual character, such as adding dialogue between the virtual character and the user or guiding the user to perform simple tasks. When the power ratio of beta waves to alpha waves in the third virtual interactive scene is within a preset range (e.g., 0-0.5), and the user's subjective feedback is positive, it indicates that the user is in a relatively relaxed state. At this point, the user can be driven into a fourth virtual interactive scene. The fourth virtual interactive scene is more interactive than the third; for example, it may allow the user to perform multi-step tasks. In practical applications, the third and fourth virtual interactive scenes can be adjusted according to the training task.

[0104] This application monitors the user's emotional regulation. When the depression is relieved, the user is driven into the interaction stage. The complexity of the virtual interaction scenario is gradually increased. From the initial virtual environment to train positive emotions, the user's ability to regulate emotions in a more realistic situation is gradually improved.

[0105] In some optional embodiments, the method provided in this application further includes: presenting a virtual transition scene in the user's field of vision before presenting the virtual interactive scene, the virtual transition scene including using virtual characters to have simple communication with the user or guiding the user into the virtual interactive scene.

[0106] Specifically, before a user transitions from the initial virtual environment to a virtual interactive scenario, the system can present a virtual transition scene within the user's field of vision, incorporating light social interaction. For example, a virtual character (such as a virtual instructor) might appear in the initial virtual environment, inviting the user to engage in simple conversation or guiding them to the next scenario (i.e., the virtual interactive scenario). Setting up virtual transition scenes makes the transition between virtual scenarios more natural, reduces user discomfort, and allows users to gradually improve their adaptability to virtual scenarios while remaining relaxed, thus helping to further solidify the positive emotional impact.

[0107] It should be noted that the interactivity of virtual interactive scenarios is stronger than that of virtual transition scenarios. Furthermore, the interactivity gradually increases from virtual transition scenarios to virtual interactive scenarios, which helps to carry out training tasks step by step, challenge users' psychological limits while ensuring user comfort, and promote the improvement of emotional adaptation and psychological resilience.

[0108] Step S17: Identify the user's state when immersed in a virtual interactive scene based on the user's EEG signals.

[0109] Optionally, after a user enters the virtual interactive scene, the EEG device continuously collects the user's brainwave signals and identifies the user's emotional state while immersed in the virtual interactive scene based on the user's brainwave signals. Specific emotion recognition methods can be found in the description of step S13 above.

[0110] Step S18: Training ends when the user's status reaches the target status or the training duration reaches the end time.

[0111] When a user's anxiety or depression gradually improves in a virtual interactive scenario, and the user's emotional state is identified as reaching the target state (such as a relaxed state) through EEG signals, or when the training time reaches the end time (such as 30 minutes), the user exits the training from the virtual reality system.

[0112] In this application, by collecting the user's electroencephalogram (EEG) signals and identifying the user's state based on these signals, the stimulation factors in the virtual reality environment are adjusted according to the identified state, thereby achieving user state regulation training. Furthermore, by monitoring changes in the user's state and providing real-time feedback on the user's EEG signals, a closed-loop regulation of the user's state is achieved, which facilitates positive intervention and improvement of the user's state.

[0113] In some optional embodiments, the method provided in this application further includes: recording changes in the user's electroencephalogram (EEG) signals and virtual environment adjustment logs during the training process, and generating an emotion change report.

[0114] Specifically, the system records changes in EEG emotional state data and corresponding virtual environment adjustment logs throughout the training process, generating an emotional change report. For example, the report includes the user's initial anxiety level, trends in physiological indicators (such as EEG and heart rate) after relaxation training, the number and magnitude of environmental adjustments at each stage, and the improvement in the user's emotional stability. This data can be used by clinicians to assess the user's progress in emotional regulation and optimize parameters for the next training session.

[0115] The method of this application will be described below through two embodiments.

[0116] Example 1: The Emotion Regulation Process for Users with Anxiety Disorder

[0117] The user puts on the EEG brain-computer interface device and VR headset and enters the VR system. The system first performs calibration and baseline signal acquisition to obtain baseline EEG data.

[0118] After calibration, the VR scene gradually transitions to the initial training environment (i.e., the initial virtual environment), such as a tranquil natural landscape (forest or beach). During this process, the user's EEG signals are collected, and signal preprocessing and frequency domain feature extraction are performed to obtain parameters such as power changes in major frequency bands such as alpha waves (8–12Hz) and beta waves (13–30Hz), as well as frontal lobe EEG asymmetry. The system continuously analyzes the user's EEG signals and identifies the user's emotional state based on frequency domain features.

[0119] The system then adjusts the stimulating elements in the VR environment in real time based on the identified emotional state to alleviate anxiety or enhance relaxation. Specific adjustment methods include:

[0120] 1. Scene Switching: When a user is detected to be in a state of high anxiety, the environment automatically switches or introduces more peaceful scene elements. For example, switching from a general scene to a tranquil natural environment: a dense forest, a babbling brook, or a sunset at the beach. These natural scenes have a stress-relieving effect, reduce environmental complexity, and make it easier for the user to calm down. As the user gradually relaxes, the system can gradually transition to a slightly more interactive scene, such as adding some safe social elements (e.g., a friendly virtual character appearing in the distance) to increase the user's adaptability.

[0121] 2. Ambient Brightness and Color Tone: The ambient lighting and color will be gently adjusted according to the user's state. For users who are anxious, the system tends to reduce stimulating light effects and high-contrast colors, using soft light and warm colors to create a sense of security. For example, the scene brightness is slightly reduced, and the light is softly diffused to avoid flickering or sudden bright visual stimulation, thereby reducing user tension. As the user's level of relaxation increases, the ambient brightness can be slightly increased to a comfortable level, allowing the user to gradually adapt to a normal lighting environment.

[0122] 3. Background Sound Effects: The system can select sound effects that help calm emotions. When the user is anxious, the background music will switch to soothing melodies or natural white noise (such as ocean waves, birdsong, and wind), with a moderate to low volume and a slow tempo to reduce physiological arousal. Sudden changes in sound or sharp sound effects should also be minimized. Once the user's mood has calmed down, some positive sound elements, such as melodious music, can be mixed in to reinforce their relaxed state.

[0123] 4. Voice Prompts: The system includes a built-in virtual voice prompt. When a user's anxiety level is detected to be high, a gentle voice prompt will be triggered to guide the user through breathing relaxation exercises or mindfulness practices. For example, a slow voice prompt might say, "Please take a deep breath and relax your shoulders," helping the user regulate their breathing rate and creating a positive feedback loop with their physiological responses. The voice content and tone are adjusted based on real-time emotional feedback: the tone is softer and the speech is shorter when the user is tense; as the user gradually relaxes, the voice can provide appropriate positive reinforcement (such as "Well done, please continue to relax") to boost the user's confidence.

[0124] The adjustment of the virtual environment and the user's physiological response form a closed-loop feedback: as the environment becomes more peaceful, the user's anxiety physiological indicators should decrease, with EEG showing a gradual increase in alpha waves and a decrease in beta waves. When the user's anxiety is relieved, for example, when the user's EEG indicators show that they have entered a relatively relaxed state for a period of time (such as an increase in the proportion of alpha waves and a remainder of beta waves below the baseline for several minutes), the system enters the next stage of the process.

[0125] Next, the scenario transitions from a purely relaxing one to one with slight interaction. Specifically, a transitional scene might appear in VR—for example, a friendly virtual guide emerges from the user's original virtual forest, inviting the user to engage in a brief conversation or guiding them to the next scene (this can be seen as adding a mild "social" element). The introduction of this transitional scene aims to gradually improve the user's adaptability to the previously anxiety-inducing situation while maintaining relaxation, but the entire process is still monitored by the system to ensure emotional stability.

[0126] In new interactive scenarios, the system continues to implement a feedback mechanism that involves real-time acquisition of EEG signals and dynamic adjustment of the virtual environment. For example, if the next stage is a simple social dialogue exercise (for users with social anxiety), the VR will simulate a safe and relaxed dialogue environment, during which EEG monitoring continues. If the user experiences mild anxiety or persistently high anxiety levels during the interaction, the virtual scene needs to be dynamically adjusted based on the different emotional changes the user experiences during the interaction, as described above, and will not be repeated here.

[0127] At the end of a complete session (e.g., after a predetermined 30-minute training session), the user exits the VR environment. The system records EEG emotional state data and corresponding environmental adjustment logs throughout the process, generating a feedback report (i.e., an emotion change report). This report is provided to clinicians to assess the user's progress in emotion regulation and to optimize parameters for the next training session.

[0128] In Example 1, the system helps users with anxiety disorder undergo relaxation training by dynamically adjusting stimuli in the VR environment. After the user wears an electroencephalogram (EEG) acquisition device (such as an EEG headset) and enters the VR environment, the system monitors their emotional state in real time and dynamically adjusts the visual and auditory stimuli in the virtual environment to gradually relax the user.

[0129] Example 2: The Emotion Regulation Process for Users with Depression

[0130] After a user with depression wears an EEG brain-computer interface device and a VR headset, the system first performs calibration and baseline signal acquisition to obtain baseline EEG data. For depressed users, the baseline EEG data may initially show certain characteristics (e.g., generally higher alpha waves and stronger alpha waves in the left frontal lobe, indicating lower brain activity and negative emotional tendencies), which the system uses as a reference starting point.

[0131] The system then creates an initial virtual environment for the user (such as a friendly and positive scene). Unlike the initial virtual environment for users with anxiety disorders (a tranquil natural environment), this scene can be slightly more lively but not overly stimulating, such as a sunny park scene: blue sky and white clouds, grass and flowers, and other tourists can be seen engaging in relaxing activities in the distance. This environment aims to provide users with a positive and safe atmosphere.

[0132] Next, the system continuously monitors the user's EEG signals, focusing on indicators related to depressive states: such as whether theta wave and low-frequency alpha wave power is consistently high, whether the response to external stimuli is sluggish (which may manifest as low total EEG power or small amplitude of change), and whether frontal alpha wave asymmetry (relative enhancement of left-sided alpha) is significant. The system identifies the user's emotional state based on the frequency domain characteristics of the collected EEG signals. If the system detects that the user is still in a state of low arousal / low mood, it prepares to initiate a series of stimulus feedback adjustments to guide the user's mood upward.

[0133] To gradually stimulate user interest and positive emotions, the system introduces or adjusts the following stimuli in the virtual environment based on EEG signal feedback, specifically including:

[0134] 1. Enrich the Scene Content: If EEG analysis shows the user is inattentive or emotionally apathetic (e.g., showing almost no emotional response to the current scene), the richness of the scene can be gradually increased. This could involve adding attention-grabbing positive elements to the initial virtual environment (e.g., a park scene), such as a cute virtual pet (puppy or kitten) running towards the user. The pet's appearance, accompanied by lively animation, can attract the user's attention. The system assesses the effect by monitoring changes in the user's EEG signals: when the user's attention is attracted, there may be a brief increase in beta waves and a decrease in alpha waves, indicating that interest has been aroused. If such changes are not detected, the system can try different stimuli, such as a beautiful kite appearing in the sky, or guiding the user to approach and look at flowers, until the EEG feedback shows some signs of user response.

[0135] 2. Ambient Brightness and Color Adjustment: Depression is often accompanied by decreased sensitivity to sensory stimuli and a subjective perception of a dim environment. To improve mood, the system appropriately increases the overall brightness and color saturation of the scene. For example, the colors of the sky and grass are made more vibrant, and the sunlight is slightly increased to enhance the sense of vitality in the scene. At the same time, the system ensures that the changes are not too sudden to avoid causing discomfort. EEG monitors the user's response to this change: if a moderate increase in brightness results in a decrease in the user's alpha waves and a slight increase in beta or gamma waves, it indicates that the visual stimulus is enhancing their alertness and mood. This positive response triggers the system to maintain or further fine-tune the brightness to a level that is comfortable for the user and slightly above the baseline.

[0136] 3. Background Sounds and Music: Sound elements play a crucial role in interventions for users with depression. The system plays background music or ambient sounds based on user preferences or generally soothing and pleasant soundscapes. For example, soft, cheerful music (such as a mix of birdsong and light music) can be added to a park setting. If the user's EEG shows a positive response to the music stimulus (such as increased activity in the fast wave bands related to emotion), the volume can be slightly increased or a change in melody can be introduced to enhance the stimulus effect. If the user shows signs of impatience or agitation with the sound (which can be judged by abnormal fluctuations or high-frequency activity in beta waves, combined with the user's possible physiological reactions), the system will promptly reduce the music volume or replace it with a softer, more natural sound to avoid overstimulation.

[0137] 4. Language Guidance and Interaction: The system's built-in virtual coach or therapist avatar will engage in appropriate voice communication with the user to guide positive thinking and emotional expression. For example, when a user is detected to be persistently depressed, the virtual coach might say in a caring tone, "Let me take you to a special place to relax." It might then guide the user for a walk in a park setting. The voice content is designed to include positive cues and encouragement, such as, "The scenery is beautiful; try taking a deep breath and feel the warm sunshine." These verbal stimuli, combined with the scene, aim to evoke the user's perception of the environment and emotional resonance. If the EEG shows that the user experiences emotional fluctuations after hearing the prompts, the system will continue to "converse" with the user as needed, such as asking simple questions to elicit a response (increasing engagement), while simultaneously monitoring their EEG responses to determine the direction of emotional changes.

[0138] After a period of stimulation, if the user's EEG characteristics show positive changes—such as a decrease in overall alpha wave power, a moderate increase in beta waves, and a reduction in alpha asymmetry between the left and right frontal lobes (indicating improved mood and increased focus)—the system determines that the user has alleviated their deep depression and is ready to enter the next scenario. At this point, the system can smoothly transition the scenario, such as from a primarily static park environment to a scenario with mild social interaction or task-oriented elements, to further solidify the mood improvement. For example, the scenario could extend to a small interactive activity in a corner of the park, where a friendly virtual object (which could be an avatar of a therapist or an acquaintance) appears before the user, inviting them to complete a simple task (such as playing fetch with a pet or helping water plants in the garden). This new scenario is more complex than the previous one, incorporating social and achievement elements, which can help users with depression experience positive emotions of interaction and success. Throughout the transition, the system will maintain monitoring of the user's EEG, specifically...

[0139] If the user adapts well to the change in environment (no new negative patterns appear in the EEG signals), the system will gradually enrich the interactive content. For example, it may add dialogue: "How did you like the game just now?" If the user can respond slightly (the system can capture simple feedback through voice recognition or body movements), this will greatly enhance their social motivation. If the EEG reflects more β and low γ activity, it indicates that the user's brain activity is increased and their emotions tend to be more positive.

[0140] If a scene transition causes user unease or stress (e.g., the user may experience anxiety in a social situation, with a high-beta, high-frequency pattern observed in the EEG), the system will adjust its strategy promptly: virtual objects can be temporarily removed from view, leaving only task-related items (such as allowing the user to play alone with their pet) to reduce social stress; or the scene can be partially relocated to a familiar corner of a park. Once the user's EEG indicates emotional stability, social elements can be reintroduced. This gentle adjustment ensures that the user does not fall back into a negative state due to scene escalation.

[0141] As users complete a series of interactive scenario exercises, they gradually emerge from their depressed mood. At the end of the training, the system saves the EEG data and environmental adjustment records of the entire process and generates a report on mood changes. The report may indicate that: the user's initial EEG characteristics were consistent with a moderate depressive state; through multi-sensory stimulation, alpha wave power gradually decreased by X% and beta wave power increased by Y% (X, Y represent numbers between 0 and 100), showing increased interest; the user experienced several positive EEG fluctuations (marking moments of pleasure) while interacting with virtual objects, all of which suggest that this VR intervention has a positive impact on the user's mood. The report also records the key adjustment measures taken by the system (such as "introducing pet interaction at the 5th minute" and "adding music stimulation at the 12th minute") and their corresponding effects. This information can help optimize personalized plans for subsequent treatments, such as determining which stimuli are most effective for the user.

[0142] In Example 2, for users with depression, the system uses a brain-computer interface to perceive the user's level of depression in real time and flexibly adjusts the scene content and stimulating elements in the VR environment (brightness, color, sound, music, virtual interaction, etc.) to gradually awaken the user from their negative state and enhance their interest and positive emotions. The entire process is also closed-loop: each environmental change is based on EEG feedback, thereby achieving dynamic intervention tailored to the individual's state and avoiding ineffective or counterproductive effects from excessively weak or strong stimuli.

[0143] In summary, this application has the following beneficial effects:

[0144] 1. Real-time and Personalized: Utilizing millisecond-level feedback from EEG signals, the system can immediately adjust the environment to match the user's current emotional state. Each user has a different EEG baseline and emotional response pattern; the system adjusts VR stimulation based on individual characteristics to provide a tailored therapeutic experience.

[0145] 2. Immersive Emotion Regulation: VR immersive environments combined with brain-computer interfaces allow users to fully engage in the intervention process. Multi-channel stimulation, including visual and auditory stimuli, creates a unified contextual experience in the brain. Combined with dynamic changes driven by brainwaves, this can more effectively influence and reshape the user's emotional responses. For example, when a user calms down, the scenery before them becomes bright and beautiful, strengthening their association between positive emotions and a relaxed state.

[0146] 3. Gradual Intervention Strategy: The system can gradually increase or decrease the intensity of stimulation based on EEG assessments, enabling targeted training. For example, for anxious users, it can gradually transition from relaxation training to situational exposure; for depressed users, it can gradually transition from sensory arousal to social interaction. This approach challenges users' psychological limits while ensuring their comfort, promoting emotional adaptation and enhancing psychological resilience.

[0147] 4. Data-driven efficacy evaluation: The physiological data and system response logs generated from each intervention provide objective evidence for efficacy evaluation and program optimization. Therapists can use the data to understand which emotions of the user have improved significantly and which stages still present difficulties, thereby adjusting subsequent treatment content. At the same time, the accumulation of a large amount of user data can further train the emotion recognition algorithm, improving the system's recognition accuracy and the effectiveness of intervention strategies.

[0148] Following the same line of thought, this application also provides a human state regulation and training device based on brain-computer interface and virtual reality, such as... Figure 2 This application provides a schematic diagram of the structure of a human state adjustment training device based on brain-computer interface and virtual reality. The human state adjustment training device 20 based on brain-computer interface and virtual reality mainly includes:

[0149] The first presentation module 21 is used to present the initial virtual environment within the user's field of vision;

[0150] Acquisition module 22 is used to acquire the brainwave signals of the user when immersed in the initial virtual environment;

[0151] The first recognition module 23 is used to recognize the user's state when immersed in the initial virtual environment based on EEG signals, and obtain the user state recognition result.

[0152] The adjustment module 24 is used to adjust the stimulus factors of the initial virtual environment based on the personnel status recognition results. The stimulus factors are used to characterize the factors that cause fluctuations in the user's status.

[0153] Module 25 is used to determine changes in the user's electroencephalogram (EEG) signals;

[0154] The second presentation module 26 is used to present a virtual interactive scene within the user's field of vision when the changes in the electroencephalogram (EEG) signal meet the conditions for improving the person's condition.

[0155] The second identification module 27 is used to identify the user's state when the user is immersed in a virtual interactive scene based on the user's brainwave signals.

[0156] Training ends module 28, which responds to when the user's status reaches the target status or the training duration reaches the end duration, and the training ends.

[0157] In one possible implementation, the person's state includes emotional state, and the first recognition module 23 can also be used for:

[0158] The EEG signals are preprocessed and frequency domain features are extracted. These features include power variation information in different frequency bands and frontal lobe asymmetry within the same frequency band. Different frequency bands include alpha waves, beta waves, theta waves, and gamma waves. Based on these frequency domain features, the user's emotional state in the initial virtual environment is identified; and / or,

[0159] Before presenting the virtual interactive scene within the user's field of vision, the method further includes: presenting a virtual transition scene within the user's field of vision, wherein the virtual transition scene includes using virtual characters to have simple interactions with the user or guiding the user into the virtual interactive scene.

[0160] In one possible implementation, the person state recognition result includes an emotion state recognition result, which is anxiety or depression. The first recognition module 23 can also be used for:

[0161] Compared to baseline EEG data, if the power of beta and gamma waves increases while the power of alpha waves decreases, the emotional state is identified as anxiety. Baseline EEG data consists of EEG signals collected when the user is immersed in a neutral, quiet virtual environment; or

[0162] Compared with baseline EEG data, if the power of alpha and theta waves increases, the frontal asymmetry of alpha waves increases, and the overall power change of the EEG signal is small, then the emotional state identification result is depression.

[0163] In one possible implementation, the person state recognition result includes the emotion state recognition result, which is anxiety or depression. The second presentation module 26 can also be used for:

[0164] When the emotion state identification result is anxiety

[0165] If the alpha wave power ratio increases and the beta wave power remains below the baseline EEG data within a preset time period, the first virtual interactive scene will be presented in the user's field of vision. The baseline EEG data is the EEG signal collected when the user is immersed in a neutral and quiet virtual scene.

[0166] If the power ratio of beta waves to alpha waves in the first virtual interactive scenario is within a preset range and the user's subjective feedback is good, then a second virtual interactive scenario will be presented in the user's field of vision. The second virtual interactive scenario is more interactive than the first virtual interactive scenario.

[0167] In one possible implementation, the second presentation module 26 can also be used for:

[0168] When the user enters the first virtual interactive scene to interact...

[0169] If the β wave power briefly increases, adjust the stimulus factors in the first virtual interactive scenario; or

[0170] If the beta wave power remains higher than the baseline EEG data and the user's heart rate increases rapidly, exit the first virtual interactive scene and switch to a relaxed, natural scene. Switch back to the first virtual interactive scene after the user's emotions have recovered.

[0171] In one possible implementation, the person state recognition result includes the emotion state recognition result, which is anxiety or depression. The second presentation module 26 can also be used for:

[0172] When the emotion state identification result is depression

[0173] If the alpha wave power decreases, the beta wave power increases, and the frontal asymmetry of the alpha wave decreases within a preset time period, then a third virtual interactive scene will be presented in the user's field of vision.

[0174] If the power ratio of beta waves to alpha waves in the third virtual interactive scenario is within a preset range and the user's subjective feedback is good, then a fourth virtual interactive scenario will be presented in the user's field of vision. The interactivity of the fourth virtual interactive scenario is stronger than that of the third virtual interactive scenario.

[0175] In one possible implementation, the person's state includes their emotional state, and the person's state recognition result includes their emotional state recognition result, which is anxiety or depression. The stimuli include auditory and visual factors. Visual factors include scene, color, and brightness, while auditory factors include music, sound effects, and voice guidance. The adjustment module 24 can also be used for:

[0176] When the emotional state is identified as anxiety, the scene is switched to a quiet, natural environment, and / or the brightness and color saturation of the scene are reduced, and / or soothing natural sounds or soft music are played, and / or voice prompts guiding the user through relaxation exercises or focusing attention are played; or

[0177] When the emotional state recognition result is depression, add positive interactive elements to the scene. Positive interactive elements are used to represent elements that promote active user interaction, and / or increase the brightness and color saturation of the scene, and / or add soothing and pleasant music, and / or play voice prompts that guide the user into positive thinking.

[0178] Figure 2 The emotion regulation device 20 based on brain-computer interface and virtual reality provided in the illustrated embodiment can be used to execute the technical solution of the method embodiment shown in this application. Its implementation principle and technical effect can be further referred to the relevant description in the method embodiment.

[0179] The above should be understood Figure 2 The division of modules in the brain-computer interface and virtual reality-based emotion regulation device 20 shown is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or physically separated. These modules can be implemented entirely in software via processing elements; entirely in hardware; or partially in software via processing elements and partially in hardware. For example, the acquisition module can be a separate processing element or integrated into a chip in an electronic device. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together or implemented independently. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0180] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, these modules can be integrated together as a System-On-a-Chip (SOC).

[0181] In the above embodiments, the processor may include, for example, a CPU, DSP, microcontroller, or digital signal processor, and may also include a GPU, embedded neural network processing unit (NPU), and image signal processor (ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as an ASIC, or one or more integrated circuits for controlling the execution of the program in this application. Furthermore, the processor may have the function of operating one or more software programs, which may be stored in a storage medium.

[0182] This application also provides an edge computing device, including: a processor and a memory, wherein the memory is used to store computer programs; and the processor is used to run the computer programs to implement the human state adjustment training method based on brain-computer interface and virtual reality provided in the embodiments of this application.

[0183] In one possible implementation, the edge computing device includes at least one of the following: a wearable display module for displaying virtual scenes; and an EEG signal acquisition module for acquiring EEG signals.

[0184] Figure 3 This is a schematic diagram of the structure of the edge computing device provided in the embodiments of this application, such as... Figure 3 As shown, the edge computing device 1200 includes a wearable display module 1210 and an EEG signal acquisition module 1220, wherein the wearable display module 1210 is used to display virtual scenes; and the EEG signal acquisition module 1220 is used to acquire EEG signals.

[0185] In one possible implementation, the edge computing device may also include a signal processing module for preprocessing the acquired EEG signals.

[0186] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to execute the human state adjustment training method based on brain-computer interface and virtual reality provided in the embodiments of this application.

[0187] The following is combined Figure 4 The exemplary electronic devices provided in the embodiments of this application are further described. Figure 4 A schematic diagram of the structure of electronic device 3000 is shown.

[0188] The aforementioned electronic device 3000 may include: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the human state adjustment training method based on brain-computer interface and virtual reality provided in the embodiments of this application by calling the program instructions.

[0189] Figure 4 A block diagram is shown of an exemplary electronic device 3000 suitable for implementing embodiments of this application. Figure 4 The electronic device 3000 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0190] like Figure 4 As shown, the electronic device 3000 is presented in the form of a general-purpose computing device. The components of the electronic device 3000 may include, but are not limited to: one or more processors 3010, memory 3020, communication bus 3040 connecting different system components (including memory 3020 and processor 3010), and communication interface 3030.

[0191] The communication bus 3040 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0192] Electronic devices 3000 typically include a variety of computer system-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0193] Memory 3020 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media). In these cases, each drive can be connected to the communication bus 3040 via one or more data media interfaces. The memory 3020 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0194] A program / utility having a set (at least one) of program modules may be stored in memory 3020. Such program modules include, but are not limited to, an operating system, one or more applications, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this application.

[0195] The electronic device 3000 can also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), and with one or more devices that enable a user to interact with the electronic device, and / or with any device that enables the electronic device to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through the communication interface 3030. Furthermore, the electronic device 3000 can also communicate through a network adapter ( Figure 3 (Not shown) communicates with one or more networks (e.g., Local Area Network (LAN), Wide Area Network (WAN), and / or public networks, such as the Internet). The aforementioned network adapter can communicate with other modules of the electronic device via the communication bus 3040. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules can be used in conjunction with the electronic device 3000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Arrays of Independent Drives (RAID) systems, tape drives, and data backup storage systems.

[0196] The processor 3010 executes various functional applications and data processing by running programs stored in the memory 3020, such as implementing the methods provided in the embodiments of this application.

[0197] It is understood that the interface connection relationships between the modules illustrated in the embodiments of this application are merely illustrative and do not constitute a structural limitation on the electronic device 3000. In other embodiments of this application, the electronic device 3000 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0198] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0199] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0200] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0201] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for training and regulating human state based on brain-computer interface and virtual reality, characterized in that, The method includes: The initial virtual environment is presented within the user's field of vision; Acquire the user's electroencephalogram (EEG) signals while the user is immersed in the initial virtual environment; Based on the EEG signals, the user's state when immersed in the initial virtual environment is identified, and the user state identification result is obtained. The stimulation factors of the initial virtual environment are adjusted based on the personnel state recognition results, and the stimulation factors are used to characterize the factors that cause fluctuations in the user's state. Determine the changes in the user's electroencephalogram (EEG) signals; When the changes in the electroencephalogram (EEG) signals meet the conditions for improving the user's condition, a virtual interactive scene is presented within the user's field of vision. The system identifies the user's state when they are immersed in the virtual interactive scene based on the user's brainwave signals. Training ends when the user's status reaches the target status or the training duration reaches the end time.

2. The method for training and regulating human state based on brain-computer interface and virtual reality according to claim 1, characterized in that, The user's state includes emotional state. The step of identifying the user's state when immersed in the initial virtual environment based on the EEG signals includes: preprocessing the EEG signals and extracting frequency domain features, the frequency domain features including power variation information in different frequency bands and frontal lobe asymmetry in the same frequency band, the different frequency bands including alpha waves, beta waves, theta waves, and gamma waves; identifying the user's emotional state when immersed in the initial virtual environment based on the frequency domain features; and / or, Before presenting the virtual interactive scene within the user's field of vision, the method further includes: presenting a virtual transition scene within the user's field of vision, wherein the virtual transition scene includes using virtual characters to have simple interactions with the user or guiding the user into the virtual interactive scene.

3. The method for training and regulating human state based on brain-computer interface and virtual reality according to claim 2, characterized in that, The personnel state recognition result includes an emotional state recognition result, which is anxiety or depression. The step of recognizing the user's emotional state when immersed in the initial virtual environment based on the frequency domain features includes: Compared to baseline EEG data, if the power of beta and gamma waves increases while the power of alpha waves decreases, the emotional state identification result is anxiety. The baseline EEG data refers to EEG signals collected when the user is immersed in a neutral and quiet virtual scene; or Compared with the baseline EEG data, if the power of alpha waves and theta waves increases, and the frontal asymmetry of alpha waves increases, while the overall power change of the EEG signal is small, then the emotional state recognition result is depression.

4. The method for training and regulating human state based on brain-computer interface and virtual reality according to claim 1, characterized in that, The personnel status recognition result includes the emotional state recognition result, which is anxiety or depression. When the changes in the electroencephalogram (EEG) signal meet the conditions for improving the personnel status, a virtual interactive scene is presented within the user's field of vision, including: When the emotional state recognition result is anxiety. If the alpha wave power ratio increases and the beta wave power remains below the baseline EEG data within a preset time period, then a first virtual interactive scene is presented in the user's field of vision. The baseline EEG data is the EEG signal collected when the user is immersed in a neutral and quiet virtual scene. If the power ratio of β waves to α waves in the first virtual interactive scene is within a preset range and the user's subjective feedback is good, then a second virtual interactive scene will be presented in the user's field of vision. The interactivity of the second virtual interactive scene is stronger than that of the first virtual interactive scene.

5. The method for training and regulating human state based on brain-computer interface and virtual reality according to claim 4, characterized in that, The method further includes: When the user enters and interacts with the first virtual interactive scene If the β-wave power briefly increases, adjust the stimulus factors of the first virtual interactive scene; or If the β wave power remains higher than the baseline EEG data and the user's heart rate increases rapidly, the user exits the first virtual interactive scene and switches to a relaxed, natural scene. The user then switches back to the first virtual interactive scene after their emotions have recovered.

6. The method for training and regulating human state based on brain-computer interface and virtual reality according to claim 1, characterized in that, The personnel status recognition result includes the emotional state recognition result, which is anxiety or depression. When the changes in the electroencephalogram (EEG) signal meet the conditions for improving the personnel status, a virtual interactive scene is presented within the user's field of vision, including: When the emotional state identification result is depression If the alpha wave power decreases, the beta wave power increases, and the frontal asymmetry of the alpha wave decreases within a preset time, then a third virtual interactive scene will be presented in the user's field of vision. If the power ratio of β waves to α waves in the third virtual interactive scene is within a preset range and the user's subjective feedback is good, then a fourth virtual interactive scene will be presented in the user's field of vision. The interactivity of the fourth virtual interactive scene is stronger than that of the third virtual interactive scene.

7. The method for training and regulating human state based on brain-computer interface and virtual reality according to claim 1, characterized in that, The personnel status includes emotional state, and the personnel status recognition result includes emotional state recognition result, which is anxiety or depression. The stimulus factors include auditory and visual factors. The visual factors include scene, color, and brightness, and the auditory factors include music, sound effects, and voice guidance. The adjustment of the stimulus factors of the initial virtual environment based on the personnel status recognition results includes: When the emotional state recognition result is anxiety, the scene is switched to a quiet natural environment, and / or the brightness and color saturation of the scene are reduced, and / or soothing natural sounds or light music are played, and / or voice prompts guiding the user to perform relaxation training or focus attention are played; or When the emotional state recognition result is depression, positive interactive elements are added to the scene. These positive interactive elements are used to represent elements that promote active user interaction, and / or increase the brightness and color saturation of the scene, and / or add soothing and pleasant music, and / or play voice prompts that guide the user into positive thinking.

8. An edge computing device, characterized in that, include: A processor and a memory, the memory being used to store a computer program; the processor being used to run the computer program to implement the human state regulation training method based on brain-computer interface and virtual reality as described in any one of claims 1-7.

9. The edge computing device according to claim 8, characterized in that, The edge computing device includes at least one of the following: Wearable display modules for displaying virtual scenes; The EEG signal acquisition module is used to acquire EEG signals.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the human state regulation training method based on brain-computer interface and virtual reality as described in any one of claims 1-7.

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