An immersive space virtual-real interaction method

Abstract

The application discloses an immersive space virtual-real interaction method and relates to the technical field of virtual reality; after the game starts, the first sub-time zone of each game stage is taken as a starting data collection window, EEG signal data of a player is collected, an alpha wave trend graph is constructed, through a series of calculation and analysis, including determining a reference frequency range, identifying an abnormal point, calculating an alpha wave variation ratio, an alpha wave evaluation value of the player in the starting data collection window is obtained, this process can accurately evaluate the brain state of the player in the starting stage of the game, the change trend of the player's immersion is predicted in advance and the intensity of the tactile feedback is adjusted in time, the player's frequent "out of the game" in the game process is avoided, the coherence and immersion of the game experience are maintained, when the alpha wave evaluation value of the player shows that the immersion may decrease, the tactile feedback is enhanced to attract attention, the game participation is improved, and the player can continuously immerse in the game.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual reality, in particular to an immersive space virtual-real interaction method. BACKGROUND

[0002] With the continuous development of science and technology, virtual reality (VR), augmented reality (AR) and other technologies are increasingly mature, and people's demand for immersive experience is also increasingly high.

[0003] In the representative application of immersive space virtual-real interaction technology - virtual racing game, players expect to obtain more realistic and immersive game experience, however, the existing immersive space virtual-real interaction technology still has the following shortcomings:

[0004] Most of them can only be adjusted after the player's immersion has obviously decreased, and cannot predict and take measures in advance. This lagging adjustment method cannot predict the trend of the player's immersion, and it is difficult to maintain the coherence and immersion of the game experience, so that the player frequently "out of the game" during the game, greatly affecting the overall experience of the game;

[0005] In addition, the existing feedback adjustment method has low accuracy and intelligence, and lacks the mining and use of a large amount of player experience data.

[0006] Therefore, an immersive space virtual-real interaction method is proposed. SUMMARY

[0007] The purpose of the present application is to solve the problems pointed out in the background art and to propose an immersive space virtual-real interaction method.

[0008] The purpose of the present application can be achieved by the following technical solution: an immersive space virtual-real interaction method, comprising:

[0009] Constructing an EEG signal feature library: during the player's virtual racing game, collecting EEG signal data at different game stages; the game stage can be divided into an adaptation stage, a competition stage and a post-race stage; the EEG signal data collected at different game stages is associated with the subjective immersion score of the corresponding player, and according to the association result, an EEG signal feature library is constructed;

[0010] Interacting with the player to evaluate: when the player starts the virtual racing game, first obtain the personal information of the current player and input it into the EEG signal feature library for matching, so as to determine the reference alpha wave frequency of the current player at different game stages; the personal information includes gender and age;

[0011] Predicting the trend of change: for a virtual racing game, different game stages are divided into different sub-time zones according to the time length of different game stages. The first sub-time zone of each game stage is taken as a starting data collection window after the player starts the game, and the trend of the EEG signal data of the player in the starting data collection window is evaluated to obtain the alpha wave evaluation value of the player in the starting data collection window.

[0012] Adjusting the intensity of haptic feedback: based on the alpha wave evaluation value of the current player in the starting data collection window, the percentage of adjusting the intensity of haptic feedback in the next sub-time zone of each game stage is determined.

[0013] As a preferred embodiment of the present application, according to the results of the association, the EEG signal feature library is constructed:

[0014] The subjective immersion score is divided into three score intervals in advance, and each score interval corresponds to a state of immersion feature; including high immersion state feature, medium immersion state feature and low immersion state feature; the EEG signal data under different state of immersion features are classified into corresponding categories respectively;

[0015] After the classification is completed, the EEG signal data under different state of immersion features are further classified according to the predetermined age range and the gender of the player, so as to construct the EEG signal feature library.

[0016] As a preferred embodiment of the present application, the reference alpha wave frequency of the current player in different game stages is determined, specifically:

[0017] After inputting the personal information of the current player into the EEG signal feature library, the EEG signal data corresponding to the personal information of the player is located in the EEG signal feature library, and the alpha wave frequency of each player in different game stages under the high state of immersion is extracted, and the mean value of the alpha wave frequency of each player in the same game stage is calculated, and the three groups of mean values are taken as the reference frequency corresponding to the alpha wave frequency of the current player in different game stages.

[0018] As a preferred embodiment of the present application, the trend of the EEG signal data of the player in the starting data collection window is evaluated, specifically:

[0019] S1: In the starting data collection window, the EEG signal data of the player is collected at a set sampling frequency, the alpha wave frequency is extracted from the original EEG signal data, the alpha wave frequency of the player at each time point in the starting data collection window is taken as a numerical point, an alpha wave trend graph is constructed, the numerical point corresponding to the alpha wave frequency at each time point in the trend graph is drawn, the adjacent numerical points are connected to obtain a frequency line, and the reference frequency matched by the personal information of the player is extracted;

[0020] S2: taking the reference frequency as a reference value, presetting an allowed fluctuation value as a calculation value, determining a reference frequency range corresponding to the player, denoted as (reference value + allowed fluctuation value, reference value - allowed fluctuation value), and drawing a threshold range line corresponding to the reference frequency range in the trend graph;

[0021] S3: identifying a numerical point outside the threshold range line as an abnormal point, extracting an alpha wave frequency of each group of abnormal points and taking an average value to obtain an abnormal frequency of the player in the initial data collection window, matching the abnormal frequency with the reference frequency range, if the matching fails, extracting the highest value and the lowest value of the reference frequency range and comparing with the abnormal frequency, if the abnormal frequency is higher than the highest value of the reference frequency range, performing difference calculation and recording as a high wave frequency, if the abnormal frequency is lower than the lowest value of the reference frequency range, performing difference calculation and taking an absolute value to record as a low wave frequency;

[0022] S4: comparing the alpha wave frequencies of adjacent two groups of numerical points, if the alpha wave frequency of the left numerical point is higher than that of the right numerical point, obtaining a slope of the corresponding frequency line and recording as a descending slope, and vice versa, recording as an ascending slope; summing up the descending slopes and the ascending slopes in the initial data collection window respectively to obtain a descending total value and an ascending total value, and performing ratio calculation with the descending total value as the numerator and the ascending total value as the denominator to obtain an alpha wave ratio of the player in the initial data collection window;

[0023] If the alpha wave ratio is greater than 1, it is determined that the alpha wave frequency of the player in the initial data collection window is in a whole downward trend, otherwise it is determined to be in a whole upward trend, and the whole downward trend and the whole upward trend correspond to a trend additional coefficient respectively.

[0024] As a preferred embodiment of the present application, an alpha wave evaluation value of the player in the initial data collection window is obtained, specifically:

[0025] S5: for the alpha wave frequency of the player at each time point in the initial data collection window, first taking an average value as an alpha wave average rate, then extracting the highest value and the lowest value of the alpha wave frequency at each time point as an alpha wave peak rate and an alpha wave valley rate, and marking the alpha wave average rate, the alpha wave peak rate and the alpha wave valley rate as u1, u2 and u3 respectively; marking the reference frequency matched by the personal information of the player as ua;

[0026] performing weighted calculation through the formula and dividing the calculation result by the integer three as the alpha wave performance value of the player in the initial data collection window; wherein is a preset influence weight factor;

[0027] S6: preset each group of frequency intervals corresponding to the high wave frequency and the low wave frequency respectively, each group of frequency intervals corresponds to a correction coefficient; after determining the high wave frequency or the low wave frequency based on the step S3, the corresponding frequency interval is matched to obtain the correction coefficient of the player in the starting data collection window;

[0028] S7: extract the alpha wave performance value of the player in the starting data collection window, and then multiply the trend additional coefficient and the correction coefficient to obtain the alpha wave evaluation value of the player in the starting data collection window.

[0029] As a preferred embodiment of the present application, the percentage of adjusting the intensity of the tactile feedback of each game stage in the next sub-time zone is determined, specifically:

[0030] The alpha wave evaluation value of the current player in the starting data collection window is matched with the preset theoretical normal range, if the matching is successful, the current trigger feedback intensity is kept unchanged to enter the next sub-time zone.

[0031] As a preferred embodiment of the present application, the percentage of adjusting the intensity of the tactile feedback of each game stage in the next sub-time zone is determined, further comprising:

[0032] If the matching fails, the matching result between the alpha wave evaluation value and the theoretical normal range is further identified, if the alpha wave evaluation value is higher than the theoretical normal range, it is determined as a higher intensity state; if the alpha wave evaluation value is lower than the theoretical normal range, it is determined as a lower intensity state;

[0033] If it is determined as a higher intensity state, by presetting an upper limit range value corresponding to each age range of different genders respectively, the personal information of the current player is extracted to determine the corresponding upper limit range value, the determined upper limit range value is added to the highest value of the theoretical normal range, and then compared with the alpha wave evaluation value, if the alpha wave evaluation value is lower, the current trigger feedback intensity is kept unchanged to enter the next sub-time zone, otherwise the difference between the alpha wave evaluation value and the highest value of the theoretical normal range is calculated, which is recorded as a percentage decrease matching value;

[0034] If it is determined as a lower intensity state, the difference between the alpha wave evaluation value and the lowest value of the theoretical normal range is calculated, and the absolute value is taken as a percentage increase matching value;

[0035] Preset each group of matching value intervals corresponding to the percentage decrease matching value and the percentage increase matching value respectively, each group of matching value intervals corresponds to an adjustment percentage respectively;

[0036] After determining the adjustment percentage, if it is determined as a higher intensity state, the current trigger feedback intensity is adjusted by the decrease of the adjustment percentage as the trigger feedback intensity of the next sub-time zone;

[0037] If it is determined that the intensity is low, the triggering feedback intensity of the next sub-time zone is adjusted by a percentage increase based on the current triggering feedback intensity.

[0038] As a preferred embodiment of the present application, it further comprises:

[0039] Feedback collection optimization: after the current player finishes the game, the subjective immersion score of the current player is collected again and input into the EEG signal feature library for updating.

[0040] Compared with the prior art, the present application has the following advantages:

[0041] The present application collects EEG signal data of the player in each game stage as the starting data collection window after the game starts, constructs an alpha wave trend chart, and obtains the alpha wave evaluation value of the player in the starting data collection window through a series of calculations and analyses, including determining the reference frequency range, identifying abnormal points, and calculating the alpha wave variation ratio. This process can accurately evaluate the brain state of the player in the starting stage of the game, predict the change trend of the player's immersion in advance and adjust the tactile feedback intensity in time, avoid the player's frequent "out of play" in the game process, maintain the coherence and immersion of the game experience, enhance the tactile feedback to attract attention when the player's alpha wave evaluation value shows that the immersion may decrease, improve the game participation, and make the player continuously immersed in the game.

[0042] The present application collects EEG signal data of the player in each game stage as the starting data collection window after the game starts, constructs an alpha wave trend chart, and obtains the alpha wave evaluation value of the player in the starting data collection window through a series of calculations and analyses, including determining the reference frequency range, identifying abnormal points, and calculating the alpha wave variation ratio. This process can accurately evaluate the brain state of the player in the starting stage of the game, predict the change trend of the player's immersion in advance and adjust the tactile feedback intensity in time, avoid the player's frequent "out of play" in the game process, maintain the coherence and immersion of the game experience, enhance the tactile feedback to attract attention when the player's alpha wave evaluation value shows that the immersion may decrease, improve the game participation, and make the player continuously immersed in the game.

[0043] The present application collects EEG signal data of the player in each game stage as the starting data collection window after the game starts, constructs an alpha wave trend chart, and obtains the alpha wave evaluation value of the player in the starting data collection window through a series of calculations and analyses, including determining the reference frequency range, identifying abnormal points, and calculating the alpha wave variation ratio. This process can accurately evaluate the brain state of the player in the starting stage of the game, predict the change trend of the player's immersion in advance and adjust the tactile feedback intensity in time, avoid the player's frequent "out of play" in the game process, maintain the coherence and immersion of the game experience, enhance the tactile feedback to attract attention when the player's alpha wave evaluation value shows that the immersion may decrease, improve the game participation, and make the player continuously immersed in the game. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to facilitate the understanding of those skilled in the art, the present application will be further described below with reference to the accompanying drawings.

[0045] Fig. 1 The flowchart of the present application;

[0046] Fig. 2 The schematic diagram of the alpha wave trend chart in the present application. DETAILED DESCRIPTION

[0047] The technical solutions of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0048] Please refer to Figs. 1-2 As shown in the figure, an immersive virtual-real interaction method comprises:

[0049] Constructing an EEG signal feature library: in the process of the player playing the virtual racing game, EEG signal data in different game stages is collected; the game stages can be divided into an adaptation stage, a competition stage and a post-race stage; the collected EEG signal data in different game stages is associated with the subjective immersion score of the corresponding player, and the subjective immersion score is set to 1-10. After the game ends, the player is asked to score his / her immersion in different time periods through a questionnaire survey. For example, when the player evaluates the immersion as 8 points or more, the corresponding EEG signal feature is defined as a high immersion state feature; when the evaluation is 3 points or less, it is regarded as a low immersion state feature; according to the association result, the EEG signal feature library is constructed;

[0050] Specifically:

[0051] The subjective immersion score is divided into three score intervals in advance, and each score interval corresponds to an immersion state feature; including a high immersion state feature, a medium immersion state feature and a low immersion state feature; the EEG signal data in different immersion state features are classified into corresponding categories respectively;

[0052] After the classification is completed, the EEG signal data in different immersion state features is further classified according to the pre-set age range and player gender of each group, so as to construct the EEG signal feature library;

[0053] It should be noted that the immersion state feature (score 7-10 points): when the player gives a score of 7-10 points, the corresponding EEG signal may show that the alpha wave frequency is relatively stable and at a relatively high level, which usually means that the brain is in a relaxed and focused state;

[0054] Medium immersion state feature (score 4-6 points): the player with a score of 4-6 points is in a medium degree of immersion state. In this state, the alpha wave frequency in the EEG signal may fluctuate to some extent, which is not as stable as in the high immersion state;

[0055] Low immersion state feature (score 1-3): When the player gives a score of 1-3, the alpha wave frequency in the EEG signal may be significantly reduced, and the fluctuation is larger;

[0056] After identifying the immersion state characteristics corresponding to different score intervals, the collected large amount of EEG signal data is sorted and classified. First, according to the high, medium and low three immersion state characteristics determined above, all EEG signal data is classified into the corresponding category;

[0057] Considering that players of different ages and genders may have differences in brain physiological structure and function, which may affect their EEG signal performance and immersion experience in immersive games, after completing the preliminary immersion state classification, the EEG signal data under different immersion state characteristics will be further classified according to the pre-set age range and player gender of each group;

[0058] Players are divided into several groups according to age, such as children group (6-12 years old), adolescents group (13-18 years old), young group (19-30 years old), middle-aged group (31-50 years old) and old group (51 years old and above). There are differences in cognitive ability, attention level and emotional regulation ability among players of different ages, which will be reflected in EEG signals;

[0059] There are also some differences in brain structure and function between men and women, which may affect their experience and EEG signal performance in immersive games. For example, women may be more sensitive in emotional perception and attention allocation, while men may have advantages in spatial cognition and action response. Therefore, further classification of data according to player gender can analyze the EEG signal characteristics of men and women in different immersion states, which can further understand the influence of gender difference on immersion and EEG signal, and provide more accurate basis for subsequent personalized prediction and adjustment.

[0060] After completing all the classification and refinement work above, a comprehensive and detailed EEG signal feature library will be built, which will contain EEG signal feature data of players of different ages and different genders in high, medium and low three immersion states, as well as corresponding subjective immersion score information. The feature library will be stored and managed by a database management system, which will facilitate subsequent data query, analysis and update. At the same time, an indexing and labeling mechanism will be established for the feature library to quickly and accurately retrieve and use the required data. By building such a rich and accurate EEG signal feature library, it provides a solid data foundation and reliable reference standard for subsequent prediction of player immersion trend and corresponding adjustment.

[0061] Interactive player evaluation: when the player starts the virtual racing game, first get the personal information of the current player and input into the EEG signal feature library for matching, so as to determine the reference alpha wave frequency of the current player in different game stages; Personal information includes gender and age;

[0062] Specifically:

[0063] After inputting the personal information of the current player into the EEG signal feature library, locate the EEG signal data that meets the personal information of the player in the EEG signal feature library, extract the alpha wave frequency of each player in different game stages under the high immersion state feature, and calculate the mean value of the alpha wave frequency of each player in the same game stage. Three groups of mean values are calculated as the reference frequency corresponding to the alpha wave frequency of the current player in different game stages;

[0064] For example, after the virtual racing game system obtains the personal information of this 16-year-old male player, it inputs it into the EEG signal feature library. The feature library starts searching and locates all the EEG signal data of male players aged 13-18 (adolescent group) among the numerous data. This step is like quickly finding the relevant book area in a large library according to the "adolescent male" classification label;

[0065] From the located data, filter out the records in the high immersion state, assuming that in the feature library, the subjective immersion score corresponding to the high immersion state is 7-10 points, find the alpha wave frequency data of different game stages (adaptation stage, competition stage and post-race stage) in these high immersion state data, for example, find 100 records that meet the conditions, of which 30 are about the start stage of the competition, 40 are about the corner driving stage, and 30 are about the sprint stage.

[0066] For the 30 alpha wave frequency data of the adaptation stage, add them up and divide by 30 to get the mean value of the alpha wave frequency of the adaptation stage, assuming that the 30 alpha wave frequency data are 9Hz, 10Hz, 9.5Hz, … After calculation, the mean value is 9Hz. In the same way, calculate the mean value of the 40 data in the competition stage, assuming it is 9.8Hz; The mean value of the 30 data in the post-race stage is assumed to be 8.7Hz.

[0067] The three mean values calculated 9Hz (adaptation stage), 9.8Hz (competition stage), and 8.7Hz (post-race stage) are respectively the reference frequencies corresponding to the alpha wave frequency of the 16-year-old male player in different stages of the virtual racing game;

[0068] Different genders and ages of players have differences in cognitive ability, attention level and brain physiological structure, which will cause different EEG signal performances in immersive games. By inputting the gender and age information of the player into the EEG signal feature library, the historical data corresponding to the player's characteristics can be screened out, so as to provide the current player with a reference alpha wave frequency that is more suitable for the individual situation;

[0069] Extracting the alpha wave frequency from the high immersion state characteristic data that meets the personal information of the player means that the determined reference frequency is based on the historical data of players with similar characteristics and in a high immersion state. In this way, when adjusting the game experience for the current player, the brain electrical characteristics of those players who successfully reached a high immersion state can be used as a reference, which helps to guide the current player into a high immersion state and enhances the attractiveness and immersion of the game to the player;

[0070] The mean value of the alpha wave frequency of each player in the same game stage is calculated, which takes advantage of the aggregation of big data. The data of a single player may be affected by accidental factors and have certain deviations. By calculating the mean value of multiple similar players, the interference of individual accidental factors can be effectively reduced, making the reference frequency more representative and stable;

[0071] Predicting the trend: for a virtual racing game, different game stages are divided into different time zones according to the length of different game stages. The first time zone of each game stage is used as the starting data collection window after the player starts the game, and the trend of the player's EEG signal data in the starting data collection window is evaluated to obtain the alpha wave evaluation value of the player in the starting data collection window.

[0072] Specifically:

[0073] S1: With the help of the dry electrode EEG acquisition module in the head-mounted device, the EEG signal data of the player is collected in the starting data collection window at a set sampling frequency. The alpha wave frequency is extracted from the original EEG signal data, and the alpha wave frequency of the player at each time point in the starting data collection window is used as a numerical point. An alpha wave trend graph is constructed, the numerical points corresponding to the alpha wave frequency at each time point in the trend graph are plotted, adjacent numerical points are connected to obtain a frequency line, and the reference frequency matched by the player's personal information is extracted. The corresponding reference frequency is extracted from the game stage where the player's current starting data collection window is located.

[0074] S2: Taking the reference frequency as the reference value and the preset allowed fluctuation value as the calculation value, the reference frequency range corresponding to the player is determined, which is represented as (reference value + allowed fluctuation value, reference value - allowed fluctuation value). The threshold range line corresponding to the reference frequency range in the trend graph is plotted.

[0075] S3: Identify numerical points outside the threshold range as outliers. Extract the alpha wave frequency for each group of outliers and take the average value to obtain the outlier frequency of the player within the initial data collection window. Match the outlier frequency with the reference frequency range. If the match fails, extract the highest and lowest values ​​of the reference frequency range and compare them with the outlier frequency. If the outlier frequency is higher than the highest value of the reference frequency range, calculate the difference and record it as a high wave frequency. If the outlier frequency is lower than the lowest value of the reference frequency range, calculate the difference and take the absolute value to record it as a low wave frequency.

[0076] S4: Compare the alpha wave frequencies of two adjacent sets of numerical points. If the alpha wave frequency of the numerical point on the left is higher than that of the numerical point on the right, obtain the slope of the corresponding frequency line and record it as the falling slope; otherwise, record it as the rising slope. Sum the falling slopes and rising slopes of each set in the initial data collection window to obtain the total falling value and the total rising value. Calculate the ratio of the falling value to the rising value to obtain the player's alpha wave variation in the initial data collection window.

[0077] If the alpha wave ratio is greater than 1, the alpha wave frequency of the player in the initial data collection window is determined to be a downward trend; otherwise, it is determined to be an upward trend. A trend addition coefficient is set for the overall downward trend and the overall upward trend.

[0078] It should be noted that the trend coefficient for the overall downward trend is set in the range of 0.834-0.972, and can be set to 0.948 here. The trend coefficient for the overall upward trend is set in the range of 1.093-1.152, and can be set to 1.098. These values ​​can be dynamically adjusted according to the actual situation.

[0079] S5: For the player's alpha wave frequency at each time point within the initial data collection window, first take the average value and record it as the alpha wave average rate. Then extract the highest and lowest values ​​of the alpha wave frequency at each time point and record them as the alpha wave peak rate and alpha wave trough rate. Mark the alpha wave average rate, alpha wave peak rate, and alpha wave trough rate as u1, u2, and u3, respectively. Mark the reference frequency obtained by matching the player's personal information as ua.

[0080] Through formula After weighted calculation, the result is divided by an integer three to obtain the player's alpha wave performance value within the initial data collection window; where... The preset influence weighting factor;

[0081] S6: Preset the frequency ranges corresponding to each group of high and low frequencies, and each frequency range corresponds to a correction coefficient; after determining the high or low frequency based on step S3, match the corresponding frequency range to obtain the correction coefficient of the player in the initial data collection window.

[0082] It should be noted that the correction coefficient range of each group of frequencies corresponding to the high wave frequency is set to 1.037-1.128, and the higher the high wave frequency, the higher the possibility of matching the correction coefficient of 1.128;

[0083] The correction coefficient range of each group of frequencies corresponding to the low wave frequency is set to 0.893-0.972, and the higher the low wave frequency, the higher the possibility of matching the correction coefficient of 0.893;

[0084] If the abnormal frequency matches the reference frequency range successfully, the correction coefficient is directly set to an integer 1.

[0085] S7: Extract the alpha wave performance value of the player in the starting data collection window, and then multiply it by the trend additional coefficient and the correction coefficient to obtain the alpha wave evaluation value of the player in the starting data collection window;

[0086] It should be noted that through detailed analysis of the alpha wave frequency, including trend judgment, abnormal point processing and comprehensive calculation of the alpha wave evaluation value, the brain state of the player in the starting stage of the game can be accurately evaluated; This helps the game system to more accurately understand the player's immersion level, attention concentration, etc., and provides a reliable basis for subsequent personalized adjustment of the game experience, for example, if the alpha wave performance value is low, the haptic feedback needs to be enhanced to attract the player's attention;

[0087] Adjust the intensity of the haptic feedback: based on the current player's alpha wave evaluation value in the starting data collection window, determine the percentage of adjustment of the haptic feedback intensity in the next sub-time zone for each game stage, and push the adjustment window to the current player. The current player adjusts the haptic feedback intensity by a corresponding percentage after determination;

[0088] Specifically:

[0089] Match the alpha wave evaluation value of the current player in the starting data collection window with the preset theoretical normal range; the theoretical normal range fluctuates up and down from the benchmark value 1, which is specifically set by technical personnel; if the match is successful, the current trigger feedback intensity remains unchanged and enters the next sub-time zone;

[0090] If the match fails, further identify the matching result between the alpha wave evaluation value and the theoretical normal range, if the alpha wave evaluation value is higher than the theoretical normal range, it is determined as a high-intensity state; if the alpha wave evaluation value is lower than the theoretical normal range, it is determined as a low-intensity state;

[0091] If it is determined that the intensity is high, a corresponding upper limit range value is determined by presetting different gender and age ranges, the upper limit range value is determined after extracting the personal information of the current player, the upper limit range value is added to the highest value of the theoretical normal range, and the alpha wave evaluation value is compared, if the alpha wave evaluation value is low, the current trigger feedback intensity is kept unchanged and the next sub-time zone is entered, otherwise the difference between the alpha wave evaluation value and the highest value of the theoretical normal range is calculated, and is recorded as a percentage drop matching value;

[0092] If it is determined that the intensity is low, the difference between the alpha wave evaluation value and the lowest value of the theoretical normal range is calculated, and the absolute value is recorded as a percentage rise matching value;

[0093] The preset percentage drop matching value and the percentage rise matching value respectively correspond to a group of matching values in the interval, and each group of matching values corresponds to an adjustment percentage;

[0094] The higher the percentage drop matching value and the percentage rise matching value, the higher the adjustment percentage corresponding to the matching;

[0095] After determining the adjustment percentage, if it is determined that the intensity is high, the trigger feedback intensity of the next sub-time zone is determined by decreasing the adjustment percentage based on the current trigger feedback intensity;

[0096] If it is determined that the intensity is low, the trigger feedback intensity of the next sub-time zone is determined by increasing the adjustment percentage based on the current trigger feedback intensity;

[0097] For example, a 25-year-old male has an alpha wave evaluation value of 1.3;

[0098] The preset theoretical normal range is based on the reference value 1 fluctuating up and down, and the fluctuation range is assumed to be ±0.2, i.e. the theoretical normal range is (0.8, 1.2), the alpha wave evaluation value of the player is 1.3, which is not within this range, and the matching fails.

[0099] Since 1.3 is higher than the theoretical normal range (0.8, 1.2), it is determined that the player is in a high intensity state;

[0100] According to the preset, the upper limit range value corresponding to the player is 0.3, and the highest value of the theoretical normal range is 1.2, and the sum of the two is 1.5;

[0101] The alpha wave evaluation value 1.3 is lower than 3.5, so the current trigger feedback intensity is kept unchanged and the next sub-time zone is entered.

[0102] Suppose another 18-year-old female has an alpha wave evaluation value of 0.5;

[0103] Similarly, the theoretical normal range is (0.8, 1.2), and the alpha wave evaluation value 0.5 is not within this range, resulting in a failed match;

[0104] 0.5 is lower than the theoretical normal range (0.8, 1.2), indicating that the player is in a lower intensity state;

[0105] Calculate the difference between the alpha wave evaluation value 0.5 and the lowest value 0.8 of the theoretical normal range, and take the absolute value to get the percentage increase matching value as |0.5-0.8|=0.3.

[0106] Assuming the preset percentage increase matching value interval and the corresponding adjustment percentage are: (0-0.2, 10%), (0.2-0.4, 20%), (0.4-0.6, 30%);

[0107] 0.3 is in the (0.2-0.4) interval, so the corresponding adjustment percentage is 20%;

[0108] In the next sub-time zone, trigger the feedback intensity to be adjusted upwards by (1+20%);

[0109] By combining the player's personal information (gender and age) to determine the upper limit range value, the differences in brain response and game experience of different individuals can be considered, and personalized tactile feedback intensity adjustment can be achieved. Different age groups and genders of players have different feelings and needs for games. This approach can better meet the unique experience of each player;

[0110] According to the matching results of the alpha wave evaluation value and the theoretical normal range, the player's state is accurately determined to be high or low intensity, and the tactile feedback intensity is adjusted accordingly. When the player is in a high intensity state, avoid overstimulation; when the player is in a low intensity state, enhance the tactile feedback to attract attention and improve game engagement, making the game experience more in line with the player's current psychological and physiological state;

[0111] During the game, as the player's state changes in real time, the tactile feedback intensity is constantly adjusted to provide dynamic game experience optimization for the player. This real-time adjustment can better maintain the player's immersion and avoid the decline in experience due to the inability of fixed tactile feedback intensity to adapt to changes in the player's state;

[0112] Scientific adjustment based on data: The entire adjustment process is based on clear numerical calculations and preset ranges and intervals, which is scientific and logical. Through rigorous data processing and rule setting, the adjustment of tactile feedback intensity is ensured to be reasonable and effective, improving the accuracy and reliability of game interaction;

[0113] After entering the next sub-time zone, continue to collect the player's EEG signal data and continuously determine the tactile feedback intensity adjustment percentage for subsequent sub-time zones.

[0114] Feedback collection optimization: after the current player finishes the game, the current player's subjective immersion score is collected again and input into the EEG signal feature library for updating;

[0115] It should be noted that according to user feedback, the EEG signal feature library is updated to continuously improve the accuracy and effectiveness of the advance adjustment of the intensity of the tactile feedback;

[0116] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and variations can be made according to the content of the specification. The specification selects and describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited by the claims and their full scope and equivalents.

Claims

1. An immersive space virtual-real interaction method, characterized in that, The application relates to a method for adjusting the intensity of haptic feedback in a virtual racing game. The method comprises the following steps: Building an EEG signal feature library: collecting EEG signal data of different game stages during the process that a player plays a virtual racing game; The game stage can be divided into an adaptation stage, a competition stage and a post-race stage; Correlating the EEG signal data collected in different game stages with the subjective immersion scores of the corresponding players, and building an EEG signal feature library according to the correlation results; Assessing the interactive player: when the player starts to play the virtual racing game, the personal information of the current player is first acquired and input into the EEG signal feature library for matching, so as to determine the reference alpha wave frequency of the current player in different game stages; the personal information includes gender and age; Predicting the trend: for a virtual racing game, different game stages are divided into respective sub-time zones according to the time length of different game stages; the first sub-time zone of each game stage is taken as a starting data collection window after the player starts to play the game, and the trend of the EEG signal data of the player in the starting data collection window is evaluated to obtain the alpha wave evaluation value of the player in the starting data collection window; 2. The immersive space virtual-real interaction method of claim 1, wherein, Adjusting the intensity of haptic feedback: based on the alpha wave evaluation value of the current player in the starting data collection window, the adjustment percentage of the intensity of haptic feedback in the next sub-time zone of each game stage is determined. According to the correlation results, the EEG signal feature library is built: The subjective immersion scores are divided into three score intervals in advance, and each score interval corresponds to a state of immersion feature; The state of immersion features include a high state of immersion feature, a medium state of immersion feature and a low state of immersion feature; the EEG signal data under different states of immersion features are classified into corresponding categories; 3. The immersive space virtual-real interaction method of claim 2, wherein, After the classification is completed, the EEG signal data under different states of immersion features are further classified according to the preset age range and the gender of the player, so as to build the EEG signal feature library. The reference alpha wave frequency of the current player in different game stages is determined as follows:

4. The immersive space virtual-real interaction method of claim 3, wherein, After the personal information of the current player is input into the EEG signal feature library, the EEG signal data corresponding to the personal information of the player is located in the EEG signal feature library, the alpha wave frequency of each player under the high state of immersion feature in different game stages is extracted, the mean value of the alpha wave frequency of each player in the same game stage is calculated, and the three groups of mean values are taken as the reference frequencies corresponding to the alpha wave frequency of the current player in different game stages. The trend of the EEG signal data of the player in the starting data collection window is evaluated as follows: S1: in the starting data collection window, the EEG signal data of the player is collected at a set sampling frequency, the alpha wave frequency is extracted from the original EEG signal data, the alpha wave frequency of the player at each time point in the starting data collection window is taken as a numerical point, an alpha wave trend graph is built, the numerical points corresponding to the alpha wave frequency at each time point in the trend graph are plotted, adjacent numerical points are connected to obtain a frequency line, and the reference frequency matched by the personal information of the player is extracted. S2: Taking the reference frequency as a reference value and a preset allowed fluctuation value as a calculation value, a reference frequency range corresponding to the player is determined, which is represented as (reference value + allowed fluctuation value, reference value - allowed fluctuation value), and a threshold range line corresponding to the reference frequency range in the trend graph is drawn; S3: Identify the value points outside the threshold range line as abnormal points, extract the alpha wave frequency of each group of abnormal points and take the average value to obtain the abnormal frequency of the player in the initial data collection window, match the abnormal frequency with the reference frequency range, if the matching fails, compare the highest value and the lowest value of the reference frequency range with the abnormal frequency, if the abnormal frequency is higher than the highest value of the reference frequency range, calculate the difference and record it as a high wave frequency, if the abnormal frequency is lower than the lowest value of the reference frequency range, calculate the difference and take the absolute value as a low wave frequency; S4: Compare the alpha wave frequencies of adjacent two groups of value points, if the alpha wave frequency of the left value point is higher than that of the right value point, obtain the slope of the corresponding frequency line and record it as a falling slope, otherwise, record it as a rising slope; sum up each group of falling slopes and rising slopes in the initial data collection window to obtain a falling total value and a rising total value, and then calculate the ratio of the falling total value to the rising total value to obtain the alpha wave ratio of the player in the initial data collection window; If the alpha wave ratio is greater than 1, it is determined that the alpha wave frequency of the player in the initial data collection window is in a overall downward trend, otherwise, it is determined to be in an overall upward trend, and the overall downward trend and the overall upward trend correspond to a trend additional coefficient respectively.

5. The immersive space virtual-real interaction method of claim 4, wherein, The alpha wave evaluation value of the player in the initial data collection window is obtained, which is specifically: S5: For the alpha wave frequency of the player at each time point in the initial data collection window, first take the average value as the alpha wave average rate, then extract the highest value and the lowest value of the alpha wave frequency at each time point as the alpha wave peak rate and the alpha wave valley rate, and mark the alpha wave average rate, the alpha wave peak rate and the alpha wave valley rate as u1, u2 and u3 respectively; Mark the reference frequency matched by the player's personal information as ua; The weighted calculation is performed by the formula After the weighted calculation, the calculation result is divided by the integer three to obtain an alpha wave performance value of the player in the starting data collection window; wherein is a preset influence weight factor; S6: Preset the interval of each group of frequencies corresponding to the high wave frequency and the low wave frequency respectively, each group of frequencies corresponding to an interval corresponds to a correction coefficient; after determining the high wave frequency or the low wave frequency based on step S3, match the interval of the corresponding frequency to obtain the correction coefficient of the player in the initial data collection window; S7: Extract the alpha wave performance value of the player in the initial data collection window, and then multiply it by the trend additional coefficient and the correction coefficient to obtain the alpha wave evaluation value of the player in the initial data collection window.

6. The immersive space virtual-real interaction method of claim 5, wherein, The adjustment percentage of the tactile feedback intensity of each game stage in the next sub-time zone is determined, which is specifically: Match the alpha wave evaluation value of the current player in the initial data collection window with the preset theoretical normal range, if the matching is successful, keep the current tactile feedback intensity unchanged and enter the next sub-time zone.

7. The immersive space virtual-real interaction method of claim 6, wherein, The adjustment percentage of the tactile feedback intensity of each game stage in the next sub-time zone also includes: If the matching fails, further identify the matching result between the alpha wave evaluation value and the theoretical normal range. If the alpha wave evaluation value is higher than the theoretical normal range, it is determined that the intensity is higher; if the alpha wave evaluation value is lower than the theoretical normal range, it is determined that the intensity is lower. If it is determined that the intensity is higher, by presetting a different upper limit range value corresponding to each age range of different genders, the personal information of the current player is extracted to determine the corresponding upper limit range value. After adding the determined upper limit range value to the highest value of the theoretical normal range, the alpha wave evaluation value is compared. If the alpha wave evaluation value is lower, the current haptic feedback intensity is kept unchanged to enter the next sub-time zone. Otherwise, the difference between the alpha wave evaluation value and the highest value of the theoretical normal range is calculated, which is recorded as a percentage drop matching value. If it is determined that the intensity is lower, the difference between the alpha wave evaluation value and the lowest value of the theoretical normal range is calculated, and the absolute value is taken to be recorded as a percentage rise matching value. The preset percentage drop matching value and the percentage rise matching value respectively correspond to each group of matching values in the interval. Each group of matching values in the interval corresponds to an adjustment percentage. After determining the adjustment percentage, if it is determined that the intensity is higher, the current haptic feedback intensity is adjusted by the adjustment percentage to be the haptic feedback intensity of the next sub-time zone. If it is determined that the intensity is lower, the current haptic feedback intensity is adjusted by the adjustment percentage to be the haptic feedback intensity of the next sub-time zone.

8. The immersive space virtual-real interaction method of claim 7, wherein, It also includes: Feedback collection optimization: After the current player finishes the game, the subjective immersion score of the current player is collected again and input into the EEG signal feature library for updating.

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