Immersive space virtual-real interaction method

Abstract

The invention discloses an immersive space virtual-real interaction method, and relates to the technical field of virtual reality. According to the method, after a game starts, a first sub-time zone of each game stage serves as an initial data collection window, EEG signal data of a player is collected, an alpha wave trend chart is constructed, and an alpha wave evaluation value of the player in the initial data collection window is obtained through a series of calculation and analysis including determination of a reference frequency range, identification of abnormal points and calculation of an alpha wave transformation ratio. The process can accurately evaluate the brain state of the player in the initial stage of the game, pre-judge the change trend of the immersion of the player in advance and adjust the tactile feedback intensity in time, so that the player is prevented from frequently playing in the game process, the continuity and immersion degree of game experience are maintained, and when the alpha wave evaluation value of the player shows that the immersion is possibly reduced, the game experience is improved. Tactile feedback is enhanced to attract attention, the game participation degree is improved, and players are continuously immersed in the game.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual reality, and specifically to an immersive spatial virtual-real interaction method. Background Art

[0002] With the continuous development of technology, technologies such as virtual reality (VR) and augmented reality (AR) are becoming increasingly mature, and people's demand for immersive experiences is also getting higher and higher.

[0003] In the representative application of immersive spatial virtual-real interaction technology - virtual racing games, players expect to obtain a more realistic and immersive gaming experience. However, the existing immersive spatial virtual-real interaction technology still has the following deficiencies: Most can only perform feedback adjustments after the player's immersion has significantly decreased, unable to anticipate and take measures in advance. This lagging adjustment method cannot predict the changing trend of the player's immersion in advance, making it difficult to maintain the coherence and immersion of the gaming experience, causing players to frequently "break out of the game" during the game process, greatly affecting the overall gaming experience; In addition, the existing feedback adjustment methods have low accuracy and intelligence, lacking the mining and utilization of a large amount of player experience data.

[0004] Therefore, an immersive spatial virtual-real interaction method is introduced. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems pointed out in the background art, and to propose an immersive spatial virtual-real interaction method.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An immersive spatial virtual-real interaction method, including: Construct an EEG signal feature library: During the process of a player playing a virtual racing game, collect EEG signal data at different game stages; the game stages can be divided into an adaptation stage, a race stage, and a post-race stage; associate the EEG signal data collected at different game stages with the subjective immersion score of the corresponding player, and construct an EEG signal feature library according to the associated results; Interactive player evaluation: When a player starts playing a 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; Predicting change trends: For a virtual racing game, different game stages are respectively divided into individual sub-time zones according to the duration of different game stages in advance. After the player starts the game, the first sub-time zone of each game stage is used as the starting data collection window, and the EEG signal data of the player within the starting data collection window is evaluated for trends to obtain the alpha wave evaluation value of the player within the starting data collection window. Adjusting the haptic feedback intensity: Based on the alpha wave evaluation value of the current player within the starting data collection window, determine the percentage of haptic feedback intensity adjustment for the next sub-time zone of each game stage.

[0007] As a preferred embodiment of the present invention, construct an EEG signal feature library according to the associated results: Pre-divide the subjective immersion score into three groups of score intervals, and each group of score intervals corresponds to an immersion state feature respectively; including high immersion state feature, medium immersion state feature, and low immersion state feature; classify the EEG signal data under different immersion state features into corresponding categories respectively; After the classification is completed, further classify the EEG signal data under different immersion state features according to the preset age range of each group and the player's gender, so as to construct an EEG signal feature library.

[0008] As a preferred embodiment of the present invention, determine the reference alpha wave frequency of the current player in different game stages, specifically: After inputting the personal information of the current player into the EEG signal feature library, locate the EEG signal data that matches the player's personal information in the EEG signal feature library, extract the alpha wave frequencies of each player under the high immersion state feature in different game stages, calculate the mean value of the alpha wave frequencies of each player in the same game stage, and use the three calculated mean values as the reference frequencies corresponding to the alpha wave frequencies of the current player in different game stages.

[0009] As a preferred embodiment of the present invention, evaluate the trend of the EEG signal data of the player within the starting data collection window, specifically: S1: Within the starting data collection window, collect the EEG signal data of the player at a set sampling frequency, extract the alpha wave frequency from the original EEG signal data, use the alpha wave frequencies of the player at each time point within the starting data collection window as numerical points to construct an alpha wave trend graph, plot the numerical points corresponding to the alpha wave frequencies at each time point in the trend graph, connect adjacent numerical points to obtain a frequency line, and at the same time extract the reference frequency matched by the player's personal information; S2: Taking the reference frequency as the reference value and the preset allowable fluctuation value as the calculated value, determine the reference frequency range corresponding to the player, expressed as (reference value + allowable fluctuation value, reference value - allowable fluctuation value), and draw the threshold range line corresponding to the reference frequency range in the trend graph; S3: Identify the numerical points outside the threshold range line as abnormal points. For each group of abnormal points, extract the α-wave frequency and then take the average to obtain the abnormal frequency of the player within the starting data collection window. Match the abnormal 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 abnormal frequency. If the abnormal frequency is higher than the highest value of the reference frequency range, perform a difference calculation and record it as the high-wave frequency. If the abnormal frequency is lower than the lowest value of the reference frequency range, perform a difference calculation and take the absolute value as the low-wave frequency; S4: Compare the α-wave frequencies of adjacent groups of numerical points. If the α-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 descending slope. Otherwise, record it as the ascending slope. Sum the descending slopes and ascending slopes of each group within the starting data collection window respectively to obtain the total descending value and the total ascending value. Calculate the ratio with the total descending value as the numerator and the total ascending value as the denominator to obtain the α-wave variation ratio of the player within the starting data collection window; If the α-wave variation ratio > 1, it is determined that the α-wave frequency of the player within the starting data collection window shows an overall downward trend. Otherwise, it is determined to show an overall upward trend. Set an additional trend coefficient corresponding to the overall downward trend and the overall upward trend respectively.

[0010] As a preferred embodiment of the present invention, obtain the α-wave evaluation value of the player within the starting data collection window, specifically: S5: For the α-wave frequency of the player at each time point within the starting data collection window, first take the average and record it as the α-wave average frequency. Then extract the highest and lowest values of the α-wave frequency at each time point and record them as the α-wave peak frequency and the α-wave valley frequency. Mark the α-wave average frequency, the α-wave peak frequency, and the α-wave valley frequency as u1, u2, and u3 respectively; Mark the reference frequency matched with the player's personal information as ua; Through the formula After performing weighted calculation, divide the calculation result by the integer three as the α-wave performance value of the player within the starting data collection window; where is a preset influence weight factor; S6: Preset the intervals where each group of frequencies corresponding to the high-wave frequency and the low-wave frequency are located. Each interval where a group of frequencies is located corresponds to a correction coefficient; After determining the high-wave frequency or the low-wave frequency based on step S3, perform the matching of the corresponding frequency interval to obtain the correction coefficient of the player within the starting data collection window; S7: Extract the α-wave performance value of the player within the starting data collection window, and then multiply it by the additional trend coefficient and the correction coefficient to obtain the α-wave evaluation value of the player within the starting data collection window.

[0011] As a preferred embodiment of the present invention, determining the percentage adjustment of the haptic feedback intensity in the next sub-time zone is specifically as follows: Match the alpha wave evaluation value of the current player within the starting data collection window with the preset theoretical normal range. If the match is successful, keep the current trigger feedback intensity unchanged and enter the next sub-time zone.

[0012] As a preferred embodiment of the present invention, determining the percentage adjustment of the haptic feedback intensity in the next sub-time zone further includes: 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 to be in a high-intensity state; if the alpha wave evaluation value is lower than the theoretical normal range, it is determined to be in a low-intensity state. If it is determined to be in a high-intensity state, each age range for different genders is preset to correspond to an upper limit range value. After extracting the personal information of the current player, determine the corresponding upper limit range value. Add the determined upper limit range value to the highest value of the theoretical normal range, and then compare it with the alpha wave evaluation value. If the alpha wave evaluation value is lower, keep the current trigger feedback intensity unchanged and enter the next sub-time zone. Otherwise, calculate the difference between the alpha wave evaluation value and the highest value of the theoretical normal range, and record it as the percentage decrease matching value. If it is determined to be in a low-intensity state, calculate the difference between the alpha wave evaluation value and the lowest value of the theoretical normal range, and take the absolute value and record it as the percentage increase matching value. Within the intervals corresponding to each group of matching values of the preset percentage decrease matching value and the percentage increase matching value respectively, each interval of the group of matching values corresponds to an adjustment percentage. After determining the adjustment percentage, if it is determined to be in a high-intensity state, on the basis of the current trigger feedback intensity, after a decrease in the adjustment percentage, it is used as the trigger feedback intensity for the next sub-time zone. If it is determined to be in a low-intensity state, on the basis of the current trigger feedback intensity, after an increase in the adjustment percentage, it is used as the trigger feedback intensity for the next sub-time zone.

[0013] As a preferred embodiment of the present invention, it further includes: Feedback collection optimization: After the current player completes the game, collect the subjective immersion score of the current player again and input it into the EEG signal feature library for update.

[0014] Compared with the prior art, the beneficial effects of the present invention are: After the game starts, the present invention uses the first sub-time zone of each game stage as the starting data collection window to collect the EEG signal data of the player, constructs an alpha wave trend graph, and through a series of calculations and analyses, including determining the reference frequency range, identifying abnormal points, and calculating the alpha wave variation ratio, obtains the alpha wave evaluation value of the player within the starting data collection window. This process can accurately evaluate the brain state of the player at the starting stage of the game, predict in advance the changing trend of the player's immersion, and timely adjust the haptic feedback intensity, avoiding the player from frequently "breaking out of the game" during the game process, maintaining the coherence and immersion of the game experience. When the alpha wave evaluation value of the player indicates that the immersion may decline, the haptic feedback is enhanced to attract attention, improve the game participation, and enable the player to continuously immerse in the game; When the player is playing a virtual racing game, the present invention collects the EEG signal data of different game stages, correlates it with the subjective immersion score of the player, divides the subjective immersion score into three intervals: high, medium, and low, corresponding to different immersion state characteristics, and further classifies it in combination with the age and gender of the player to construct an EEG signal feature library. In this way, a large amount of player experience data can be mined, providing a basis for subsequent prediction, and solving the problem that the prior art lacks the mining and utilization of player experience data; The present invention determines the reference alpha wave frequency of different game stages by obtaining the player's personal information and inputting it into the EEG signal feature library for matching. Utilizing the advantage of big data aggregation to reduce the interference of individual accidental factors, providing a reference frequency that suits the individual situation of the current player, and the adjustment based on this is more targeted, solving the problem of low accuracy and intelligence level of the existing feedback adjustment method. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the drawings.

[0016] Figure 1 is a flowchart of the present invention; Figure 2 is a schematic diagram of the alpha wave trend graph in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0018] Please refer to Figure 1-2 as shown, an immersive spatial virtual-real interaction method includes: Constructing an EEG signal feature library: During the process of a player playing a virtual racing game, collect EEG signal data in different game stages. The game stages can be divided into an adaptation stage, a race stage, and a post-race stage. Associate the EEG signal data collected in different game stages with the subjective immersion score of the corresponding player. The subjective immersion score is set from 1 to 10. After the game ends, let the player rate their immersion at different times through a questionnaire survey. For example, when the player rates the immersion as above 8 points, define the corresponding EEG signal features as high-immersion state features; when the rating is below 3 points, it is regarded as low-immersion state features. According to the associated results, construct an EEG signal feature library; Specifically: Pre-divide the subjective immersion score into three groups of score intervals, and each group of score intervals corresponds to an immersion state feature respectively, including high-immersion state features, medium-immersion state features, and low-immersion state features. Classify the EEG signal data under different immersion state features into the corresponding categories respectively; After the classification is completed, further classify the EEG signal data under different immersion state features according to the preset age ranges of each group and the player's gender, so as to construct an EEG signal feature library; It should be noted that for 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 α-wave frequency is relatively stable and at a relatively high level, which usually means that the brain is in a relaxed and focused state; For the medium-immersion state feature (score 4 - 6 points): Players with a score of 4 - 6 points are in a medium level of immersion. In this state, the α-wave frequency in the EEG signal may show certain fluctuations and is not as stable as in the high-immersion state; For the low-immersion state feature (score 1 - 3 points): When the player gives a score of 1 - 3 points, at this time, the α-wave frequency in the EEG signal may decrease significantly and fluctuate greatly; After clarifying the immersion state features corresponding to different score intervals, start to organize and classify the large amount of collected EEG signal data. First, according to the above-determined high, medium, and low three immersion state features, classify all the EEG signal data into the corresponding categories respectively; Considering that players of different ages and genders may have differences in brain physiological structures and functions, and these differences may affect their EEG signal performance and immersion experience in immersive games. Therefore, after completing the preliminary immersion state classification, the EEG signal data under different immersion state features will be further refined and classified according to the preset age ranges of each group and the player's gender; Players are divided into several groups according to age, such as children's group (6 - 12 years old), teenagers' group (13 - 18 years old), young people's group (19 - 30 years old), middle-aged group (31 - 50 years old) and elderly group (51 years old and above). Players of different ages have differences in cognitive ability, attention level and emotion regulation ability, etc. These differences will be reflected in EEG signals; There are also certain differences in brain structure and function between men and women, and these differences 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 motor response. Therefore, further classify the data according to the gender of players, and analyze the EEG signal characteristics of men and women in different immersive states respectively. This can more deeply understand the impact of gender differences on immersion and EEG signals, and provide a more accurate basis for subsequent personalized prediction and adjustment.

[0019] After completing all the above classification and refinement work, a comprehensive and detailed EEG signal feature library will be constructed. This feature library will contain EEG signal feature data of players of different ages and genders in high, medium and low immersion states, as well as corresponding subjective immersion score information. The feature library will be stored and managed using a database management system for convenient subsequent data query, analysis and update. At the same time, an indexing and annotation mechanism will also be established for the feature library to quickly and accurately retrieve and use the required data. By constructing such a rich and accurate EEG signal feature library, it provides a solid data foundation and reliable reference standard for subsequent prediction of the change trend of players' immersion and corresponding adjustment.

[0020] Interactive player evaluation: When the player starts the virtual racing game, first obtain the current player's personal information 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; Specifically: After inputting the current player's personal information into the EEG signal feature library, locate the EEG signal data that matches the player's personal information in the EEG signal feature library, extract the alpha wave frequency of each player at different game stages under the high immersion state characteristics, calculate the mean value of the alpha wave frequency of each player at the same game stage, and use the three calculated mean values as the reference frequencies corresponding to the alpha wave frequency of the current player at different game stages; 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 to retrieve and locates the EEG signal data of all male players aged 13 - 18 (teenager group) among numerous data. This step is like quickly finding the relevant book area in a large library according to the classification label of "teenage male"; From the located data, filter out the records under the characteristics of high immersion state. Assume that in the feature library, the subjective immersion score corresponding to the high immersion state is 7 - 10 points. Among these high immersion state data, find the alpha wave frequency data in different game stages (adaptation stage, race stage, and post-race stage). For example, 100 qualified records are found, among which 30 are about the start stage of the race, 40 are about the cornering stage, and 30 are about the sprint stage.

[0021] For these 30 alpha wave frequency data in the adaptation stage, add them up and divide by 30 to get the average alpha wave frequency in the adaptation stage. Assume these 30 alpha wave frequency data are 9Hz, 10Hz, 9.5Hz... After calculation, the average value is 9Hz. Using the same method, calculate the average value of the 40 data in the race stage, assume it is 9.8Hz; the average value of the 30 data in the post-race stage, assume it is 8.7Hz.

[0022] The three calculated average values, 9Hz (adaptation stage), 9.8Hz (race stage), and 8.7Hz (post-race stage), are respectively used as the reference frequencies corresponding to the alpha wave frequencies of this 16-year-old male player in different stages of the virtual racing game; Players of different genders and ages have differences in cognitive ability, attention level, and brain physiological structure. These differences will lead to different EEG signal performances in immersive games. By inputting the gender and age information of the player into the EEG signal feature library, it is possible to specifically screen out the historical data that matches the characteristics of the player, so as to provide a reference alpha wave frequency that is more suitable for the individual situation of the current player; Extracting the alpha wave frequency from the high immersion state characteristic data that conforms to the personal information of the player means that the determined reference frequency is based on the historical data of players with similar characteristics to the current player and in a high immersion state; in this way, when adjusting the game experience for the current player, it is possible to refer to the EEG characteristics of those similar players who have successfully reached a high immersion state, which helps to guide the current player into a high immersion state and enhance the attractiveness and immersion of the game for this player; Calculate the average value of the alpha wave frequencies of eligible players in the same game stage, leveraging the aggregation advantage of big data. The data of a single player may be affected by accidental factors and there may be certain deviations. By calculating the average value of multiple similar players, the interference of such individual accidental factors can be effectively reduced, making the obtained reference frequency more representative and stable; Predict the change trend: For a virtual racing game, divide different game stages into respective sub-time zones in advance according to the duration of different game stages. After the player starts the game, use the first sub-time zone of each game stage as the starting data collection window, and conduct a trend evaluation on the EEG signal data of the player within the starting data collection window to obtain the alpha wave evaluation value of the player within the starting data collection window; Specifically: S1: With the help of the dry electrode EEG acquisition module in the head-mounted device, within the starting data collection window, collect the EEG signal data of the player at the set sampling frequency, extract the alpha wave frequency from the original EEG signal data, use the alpha wave frequencies of the player at each time point within the starting data collection window as numerical points to construct an alpha wave trend graph, plot the numerical points corresponding to the alpha wave frequencies at each time point within the trend graph, connect adjacent numerical points to obtain a frequency line, and at the same time extract the reference frequency matched with the player's personal information; extract the corresponding reference frequency according to the game stage where the player's current starting data collection window is located; S2: Taking the reference frequency as the reference value and presetting the allowable fluctuation value as the calculated value, determine the reference frequency range corresponding to the player, expressed as (reference value + allowable fluctuation value, reference value - allowable fluctuation value), and draw the threshold range line corresponding to the reference frequency range within the trend graph; S3: Identify the numerical points outside the threshold range line as abnormal points. After extracting the alpha wave frequencies for each group of abnormal points and taking the average value, obtain the abnormal frequency of the player within the starting data collection window. Match the abnormal frequency with the reference frequency range. If the match fails, extract the highest value and the lowest value of the reference frequency range and compare them with the abnormal frequency. If the abnormal frequency is higher than the highest value of the reference frequency range, perform a difference calculation and record it as the high wave frequency. If the abnormal frequency is lower than the lowest value of the reference frequency range, perform a difference calculation and take the absolute value and record it as the low wave frequency; S4: Compare the alpha wave frequencies of adjacent pairs of numerical points. If the alpha wave frequency of the numerical point on the left is higher than the alpha wave frequency of the numerical point on the right, obtain the slope of the corresponding frequency line and record it as the descending slope. Otherwise, record it as the ascending slope; sum the descending slopes and ascending slopes respectively within the starting data collection window to obtain the total descending value and the total ascending value. Calculate the ratio with the total descending value as the numerator and the total ascending value as the denominator to obtain the alpha wave change ratio of the player within the starting data collection window; If the alpha wave ratio > 1, it is determined that the alpha wave frequency of the player in the starting data collection window shows an overall downward trend; otherwise, it is determined to be an overall upward trend. An additional trend coefficient is set for each of the overall downward trend and the overall upward trend. It should be noted that the range of the additional trend coefficient for the overall downward trend is set between 0.834 and 0.972, and it can be set to 0.948 here. The range of the additional trend coefficient for the overall upward trend is set between 1.093 and 1.152, and it is set to 1.098. Subsequently, it can be dynamically adjusted according to the actual situation.

[0023] S5: For the alpha wave frequencies of the player at each time point within the starting data collection window, first take the average value and denote it as the average alpha wave rate. Then extract the highest value and the lowest value of the alpha wave frequencies at each time point, and denote them as the peak alpha wave rate and the trough alpha wave rate, respectively. Denote the average alpha wave rate, the peak alpha wave rate, and the trough alpha wave rate as u1, u2, and u3, respectively; denote the reference frequency matched by the player's personal information as ua. Through the formula After weighted calculation, divide the calculation result by the integer three to obtain the alpha wave performance value of the player within the starting data collection window; where is a preset influence weight factor. S6: Preset the frequency ranges corresponding to the high wave frequency and the low wave frequency respectively, and each frequency range corresponds to a correction coefficient; after determining the high wave frequency or the low wave frequency based on step S3, perform the matching of the corresponding frequency range to obtain the correction coefficient of the player within the starting data collection window. It should be noted that the range of the correction coefficient for the frequency ranges corresponding to the high wave frequency is set between 1.037 and 1.128, and the higher the high wave frequency, the higher the possibility of matching the correction coefficient of 1.128. The range of the correction coefficient for the frequency ranges corresponding to the low wave frequency is set between 0.893 and 0.972, and the higher the low wave frequency, the higher the possibility of matching the correction coefficient of 0.893. If the abnormal frequency matches the reference frequency range successfully, the correction coefficient is directly taken as the integer 1.

[0024] S7: Extract the alpha wave performance value of the player within the starting data collection window, and then multiply it by the additional trend coefficient and the correction coefficient to obtain the alpha wave evaluation value of the player within the starting data collection window. It should be noted that by analyzing the α-wave frequency in detail, including trend judgment, handling of abnormal points, and comprehensively calculating the α-wave evaluation value, the brain state of the player at the initial 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 α-wave performance value is low, it is necessary to enhance the tactile feedback to attract the player's attention; Adjust the tactile feedback intensity: Based on the α-wave evaluation value of the current player within the initial data collection window, determine the percentage adjustment of the tactile feedback intensity in the next sub-time zone for each game stage, and push an adjustment window to the current player. After the current player determines, adjust the tactile feedback intensity by the corresponding percentage; Specifically: Match the α-wave evaluation value of the current player within the initial data collection window with the preset theoretical normal range; the theoretical normal range fluctuates within an upper and lower range with a reference value of 1, which is specifically set by technical personnel; if the match is successful, keep the current trigger feedback intensity unchanged and enter the next sub-time zone; If the match fails, further identify the matching result between the α-wave evaluation value and the theoretical normal range. If the α-wave evaluation value is higher than the theoretical normal range, it is determined to be in a high-intensity state; if the α-wave evaluation value is lower than the theoretical normal range, it is determined to be in a low-intensity state; If it is determined to be in a high-intensity state, by presetting that each age range of different genders corresponds to an upper limit range value, after extracting the personal information of the current player, determine the corresponding upper limit range value. Add the determined upper limit range value to the highest value of the theoretical normal range, and then compare it with the α-wave evaluation value. If the α-wave evaluation value is low, keep the current trigger feedback intensity unchanged and enter the next sub-time zone. Otherwise, calculate the difference between the α-wave evaluation value and the highest value of the theoretical normal range, and record it as the percentage decrease matching value; If it is determined to be in a low-intensity state, calculate the difference between the α-wave evaluation value and the lowest value of the theoretical normal range, and take the absolute value and record it as the percentage increase matching value; Preset groups 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; The higher the percentage decrease matching value and the percentage increase matching value, the higher the corresponding adjustment percentage obtained by matching; After determining the adjustment percentage, if it is determined to be in a high-intensity state, on the basis of the current trigger feedback intensity, after a decrease in the adjustment percentage, use it as the trigger feedback intensity for the next sub-time zone; If it is determined to be in a low-intensity state, on the basis of the current trigger feedback intensity, after an increase in the adjustment percentage, use it as the trigger feedback intensity for the next sub-time zone; For example, a 25-year-old male with an alpha wave evaluation value of 1.3; The preset theoretical normal range fluctuates up and down with a reference value of 1. Assuming the fluctuation range is ±0.2, that is, the theoretical normal range is (0.8, 1.2). The player's alpha wave evaluation value of 1.3 is not within this range, so the match fails.

[0025] Since 1.3 is higher than the theoretical normal range (0.8, 1.2), it is determined that the player is in a higher intensity state; 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. Adding the two gives 1.5; The alpha wave evaluation value of 1.3 is lower than 3.5, so the current trigger feedback intensity remains unchanged and enters the next sub-time zone.

[0026] Suppose another 18-year-old female with an alpha wave evaluation value of 0.5; Similarly, the theoretical normal range is (0.8, 1.2). The alpha wave evaluation value of 0.5 is not within this range, so the match fails; 0.5 is lower than the theoretical normal range (0.8, 1.2), so it is determined that the player is in a lower intensity state; Calculate the difference between the alpha wave evaluation value of 0.5 and the lowest value of the theoretical normal range of 0.8, and take the absolute value to get the percentage increase matching value of |0.5 - 0.8| = 0.3.

[0027] Suppose the intervals where the preset percentage increase matching values are located and the corresponding adjustment percentages are: (0 - 0.2, 10%), (0.2 - 0.4, 20%), (0.4 - 0.6, 30%); 0.3 is in the interval (0.2 - 0.4), so the corresponding adjustment percentage is 20%; In the next sub-time zone, the trigger feedback intensity is adjusted upward by (1 + 20%); By combining the player's personal information (gender and age) to determine the upper limit range value, it is possible to take into account the differences in brain responses and game experiences among different individuals, and achieve personalized adjustment of tactile feedback intensity. Players of different ages and genders have different feelings and needs for the game, and this method can better meet the unique experiences of each player; Based on the matching result of the alpha wave evaluation value and the theoretical normal range, accurately judge whether the player's state is of higher or lower intensity, and then adjust the tactile feedback intensity accordingly. When the player is in a higher intensity state, avoid excessive stimulation; when the player is in a lower intensity state, enhance the tactile feedback to attract attention and improve game participation, making the game experience more in line with the player's current psychological and physiological state; During the game process, as the player's state changes in real time, the haptic feedback intensity is continuously adjusted to provide the player with an optimized dynamic gaming experience. This real-time adjustment can better maintain the player's immersion and avoid a decline in the experience caused by the inability of a fixed haptic feedback intensity to adapt to the changes in the player's state; Scientific adjustment based on data: The entire adjustment process is based on clear numerical calculations and preset ranges and intervals, featuring scientificity and logic. Through rigorous data processing and rule setting, it ensures that the adjustment of the haptic feedback intensity is both reasonable and effective, improving the accuracy and reliability of game interaction; After entering the next sub-time zone, continue to collect the player's EEG signal data and continuously determine the adjustment percentage of the haptic feedback intensity for each subsequent sub-time zone.

[0028] Feedback collection optimization: After the current player completes the game, collect the current player's subjective immersion score again and input it into the EEG signal feature library for update; 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 early adjustment of the haptic feedback intensity; The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific implementation manners described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An immersive space virtual-reality interaction method, characterized in that: include: Construct an EEG signal feature library: When players are playing a virtual racing game, collect EEG signal data at different game stages; The game phase can be divided into the adaptation phase, the competition phase, and the post-game phase; Correlate the EEG signal data collected at different game stages with the corresponding players' subjective immersion scores, and build an EEG signal feature library based on the correlation results; Interactive player evaluation: When a player starts playing a virtual racing game, the current player’s personal information is first obtained and entered into the EEG signal feature library for matching, thereby determining the current player’s reference alpha wave frequency at different game stages; personal information includes gender and age; Predicting the changing trend: For a virtual racing game, the different game stages are divided into sub-time zones according to their duration in advance. After the player starts the game, the first sub-time zone of each game stage is used as the starting data collection window, and the EEG signal data of the player in the starting data collection window is evaluated for trend, and the alpha wave evaluation value of the player in the starting data collection window is obtained; Adjust tactile feedback intensity: Based on the current player's alpha wave evaluation value within the initial data collection window, determine the tactile feedback intensity adjustment percentage in the next sub-time zone for each game stage.

2. The immersive space virtual-reality interaction method according to claim 1, characterized in that: According to the correlation results, the EEG signal feature library is constructed: The subjective immersion score is pre-divided into three groups of score intervals, and each group of score intervals corresponds to an immersion state feature; Including high immersion state characteristics, medium immersion state characteristics and low immersion state characteristics; EEG signal data under different immersion state characteristics are classified into corresponding categories; After the classification is completed, the EEG signal data under different immersion state characteristics are further classified according to the preset age ranges of each group and the gender of the players, thereby constructing an EEG signal feature library.

3. The immersive space virtual-reality interaction method according to claim 2, characterized in that: Determine the reference alpha wave frequency of the current player at different game stages, specifically: After inputting the current player's personal information into the EEG signal feature library, the EEG signal data that matches the player's personal information is located in the EEG signal feature library, and the α wave frequencies of each player at different game stages under high immersion state characteristics are extracted. The α wave frequencies of each player at the same game stage are averaged, and the three sets of averages calculated are used as reference frequencies corresponding to the α wave frequencies of the current player at different game stages.

4. The immersive space virtual-reality interaction method according to claim 3, characterized in that: Perform trend evaluation on the player's EEG signal data within the initial data collection window, specifically: S1: In the initial data collection window, the EEG signal data of the player is collected at the set sampling frequency, the α wave frequency is extracted from the original EEG signal data, the α wave frequency of the player at each time point in the initial data collection window is used as a numerical point, an α wave trend graph is constructed, the numerical point corresponding to the α wave frequency at each time point in the trend graph is plotted, the frequency line is obtained by connecting adjacent numerical points, and the reference frequency matched by the player's personal information is extracted at the same time; S2: Taking the reference frequency as the base value and the preset allowable fluctuation value as the calculated value, determine the reference frequency range corresponding to the player, expressed as (base value + allowable fluctuation value, base value - allowable fluctuation value), and draw the threshold range line corresponding to the reference frequency range in the trend graph; S3: Identify the numerical points outside the threshold range line and record them as abnormal points. For each group of abnormal points, extract the α wave frequency 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 match fails, extract the highest and lowest values ​​of the reference frequency range and compare them 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 the 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 and record it as the low wave frequency. S4: Compare the α wave frequencies of two adjacent groups of numerical points. If the α 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 descending slope. Otherwise, record it as the ascending slope. Sum the descending slopes and ascending slopes of each group in the initial data collection window to obtain the total descending value and the total ascending value. The total descending value is used as the numerator and the total ascending value is used as the denominator to calculate the ratio to obtain the α wave change ratio of the player in the initial data collection window. If the α wave change ratio is greater than 1, the α wave frequency of the player in the initial data collection window is judged to be in an overall downward trend, otherwise it is judged to be in an overall upward trend. A trend additional coefficient is set to correspond to the overall downward trend and the overall upward trend respectively.

5. The immersive space virtual-reality interaction method according to claim 4, characterized in that: Get the player's alpha wave evaluation value within the initial data collection window, specifically: S5: For the α wave frequency of the player at each time point within the initial data collection window, first take the average value and record it as the α wave average rate, then extract the highest and lowest values ​​of the α wave frequency at each time point, record them as the α wave peak rate and α wave trough rate, and mark the α wave average rate, α wave peak rate and α wave trough rate as u1, u2 and u3 respectively; The reference frequency matched with the player's personal information is marked as ua; By formula After weighted calculation, the result is divided by the integer three as the player's alpha wave performance value within the initial data collection window; is the preset influence weight factor; S6: Preset the intervals of each group of frequencies corresponding to the high wave frequency and the low wave frequency, and each group of frequencies corresponds to a correction coefficient; after determining the high wave frequency or the low wave frequency based on step S3, match the intervals of the corresponding frequencies to obtain the correction coefficient of the player in the initial data collection window; S7: Extract the player's alpha wave performance value within the initial data collection window, and then multiply it by the trend additional coefficient and the correction coefficient to obtain the player's alpha wave evaluation value within the initial data collection window.

6. The immersive space virtual-reality interaction method according to claim 5, characterized in that: Determine the adjustment percentage of the haptic feedback intensity in the next sub-time zone for each game phase, specifically: The alpha wave evaluation value of the current player in the initial data collection window is matched with the preset theoretical normal range. If the match is successful, the current trigger feedback intensity is kept unchanged to enter the next sub-time zone.

7. The immersive space virtual-reality interaction method according to claim 6, characterized in that: Determine the adjustment percentage of the haptic feedback intensity in the next sub-time zone for each game phase, and also include: If the match fails, the matching result between the α wave evaluation value and the theoretical normal range is further identified. If the α wave evaluation value is higher than the theoretical normal range, it is determined to be a high intensity state; if the α wave evaluation value is lower than the theoretical normal range, it is determined to be a low intensity state; If it is determined to be a high intensity state, each age range of different genders is preset to correspond to an upper limit range value, and the corresponding upper limit range value is determined after extracting the personal information of the current player. The determined upper limit range value is added to the highest value of the theoretical normal range, and then compared with the α wave evaluation value. If the α wave evaluation value is low, the current trigger feedback intensity is kept unchanged to enter the next sub-time zone. Otherwise, the difference between the α wave evaluation value and the highest value of the theoretical normal range is calculated and recorded as the percentage decrease matching value; If it is determined to be a low-intensity state, the difference between the α wave evaluation value and the lowest value of the theoretical normal range is calculated, and the absolute value is taken and recorded as the percentage increase matching value; The preset percentage decreasing matching value and the percentage increasing matching value correspond to the intervals of the groups of matching values, and each interval of the groups of matching values ​​corresponds to an adjustment percentage; After determining the adjustment percentage, if it is determined to be a high intensity state, then based on the current trigger feedback intensity, the adjustment percentage is decreased and used as the trigger feedback intensity for the next sub-time zone; If it is determined to be in a low intensity state, the current trigger feedback intensity is adjusted by an increased percentage and used as the trigger feedback intensity for the next sub-time zone.

8. The immersive space virtual-reality interaction method according to claim 7, characterized in that: Also includes: Feedback collection optimization: After the current player completes the game, the current player’s subjective immersion score is collected again and input into the EEG signal feature library for update.

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