A game adjusting method, device and storage medium

Abstract

The application discloses a game adjusting method and device and a storage medium, and relates to the technical field of games, and can improve the exercise efficiency of attention deficit hyperactivity disorder.

Description

Technical Field

[0001] This invention relates to the technical field of brain-computer interfaces, and more particularly to a game control method, device, and storage medium. Background Technology

[0002] Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by an inability to maintain attention, hyperactivity, and impulsivity.

[0003] Since ADHD often develops in childhood, games are now widely used to help patients (especially children) with their rehabilitation. Designers have adapted the gameplay to ADHD, and patients can choose games based on their own interests to help them with their rehabilitation.

[0004] The difficulty of these games is usually fixed when the game is designed. However, since different patients have different abilities and the same patient may be in different situations, if the game is too difficult or too easy, the patient will lose interest in continuing the game and reduce the effectiveness of rehabilitation exercises. Subsequently, the designers will adjust the difficulty of the game based on the patient's exercise results and feedback, or select suitable games for the patient. This process is relatively complicated and time-consuming. Summary of the Invention

[0005] In view of this, the present invention provides a game adjustment method, device and storage medium to improve the efficiency of game adjustment designed for ADHD.

[0006] A first aspect of the present invention provides a game-based adjustment method, wherein the game library contains multiple game programs for training purposes targeting attention deficit hyperactivity disorder (ADHD), the method comprising:

[0007] During the operation of the game program for the user, a brain-computer interface is invoked to collect the user's brain signals.

[0008] Preprocessing is performed on the electroencephalogram (EEG) signals;

[0009] If preprocessing is completed, multiple feature values ​​related to the attention deficit hyperactivity disorder are extracted from the EEG signal;

[0010] The difficulty of training the attention deficit hyperactivity disorder (ADHD) in the game program is adjusted based on the aforementioned feature values.

[0011] If the game program ends, a first feature vector related to the attention deficit hyperactivity disorder is constructed for the user based on multiple feature values;

[0012] Based on multiple first feature vectors within the current time period, game programs are selected for the user from the game library to continue training for the attention deficit hyperactivity disorder in the next time period.

[0013] A second aspect of the present invention provides a game-based adjustment device having a game library containing multiple game programs for training purposes targeting attention deficit hyperactivity disorder (ADHD), the device comprising:

[0014] The EEG signal acquisition module is used to call the brain-computer interface to acquire EEG signals from the user during the operation of the game program in front of the user.

[0015] A preprocessing module is used to perform preprocessing on the electroencephalogram (EEG) signals;

[0016] The feature extraction module is used to extract multiple feature values ​​related to the attention deficit hyperactivity disorder from the electroencephalogram (EEG) signal if preprocessing is completed.

[0017] The difficulty adjustment module is used to adjust the difficulty of the game program for training the attention deficit hyperactivity disorder based on the feature values.

[0018] A feature vector construction module is used to construct a first feature vector related to the attention deficit hyperactivity disorder for the user based on multiple feature values ​​if the game program ends running.

[0019] The game program filtering module is used to filter game programs for the user in the game library based on multiple first feature vectors within the current time period, so as to continue training for the attention deficit hyperactivity disorder in the next time period.

[0020] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0021] At least one processor; and

[0022] A memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the game adjustment method as described in the first aspect above.

[0024] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the game adjustment method as described in the first aspect above.

[0025] A fifth aspect of the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the game adjustment method as described in the first aspect above.

[0026] In this embodiment, during the user-facing operation of the game program, a brain-computer interface is invoked to collect the user's electroencephalogram (EEG) signals; preprocessing is performed on the EEG signals; if preprocessing is complete, multiple feature values ​​related to Attention Deficit Hyperactivity Disorder (ADHD) are extracted from the EEG signals; the difficulty of the game program for ADHD training is adjusted based on the feature values; if the game program ends, a first feature vector related to ADHD is constructed for the user based on the multiple feature values; and game programs are selected from the game library for the user based on the multiple first feature vectors within the current time period, so that ADHD training can continue in the next time period. This embodiment dynamically adjusts the difficulty of the game program based on the feature values ​​of the EEG signals when the user operates the game program in the short term, and selects game programs based on the first feature vectors of the EEG signals when the user operates the game program in the long term. This enables the game program to adapt to the user's ability and state, correcting ADHD training in the short and long term, and effectively improving the training efficiency of ADHD.

[0027] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of a game adjustment method provided in Embodiment 1 of the present invention.

[0030] Figure 2 This is a schematic diagram of a game adjustment device provided in Embodiment 2 of the present invention.

[0031] Figure 3 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate so that the embodiments of the invention described herein can cover implementations in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] See Figure 1 The diagram shows a flowchart of a game adjustment method provided in Embodiment 1 of the present invention. The method can be executed by a game adjustment device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0036] Typically, this electronic device is a cloud server with a game library containing various games designed to train individuals with Attention Deficit Hyperactivity Disorder (ADHD). Specifically, these games can be designed based on psychological paradigms such as Go / no-go tasks, stroop tasks, flanker tasks, and n-back tasks.

[0037] These game programs can be distributed to clients on edge devices (such as personal computers, game consoles, etc.) as needed. The edge devices are connected to terminals with brain-computer interfaces (such as headbands) via wired or wireless means (such as Bluetooth).

[0038] Due to the varying configurations of edge devices, these games are mostly small, standalone games designed to broaden their applicability.

[0039] Edge devices and cloud servers belong to a hybrid architecture that combines edge computing and cloud computing, enabling continuous data-driven optimization while ensuring real-time performance.

[0040] like Figure 1 As shown, the method includes:

[0041] Step 101: During the operation of the game program in front of the user, the brain-computer interface is invoked to collect the user's brain signals.

[0042] In this embodiment, the user (such as an ADHD patient) wears a terminal with a brain-computer interface, starts a game program, and performs rehabilitation exercises for ADHD according to the game program's instructions. During this process, the brain-computer interface continuously collects single-channel or multi-channel EEG signals from the user. The terminal transmits the user's EEG signals to an edge device in real time via Bluetooth or other means. The edge device then transmits the user's EEG signals to a cloud server via protocols such as HTTPS (Hypertext Transfer Protocol Secure).

[0043] Taking a single channel as an example, it can collect EEG signals from the user under the Fp2 (right temporal pole) channel, with a sampling rate of 250Hz and a contact impedance of less than 5 kΩ.

[0044] By collecting single-channel EEG signals, the complexity of the algorithm can be effectively reduced while ensuring the accuracy of game adjustment, thus increasing user comfort and convenience.

[0045] Step 102: Perform preprocessing on the EEG signals.

[0046] In this embodiment, one or more preprocessing steps can be performed on the EEG signal to improve the quality of the EEG signal.

[0047] In practice, a power frequency notch filter at frequencies such as 50Hz can be used to filter the EEG signal, eliminating power frequency interference and its harmonics from the power grid.

[0048] If filtering is completed, artifact removal of EEG signals can be performed using sliding window ANOVA, where the window length is 1 second and the threshold is 3 times the baseline variance.

[0049] If artifact removal is completed, the EEG signal can be bandpass filtered using a frequency band of 2-35Hz to retain the main energy region in the frequency band while suppressing noise outside the frequency band, thus completing the preprocessing.

[0050] Step 103: If preprocessing is completed, extract multiple feature values ​​related to attention deficit hyperactivity disorder from the EEG signal.

[0051] During the preprocessing of EEG signals, multiple feature values ​​related to attention deficit hyperactivity disorder can be extracted from the EEG signals.

[0052] For example, multiple eigenvalues ​​include the band ratio θ / β between the θ band and the β band, and the differential slope β of the β band. shope And entropy.

[0053] Among them, the band ratio θ / β between the theta band and the beta band is a physiological indicator of prefrontal inhibitory control function. In game programs, it manifests as the number of times the character's attention wanders and the frequency of task interruption. In ADHD rehabilitation exercises, it helps to improve sustained attention.

[0054] Differential slope β in the β band shope The physiological significance of this is that it reflects cognitive load and anxiety level. In the context of game programs, it manifests as operational error rate and decision hesitation time. In the rehabilitation training of ADHD, it helps to reduce anxiety and optimize decision-making ability.

[0055] The physiological significance of entropy is brainwave complexity (fatigue / focus). In game programming, it manifests as game rhythm adaptability, reaction speed fluctuations, etc., and it helps to enhance state stability in ADHD rehabilitation training.

[0056] In this example, the theta band (e.g., 4-7 Hz) and the beta band (e.g., 12-30 Hz) can be identified in the EEG signal.

[0057] On the one hand, the ratio between the power of the θ band and the power of the β band is calculated, which is used as the band ratio θ / β between the θ band and the β band.

[0058] Furthermore, the band ratio θ / β can be filtered to shield transient noise in physiological signals (such as blinking and limb movement artifacts).

[0059] Taking sliding window filtering as an example, the weighted average of the band ratio θ / β over the most recent 15 seconds can be calculated (recent data has a higher weight), and band ratios θ / β that exceed the mean ± 2.5 times the standard deviation can be removed.

[0060] On the other hand, within a preset time window, the ratio between the magnitude of the power change in the β band and the width of the time window is calculated, and this ratio is used as the differential slope β of the β band. shope At this time, β shope =ΔP β / Δt, where ΔP β Δt represents the amplitude of the power variation in the β band, and Δt represents the width of the time window (e.g., 1 s).

[0061] On the other hand, the sample entropy of the EEG signal is calculated as the entropy value Entropy. At this time, Entropy(m,r,N)=-ln(B / A), where m is the embedding dimension (usually taken as 2), r is the similarity tolerance (taken as 0.1-0.25 times the standard deviation of the EEG signal), A is the number of m+1 dimension subsequence matches, and B is the number of m dimension subsequence matches.

[0062] Step 104: Adjust the difficulty of the game program for training attention deficit hyperactivity disorder based on the feature values.

[0063] In this embodiment, the difficulty of training ADHD in the game program is a configurable parameter, such as the enemy's attack speed, the complexity of the task, the reaction time window of the virtual character, etc. Different feature values ​​have different physiological meanings and roles in ADHD rehabilitation training. Therefore, the difficulty of training ADHD in the game program can be adjusted according to at least one feature value.

[0064] In one approach, the change Δ(θ / β) between two adjacent band ratios can be calculated, that is, the change Δ(θ / β) between two adjacent band ratios is obtained by subtracting the previous band ratio θ / β from the next band ratio θ / β.

[0065] If the change Δ(θ / β) is greater than the first measurement threshold T1 (T1 is greater than 0 and usually less than 1, such as 0.15), the user's attention is significantly distracted. Then, the product between the step size and the first ratio is added to the current difficulty of training ADHD in the game program, which is used as the new difficulty of training ADHD in the game program.

[0066] Among them, the first measurement threshold T1 is a dynamic threshold that can be dynamically adjusted based on the user's historical exercise data. For example, the first measurement threshold T1 for children is larger, relaxed to 2.0 times the standard deviation of θ / β.

[0067] Therefore, the process of adjusting the difficulty of a game program to train attention deficit hyperactivity disorder can be represented as: k t+1 =k t +α1Δ, where, k t+1 Let k be the difficulty level of the game program training for Attention Deficit Hyperactivity Disorder (i.e., the new difficulty) at time t+1. t Let α1 be the difficulty level of the game program training for Attention Deficit Hyperactivity Disorder (i.e., the current difficulty) at time t, α1 be the first ratio, such as 0.3, and Δ be the step size of the difficulty adjustment, representing the magnitude of each difficulty change.

[0068] Δ is a hyperparameter that can be determined through experiments or experience. A larger Δ results in a faster response but leads to more drastic fluctuations in difficulty, which may cause user discomfort. A smaller Δ makes the adjustment smoother but reduces the response speed.

[0069] If the change Δ(θ / β) is less than the second measurement threshold T2 (T2 is less than 0 and usually greater than -1, such as -0.1), then the product between the difficulty of the current training of ADHD in the game program and the second ratio is calculated as the difficulty of the new training of ADHD in the game program. This dynamically adapts to the user's improved attention state, prevents frustration caused by excessive difficulty, and consolidates the training effect of ADHD.

[0070] This embodiment adjusts the difficulty of rehabilitation exercises for attention deficit hyperactivity disorder (ADHD) in a timely and stable manner based on the change between two adjacent band ratios, thereby improving the effectiveness of rehabilitation exercises for ADHD.

[0071] Therefore, the process of adjusting the difficulty of a game program to train attention deficit hyperactivity disorder can be represented as: k t+1 =α2k t , where k t+1 Let k be the difficulty level of the game program training for Attention Deficit Hyperactivity Disorder (i.e., the new difficulty) at time t+1. t Let α2 be the difficulty level of the game program training for Attention Deficit Hyperactivity Disorder at time t (i.e., the current difficulty), and let α2 be the second ratio, such as 0.8.

[0072] In one example, the game program is the Flanker task game (impulse control training). The game is designed for users to quickly determine the direction of the central arrow (such as "←←→←←") and suppress their instinctive reaction to the interfering arrows on both sides. The parameters representing the difficulty are the number of arrows and the intensity of interference (such as the proportion of the arrows on both sides that are in the same direction as the central arrow).

[0073] When a user's theta wave power significantly decreases and beta wave power increases, it indicates improved prefrontal inhibitory control and reduced impulsivity. At this time, the change in the ratio between two adjacent wavebands, Δ(θ / β), will significantly decrease, triggering a reduction in the difficulty of training for attention deficit hyperactivity disorder (ADHD).

[0074] The significance of reducing the difficulty of training for attention deficit hyperactivity disorder (ADHD):

[0075] Preventing excessive challenges: If users have already been able to effectively suppress interference (the θ / β ratio decreases), further increasing the difficulty may lead to anxiety or mistakes, which may weaken the training effect.

[0076] Strengthen positive feedback: Reduce the intensity of interference (such as reducing the number of arrows on both sides) to enable users to successfully complete tasks continuously, thereby increasing their confidence and participation.

[0077] Maintaining the optimal challenge zone: By reducing the difficulty, keeping the task at a level slightly below current ability, neural plasticity is promoted (such as strengthening the connection between the prefrontal cortex and the basal ganglia).

[0078] In one example, the game program is an N-back task game (working memory training). The game is designed so that users need to memorize consecutive numbers or shapes and determine whether the current stimulus is the same as the previous N positions. The parameters representing the difficulty are the N value (memory load), stimulus presentation speed, etc.

[0079] When a user exhibits theta wave suppression and beta wave activation enhancement, it indicates improved working memory efficiency and reduced cognitive load. At this time, the change in the ratio between two adjacent wavebands, Δ(θ / β), will significantly decrease, triggering a reduction in the difficulty of training for attention deficit hyperactivity disorder (ADHD).

[0080] The significance of reducing the difficulty of training for attention deficit hyperactivity disorder (ADHD):

[0081] Avoid cognitive overload: If users can already efficiently complete high N-value tasks (such as 3-back), further increasing the N value may lead to information overload, which may reduce the training effect.

[0082] Consolidate memory strategies: Reduce the N value (e.g., from 3-back to 2-back) to allow users to focus on optimizing memory strategies (e.g., grouping encoding) rather than simply coping with the load.

[0083] Balanced training intensity: By reducing the difficulty, the user is kept in a "flow state", which promotes dopamine secretion and enhances long-term training motivation.

[0084] In one example, the game program is a Go / No-go task game (attention persistence training). The game is designed so that the user needs to react quickly to "Go" stimuli (such as green circles) while suppressing the impulse to click on "No-go" stimuli (such as red circles). The parameters representing the difficulty are the frequency of "No-go" stimuli, reaction time window, etc.

[0085] When a user's theta wave inhibition is enhanced and beta wave activation is stable, it indicates sustained improvement in attention and reduced impulsivity. At this point, the change in the ratio between two adjacent wavebands, Δ(θ / β), will decrease significantly, triggering a reduction in the difficulty of training for attention deficit hyperactivity disorder (ADHD).

[0086] The significance of reducing the difficulty of training for attention deficit hyperactivity disorder (ADHD):

[0087] Preventing habituation: If users can effectively suppress "No-go" stimuli (e.g., error rate <10%), continuing to increase the frequency may lead to automatic reactions and weaken the training effect.

[0088] Increase task diversity: reduce the frequency of "No-go" responses, introduce dynamic goals (such as randomly moving stimuli), and train users to maintain attention in different contexts.

[0089] Optimize the reward mechanism: By reducing the difficulty and increasing success feedback (such as combo rewards), the reward sensitivity of the prefrontal-striatal circuit is enhanced.

[0090] Furthermore, difficulty reduction does not simplify the task, but rather matches the level of challenge with the user's current cognitive state, achieving dynamic adaptability.

[0091] For example, when θ / β decreases from 4.0 to 3.5 (Δ<-0.1), the N value of the N-back task can be reduced from 3 to 2, while shortening the reaction time window (e.g., from 1 second to 0.8 seconds) to maintain the overall cognitive load.

[0092] By gradually adjusting the difficulty level, the user's baseline capabilities can be improved step by step, achieving long-term intervention effects.

[0093] For example, in the initial stage: θ / β=4.5, K=0.8 (low difficulty); in the middle stage of training: θ / β=3.8, K=1.2 (medium difficulty); in the later stage of training: θ / β=3.0, K=1.5 (high difficulty).

[0094] In another approach, the duration during which the statistical entropy value Entropy is greater than the third metric threshold T3 (T3 is a positive value, such as 2.5) is measured.

[0095] If the duration is greater than the fourth metric threshold T4 (T4 is a positive value, such as 30s), then subtract the ratio between the entropy value and the upper limit value from 1 to obtain the adjustment coefficient.

[0096] The product of the difficulty of the current training of ADHD in the game program, the third ratio, and the adjustment coefficient is calculated as the difficulty of the new training of ADHD in the game program.

[0097] Therefore, the process of adjusting the difficulty of a game program to train attention deficit hyperactivity disorder can be represented as: k t+1 =α3k t (1-Entropy / m), where k t+1 Let k be the difficulty level of the game program training for Attention Deficit Hyperactivity Disorder (i.e., the new difficulty) at time t+1. t Let α3 be the difficulty level of the game program training for Attention Deficit Hyperactivity Disorder at time t (i.e., the current difficulty level), where α3 is the third ratio, such as 0.9, and m is the upper limit value, such as 5.

[0098] In one example, the game program is a rhythm matching game (attention persistence training). The game is designed so that users need to tap the screen in time with the music. The rhythm complexity (such as beat intervals and multi-track synchronization) increases with the difficulty.

[0099] When the entropy value of a user's EEG signal is greater than 2.5, it indicates that the complexity of brain signals has increased, and the user may be in a state of fatigue or distraction. In this case, compensation should be made to reduce the difficulty of training for attention deficit hyperactivity disorder.

[0100] The significance of compensating for the difficulty of training in attention deficit hyperactivity disorder (ADHD):

[0101] Reduce rhythm complexity: Increase the beat interval (e.g., from 0.5 seconds to 0.8 seconds) to reduce multi-track synchronization requirements, giving the user's brain more time to process information.

[0102] Insert rest intervals: After a continuous high entropy phase, a 10-second rest period is automatically inserted, playing soothing music to help users restore prefrontal cortex resources.

[0103] Provide visual assistance: Display the beat trajectory line on the screen to reduce reliance on auditory attention and alleviate cognitive overload.

[0104] In another example, the game is a memory search game (working memory training), which is designed for users to memorize the location of a target in a 3x3 grid and accurately click on it after distractions appear.

[0105] When a user makes multiple consecutive errors or experiences prolonged reaction time, accompanied by an entropy value greater than 2.5, it indicates that the working memory load is approaching its limit. At this point, compensation should be made to reduce the difficulty of training attention deficit hyperactivity disorder.

[0106] The significance of compensating for the difficulty of training in attention deficit hyperactivity disorder (ADHD):

[0107] Reduce memory load: Shrink the grid from 3x3 to 2x2 to reduce the number of locations that need to be memorized.

[0108] Extend the reaction time window: from 1 second to 1.5 seconds to reduce anxiety caused by time pressure.

[0109] Introducing a prompting mechanism: After a user makes consecutive mistakes, the target area is highlighted for 2 seconds to aid memory reinforcement.

[0110] In yet another example, the game program is a multi-tasking game (performing functional training).

[0111] Game design: Users need to complete math problems (such as mental arithmetic) and graphic matching tasks at the same time, with task priorities changing dynamically.

[0112] An increase in task switching error rate accompanied by an entropy value > 2.5 indicates an imbalance in the allocation of resources in the prefrontal cortex. In this case, it is necessary to compensate for the difficulty in reducing the training of attention deficit hyperactivity disorder.

[0113] The significance of compensating for the difficulty of training in attention deficit hyperactivity disorder (ADHD):

[0114] Simplify task combinations: Temporarily remove low-priority tasks (such as image matching) and focus on a single task (mental arithmetic).

[0115] Provide task priority hints: Display the current task priority icon at the edge of the screen to reduce decision-making load.

[0116] Insert step-by-step instructions: Break down complex tasks into sub-steps (such as "do addition first, then subtraction") to reduce working memory requirements.

[0117] Furthermore, as an indicator of fatigue, an elevated entropy value (>2.5) is usually associated with brain signal desynchronization, reflecting depletion of prefrontal cortex resources. For example, in healthy individuals, after working continuously for 1 hour, the entropy value may rise from 1.8 to 2.6, accompanied by a decline in attention.

[0118] The compensation operation is time-sensitive, assessing progressive fatigue every 5 minutes, while triggering it upon completion of a level can respond to sudden distractions (such as external interference).

[0119] For example, after a user answers 5 questions correctly in a row, the entropy value may increase due to overconfidence. Triggering compensation at this time can prevent subsequent errors.

[0120] Through periodic adjustments, the system can gradually train users to maintain a low entropy value (<2.0), reflecting a reduction in brain signal complexity (i.e., greater focus), thus achieving long-term intervention effects.

[0121] For example, after 4 weeks of training, the average entropy value of children with ADHD may decrease from 3.0 to 2.2, accompanied by a 25% increase in attention test scores.

[0122] Step 105: If the game program ends, construct a first feature vector related to Attention Deficit Hyperactivity Disorder for the user based on multiple feature values.

[0123] When the game program ends, a first feature vector U related to attention deficit hyperactivity disorder can be constructed for the user based on multiple feature values ​​during the game program's operation.

[0124] In the specific implementation, the sub-vectors in the first feature vector U include the attention concentration AttnIndex, the impulse control ability ImpulseControl, and the working memory capacity WorkMem, that is, U=(AttnIndex, ImpulseControl,WorkMem).

[0125] Among them, AttnIndex represents the user's sustained focus when operating the game program, ImpulseControl represents the user's instinctive response to suppressing interfering stimuli when operating the game program, and WorkingMemory represents the user's short-term memory capacity when operating the game program.

[0126] Attention deficit hyperactivity disorder (ADHD) exhibits significant deficiencies in attention concentration (AttnIndex), impulse control, and working memory (WorkMem). These parameters can effectively cover the rehabilitation assessment of ADHD, ensuring clinical applicability. Furthermore, given the low information content and limited controllable dimensions of single-channel EEG signals, these parameters serve as core control dimensions, providing data support for single-channel EEG signals.

[0127] In one case, the band ratio θ / β is dominant, and the differential slope β is... shope In addition to entropy, the band ratio θ / β and the differential slope β are expressed using linear or nonlinear methods. shope It is integrated with the entropy value to form the user's attention concentration attnIndex, which is related to attention deficit hyperactivity disorder.

[0128] Among them, the concentration level AttnIndex is negatively correlated with the band ratio θ / β. Therefore, the stronger the θ wave suppression, the more concentrated the attention.

[0129] The so-called band ratio θ / β as the main factor, and the differential slope β shope In addition to entropy, this refers to the fact that in the attention concentration AttnIndex, the weight (influence) of the band ratio θ / β is greater than that of the differential slope β. shope The weight (influence) of entropy, and the weight (influence) of entropy.

[0130] For example, AttnIndex = 0.5 × 1 / (θ / β) + 0.3 × β shope +0.2×1 / Entropy.

[0131] In some cases, the differential slope β shope The weight (influence) of Entropy and the weight (influence) of Entropy can be 0.

[0132] In another case, with the differential slope β shope The primary method is based on the band ratio θ / β, with entropy as a secondary factor. Linear or nonlinear approaches are used to combine the band ratio θ / β and the differential slope β. shope It integrates with entropy to form ImpulseControl, a user's impulse control ability associated with attention deficit hyperactivity disorder.

[0133] Among them, impulse control ability is negatively correlated with the differential slope β. shope The smoother the transition, the more stable the decision-making process.

[0134] The so-called differential slope β shope The primary component, with band ratio θ / β and entropy as secondary components, refers to the differential slope β in impulse control. shope The weights (influences) of each band ratio θ / β are greater than the weights (influences) of the band ratio θ / β and the weights (influences) of the entropy value.

[0135] In some cases, the weights (influences) of the band ratio θ / β and the entropy value can be 0.

[0136] In another case, the entropy value Entropy is dominant, along with the band ratio θ / β and the differential slope β. shope As a supplement, linear or nonlinear methods are used to adjust the band ratio θ / β and the differential slope β. shope The entropy value is integrated with the working memory capacity WorkMem associated with the user's attention deficit hyperactivity disorder.

[0137] Among them, the working memory capacity WorkMem is negatively correlated with the entropy value Entropy. Therefore, the lower the entropy value Entropy, the higher the information processing efficiency.

[0138] The so-called entropy value as the main factor, the band ratio θ / β and the differential slope β shope "Secondary" means that in the working memory capacity WorkMem, the weight (influence) of the entropy value is greater than the weight (influence) of the band ratio θ / β and the differential slope β. shope Weight (influence).

[0139] In some cases, the weight (influence) of the band ratio θ / β and the differential slope β shope The weight (influence) can be 0.

[0140] Step 106: Based on multiple first feature vectors within the current time period, select game programs for the user from the game library so that training for attention deficit hyperactivity disorder can continue in the next time period.

[0141] In practical applications, the process of users using game programs for rehabilitation training of attention deficit hyperactivity disorder can be divided into multiple time periods. In each time period, users can operate the game program multiple times, accumulating multiple first feature vectors.

[0142] At the end of each time period, the effectiveness of the user's rehabilitation training for attention deficit hyperactivity disorder (ADHD) using the game program can be evaluated based on multiple first feature vectors. Based on this, game programs can be selected for the user to continue training for ADHD in the next time period.

[0143] In one embodiment of the present invention, each game program in the game library is configured with a second feature vector G related to attention deficit hyperactivity disorder, and the dimension of the second feature vector G of the game program is the same as the dimension of the user's first feature vector U.

[0144] When the subvectors in the first feature vector U include attention concentration AttnIndex, impulse control ability ImpulseControl, and working memory capacity WorkMem, the subvectors in the second feature vector G include attention concentration AttnIndex, impulse control ability ImpulseControl, and working memory capacity WorkMem, that is, G=(AttnIndex,ImpulseControl,WorkMem).

[0145] Among them, AttnIndex represents the sustained focus required when operating the game program, ImpulseControl represents the strength of the instinctive response to suppressing interfering stimuli when operating the game program, and WorkingMemory represents the short-term memory requirement when operating the game program.

[0146] In one example, the game program is a number matrix reproduction game of number memory. Its function / gameplay is as follows: the user observes randomly flashing numbers (1-9) in a 3x3 grid, and after the flashing ends, the user must click the numbers in sequence. As the difficulty increases, the grid expands to 4x4 and adds distracting numbers (such as repeating numbers or false positions).

[0147] In this example, the attention concentration AttnIndex is a high weight of 0.6, requiring continuous focus on the position of the number. The stronger the inhibition of the θ / β ratio, the more accurate the memory.

[0148] ImpulseControl has a medium weight of 0.3, which requires suppressing accidental touches that could interfere with the numbers. A flat βslope can reduce misjudgments.

[0149] The working memory capacity WorkMem has a high weight of 0.5, indicating that multiple rounds of numerical sequence need to be memorized. The low entropy reflects high information processing efficiency.

[0150] Therefore, the second eigenvector of the digital matrix reproduction is G = (0.6, 0.3, 0.5).

[0151] In another example, the game program is a target range precision training exercise in a shooting game. Its function / gameplay is as follows: the user must shoot when a moving target appears, while avoiding shooting hostage targets. As the difficulty increases, the target movement speed increases, and camouflaged targets (such as targets with a similar color to the background) are added.

[0152] In this example, the attention concentration AttnIndex is set to a medium weight of 0.4, indicating the need for rapid target identification. A stable θ / β ratio can improve shooting accuracy.

[0153] Impulse control ability has a high weight of 0.5, indicating a need to suppress impulsive shooting, β slope A gentle slope can reduce accidental injury.

[0154] The working memory capacity WorkMem has a low weight of 0.1, indicating that the task mainly depends on reaction speed and has a low memory load.

[0155] Therefore, the second feature vector of the target range precision training is G = (0.4, 0.5, 0.1).

[0156] In another example, the game program is a board-based strategy game called "Surrounding Battle," where the user moves pieces on a 5x5 board and wins by surrounding the opponent's pieces. As the difficulty increases, the board expands to 6x6, and special rules are added (such as "portal" pieces).

[0157] In this example, the attention focus AttnIndex has a medium weight of 0.3, requiring observation of the entire chessboard, and the stable θ / β ratio allows for optimized decision-making.

[0158] Impulse Control has a medium weight of 0.3, so impulsive moves should be avoided and a flat βslope can reduce errors.

[0159] The working memory capacity WorkMem is high-weighted at 0.6, requiring the memorization of the opponent's historical moves. The low entropy reflects the depth of the strategy.

[0160] Therefore, the second eigenvector of the chessboard encirclement battle is G = (0.3, 0.3, 0.6).

[0161] In this embodiment, step 106 may include the following steps:

[0162] Step 1061: For each first feature vector within the current time period, query the baseline value of each sub-vector in the first feature vector within the current time period.

[0163] In this embodiment, a dynamic baseline value can be set for the current user to evaluate the baseline situation of the user's rehabilitation training for attention deficit hyperactivity disorder. Then, in the current time period, the baseline values ​​of each sub-vector in the first feature vector that are effective in the current time period can be queried. That is, each sub-vector in the first feature vector has a corresponding baseline value set.

[0164] Step 1062: For each sub-vector in the first feature vector, calculate the deviation value of the sub-vector from the baseline value.

[0165] In this embodiment, each first feature vector within the current time period can be traversed. For each sub-vector in the current first feature vector, the degree to which the sub-vector deviates from the corresponding baseline value can be calculated and recorded as the deviation value.

[0166] For example, the average value of each sub-vector of multiple first feature vectors within the current time period can be calculated to obtain an average vector. The difference between the average vector corresponding to each sub-vector of the first feature vector and the baseline value can be calculated as a first intermediate value. The standard deviation of multiple historical baseline values ​​and the square root of the number of multiple first feature vectors within the current time period can be calculated to obtain a second intermediate value. The ratio between the first intermediate value and the second intermediate value can be calculated as the deviation value of the sub-vector of the first feature vector from the baseline value.

[0167] In this example, the deviation of the sub-vectors in the first feature vector from the baseline value can be expressed as:

[0168] ;

[0169] Where z is the deviation of the sub-vector in the first eigenvector from the baseline value. σ is the average of the sub-vectors among the multiple first eigenvectors, μ is the baseline value, σ is the standard deviation of the multiple historical baseline values, and n is the number of the multiple first eigenvectors.

[0170] In this example, the nonlinear distance from the baseline value to a series of sub-vectors in the first feature vector is expressed in units of the standard deviation of the baseline value, which truly reflects the relative standard distance of the series of sub-vectors in the first feature vector from the baseline value.

[0171] Step 1063: Identify significant changing trends in each sub-vector of the first feature vector based on the deviation value.

[0172] For each sub-vector in the first feature vector, the sub-vectors in the first feature vector are identified based on the deviation of the sub-vectors from the baseline value, which shows a significant trend of change.

[0173] In a specific implementation, a first significance threshold and a second significance threshold can be set.

[0174] The first significance threshold is negative, and the second significance threshold is positive. The first significance threshold and the second significance threshold are opposites of each other. For example, the first significance threshold is -1.96 and the second significance threshold is 1.96.

[0175] The deviation values ​​of the sub-vectors in the first feature vector from the baseline value are compared with the first significance threshold and the second significance threshold, respectively.

[0176] If the deviation value is less than the first significance threshold, then the trend of significant change of each sub-vector in the first feature vector is determined as optimization.

[0177] If the deviation value is greater than the second significance threshold, then the significant change trend of each sub-vector in the first feature vector is determined to be deterioration.

[0178] Step 1064: Identify the training effect of the user's training on attention deficit hyperactivity disorder in the current time period based on the changing trend of each sub-vector in the multiple first feature vectors within the current time period.

[0179] In this embodiment, the changing trends of each sub-vector in multiple first feature vectors within the current time period can be statistically analyzed to identify the training effect of the user's training on attention deficit hyperactivity disorder within the current time period.

[0180] In the specific implementation, multiple first feature vectors within the current time period are labeled with change tags.

[0181] The change label includes an optimization label or a degradation label; the optimization label indicates that the change trend of each sub-vector in the first feature vector is optimization, and the degradation label indicates that the change trend of at least one sub-vector in the first feature vector is degradation.

[0182] If the proportion of optimized tags is greater than the preset first proportion threshold (e.g., 70%), then it is determined that the training effect of the user on attention deficit hyperactivity disorder in the current time period has improved.

[0183] If the proportion of optimized tags is less than the preset second proportion threshold (e.g., 50%), then it is determined that the training effect of the user's training for attention deficit hyperactivity disorder in the current time period has deteriorated.

[0184] Step 1065: If the training effect is improved, update the baseline value based on each sub-vector of the multiple first feature vectors within the current time period.

[0185] If the user's training effect on attention deficit hyperactivity disorder (ADHD) is improved within the current time period, the baseline value can be updated based on the sub-vectors of multiple first feature vectors within the current time period. This allows the baseline value to dynamically change with the user's state, thereby improving the training effect on ADHD.

[0186] In the specific implementation, the average vector corresponding to each sub-vector of the multiple first feature vectors within the current time period is assigned to the baseline value.

[0187] For example, after a user operates the game program multiple times, the mean (i.e., average vector) of attention concentration AttnIndex decreases from 4.0 to 3.5 (deviation z=-2.5), the mean (i.e., average vector) of impulse control ability decreases from 0.8 to 0.5 (deviation z=-3.0), and the mean (i.e., average vector) of working memory capacity WorkMem decreases from 2.8 to 2.0 (deviation z=-2.2).

[0188] At this point, the baseline value is updated to (3.5, 0.5, 2.0) to maintain the current game difficulty.

[0189] Step 1066: If the training effect worsens, calculate the similarity between multiple first feature vectors within the current time period and the second feature vectors of each game program in the game library.

[0190] If a user's training effect on attention deficit hyperactivity disorder worsens during the current time period, the similarity between multiple first feature vectors and the second feature vectors of various game programs in the game library can be calculated using methods such as cosine angle and Pearson correlation coefficient, thereby achieving matching between the user and the game program.

[0191] For example, for users with a high need for impulse control, design-related games can be recommended to them.

[0192] Taking the cosine angle as an example, the similarity between multiple first feature vectors within the current time period and the second feature vectors of various game programs in the game library can be represented as follows:

[0193] ;

[0194] Where U is the first feature vector, G is the second feature vector, and Similarity(U,G) is the similarity between the first feature vector U and the second feature vector G.

[0195] Step 1067: Select the most similar game programs for the user to continue training for attention deficit hyperactivity disorder in the next time period.

[0196] In this embodiment, the similarity of each game program can be sorted from high to low to obtain a game sequence, and the game programs with the highest ranking (i.e., the game programs with the highest similarity) can be selected from the game sequence.

[0197] In general, to improve user experience, multiple game programs with the highest similarity can be deduplicated, removing game programs that the user has actually played in one or more recent time periods.

[0198] Several selected game programs will be recommended to the user for selection, and training for attention deficit hyperactivity disorder will continue in the next time period.

[0199] In one example, when the attention span AttnIndex decreases, such as when θ / β remains greater than 3.5, games with low distraction tasks can be prioritized for the user, such as fixed arrow directions and reduced number of distracting arrows (from 5 to 3).

[0200] When impulse control decreases, such as β slope If the score is consistently above 0.8, games with lower feedback intensity can be recommended to the user, such as reducing the volume of error sound effects to avoid overstimulating the prefrontal cortex.

[0201] When the working memory capacity WorkMem is insufficient, such as when the working memory capacity WorkMem is consistently greater than 2.5, a game program with memory enhancement levels can be recommended to the user, such as a 3-second countdown to memorize the direction of an arrow, and a short-term reward (such as double points) after completion.

[0202] In another example, after multiple user interactions with the game, the mean (mean vector) of AttnIndex (attention concentration) increased from 3.0 to 3.8 (deviation z=2.1), the mean (mean vector) of ImpulseControl increased from 0.5 to 0.9 (deviation z=2.4), and the mean (mean vector) of WorkMem (working memory capacity) decreased from 1.8 to 2.6 (deviation z=2.0).

[0203] At this point, recommend games with low impulsive demand to the user again, such as "Target Training" and "Flanker Missions". If the user has recently participated in "Flanker Missions", then "Flanker Missions" can be further excluded.

[0204] In this embodiment, during the user-facing operation of the game program, a brain-computer interface is invoked to collect the user's electroencephalogram (EEG) signals; preprocessing is performed on the EEG signals; if preprocessing is complete, multiple feature values ​​related to Attention Deficit Hyperactivity Disorder (ADHD) are extracted from the EEG signals; the difficulty of the game program for ADHD training is adjusted based on the feature values; if the game program ends, a first feature vector related to ADHD is constructed for the user based on the multiple feature values; and game programs are selected from the game library for the user based on the multiple first feature vectors within the current time period, so that ADHD training can continue in the next time period. This embodiment dynamically adjusts the difficulty of the game program based on the feature values ​​of the EEG signals when the user operates the game program in the short term, and selects game programs based on the first feature vectors of the EEG signals when the user operates the game program in the long term. This enables the game program to adapt to the user's ability and state, correcting ADHD training in the short and long term, and effectively improving the training efficiency of ADHD.

[0205] Example 2

[0206] See Figure 2 The diagram shows a schematic of a game regulation device according to Embodiment 2 of the present invention. The game library contains multiple game programs for training individuals with attention deficit hyperactivity disorder (ADHD), such as... Figure 2 As shown, the device includes:

[0207] The EEG signal acquisition module 201 is used to call the brain-computer interface to acquire EEG signals from the user during the operation of the game program in front of the user.

[0208] Preprocessing module 202 is used to perform preprocessing on the electroencephalogram (EEG) signal;

[0209] The feature extraction module 203 is used to extract multiple feature values ​​related to the attention deficit hyperactivity disorder from the electroencephalogram signal if preprocessing is completed.

[0210] Difficulty adjustment module 204 is used to adjust the difficulty of the game program for training the attention deficit hyperactivity disorder based on the feature value;

[0211] The feature vector construction module 205 is used to construct a first feature vector related to the attention deficit hyperactivity disorder for the user based on multiple feature values ​​if the game program ends running.

[0212] The game program screening module 206 is used to screen game programs for the user in the game library based on multiple first feature vectors in the current time period, so as to continue training for the attention deficit hyperactivity disorder in the next time period.

[0213] In one embodiment of the present invention, the plurality of feature values ​​include the band ratio between the θ band and the β band, the differential slope of the β band, and the entropy value. The feature value extraction module 203 includes:

[0214] A band recognition module is used to identify the theta band and beta band in the electroencephalogram (EEG) signal.

[0215] The band ratio calculation module is used to calculate the ratio between the power of the θ band and the power of the β band, which is used as the band ratio between the θ band and the β band.

[0216] The differential slope calculation module is used to calculate the ratio between the amplitude of the power change in the β band and the width of the time window within a preset time window, and use it as the differential slope of the β band.

[0217] The entropy calculation module is used to calculate the sample entropy of the EEG signal as the entropy value.

[0218] In one embodiment of the present invention, the difficulty adjustment module 204 includes:

[0219] The change calculation module is used to calculate the change between two adjacent band ratios.

[0220] The difficulty increase module is used to add the product between the step size and the first ratio to the current difficulty of training the attention deficit hyperactivity disorder in the game program if the change amount is greater than the first measurement threshold, so as to serve as the new difficulty of training the attention deficit hyperactivity disorder in the game program.

[0221] The difficulty reduction module is used to calculate the product between the current difficulty of the game program in training the attention deficit hyperactivity disorder and the second ratio if the change amount is less than the second measurement threshold, and use this product as the new difficulty of the game program in training the attention deficit hyperactivity disorder.

[0222] The duration statistics module is used to count the duration when the entropy value is greater than the third metric threshold.

[0223] The adjustment coefficient calculation module is used to subtract the ratio between the entropy value and the upper limit value from 1 if the duration is greater than the fourth measurement threshold, so as to obtain the adjustment coefficient.

[0224] The difficulty update module is used to calculate the product between the current difficulty of the game program in training the attention deficit hyperactivity disorder, the third ratio, and the adjustment coefficient, as the new difficulty of the game program in training the attention deficit hyperactivity disorder.

[0225] In one embodiment of the present invention, the sub-vectors in the first feature vector include attention concentration, impulse control ability, and working memory capacity.

[0226] The feature vector construction module 205 includes:

[0227] An attention concentration generation module is used to integrate the band ratio, the differential slope, and the entropy value as the main components to form the user's attention concentration related to attention deficit hyperactivity disorder (ADHD).

[0228] An impulse control ability generation module is used to integrate the band ratio, the differential slope, and the entropy value as the main components, the band ratio, and the entropy value as auxiliary components, to form the user's impulse control ability related to the attention deficit hyperactivity disorder.

[0229] The working memory capacity generation module is used to integrate the entropy value as the main component and the band ratio and the differential slope as secondary components to form the working memory capacity of the user related to the attention deficit hyperactivity disorder.

[0230] In one embodiment of the present invention, each game program in the game library is configured with a second feature vector related to the attention deficit hyperactivity disorder, wherein the sub-vectors of the second feature vector include attention concentration, impulse control ability and working memory capacity.

[0231] The game program filtering module 206 includes:

[0232] The baseline value query module is used to query the effective baseline values ​​of each sub-vector in the first feature vector within the current time period;

[0233] The deviation value calculation module is used to calculate the deviation value of each sub-vector in the first feature vector from the baseline value.

[0234] A trend identification module is used to identify significant trends in each sub-vector of the first feature vector based on the deviation value.

[0235] The training effect recognition module is used to identify the training effect of the user on the attention deficit hyperactivity disorder training in the current time period based on the changing trend of each sub-vector in the multiple first feature vectors in the current time period.

[0236] The baseline value update module is used to update the baseline value based on each sub-vector of the multiple first feature vectors in the current time period if the training effect is improved.

[0237] The similarity calculation module is used to calculate the similarity between multiple first feature vectors within the current time period and the second feature vectors of each game program in the game library if the training effect deteriorates.

[0238] The similarity filtering module is used to filter the multiple game programs with the highest similarity for the user, so that the user can continue to train for the attention deficit hyperactivity disorder in the next time period.

[0239] In one embodiment of the present invention, the deviation value calculation module includes:

[0240] The average vector calculation module is used to calculate the average value of each sub-vector of multiple first feature vectors within the current time period to obtain the average vector;

[0241] The first intermediate value calculation module is used to calculate the difference between the average vector corresponding to each sub-vector in the first feature vector and the baseline value, as the first intermediate value;

[0242] The second intermediate value calculation module is used to calculate the square root of the standard deviation of multiple historical baseline values ​​and the number of multiple first feature vectors in the current time period to obtain the second intermediate value.

[0243] The ratio calculation module is used to calculate the ratio between the first intermediate value and the second intermediate value, which is used as the deviation value of the sub-vector in the first feature vector from the baseline value.

[0244] The baseline value update module is also used for:

[0245] The average vector corresponding to each sub-vector of the multiple first feature vectors within the current time period is assigned to the baseline value.

[0246] In one embodiment of the present invention, the trend recognition module includes:

[0247] The optimization determination module is used to determine the significant change trend of each sub-vector in the first feature vector as optimization if the deviation value is less than the first significance threshold.

[0248] The degradation determination module is used to determine that if the deviation value is greater than the second significance threshold, the significant change trend of each sub-vector in the first feature vector is considered as degradation.

[0249] In one embodiment of the present invention, the exercise effect recognition module includes:

[0250] A change labeling module is used to label multiple first feature vectors within the current time period with change labels; the change labels include optimization labels or degradation labels; the optimization label indicates that the change trend of each sub-vector in the first feature vector is optimization, and the degradation label indicates that the change trend of at least one sub-vector in the first feature vector is degradation;

[0251] The improvement determination module is used to determine that the training effect of the user's training on the attention deficit hyperactivity disorder in the current time period is improved if the proportion of the optimized label is greater than a preset first proportion threshold.

[0252] The "deterioration determination module" is used to determine that the training effect of the user's training on the attention deficit hyperactivity disorder in the current time period has deteriorated if the proportion of the optimized label is less than a preset second proportion threshold.

[0253] The game adjustment device provided in this embodiment of the invention can execute the game adjustment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the game adjustment method.

[0254] Example 3

[0255] See Figure 3 This diagram illustrates a structural schematic of an electronic device according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0256] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0257] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0258] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as game modulation methods.

[0259] In some embodiments, the game adjustment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the game adjustment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the game adjustment method by any other suitable means (e.g., by means of firmware).

[0260] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0261] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0262] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0263] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0264] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0265] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0266] Example 4

[0267] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the game adjustment method provided in any embodiment of this invention.

[0268] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0269] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0270] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A game adjustment method characterized by, The method comprises the following steps: In the process of running the game program for the user, the brain-computer interface is called to collect the electroencephalogram of the user; The electroencephalogram is preprocessed; If the preprocessing is completed, a plurality of characteristic values related to the attention deficit hyperactivity disorder are extracted from the electroencephalogram, wherein the plurality of characteristic values include the band ratio between the theta band and the beta band, the differential slope of the beta band, and the entropy value; The difficulty of the game program in exercising the attention deficit hyperactivity disorder is adjusted according to the characteristic values; If the game program ends running, a first characteristic vector related to the attention deficit hyperactivity disorder is constructed for the user according to the plurality of characteristic values, wherein the sub-vectors in the first characteristic vector include the attention concentration degree, the impulse control ability, and the working memory capacity; According to the plurality of first characteristic vectors in the current time period, a game program is screened for the user in the game library to continue exercising the attention deficit hyperactivity disorder in the next time period; The step of extracting a plurality of characteristic values related to the attention deficit hyperactivity disorder from the electroencephalogram comprises the following steps: The theta band and the beta band are identified in the electroencephalogram; The ratio between the power of the theta band and the power of the beta band is calculated as the band ratio between the theta band and the beta band; In a preset time window, the ratio between the change amplitude of the power of the beta band and the width of the time window is calculated as the differential slope of the beta band; The sample entropy of the electroencephalogram is calculated as the entropy value; The step of constructing a first characteristic vector related to the attention deficit hyperactivity disorder for the user according to the plurality of characteristic values comprises the following steps: The band ratio, the differential slope, and the entropy value are fused into the attention concentration degree of the user related to the attention deficit hyperactivity disorder, with the band ratio as the main factor and the differential slope and the entropy value as the auxiliary factors; The band ratio, the differential slope, and the entropy value are fused into the impulse control ability of the user related to the attention deficit hyperactivity disorder, with the differential slope as the main factor and the band ratio and the entropy value as the auxiliary factors; The band ratio, the differential slope, and the entropy value are fused into the working memory capacity of the user related to the attention deficit hyperactivity disorder, with the entropy value as the main factor and the band ratio and the differential slope as the auxiliary factors; Each game program in the game library is configured with a second characteristic vector related to the attention deficit hyperactivity disorder, wherein the sub-vectors in the second characteristic vector include the attention concentration degree, the impulse control ability, and the working memory capacity; The step of screening a game program for the user in the game library according to the plurality of first characteristic vectors in the current time period to continue exercising the attention deficit hyperactivity disorder in the next time period comprises the following steps: The baseline values of each sub-vector in the first characteristic vector in the current time period are queried; For each sub-vector in the first characteristic vector, the deviation value of the sub-vector in the first characteristic vector from the baseline value is calculated; identify a change trend of each sub-vector in the first feature vector according to the deviation value; identify an exercise effect of the user on the attention deficit hyperactivity disorder in the current time period according to the change trend of each sub-vector in the first feature vector in the current time period; if the exercise effect is getting better, update the baseline value according to each sub-vector of the first feature vector in the current time period; if the exercise effect is getting worse, calculate a similarity between the first feature vector in the current time period and the second feature vector of each game program in the game library; select the game programs with the highest similarity for the user to continue the exercise on the attention deficit hyperactivity disorder in the next time period.

2. The method of claim 1, wherein, the adjusting the difficulty of the game program in exercising the attention deficit hyperactivity disorder according to the feature value, comprises: calculate a change amount between two adjacent wave band ratios; if the change amount is greater than a first measurement threshold, add a product between a step length and a first proportion to a current difficulty of the game program in exercising the attention deficit hyperactivity disorder as a new difficulty of the game program in exercising the attention deficit hyperactivity disorder; if the change amount is less than a second measurement threshold, calculate a product between the current difficulty of the game program in exercising the attention deficit hyperactivity disorder and a second proportion as the new difficulty of the game program in exercising the attention deficit hyperactivity disorder; statistically calculate a time length when the entropy value is greater than a third measurement threshold; if the time length is greater than a fourth measurement threshold, obtain an adjusting coefficient by subtracting a ratio between the entropy value and an upper limit value from 1; calculate a product between the current difficulty of the game program in exercising the attention deficit hyperactivity disorder, a third proportion and the adjusting coefficient as the new difficulty of the game program in exercising the attention deficit hyperactivity disorder.

3. The method of claim 1, wherein, the calculating the deviation value of each sub-vector in the first feature vector from the baseline value, comprises: calculate an average value of each sub-vector of the first feature vector in the current time period to obtain an average vector; calculate a difference between the average vector corresponding to each sub-vector in the first feature vector and the baseline value as a first intermediate value; calculate a square root of a number of the first feature vector in the current time period and a standard deviation of a plurality of historical baseline values to obtain a second intermediate value; calculate a ratio between the first intermediate value and the second intermediate value as the deviation value of each sub-vector in the first feature vector from the baseline value; the updating the baseline value according to each sub-vector of the first feature vector in the current time period, comprises: assign the average vector corresponding to each sub-vector of the first feature vector in the current time period to the baseline value.

4. The method of claim 1, wherein, the identifying a change trend of each sub-vector in the first feature vector according to the deviation value, comprises: If the deviation value is less than a first significant threshold, it is determined that each sub-vector in the first feature vector has an optimization trend of significance change; If the deviation value is greater than a second significant threshold, it is determined that each sub-vector in the first feature vector has a deterioration trend of significance change.

5. The method of claim 1, wherein, The identifying the exercise effect of the user on the attention deficit hyperactivity disorder in the current time period according to the change trends of each sub-vector in the plurality of first feature vectors in the current time period comprises: labeling change labels for the plurality of first feature vectors in the current time period; the change labels comprise optimization labels or deterioration labels; the optimization label indicates that the change trends of each sub-vector in the first feature vector are all optimization, and the deterioration label indicates that the change trend of at least one sub-vector in the first feature vector is deterioration; If the proportion of the optimization labels is greater than a preset first proportion threshold, it is determined that the exercise effect of the user on the attention deficit hyperactivity disorder in the current time period is getting better; If the proportion of the optimization labels is less than a preset second proportion threshold, it is determined that the exercise effect of the user on the attention deficit hyperactivity disorder in the current time period is getting worse.

6. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the game adjustment method according to any one of claims 1-5.

7. A computer readable storage medium characterized by, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the game adjustment method according to any one of claims 1-5.

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