A cognitive compensation evaluation and regulation system

Abstract

This application relates to the field of neuromodulation technology, and in particular to a cognitive compensation assessment and regulation system. The cognitive compensation assessment system includes: a task module for distinguishing different types of cognitive compensation modes and triggering target task paradigms that activate different cognitive compensation modes; a data acquisition module for collecting behavioral data and brain imaging data of the user when performing the target task paradigm; a cognitive compensation calculation module for calculating the cognitive compensation effect exhibited by the user based on the collected behavioral data and brain imaging data to obtain a cognitive compensation index; and a cognitive compensation monitoring module for monitoring the cognitive compensation index, determining whether the target compensation effect has been triggered in the current target task paradigm, and assessing the user's cognitive compensation ability based on the user's behavioral data and the triggering status of the target compensation effect, thereby enabling adaptive regulation of the user's cognitive compensation ability.

Description

Technical Field

[0001] This application relates to the field of neuromodulation technology, and in particular to a cognitive compensation assessment and modulation system. Background Technology

[0002] Cognition refers to a series of psychological and neural processes by which an individual acquires, processes, stores, and utilizes information; it is the process by which humans process information. Cognitive ability determines the speed and quality of an individual's information acquisition and processing, and is a prerequisite for the realization of higher functions such as learning, decision-making, emotion regulation, and social interaction. It not only supports basic activities in daily life but also directly affects an individual's adaptability and problem-solving abilities in complex environments. From the perspective of cognitive neuroscience, the series of cognitive processes are not the functional products of a single brain region, but rather a complex process achieved through the dynamic interaction of multiple functional systems distributed throughout the brain.

[0003] Normal cognitive aging refers to the gradual cognitive changes experienced by individuals with age in the absence of clear neurological diseases or severe pathological damage. From a behavioral perspective, numerous studies have consistently found that processing speed, attentional control, working memory, and episodic memory are the functional areas most susceptible to impact during cognitive aging.

[0004] Existing studies have identified typical patterns of cognitive compensation during aging, but there are currently no systematic and sophisticated solutions based on these compensation patterns in existing technologies. Summary of the Invention

[0005] The purpose of this application is to provide a cognitive compensation assessment and regulation system. This system uses a specially designed target task paradigm as a trigger to collect relevant behavioral data and brain imaging data of users, calculates and monitors the cognitive compensation effect and related indicators of users, and promotes the occurrence of cognitive compensation in the form of neural regulation when the cognitive compensation ability of users is insufficient, thereby realizing cognitive compensation when cognitive function is impaired or resources are insufficient.

[0006] In some embodiments, this application provides a cognitive compensation assessment system, the system comprising: a task module, configured to distinguish different types of cognitive compensation modes and trigger target task paradigms that activate different cognitive compensation modes; a data acquisition module, configured to collect behavioral data and brain imaging data of the user when performing the target task paradigm; a cognitive compensation calculation module, configured to calculate the cognitive compensation effect exhibited by the user based on the collected behavioral data and brain imaging data, to obtain a cognitive compensation index, the cognitive compensation index including at least one of a degradation index of the brain region of interest in the task, an index indicating whether a compensation mode appears, and a comprehensive cognitive level index; and a cognitive compensation monitoring module, configured to monitor the cognitive compensation index, determine whether a target compensation effect is triggered in the current target task paradigm, and assess the user's cognitive compensation ability based on the user's behavioral data and the triggering status of the target compensation effect.

[0007] In some embodiments, the system further includes a cognitive compensation generalization testing module for evaluating the generalization ability of the cognitive compensation results formed by the user under different test task paradigms. The cognitive compensation generalization testing module includes a cognitive compensation generalization testing unit and a cognitive compensation adjustment unit. The cognitive compensation generalization testing unit is used to perform cognitive compensation testing on the constructed test task paradigm and obtain the user's cognitive compensation ability. The cognitive compensation adjustment unit is used to dynamically adjust the task parameters of the target task paradigm according to the cognitive compensation ability.

[0008] In some embodiments, the cognitive compensation calculation module includes a task-interested brain region activation level calculation unit, a compensation level calculation unit, and a behavioral performance calculation unit; the task-interested brain region activation level calculation unit is used to calculate the activation level of brain regions related to the target task paradigm, and is used to measure the degradation index of the task-interested brain regions; the compensation level calculation unit is used to calculate the compensation level of different types of cognitive compensation patterns, and is used to measure whether the compensation pattern has an index; the performance calculation unit is used to calculate the behavioral performance of different target task paradigms, and is used to measure the comprehensive cognitive level index.

[0009] In some embodiments, the cognitive compensation calculation module further includes a brain region segmentation unit, which includes a task-interested brain region segmentation subunit and a compensation-interested brain region segmentation subunit. The task-interested brain region segmentation subunit is used to segment brain regions into task-dependent task-interested brain regions for different types of cognitive compensation modes and target task paradigms. The task-interested brain regions are baseline regions for functional activation, representing core cognitive processing brain network regions necessary for the normal execution of inhibitory control tasks. The compensation-interested brain region segmentation subunit is used to segment brain regions into compensation-theory-dependent compensation-interested brain regions for different types of cognitive compensation modes and target task paradigms. The compensation-interested brain regions are potential compensation participation regions, reflecting compensation brain network regions that reflect higher-order cognitive control and strategy regulation. The cognitive compensation calculation module is used to analyze the activation patterns and relative changes of the task-interested brain regions and the compensation-interested brain regions during the target task paradigm process, determine whether there is an activation shift trend, and judge whether the compensation mechanism of the corresponding cognitive compensation mode is triggered during the inhibitory control process.

[0010] In some embodiments, the cognitive compensation calculation module further includes a brain region activation calculation unit, which includes an activation level calculation subunit and an activation level mapping subunit. The activation level calculation subunit processes the acquired brain imaging data to obtain time-series data from multiple channels. Based on the start time, duration, and event label of each event in the target task paradigm, it defines an event function. The event function is convolved with the dynamic response function to obtain a single-trial regression quantity representing the predicted response of a single event in the brain imaging data signal, which is then combined into a design matrix. A generalized linear model is established for the design matrix corresponding to the brain imaging data signal of each channel, and a method is used... Least squares are used to obtain parameter estimates as a quantitative indicator of the activation level of each channel. The activation level mapping subunit is used to construct the mapping relationship between cortical vertices and channels in standard space based on the imaging principle and projection rules of brain imaging data. The activation level of each channel is projected to each vertex in standard space. Under the constraint of brain region segmentation results, the activation levels in brain regions of interest in the task are converged to obtain brain region-level features reflecting the core processing of the task. Neural signals in brain regions of interest in compensation are converged to obtain the participation level of potential compensation areas in the task process. Based on the participation level, it is determined whether the corresponding cognitive compensation mechanism is triggered during the inhibitory control process.

[0011] In some embodiments, the cognitive compensation monitoring module includes a behavior monitoring unit, an activation monitoring unit, a compensation monitoring unit, and a fusion monitoring unit. The behavior monitoring unit monitors the user's behavioral performance indicators in different types of cognitive compensation modes within the target task paradigm, and monitors the user's explicit cognitive execution ability under current task conditions. The activation monitoring unit monitors the activation level indicators of the user's task-interested brain regions, reflecting the participation level of the core task processing brain network. The compensation monitoring unit characterizes the additional participation indicators of potential compensation-interested brain regions during the task process, reflecting whether the brain is maintaining task performance through functional reorganization. The fusion monitoring unit determines the user's cognitive compensation ability based on the user's behavioral performance indicators, the activation level indicators of task-interested brain regions, and the additional participation indicators.

[0012] In some embodiments, the different types of cognitive compensation modes include a first compensation mode and a second compensation mode, the first compensation mode and the second compensation mode including a mode of reduced hemispheric asymmetry in brain activation and a mode of brain activation shifting from the posterior sensory area to the anterior higher control area.

[0013] In some embodiments, the task module further includes a first task parameter unit, a first task unit, a second task parameter unit, and a second task unit; the first task parameter unit is used to set and adjust the core parameters of the target task paradigm of the first compensation mode; the first task unit is used to trigger and start the target task paradigm and process of the first compensation mode; the second task parameter unit is used to set and adjust the core parameters of the target task paradigm of the second compensation mode; the second task unit is used to trigger and start the target task paradigm and process of the second compensation mode.

[0014] In some embodiments, the data acquisition module further includes a behavioral data acquisition unit and a brain imaging data acquisition unit; the behavioral data acquisition unit is used to collect the user's behavioral performance when completing the target task paradigm, the behavioral performance including at least one of reaction time, accuracy rate, error type and task completion strategy; the brain imaging data acquisition unit is used to continuously collect functional near-infrared brain imaging data generated by the user during the completion of the target task paradigm and form time series data to reflect the changes in brain blood oxygen dynamics of the user under different target task paradigms.

[0015] This application also provides a cognitive compensation regulation system, which includes the cognitive compensation assessment system described in any of the above embodiments. The cognitive compensation regulation system further includes a cognitive compensation promotion module, which is used to determine neural regulation parameters when the cognitive compensation ability assessed by the cognitive compensation assessment system is insufficient, and to perform neural regulation on the user based on the neural regulation parameters. The cognitive compensation promotion module includes a neural regulation triggering unit, a neural regulation parameter setting unit, and a neural regulation parameter execution unit. The neural regulation triggering unit is used to trigger the cognitive compensation promotion module to perform neural regulation on the user when the cognitive compensation ability determines that the user's behavioral ability does not meet preset conditions and the target compensation effect also does not meet preset conditions. The neural regulation parameter setting unit is used to initialize and adjust the regulation parameters of the neural regulation, which include at least one of regulation channel, regulation wavelength, regulation intensity, regulation position, and regulation duration. The neural regulation parameter execution unit is used to perform neural regulation on the user based on the determined regulation parameters.

[0016] In the above embodiments, a specially designed target task paradigm is used as a trigger to collect relevant user behavior data and brain imaging data, calculate and monitor the user's cognitive compensation effect and related indicators, and promote the occurrence of cognitive compensation in the form of neural modulation when the user's cognitive compensation ability is insufficient, thereby realizing cognitive compensation when cognitive function is impaired or resources are insufficient. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:

[0018] Figure 1 This is a schematic diagram of a cognitive compensation assessment system module provided in one embodiment of this application;

[0019] Figure 2 This is a schematic diagram of brain region segmentation provided in one embodiment of this application;

[0020] Figure 3 This is a schematic diagram of the HAROLD-related brain regions of interest provided in one embodiment of this application;

[0021] Figure 4 This is a schematic diagram of the PASA-related brain regions of interest provided in one embodiment of this application;

[0022] Figure 5 This is a schematic diagram of the light stimulation position provided in one embodiment of this application;

[0023] Figure 6 This is a schematic diagram of a cognitive function task library provided in one embodiment of this application;

[0024] Figure 7 This is a schematic diagram illustrating a PASA paradigm example provided in one embodiment of this application;

[0025] Figure 8 This is a schematic diagram of a cognitive compensation regulation system module provided in another embodiment of this application;

[0026] Figure 9 This is a schematic diagram illustrating an example of the HAROLD paradigm provided in one embodiment of this application;

[0027] Figure 10 This is a schematic diagram of a cognitive compensation assessment and regulation system module provided in one embodiment of this application;

[0028] Figure 11 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0030] The technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0031] This solution does not aim to obtain disease diagnosis results or health status. It is a system that processes user behavioral data and brain imaging data to achieve cognitive compensation assessment and regulation. All methods and steps involved in the system are data processing methods implemented by computers and other devices.

[0032] It should be fully understood that the user behavior data and brain imaging data involved in this application (including but not limited to user reaction time, accuracy rate, error type and task completion strategy, user functional near-infrared brain imaging data and user personal information, etc.) are all information and data authorized by the user or fully authorized by all parties. The use of user information shall comply with the privacy policies and practices of the industry that are generally considered to meet or exceed the requirements for maintaining user privacy. The collection, use and processing of related data shall comply with relevant laws, regulations and standards, and provide corresponding operation access points for users to choose to authorize or refuse.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0034] It should be noted that the neural modulation method provided in this application can specifically be light stimulation modulation, and other types of neural modulation methods can also be used in other embodiments, not limited to light stimulation. In the following embodiments, light stimulation is mainly used as an example of neural modulation.

[0035] In some embodiments, see Figure 1 This application provides a cognitive compensation assessment system 100, the system 100 comprising:

[0036] Task module 101 is used to distinguish different types of cognitive compensation modes and trigger the target task paradigm that starts different cognitive compensation modes.

[0037] The data acquisition module 102 is used to collect behavioral data and brain imaging data of the user when performing the target task paradigm.

[0038] The cognitive compensation calculation module 103 is used to calculate the cognitive compensation effect exhibited by the user based on the collected behavioral data and brain imaging data, and obtain a cognitive compensation index. The cognitive compensation index includes at least one of the following: a degradation index of the brain region of interest in the task, an index indicating whether a compensation pattern appears, and a comprehensive index of cognitive level.

[0039] The cognitive compensation monitoring module 104 is used to monitor the cognitive compensation indicators, determine whether the target compensation effect is triggered in the current target task paradigm, and evaluate the user's cognitive compensation ability based on the user's behavioral data and the triggering of the target compensation effect.

[0040] Specifically, task module 101 is used to trigger and distinguish different types of cognitive compensation modes. In some embodiments, the different types of cognitive compensation modes include a first compensation mode and a second compensation mode, which include the brain activation hemispheric asymmetry reduction mode (HAROLD) and the brain activation shift from the posterior sensory area to the anterior higher control area mode (PASA). Furthermore, the different types of cognitive compensation modes have different task paradigms.

[0041] In some embodiments, the task module 101 further includes a first task parameter unit, a first task unit, a second task parameter unit, and a second task unit. The first task parameter unit is used to set and adjust the core parameters of the target task paradigm of the first compensation mode. The first task unit is used to trigger and start the target task paradigm and process of the first compensation mode. The second task parameter unit is used to set and adjust the core parameters of the target task paradigm of the second compensation mode. The second task unit is used to trigger and start the target task paradigm and process of the second compensation mode. Specifically, the task module 101 is used to trigger and distinguish different types of cognitive compensation modes, and includes four units: the first task parameter unit can be a HAROLD task unit, the second task parameter unit can be a PASA task unit, and the second task parameter unit can be a PASA task parameter unit. In a specific embodiment, the HAROLD task parameter unit is used to adjust the core parameters in the task paradigm that triggers the HAROLD compensation mode, and the HAROLD task unit is used to trigger the task paradigm and process of the HAROLD compensation mode. The PASA task parameter unit is used to adjust the core parameters in the task paradigm that triggers the PASA compensation mode. The PASA task unit is used to trigger the task paradigm and process of the PASA compensation mode.

[0042] In a specific embodiment, for the first compensation mode (HAROLD compensation mode), this application designs a task paradigm specifically for triggering and monitoring the HAROLD compensation effect, named the HAROLD paradigm (target task paradigm). See [link to relevant documentation]. Figure 9 The basic logic behind this task paradigm is that the cognitive processing triggered within it is primarily unilateral, requiring a shift to a collaborative activity pattern involving both hemispheres when a cognitively aged user wants to achieve performance levels comparable to non-cognitively aged individuals. Therefore, this paradigm includes a series of cognitive functions reliant on classic unilateral activity (such as semantic processing, unilateral sensory information retrieval, and inhibitory control). Based on these cognitive functions, parameters such as task rule complexity, interference intensity, or time pressure are adjusted to continuously increase the cognitive load, gradually limiting the cognitive resources of the unilateral hemisphere. At this point, if a cognitively aged user wishes to maintain cognitive performance within the normal range of non-cognitively aged individuals, they need to recruit the contralateral hemisphere to participate in cognitive processing, thus shifting the cognitive processing from unilateral dominance to a collaborative activity pattern involving both hemispheres. Taking inhibitory control as an example, this application designs several experimental condition blocks: no load (L0), low load (L1), medium load (L2), and high load (L3). In each experimental block, the user needs to perform actions according to the rules of that block.

[0043] In one specific embodiment, for the second compensation mode (PASA compensation mode), this application designs a task paradigm specifically for the triggering and monitoring of PASA compensation effects (see [link to relevant documentation]). Figure 7 This is named the PASA paradigm (Target Task Paradigm). The basic logic behind this paradigm is that the cognitive processing induced in this paradigm is primarily driven by the activity of the occipital cortex. When a cognitively aged user wants to reach the performance range of a cognitively aged user, it needs to shift to an activity mode that additionally mobilizes the prefrontal cortex. Therefore, this paradigm includes a series of cognitive functions that rely on classical posterior hemisphere activity (such as face recognition, image naming, and visual separation processing). Based on these cognitive functions, by adjusting parameters such as task rule complexity, interference intensity, or time pressure, the cognitive load is continuously increased, and the cognitive resources of the posterior hemisphere are gradually limited. At this point, if a cognitively aged user wants to maintain their cognitive performance within the normal range of a cognitively aged user, it needs to recruit the anterior hemisphere to participate in cognitive processing, thereby prompting the cognitive processing to shift from a posterior hemisphere-dominated mode to a collaborative activity mode involving both hemispheres. Taking face recognition as an example, this application designed several experimental condition blocks: no load (L0), low load (L1), medium load (L2), and high load (L3). Among them, the face corresponding to high load (L3) can be a real face that meets privacy compliance requirements. The PASA paradigm example is as follows. Figure 7 As shown, for privacy compliance reasons, the following... Figure 7 The high-load (L3) task in the PASA paradigm corresponds to partially occluded faces. This does not mean that users are actually performing face recognition on occluded faces when performing the high-load (L3) task in the PASA paradigm. Users are actually performing face recognition on complete, real faces. In each experimental block, users need to process faces according to the rules of that experimental block and answer corresponding questions.

[0044] In the above embodiments, by performing the above tasks under different task load conditions, standardized induction conditions can be provided for the subsequent calculation and monitoring of cognitive compensation effects.

[0045] In some embodiments, the system further includes a task presentation module. This module presents specific tasks to the user in a visual or multimodal format according to the task paradigm parameters set in the task module, and controls the start, end, and stimulus sequence (neuromodulation sequence) of the task to ensure the standardization and repeatability of the experimental process. Specifically, in this task presentation module, the task paradigm is presented in a standardized manner according to a preset task paradigm structure and task parameters. Based on pre-built task identifiers, task logic, and task parameter interfaces, tasks are uniformly configured and presented to ensure consistency, repeatability, and traceability of different tasks during execution. The task presentation flow is generated based on the task execution structure defined in the task logic. Furthermore, the specific parameters of the task are configured through the task parameter interface. The parameter configuration includes at least cognitive load level, stimulus presentation duration, stimulus interval, number of trials, and task duration, and can be adjusted according to evaluation needs or user status. After completing the above task invocation, flow generation, and parameter configuration, the task presentation module presents task stimuli (task modulation) to the user according to the generated task flow and guides the user to complete the corresponding operations.

[0046] In some embodiments, the data acquisition module 102 further includes a behavioral data acquisition unit and a brain imaging data acquisition unit. The behavioral data acquisition unit is used to collect the user's behavioral performance when completing a target task paradigm, including at least one of reaction time, accuracy rate, error type, and task completion strategy. The brain imaging data acquisition unit is used to continuously collect functional near-infrared brain imaging data generated by the user during the completion of the target task paradigm and form time-series data to reflect changes in the user's brain blood oxygen dynamics under different target task paradigms.

[0047] Specifically, the data acquisition module 102 is used to collect behavioral data and brain imaging data during the completion of the task paradigm. Specifically, the data acquisition module includes a behavioral data acquisition unit and a brain imaging data acquisition unit. The behavioral data acquisition unit is used to collect the user's behavioral performance during the completion of the target task paradigm. The behavioral data refers to the behavioral data generated by the user when completing the experimental task. The behavioral data includes at least reaction time, accuracy rate, error type, and indicators related to task completion strategies. The brain imaging data acquisition unit is used to continuously collect functional near-infrared brain imaging data generated by the user during the completion of the target task paradigm and form time-series data to reflect the changes in brain blood oxygen dynamics under different target task paradigms.

[0048] During the execution of the aforementioned tasks, behavioral data and brain imaging data are collected. The behavioral data records the user's performance during the cognitive task and includes at least the following data types: event-labeled data, used to record the attributes of various stimuli and additional events presented during the experiment; time-stamped data, used to record the timestamps of the event-labeled data during the experiment; user reaction data, used to record the user's actions on the reactor during the experiment; and user reaction time-stamped data, used to record the timestamps of the user's reactions during the experiment. The cognitive functional brain imaging data is functional near-infrared brain imaging data (fNIRS), used to record changes in brain blood oxygen dynamics under different cognitive task conditions. It includes oxyhemoglobin signals and deoxyhemoglobin signals, and is continuously collected during task execution to form corresponding time-series data.

[0049] In some specific embodiments, functional near-infrared brain imaging (fIRBIM) is preferred as a means of monitoring neural states rather than electroencephalography (EEG). On the one hand, fIRBIM reflects neural activity by detecting changes in brain oxygenation dynamics, is insensitive to light stimulation and its interference, and is suitable for stable operation under light stimulation conditions. On the other hand, fIRBIM has good spatial localization capabilities, directly reflecting changes in the metabolic response of specific cortical brain regions during cognitive tasks and stimulus interventions, and typically exhibits higher stability and reproducibility in terms of temporal scale, spatial localization, and consistency across experimental conditions. Furthermore, fIRBIM is suitable for long-term, continuous, and low-burden monitoring, which is beneficial for constructing long-term cognitive training systems.

[0050] In some embodiments, the cognitive compensation calculation module 103 is used to quantitatively calculate and characterize the cognitive compensation effect exhibited by the user to obtain a cognitive compensation index. In a specific embodiment, the cognitive compensation calculation module includes a task-interested brain region activation degree calculation unit, a compensation degree calculation unit, and a behavioral performance calculation unit. The task-interested brain region activation degree calculation unit is used to calculate the activation degree of brain regions related to the target task paradigm to measure the degradation index of the task-interested brain regions; the compensation degree calculation unit is used to calculate the compensation degree of different types of cognitive compensation patterns to measure whether the compensation pattern has an index; the performance calculation unit is used to calculate the behavioral performance of different target task paradigms to measure the comprehensive cognitive level index.

[0051] Specifically, the cognitive compensation calculation module 103 includes a task-interested brain region activation level calculation unit, a compensation level calculation unit, and a behavioral performance calculation unit. The task-interested brain region activation level calculation unit is used to calculate the activation level related to the task, which is used to measure the degradation of task-dependent brain regions; the compensation level calculation unit is used to calculate the HAROLD and PASA compensation levels, which can be used to measure whether a compensation pattern has occurred; the behavioral performance calculation unit is used to calculate behavioral performance under different task paradigms, which can be used to measure the overall cognitive level.

[0052] In one specific embodiment, the cognitive compensation calculation module further includes a brain region segmentation unit, which comprises a task-interested brain region segmentation subunit and a compensation-interested brain region segmentation subunit. The task-interested brain region segmentation subunit is used to segment brain regions into task-dependent task-interested brain regions for different types of cognitive compensation modes and target task paradigms. The task-interested brain regions are baseline regions for functional activation, representing the core cognitive processing brain network regions necessary for the normal execution of the inhibitory control task. The compensation-interested brain region segmentation subunit is used to segment brain regions into compensation-theory-dependent compensation-interested brain regions for different types of cognitive compensation modes and target task paradigms. The compensation-interested brain regions are potential compensation participation regions, reflecting compensation brain network regions that reflect higher-order cognitive control and strategy regulation. The cognitive compensation calculation module is used to analyze the activation patterns and relative changes of the task-interested brain regions and the compensation-interested brain regions during the target task paradigm process, determine whether there is an activation shift trend, and judge whether the compensation mechanism of the corresponding cognitive compensation mode is triggered during the inhibitory control process.

[0053] In some embodiments, the cognitive compensation calculation module further includes a brain region segmentation unit, which uses a preset brain region segmentation atlas (Destrieux) to divide the standard space into several brain regions. , For the corresponding set, such as formula (1) and Figure 2 As shown, where, Figure 2 The brain regions are divided into areas for display, with different numbers representing different brain regions.

[0054] (1)

[0055] In some embodiments, the brain region segmentation unit includes a task-interested brain region segmentation subunit and a compensatory interest brain region segmentation subunit. In practice, brain regions of interest related to HAROLD are first defined. In the HAROLD paradigm, two types of brain regions of interest will be defined: one type is task-dependent, i.e., task-interested brain regions. One type of brain region of interest is the compensatory brain region of interest, which depends on the Harold compensation theory. Taking the inhibition of motor control as an example, the brain regions of interest are task-dependent, i.e., the task-interested brain regions. Defined as the bilateral dorsolateral prefrontal cortex. And the bilateral inferior frontal gyrus. , such as formula (2) and Figure 3 The area shown in red indicates the brain regions of interest dependent on Harold's compensation theory, also known as compensatory regions of interest. Defined as the bilateral postcentral gyrus. , such as formula (3) and Figure 3 The yellow area in the middle shows the brain regions of interest. First, task-dependent brain regions of interest are used as the baseline regions for functional activation to characterize the core cognitive processing networks necessary for the normal execution of inhibitory control tasks. Then, brain regions of interest dependent on HAROLD compensation theory, namely compensatory brain regions of interest, are introduced. This study identifies potential compensatory regions. By comparing the activation patterns and hemispheric distribution characteristics of two types of brain regions of interest during the task, it analyzes whether there is a trend of shifting from unilateral dominant activation to bilateral synergistic activation, thereby determining whether the compensatory mechanism described by Harold is triggered during inhibitory control. Combining task functional localization with the theoretical assumptions of compensation provides a clear neurophysiological explanation for subsequent assessments of compensation intensity and analysis of individual differences.

[0056] (2)

[0057] (3)

[0058] Furthermore, PASA defines brain regions of interest. In the PASA paradigm, two types of brain regions of interest will be defined: one type is task-dependent, i.e., task-related brain regions of interest. One type of brain region of interest is the compensatory brain region of interest, which depends on the PASA compensation theory. Taking the face recognition version as an example, the brain regions of interest, depending on the task, are defined as the bilateral fusiform gyrus. , such as formula (4) and Figure 4 The red area in the middle indicates the brain region of interest dependent on the PASA compensation theory, defined as the bilateral dorsolateral prefrontal cortex. Bilateral inferior frontal gyrus Bilateral superior frontal gyrus Lateral occipital cortex on both sides The bilateral medial occipital cortex , such as formula (5) and Figure 4 The yellow area in the middle indicates the brain region of interest. First, task-dependent brain regions of interest are used as the functional benchmark regions for primary perceptual processing, representing the core processing of the posterior visual pathway in face recognition tasks. Then, brain regions of interest dependent on PASA compensation theory are introduced as anterior compensatory regions reflecting higher-order cognitive control and strategy regulation. By comparing the activation intensity and relative changes of these two types of brain regions of interest during task execution, the study analyzes whether there is a trend of shifting from posterior perceptual processing to prefrontal control processing as task load or individual cognitive state changes. This characterizes the compensatory reorganization pattern described by PASA theory, characterized by weakened posterior activation and enhanced prefrontal activation.

[0059] (4)

[0060] (5)

[0061] In some embodiments, the cognitive compensation calculation module further includes a brain region activation calculation unit, which includes an activation degree calculation subunit and an activation degree mapping subunit. The activation level calculation subunit processes the acquired brain imaging data to obtain time-series data for multiple channels. It defines an event function based on the start time, duration, and event label of each event in the target task paradigm. The event function is convolved with the dynamic response function to obtain a single-trial regression quantity representing the predicted response of a single event in the brain imaging data signal, which is then combined into a design matrix. A generalized linear model is established for the design matrix corresponding to the brain imaging data signal of each channel, and the parameter estimate is obtained using least squares as a quantitative indicator of the activation level of each channel. The activation level mapping subunit, based on the imaging principle and projection rules of the brain imaging data, constructs a mapping relationship between cortical vertices and channels in a standard space. It projects the activation level of each channel to each vertex in the standard space. Under the constraint of brain region segmentation results, it converges the activation levels in brain regions of interest in the task to obtain brain region-level features reflecting the core processing of the task. It also converges neural signals in brain regions of interest in compensation to obtain the participation level of potential compensatory regions in the task process. Based on the participation level, it determines whether the corresponding cognitive compensation mechanism is triggered during the inhibitory control process.

[0062] Specifically, the brain activation level is estimated based on the task paradigm. This involves event-related modeling and single-trial activation level estimation based on a generalized linear model. The activation level calculation subunit processes the acquired brain imaging data to obtain multi-channel time-series data. Specifically, after standard preprocessing of the acquired brain imaging data (fNIRS signals), the following is obtained: Time series data of each channel The matrix formed As shown in formula (6).

[0063] (6)

[0064] As shown in formula (7), for each event in the experiment Define its start time Duration and event tags (e.g., "No-load test conditions_First trial"). As shown in formula (8), This indicates whether the nth experimental event is active at time t, with 1 indicating the event occurring and 0 indicating it not occurring.

[0065] (7)

[0066] (8)

[0067] Since the blood oxygenation changes measured by fNIRS are the result of neural activity filtered by a hemodynamic system, a hemodynamic response function is introduced. This is used to characterize the temporal response pattern of a single transient neural activation. Subsequently, by convolving the event function with the response function, the ... The predicted response of an event in the fNIRS signal, i.e., the regression value of a single trial. And combine them into a design matrix As shown in formulas (9) and (10).

[0068] (9)

[0069] (10)

[0070] Then, for the fNIRS signal of each channel... A generalized linear model is established, and the parameter estimates obtained by the least squares method are used as a quantitative indicator of activation level. , cannot be designed matrix The explanation part is As shown in formula (11).

[0071] (11)

[0072] Subsequently, the mapping between channels and the cerebral cortex is performed. Based on the principles of fNIRS imaging and projection rules, the mapping relationship between cortical vertices and channels is constructed in the standard space, and the activation level of each channel is projected to each vertex in the standard space.

[0073] Constrained by brain region segmentation results, activation levels in task-dependent brain regions of interest are aggregated to obtain brain region-level features reflecting the core processing of the task. The same calculations are performed on neural signals in brain regions of interest dependent on compensation theory to characterize the involvement of potential compensatory regions in the task process.

[0074] Specifically, in the HAROLD paradigm, taking the inhibition-controlled motion version as an example, The activation level of the HAROLD paradigm task-dependent brain region corresponding to the nth event, and the activation level of task dependence. Defined as the average activation level of brain regions related to inhibitory control minus the baseline, as shown in Equation (12), where Brain regions of interest for the task Belonging to the brain region of interest A collection of voxels, This represents the activation level of the vth voxel at the nth event.

[0075] See formula (13) and formula (14). The HAROLD paradigm compensation theory for the nth event depends on the activation level of the brain region. This belongs to the brain region of interest in the task. A collection of voxels, The degree of HAROLD compensation corresponding to the nth event, and the degree of compensation dependent on compensation theory. The tendency defined as lateralization is determined by the degree of activation dependent on compensatory theory. This index is calculated; the smaller the value, the greater the compensation. as well as The HAROLD compensation theory for the left or right side depends on the activation level of the brain region. When the former is the left side, the latter is the right side, as shown in formulas (13) and (14).

[0076]

[0077]

[0078] (14)

[0079] In the PASA paradigm, taking the facial recognition version as an example, The activation level of the PASA paradigm task-dependent brain region corresponding to the nth event. Belonging to the brain region of interest The set of voxels, the degree of task-dependent activation It is defined as the average activation level of the brain regions related to inhibitory control after subtracting the baseline, as shown in Equation (15).

[0080] The PASA paradigm compensation theory for the nth event depends on the activation level of the brain region. Belonging to the brain region of interest The set of voxels, and the degree of compensation dependent on the compensation theory. The trend defined as the difference before and after is determined by the degree of activation of compensatory theory dependence. This is calculated; the larger the index, the greater the compensation amount. The degree of PASA compensation corresponding to the nth event. as well as The PASA compensation theory for the anterior or posterior side depends on the degree of activation of the brain region. When the former is anterior, the latter is posterior, as shown in formulas (16) and (17).

[0081]

[0082]

[0083] (17)

[0084] The calculation of user behavior in the HAROLD and PASA paradigms mainly focuses on correct response and reaction time, as shown in formulas (18) and (19). As a comprehensive behavioral indicator of the HAROLD paradigm, For the behavior indicators at the nth event, such as reaction time or judgment labels, As a comprehensive behavioral indicator of the PASA paradigm, For the nth event, the behavioral indicators, such as reaction time or judgment labels.

[0085]

[0086]

[0087] In some embodiments, the cognitive compensation monitoring module 104 is used to continuously monitor and determine the compensation indicators output by the cognitive compensation calculation module. The cognitive compensation monitoring module includes a behavior monitoring unit, an activation monitoring unit, a compensation monitoring unit, and a fusion monitoring unit. The behavior monitoring unit is used to monitor the user's behavioral performance indicators in different types of cognitive compensation modes within the target task paradigm, and to monitor the user's explicit cognitive execution ability under the current task conditions. The activation monitoring unit is used to monitor the activation level indicators of the brain regions of interest in the user's task, reflecting the participation level of the core processing brain network of the task. The compensation monitoring unit is used to characterize the additional participation indicators of potential compensation interest brain regions during the task process, reflecting whether the brain is maintaining task performance through functional reorganization. The fusion monitoring unit is used to determine the user's cognitive compensation ability based on the user's behavioral performance indicators, the activation level indicators of the brain regions of interest in the task, and the additional participation indicators.

[0088] Specifically, the cognitive compensation monitoring module can include two units: a HAROLD monitoring unit and a PASA monitoring unit. The HAROLD monitoring unit determines whether the user has induced and triggered the HAROLD compensation effect in the current task; the PASA monitoring unit determines whether the user has induced and triggered the PASA compensation effect in the current task. When the monitoring module detects that the user's behavior is significantly worse than normal and there is no obvious induction and triggering of cognitive compensation, the cognitive compensation monitoring module will generate a judgment result of insufficient compensation and send a trigger signal to the cognitive compensation promotion module to initiate the subsequent cognitive compensation promotion process.

[0089] The cognitive compensation monitoring module includes a behavior monitoring unit, an activation monitoring unit, a compensation monitoring unit, and a fusion monitoring unit. It first monitors behavioral performance, then monitors the activation level of task dependence and the compensation level of theoretical dependence. Specifically, the behavior monitoring unit monitors the user's behavioral performance indicators in different types of cognitive compensation modes within the target task paradigm. This monitors the user's explicit cognitive execution ability under the current task conditions; that is, the system first monitors the user's behavioral performance in the HAROLD and PASA paradigms, including indicators such as reaction accuracy and reaction time, to characterize the user's explicit cognitive execution ability under the current task conditions. The activation monitoring unit monitors the activation level indicators of the brain regions of interest in the task, reflecting the participation level of the core task processing brain network. At the neural level, the system simultaneously monitors the activation level of task dependence, reflecting the participation level of the core task processing network. The compensation monitoring unit characterizes the additional participation indicators of potential compensatory brain regions of interest during the task process, reflecting whether the brain is maintaining task performance through functional reorganization. The system further monitors the degree of compensation based on the theory of compensation, which is used to characterize the additional involvement of potential compensatory brain regions during the task, thereby reflecting whether the brain is maintaining task performance through functional reorganization. The fusion monitoring unit is used to judge the user's cognitive compensation ability based on the user's behavioral performance indicators, the activation degree indicators of brain regions of interest in the task, and the additional involvement indicators. That is, based on data from the past database, the three types of indicators are standardized, as shown in formulas (20) to (22). Among them, in formula (20), For standardized behavioral indicators, As the original behavioral indicators, This represents the corresponding average performance within the group. Let be the corresponding standard deviation in the group. In formula (21), The standardized activation metric The original activation index, This represents the corresponding average performance within the group. Let be the corresponding standard deviation in the group. In formula (22), The standardized compensation indicator. As the original behavioral indicators, This represents the corresponding average performance within the group. is the corresponding standard deviation in the population. H and P represent HAROLD and PASA, respectively.

[0090] In the above embodiments, by jointly analyzing the relationship between the three types of indicators, and following the pattern that behavior is the result, activation is the foundation, and compensation is the mechanism, the monitoring module unifies the three. Referring to formula (23), the system performs state checks according to the following logic. The determination is based on the respective thresholds: when the behavior is stable, the task activation is normal, and the compensation level is low, it is determined to be a normal processing state; when the behavior is stable, the task activation decreases, and the compensation level increases, it is determined to be an effective compensation state; when the behavior decreases and the compensation level increases, it is determined to be a state of excessive compensation load or risk of compensation failure; when both the behavior and the compensation level decrease, it is determined to be a state of weakened compensation ability. This represents the threshold for judging abnormal behavioral indicators. This represents the threshold for determining abnormal activation levels. This represents the threshold when the degree of compensation is abnormal.

[0091] (20)

[0092] (twenty one)

[0093] (twenty two)

[0094] (twenty three)

[0095] In some embodiments, see Figure 8 This application also provides a cognitive compensation regulation system 200, which includes a cognitive compensation assessment system provided in the above embodiments. The cognitive compensation regulation system further includes a cognitive compensation promotion module 105. The cognitive compensation promotion module is used to determine neural regulation parameters when the cognitive compensation ability assessed by the cognitive compensation assessment system is insufficient, and to perform neural regulation on the user based on the neural regulation parameters. The cognitive compensation promotion module includes a neural regulation triggering unit, a neural regulation parameter setting unit, and a neural regulation parameter execution unit. The neural regulation triggering unit is used to trigger the cognitive compensation promotion module to perform neural regulation on the user when the cognitive compensation ability determines that the user's behavioral ability does not meet preset conditions and the target compensation effect also does not meet preset conditions. The neural regulation parameter setting unit is used to initialize and adjust the regulation parameters of the neural regulation, which include at least one of regulation channel, regulation wavelength, regulation intensity, regulation position, and regulation duration. The neural regulation parameter execution unit is used to perform neural regulation on the user based on the determined regulation parameters.

[0096] Specifically, the cognitive compensation enhancement module is used to perform neural modulation on the user, and the specific neural modulation can be light stimulation. The cognitive compensation enhancement module includes a neural modulation triggering unit, a neural modulation parameter setting unit, and a neural modulation parameter execution unit. When the neural modulation is light stimulation, the neural modulation parameter setting unit can be a light stimulation parameter unit, used to initialize and adjust the light stimulation parameter settings, which at least include the stimulation channel, stimulation wavelength, stimulation intensity, stimulation location, and stimulation duration; the neural modulation parameter execution unit can specifically be a light stimulation execution unit, responsible for performing light stimulation on the user based on the determined light stimulation parameters.

[0097] After the user assessment is completed, the system, based on the user's cognitive compensation index judgment results, triggers the neural modulation parameter execution unit in the cognitive compensation promotion module to intervene in the two types of users with insufficient behavioral performance, thereby promoting their cognitive compensation.

[0098] The cognitive compensation enhancement module initializes and adjusts the modulation parameters (stimulation parameters) through a neural modulation parameter setting unit (specifically, a photostimulation parameter unit), and the neural modulation parameter execution unit (photostimulation execution unit) implements neural modulation (photostimulation) according to the determined parameters. Based on the user's current lack of cognitive compensation patterns, the system determines the modulation location (stimulation location) of neural modulation (photostimulation) by combining task-dependent brain regions and compensation theory-dependent brain regions. Within a preset safety range, it adaptively adjusts parameters such as modulation intensity (stimulation intensity) and modulation time (stimulation duration), thereby achieving individualized and controllable neural modulation (photostimulation intervention).

[0099] Optical stimulation is a non-invasive neuromodulation technique that uses near-infrared or red light of specific wavelengths to irradiate the cerebral cortex, thereby regulating neural activity and metabolic states. In recent years, with a deeper understanding of mitochondrial function, neural metabolism, and cerebral blood flow regulation mechanisms, optical stimulation has been gradually introduced into the field of cognitive neuroscience and is considered a regulatory technology with potential cognitive enhancement and neuroprotective effects. From a neurobiological perspective, this process does not directly induce neuronal firing, but rather provides a more favorable physiological environment for neural activity by regulating neuronal metabolism and excitability thresholds. Therefore, the effect of optical stimulation is closer to state regulation than forced stimulation, which makes it safer and more tolerable in cognitive modulation applications. Existing non-invasive brain stimulation techniques primarily aim to enhance activation of a specific brain region or improve immediate task performance, without being designed around cognitive compensation mechanisms. When individuals fail to naturally activate compensatory modes such as HAROLD or PASA during cognitive tasks, existing technologies lack clear judgment criteria and intervention logic, making it difficult to proactively intervene at appropriate times to induce or strengthen the compensatory process.

[0100] Specifically, light stimulation parameters Defined as the wavelength of light stimulation Light stimulation intensity Location of light stimulation and duration of light stimulation Light stimulation wavelength Set to safe and effective wavelength Unless there are special circumstances, the location of light stimulation will not change dynamically with the level of compensation; To adaptively adjust based on different compensatory brain regions and levels, as well as different task dependencies, such as Figure 5 As shown; light stimulation intensity With light stimulation time The intensity of light stimulation will change dynamically with the state of compensation; when the state of compensation is weaker, the intensity of light stimulation will increase. and light stimulation time In the safe zone The number of cases increases continuously, among which This refers to the user's cognitive compensation ability, as shown in formulas (24) to (27).

[0101] (twenty four)

[0102] (25)

[0103] (26)

[0104] (27)

[0105] In some embodiments, the system further includes a cognitive compensation generalization testing module for evaluating the generalization ability of the cognitive compensation results formed by the user under different test task paradigms. The cognitive compensation generalization testing module includes a cognitive compensation generalization testing unit and a cognitive compensation adjustment unit. The cognitive compensation generalization testing unit is used to perform cognitive compensation testing on the constructed test task paradigm and obtain the user's cognitive compensation ability. The cognitive compensation adjustment unit is used to dynamically adjust the task parameters of the target task paradigm according to the cognitive compensation ability.

[0106] Specifically, the cognitive compensation generalization testing module is used to evaluate the generalization ability of the cognitive compensation effect formed by users in different task scenarios. It includes two units: the cognitive compensation generalization testing unit and the cognitive compensation adjustment unit. The generalization task unit is used to enable users to perform cognitive operations under new cognitive task conditions, and the generalization effect calculation unit is used to calculate whether users can benefit from the promotion of cognitive compensation in new cognitive scenarios.

[0107] The purpose of this application is to enhance and enable users with cognitive aging to learn the HAROLD and PASA compensatory patterns. Therefore, additional cognitive compensation generalization tests are needed to record the generalization ability of this application. In this part, users need to complete task paradigms representing different cognitive abilities.

[0108] First, a cognitive function task library is constructed to uniformly store, organize, call, and manage task paradigms for different cognitive functions, thereby building a structured, scalable, and reusable cognitive function task system. For example... Figure 6 As shown, the cognitive function task library includes multiple types of cognitive paradigms, including the first paradigm, the second paradigm, and the nth paradigm (n is a positive integer greater than or equal to 1). The cognitive function task library adopts a multi-layered structured storage method and includes at least the following information levels.

[0109] Task Identification Layer: This layer assigns unique identification information to each task paradigm to distinguish and reference different cognitive tasks. It includes at least task number, task name, and task type fields, used to uniquely locate specific task paradigms during task invocation, data recording, and subsequent analysis. This ensures consistency and traceability of tasks across different execution rounds and system modules. See also... Figure 3 For the HAROLD paradigm, taking the DRM paradigm as an example, the task identifier layer information for this task is "DRM paradigm".

[0110] Task Logic Layer: The task logic layer describes the execution flow and structural rules of the task paradigm, including task phase division, trial structure, stimulus condition combination methods, and randomization rules, to define the complete execution logic of the cognitive task. Through the task logic layer, the system can present tasks according to a consistent process under different users and operating environments, thereby ensuring the stability and repeatability of the cognitive process induced by the task. Taking the DRM paradigm as an example, the task logic layer of this task is divided into two parts. First, the user learns a series of semantically related words. Then, after a period of time, a testing phase is conducted, presenting words to the user and asking them to judge whether they are previously learned words. There are three types of test words: previously learned words, semantically related words that have not been previously learned, and words that have neither been learned nor semantically related.

[0111] Task Parameter Interface Layer: This layer defines the parameter interfaces in the task paradigm that can be externally called and dynamically adjusted. These include parameter settings such as task difficulty, stimulus timing, number of trials, and stimulus interval, and limits the value range and default configuration of each parameter. Through this layer, the system can generate task instances with different parameter configurations without changing the task's logical structure, adapting to different assessment stages, individual states, or intervention needs. Taking the DRM paradigm as an example, the task parameter interface layer includes encoding modality (the sensory modality of word presentation during the encoding stage, such as visual or auditory), presentation time (the time for word presentation, such as 2000 milliseconds), retrieval interval (the interval between encoding stages before entering the retrieval stage, such as 30 minutes), vocabulary size (the total number of vocabulary entries in the task, such as 8), and word count (the number of words in each vocabulary, such as 6).

[0112] Task Tagging Layer: The task tagging layer is used to functionally label task paradigms, associating each task with its corresponding cognitive functions and sub-functions, and indicating the cognitive function. Through the task tagging layer, the system can classify, summarize, and interpret behavioral and brain imaging data based on cognitive function dimensions during subsequent data analysis and risk assessment. Taking the DRM paradigm as an example, it first measures the user's ability to extract and process semantics, which is the primary cognitive function measured in this task paradigm; secondly, it can be used to measure the user's false memories and long-term memory; finally, the stimulus presentation is visual or auditory, which can be used to roughly measure the user's visual or auditory sensory information processing ability.

[0113] Based on the user's generalization performance, we can further revert to the target task paradigm, such as the HAROLD paradigm and PASA paradigm, and set the task parameters accordingly. If the generalization effect is poor, we need to reduce the difficulty of the HAROLD paradigm and PASA paradigm; otherwise, we can continue to increase the difficulty.

[0114] In one specific embodiment, see Figure 10 This application provides a schematic diagram of the main modules of a cognitive compensation assessment and regulation system in one embodiment. This application presents a closed-loop system based on stable cognitive compensation patterns (i.e., HAROLD and PASA), namely a cognitive compensation assessment and regulation system. The system uses a specially designed task paradigm as an inducement to collect corresponding behavioral and functional near-infrared brain imaging data, calculates and monitors the effects and related indicators of cognitive compensation, and promotes cognitive compensation through light stimulation when cognitive compensation ability is insufficient. Finally, the generalization effect of cognitive compensation is tested on an independent generalization task, and the corresponding parameters in the task paradigm are further adjusted based on the generalization effect. Specifically, it includes the following modules.

[0115] The HP task (task module) is used to distinguish different types of cognitive compensation modes and contains four units. The HAROLD task parameter unit (first task parameter unit) is used to adjust the core parameters in the task paradigm for inducing and triggering the HAROLD compensation mode (first compensation mode); the HAROLD task unit (second task unit) is used to determine the experimental paradigm and procedure for inducing and triggering the HAROLD compensation mode (second compensation mode). The PASA task parameter unit (second task parameter unit) is used to adjust the core parameters in the task paradigm for inducing and triggering the PASA compensation mode. The PASA task unit (second task unit) is used to determine the experimental paradigm and procedure for inducing the PASA compensation mode. By performing these tasks under different task load conditions, standardized induction conditions can be provided for the subsequent calculation and monitoring of cognitive compensation effects.

[0116] The task presentation module is used to present specific tasks to users in a visual or multimodal form according to the task paradigm parameters set in the HP task module, and to control the start, end and stimulus sequence of the task to ensure the standardization and repeatability of the experimental process.

[0117] The data acquisition module is used to collect data when completing the task paradigm, which includes two parts: behavioral data, which refers to the behavioral data generated by the user when completing the experimental task, including at least reaction time, accuracy, error type and task completion strategy related indicators; and brain imaging data, which refers to the functional near-infrared brain imaging data generated by the user during the completion of the experimental task, to reflect the changes in brain blood oxygen dynamics under different task conditions.

[0118] The cognitive compensation calculation module is used to quantitatively calculate and characterize the cognitive compensation effect exhibited by users. It includes three calculation parts: activation level, which is used to calculate the activation level related to the task and can be used to measure the degradation of task-dependent brain regions; compensation level, which is used to calculate the compensation level of HAROLD and PASA and can be used to measure whether the compensation pattern has appeared; and behavioral performance, which is used to calculate the behavioral performance under different task paradigms and can be used to measure the overall cognitive level.

[0119] The cognitive compensation monitoring module continuously monitors and determines the compensation indicators output by the cognitive compensation calculation module. It comprises two units: a HAROLD monitoring unit to determine whether the user has exhibited the HAROLD compensation effect in the current task, and a PASA monitoring unit to determine whether the user has exhibited the PASA compensation effect. When the monitoring module detects that the user's behavior is significantly worse than normal and there is no obvious trigger for cognitive compensation, it generates a determination of insufficient compensation and sends a trigger signal to the cognitive compensation promotion module to initiate the subsequent cognitive compensation promotion process.

[0120] The cognitive compensation enhancement module is used to provide light stimulation to the user and comprises two units. The light stimulation parameter unit is used to initialize and adjust the light stimulation parameter settings, which include at least the stimulation channel, stimulation wavelength, stimulation intensity, stimulation location, and stimulation duration. The light stimulation execution unit is responsible for providing light stimulation to the user based on the determined light stimulation parameters.

[0121] The cognitive compensation generalization testing module is used to evaluate the generalization ability of users' cognitive compensation effects in different task scenarios. It includes two units: a generalization task unit, which enables users to perform cognitive operations under new cognitive task conditions; and a generalization effect calculation unit, which calculates whether users can benefit from the promotion of cognitive compensation in new cognitive scenarios.

[0122] It is understood that the computer device used to implement the user interaction system solution provided in this application can be a server, and its internal structure diagram can be as follows: Figure 11 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores relevant data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the method provided in this application.

[0123] Those skilled in the art will understand that Figure 11The structures shown are merely block diagrams of a portion of the structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. The computer device may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program may include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0124] It should be understood that the processor mentioned in the embodiments of this application can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A cognitive compensation assessment system, characterized in that, The system includes: The task module is used to distinguish different types of cognitive compensation modes and trigger target task paradigms that activate different cognitive compensation modes. The data acquisition module is used to collect behavioral data and brain imaging data of users when performing the target task paradigm; The cognitive compensation calculation module is used to calculate the cognitive compensation effect exhibited by the user based on the collected behavioral data and brain imaging data, and obtain cognitive compensation indicators. These indicators include at least one of the following: a regression indicator for task-interested brain regions, an indicator of the presence of compensation patterns, and a comprehensive cognitive level indicator. The cognitive compensation calculation module also includes a brain region segmentation unit, which comprises a task-interested brain region segmentation subunit and a compensation-interested brain region segmentation subunit. The task-interested brain region segmentation subunit is used to segment brain regions into task-dependent task-interested brain regions based on the target task paradigm of different types of cognitive compensation patterns. The task-interested brain regions are the basis for functional activation. The quasi-region is used to characterize the core cognitive processing brain network regions necessary for the normal execution of the inhibitory control task; the sub-unit for dividing the brain region of interest into compensatory brain regions based on compensation theory is used to divide the brain region into compensatory brain regions of interest for target task paradigms with different types of cognitive compensation modes, wherein the compensatory brain regions of interest are potential compensatory participation regions as compensatory brain network regions reflecting higher-order cognitive control and strategy regulation; the cognitive compensation calculation module is used to analyze the activation patterns and relative changes of the task-interested brain regions and the compensatory brain regions of interest in the target task paradigm process, determine whether there is an activation shift trend, and determine whether the compensatory mechanism of the corresponding cognitive compensation mode is triggered during the inhibitory control process. The cognitive compensation monitoring module is used to monitor the cognitive compensation indicators, determine whether the target compensation effect is triggered in the current target task paradigm, and evaluate the user's cognitive compensation ability based on the user's behavioral data and the triggering of the target compensation effect.

2. The system according to claim 1, characterized in that, The system also includes a cognitive compensation generalization testing module, which is used to evaluate the generalization ability of the cognitive compensation results formed by the user in different test task paradigms. The cognitive compensation generalization testing module includes a cognitive compensation generalization testing unit and a cognitive compensation adjustment unit. The cognitive compensation generalization test unit is used to perform cognitive compensation tests on the constructed test task paradigm and obtain the user's cognitive compensation ability. The cognitive compensation adjustment unit is used to dynamically adjust the task parameters of the target task paradigm based on the cognitive compensation ability.

3. The system according to claim 1, characterized in that, The cognitive compensation calculation module includes a task-interested brain region activation degree calculation unit, a compensation degree calculation unit, and a behavioral performance calculation unit. The task-interested brain region activation calculation unit is used to calculate the activation level of brain regions related to the target task paradigm, and to measure the degradation index of the task-interested brain regions. The compensation degree calculation unit is used to calculate the compensation degree of different types of cognitive compensation patterns, and to measure whether the compensation pattern has an indicator. The performance calculation unit is used to calculate the behavioral performance of different target task paradigms, which is used to measure the comprehensive index of cognitive level.

4. The system according to claim 1, characterized in that, The cognitive compensation calculation module also includes a brain region activation calculation unit, which includes an activation degree calculation subunit and an activation degree mapping subunit. The activation level calculation subunit is used to process the collected brain imaging data to obtain time series data of multiple channels. Based on the start time, duration and event label of each event in the target task paradigm, an event function is defined. The event function is convolved with the dynamic response function to obtain the single-trial regression of the predicted response representation of a single event in the brain imaging data signal and combined into a design matrix. A generalized linear model is established for the design matrix corresponding to the brain imaging data signal of each channel, and the parameter estimate is obtained by least squares as a quantitative index of the activation level of each channel. The activation level mapping subunit is used to construct a mapping relationship between cortical vertices and channels in a standard space based on the imaging principle and projection rules of brain imaging data. The activation level of each channel is projected to each vertex in the standard space. Under the constraint of brain region segmentation results, the activation levels in brain regions of interest in the task are converged to obtain brain region-level features reflecting the core processing of the task. Neural signals in brain regions of interest in compensation are converged to obtain the participation level of potential compensation areas in the task process. Based on the participation level, it is determined whether the corresponding cognitive compensation mechanism is triggered during the inhibitory control process.

5. The system according to claim 1, characterized in that, The cognitive compensation monitoring module includes a behavior monitoring unit, an activation monitoring unit, a compensation monitoring unit, and a fusion monitoring unit; The behavior monitoring unit is used to monitor the user's behavioral performance indicators in different types of cognitive compensation modes of the target task paradigm, and to monitor the user's explicit cognitive execution ability under the current task conditions. The activation monitoring unit is used to monitor the activation level of brain regions of interest in the user's task, which reflects the level of participation of the core processing brain network of the task. The compensation monitoring unit is used to characterize the additional participation indicators of brain regions of interest that may compensate during the task, reflecting whether the brain is maintaining task performance through functional reorganization. The fusion monitoring unit is used to determine a user's cognitive compensation ability based on the user's behavioral performance indicators, the activation level of brain regions of interest in the task, and additional participation indicators.

6. The system according to claim 1, characterized in that, The different types of cognitive compensation modes include a first compensation mode and a second compensation mode, which include a brain activation hemispheric asymmetry reduction mode and a brain activation shift from the posterior sensory area to the anterior higher control area mode.

7. The system according to claim 6, characterized in that, The task module further includes a first task parameter unit, a first task unit, a second task parameter unit, and a second task unit; The first task parameter unit is used to set and adjust the core parameters of the target task paradigm of the first compensation mode; The first task unit is used to trigger and start the target task paradigm and process of the first compensation mode; The second task parameter unit is used to set and adjust the core parameters of the target task paradigm of the second compensation mode; The second task unit is used to trigger and start the target task paradigm and process of the second compensation mode.

8. The system according to claim 1, characterized in that, The data acquisition module also includes a behavioral data acquisition unit and a brain imaging data acquisition unit; The behavioral data collection unit is used to collect the user's behavioral performance when completing the target task paradigm. The behavioral performance includes at least one of reaction time, accuracy rate, error type and task completion strategy. The brain imaging data acquisition unit is used to continuously acquire functional near-infrared brain imaging data generated by the user during the completion of the target task paradigm and form time series data to reflect the changes in brain blood oxygen dynamics under different target task paradigms.

9. A cognitive compensatory regulation system, characterized in that, The cognitive compensation regulation system includes the cognitive compensation assessment system according to any one of claims 1 to 8, and the cognitive compensation regulation system further includes: a cognitive compensation promotion module, used to determine neural regulation parameters when the cognitive compensation ability assessed by the cognitive compensation assessment system is insufficient, and to perform neural regulation on the user according to the neural regulation parameters; the cognitive compensation promotion module includes a neural regulation triggering unit, a neural regulation parameter setting unit, and a neural regulation parameter execution unit. The neural modulation triggering unit is used to trigger the cognitive compensation promotion module to perform neural modulation on the user when the cognitive compensation ability determines that the user's behavioral ability does not meet the preset conditions and the target compensation effect does not meet the preset conditions. The neural modulation parameter setting unit is used to initialize and adjust the modulation parameters of neural modulation, wherein the modulation parameters include at least one of modulation channel, modulation wavelength, modulation intensity, modulation position and modulation duration; The neural modulation parameter execution unit is used to perform neural modulation on the user based on the determined modulation parameters.

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