An auxiliary system, method, and medium for emotional cognitive intervention

Abstract

This application provides an auxiliary system, method, and medium for emotion-cognitive intervention, relating to the field of brain-computer interface technology. The method includes: monitoring rehabilitation training instructions; responding to the rehabilitation training instructions, presenting a first stimulus scheme to the target subject based on an emotion-cognitive paradigm, and collecting the target subject's EEG signals to be evaluated while passively receiving the first stimulus scheme; performing PAC analysis on the EEG signals to be evaluated to obtain the target subject's first PAC characteristics; obtaining the target subject's characteristic lead pairs and characteristic frequency band pairs based on the first PAC characteristics and baseline PAC characteristics; responding to the rehabilitation training instructions, evaluating the target subject's emotion regulation function based on emotion regulation quantification rules to obtain a cognitive reappraisal ability score; and generating an individualized intervention plan for the target subject based on the characteristic lead pairs, characteristic frequency band pairs, and cognitive reappraisal ability score, for use in implementing the target subject's intervention training.

Description

Technical Field

[0001] This application relates to the field of brain-computer interface technology, and in particular to an auxiliary system, method and medium for emotional cognition intervention. Background Technology

[0002] Emotional cognitive disorders are a common syndrome worldwide, characterized by high incidence and relapse rates, placing a heavy burden on public life and the socioeconomic system. Current interventions mainly include pharmacological, psychological, and neuromodulation approaches. These methods can alleviate symptoms to some extent, but their effectiveness is often influenced by individual differences.

[0003] Existing intervention models for emotional cognitive disorders are insufficient to meet the needs of diverse individuals, and their training effects are generally poor. Therefore, providing an auxiliary method for emotional cognitive intervention to improve its effectiveness is of significant practical importance. Summary of the Invention

[0004] This application provides an auxiliary system, method, and medium for emotion cognition intervention, which can improve the effectiveness of emotion cognition intervention.

[0005] In a first aspect, embodiments of this application provide an auxiliary system for emotion cognition intervention, comprising: The detection module is used to monitor rehabilitation training instructions; The brain function assessment module is used to respond to the rehabilitation training instructions, present a first stimulus scheme to the target subject based on the emotional cognitive paradigm, and collect the target subject's EEG signals to be assessed when passively receiving the first stimulus scheme; perform phase amplitude coupling (PAC) analysis on the EEG signals to be assessed to obtain the first PAC features of the target subject; and obtain the characteristic lead pairs and characteristic frequency band pairs of the target subject based on the first PAC features and the baseline PAC features; the baseline PAC features are obtained by performing PAC analysis on the baseline EEG signals of the baseline subject when passively receiving the first stimulus scheme presented to the baseline subject based on the emotional cognitive paradigm. The emotion regulation assessment module is used to respond to the rehabilitation training instructions and evaluate the emotion regulation function of the target subject based on the emotion regulation quantification rules to obtain a cognitive reappraisal ability score. The intervention strategy decision-making module is used to generate an individual intervention plan for the target subject based on the feature lead pairs, the feature frequency band pairs, and the cognitive reappraisal ability score, so as to implement the intervention training for the target subject.

[0006] The auxiliary system for emotion-cognitive intervention provided in this application includes: a detection module for monitoring rehabilitation training instructions; a brain function assessment module for responding to the rehabilitation training instructions, presenting a first stimulus scheme to the target subject based on an emotion-cognitive paradigm, and collecting the target subject's EEG signal to be assessed when passively receiving the first stimulus scheme; performing phase-amplitude coupled-phase (PAC) analysis on the EEG signal to be assessed to obtain the target subject's first PAC feature; and obtaining the target subject's characteristic lead pair and characteristic frequency band pair based on the first PAC feature and the baseline PAC feature; the baseline PAC feature is obtained by analyzing the first stimulus scheme presented to the baseline subject based on the emotion-cognitive paradigm when the baseline subject passively receives the first stimulus scheme. The system utilizes baseline EEG signals during stimulation protocols to perform PAC analysis and obtain PAC features. An emotion regulation assessment module, responding to rehabilitation training instructions, evaluates the target subject's emotion regulation function based on emotion regulation quantification rules, obtaining a cognitive reappraisal ability score. An intervention strategy decision-making module generates an individualized intervention plan for the target subject based on the feature lead pairs, feature frequency band pairs, and the cognitive reappraisal ability score, for implementing intervention training. By acquiring the subject's brain function and emotion regulation state, the system can more comprehensively detect and assess the subject's emotional and cognitive state, generating corresponding individualized intervention plans, improving the accuracy of emotional and cognitive intervention, and ultimately enhancing its effectiveness.

[0007] In one possible implementation, the system further includes: The initial state assessment module is used to obtain the initial emotional and cognitive level information of the target subject based on the target subject's emotional and cognitive rehabilitation type. The training status assessment module is used to obtain the target subject's stage emotional cognitive level information based on the target subject's emotional cognitive rehabilitation type after completing an intervention training session; if the previous intervention training is deemed effective based on the stage emotional cognitive level information and the initial emotional cognitive level information, then a new rehabilitation training instruction is generated for the target subject.

[0008] The auxiliary system for emotional cognitive intervention provided in this embodiment further includes: an initial state assessment module, used to obtain initial emotional cognitive level information of the target subject based on the target subject's emotional cognitive rehabilitation type; a training state assessment module, used to obtain stage emotional cognitive level information of the target subject based on the target subject's emotional cognitive rehabilitation type after completing one intervention training session; if the previous intervention training is deemed effective based on the stage emotional cognitive level information and the initial emotional cognitive level information, then a new rehabilitation training instruction is generated for the target subject. This system, based on the target subject's emotional cognitive rehabilitation type, determines initial and stage emotional cognitive level information, and generates new rehabilitation training instructions for the target subject when the previous intervention training is deemed effective, providing a dynamic management mechanism for rehabilitation training and further improving the effectiveness of emotional cognitive intervention.

[0009] In one possible implementation, the brain function assessment module is specifically used for: The modulation index (MI) method based on the Körbeck-Leibler KL divergence was used to calculate the PAC features corresponding to the EEG signal and each lead pair and the preset frequency band pair.

[0010] The emotional cognition intervention auxiliary system provided in this embodiment includes a brain function assessment module that specifically calculates the PAC features corresponding to each lead pair and preset frequency band pairs using the modulation index (MI) method based on the Kohlbek-Leibler KL divergence. This system uses the Kohlbek-Leibler KL divergence-based modulation index (MI) method to calculate the PAC features corresponding to each lead pair and preset frequency band pairs, and determines the PAC features through the KL divergence-based MI method. This provides a whole-brain level cross-frequency coupling analysis mechanism, offering precise parameter basis for determining emotional cognition intervention schemes and further improving the effectiveness of emotional cognition intervention.

[0011] In one possible implementation, the frequency band pair includes a preceding value and a following value; the preceding value is less than the following value; the brain function assessment module is specifically used for: Select the lead pairs one by one. For each selected lead pair, select the frequency band pairs one by one. For each selected frequency band pair, perform the following operations: The EEG signal is subjected to low-frequency filtering according to the previous value of the current frequency band pair to obtain a low-frequency signal, and the EEG signal is subjected to high-frequency filtering according to the subsequent value of the current frequency band pair to obtain a high-frequency signal. Information is extracted from the low-frequency signal and the high-frequency signal based on the Hilbert transform to obtain the phase time series information of the low-frequency signal and the amplitude time series information of the high-frequency signal. Based on the phase time series information, phase binning statistics are performed on the amplitude time series information to obtain amplitude distribution information; The amplitude distribution information is compared with the preset uniform distribution information to obtain the PAC features corresponding to the lead pair and frequency band pair.

[0012] The auxiliary system for emotion cognition intervention provided in this embodiment includes a frequency band pair comprising a pre-value and a post-value; the pre-value is less than the post-value; the system sequentially selects lead pairs, and for each selected lead pair, sequentially selects frequency band pairs, performing the following operations for each selected frequency band pair: performing low-frequency filtering on the EEG signal according to the pre-value of the current frequency band pair to obtain a low-frequency signal, and performing high-frequency filtering on the EEG signal according to the post-value of the current frequency band pair to obtain a high-frequency signal; based on Hill... The Bert transform extracts information from the low-frequency signal and the high-frequency signal respectively, obtaining the phase time series information of the low-frequency signal and the amplitude time series information of the high-frequency signal; phase binning statistics are performed on the amplitude time series information based on the phase time series information to obtain amplitude distribution information; the amplitude distribution information is compared with the preset uniform distribution information to obtain the PAC features corresponding to the lead pair and frequency band pair. This can accurately and efficiently determine the PAC features of each lead pair on the typical frequency band pair, which can further improve the effect of emotion cognition intervention.

[0013] In one possible implementation, the EEG signal includes the EEG signal to be evaluated and the reference EEG signal; the brain function assessment module is specifically used for: Based on the first PAC feature and the reference PAC feature, the difference PAC feature corresponding to the target information is determined; the target information is an information combination consisting of a lead pair and a frequency band pair, constructed by selecting the lead pairs one by one and selecting the frequency band pairs for each selected lead pair one by one. The maximum value among the difference PAC features is selected as the target PAC feature; The lead pairs included in the target target points corresponding to the target PAC features are taken as the feature lead pairs, and the frequency band pairs included in the target target points corresponding to the target PAC features are taken as the feature frequency band pairs.

[0014] The emotional cognition intervention auxiliary system provided in this embodiment includes an EEG signal comprising the EEG signal to be evaluated and a reference EEG signal. The brain function assessment module is specifically configured to: determine a difference PAC feature corresponding to the target information based on the first PAC feature and the reference PAC feature; the target information is constructed by selecting each lead pair and each frequency band pair for each selected lead pair, forming an information combination consisting of one lead pair and one frequency band pair; selecting the maximum value among the difference PAC features as the target PAC feature; and using the lead pairs included in the target target point corresponding to the target PAC feature as the feature lead pairs and the frequency band pairs included in the target target point corresponding to the target PAC feature as the feature frequency band pairs. This system determines the difference PAC feature corresponding to the target information based on the first PAC feature and the benchmark PAC feature. The target information is constructed by selecting each lead pair and then selecting each frequency band pair for each selected lead pair, forming an information combination consisting of a lead pair and a frequency band pair. The maximum value in the difference PAC feature is selected as the target PAC feature. The lead pairs included in the target target point corresponding to the target PAC feature are taken as the feature lead pairs, and the frequency band pairs included in the target target point corresponding to the target PAC feature are taken as the feature frequency band pairs. This system can efficiently identify PAC features that are significantly different between the target subject and the benchmark subject, and further accurately locate the feature lead pairs and feature frequency band pairs, thereby further improving the effect of emotion cognition intervention.

[0015] In one possible implementation, the intervention strategy decision module is specifically used for: If the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the first type, then a single neuromodulation plan is generated as the individual intervention plan for the target subject; the first type represents that the subject has abnormal PAC characteristics and normal emotion regulation function. If the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the second type, then a single emotion regulation ability training program is generated as the individual intervention program for the target subject; the second type represents that the subject's PAC characteristics are normal, but the emotion regulation function is abnormal. If the characteristic lead pair, the characteristic frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the third type, then a simultaneous joint intervention plan for training neural regulation and emotion regulation ability is generated as the individual intervention plan for the target subject; the third type represents the subject's abnormal PAC characteristics and abnormal emotion regulation function.

[0016] The system, if the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the first type, then generates a single neuromodulation scheme as an individual intervention plan for the target subject; the first type indicates that the subject's PAC characteristics are abnormal, but the emotion regulation function is normal; if the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the second type, then generates a single emotion regulation ability training plan as an individual intervention plan for the target subject; the second type indicates that the subject's PAC characteristics are normal, but the emotion regulation function is abnormal; if the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the second type, then generates a single emotion regulation ability training plan as an individual intervention plan for the target subject; the second type indicates that the subject's PAC characteristics are normal, but the emotion regulation function is abnormal; if the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the second type, then generates a single emotion regulation ability training plan as an individual intervention plan for the target subject. Cognitive reappraisal ability score determines that the target subject belongs to the third type. A simultaneous joint intervention plan combining neural modulation and emotion regulation ability training is then generated as the individual intervention plan for the target subject. The third type represents abnormal PAC characteristics and abnormal emotion regulation function in the subject. By classifying the target subject according to feature lead pairs, feature frequency band pairs, and cognitive reappraisal ability score, and dynamically constructing individual intervention plans based on the target subject's category, it is possible to integrate neural modulation and emotion regulation ability training, providing more targeted intervention for the subject's emotional cognition, improving the accuracy of emotional cognition intervention, and further enhancing the effectiveness of emotional cognition intervention.

[0017] In one possible implementation, the neuromodulation scheme includes employing the CFC-tACS method to determine the target stimulation frequency and target location based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, and outputting the stimulation signal in the form of nested PAC waveforms of low-frequency and high-frequency signals.

[0018] The auxiliary system for emotion-cognitive intervention provided in this embodiment includes a neuromodulation scheme employing the CFC-tACS method. Based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, the system determines the target stimulus frequency and target location, and outputs the stimulus signal in the form of nested PAC waveforms of low-frequency and high-frequency signals. This system provides a neuromodulation implementation mechanism that can improve the accuracy of neuromodulation and further enhance the effectiveness of emotion-cognitive intervention.

[0019] In one possible implementation, the stimulation signal is a signal formed by modulating the amplitude of the high-frequency signal with the phase of the low-frequency signal.

[0020] The emotional cognition intervention auxiliary system provided in this embodiment uses a stimulus signal formed by phase modulation of a high-frequency signal amplitude using a low-frequency signal. This system, based on phase modulation to form the stimulus signal, integrates low-frequency and high-frequency signals, further enhancing the effectiveness of emotional cognition intervention.

[0021] In one possible implementation, the emotion cognition paradigm includes multiple stimulus subtasks; the stimulus subtasks include single-layer emotion cognition stimulus subtasks and two-layer emotion cognition stimulus subtasks; the single-layer emotion cognition stimulus subtasks are used to detect weak logical emotion cognitions of the subject based on one region of interest; the two-layer emotion cognition stimulus subtasks are used to detect strong logical emotion cognitions of the subject based on two regions of interest; the single-layer emotion cognition stimulus subtasks include low-memory single-layer emotion cognition stimulus subtasks and high-memory single-layer emotion cognition stimulus subtasks; the two-layer emotion cognition stimulus subtasks include low-memory two-layer emotion cognition stimulus subtasks and high-memory two-layer emotion cognition stimulus subtasks; The low-memory single-layer emotional cognition stimulus subtask represents the subject selecting the corresponding response item based on the presented first emotional stimulus map area; the number of types of emotional images included in the first emotional stimulus map area is a first value. The high-memory single-layer emotional cognition stimulus subtask represents the subject selecting the corresponding response item based on the presented second emotional stimulus map area; the number of types of emotional images included in the second emotional stimulus map area is a second value; the first value is less than the second value; The low-memory two-layer emotional cognition stimulus subtask represents the participants selecting response items corresponding to the first non-emotional stimulus area based on the presented third emotional stimulus area; the number of types of emotional images included in the third emotional stimulus area is a third numerical value. The high-memory two-layer emotional cognition stimulus subtask represents the participants selecting response items corresponding to the presented second non-emotional stimulus area based on the presented fourth emotional stimulus area; the fourth emotional stimulus area includes the number of types of emotional images, which is a fourth value; the third value is less than the fourth value.

[0022] The auxiliary system for emotion cognition intervention provided in this embodiment includes an emotion cognition paradigm comprising multiple stimulus subtasks. These subtasks include single-layer and double-layer emotion cognition subtasks. The single-layer subtask is used to detect weak logical emotion cognition in the subject based on one region of interest. The double-layer subtask is used to detect strong logical emotion cognition in the subject based on two regions of interest. The single-layer subtask includes low-memory and high-memory subtasks. The double-layer subtask includes low-memory and high-memory subtasks. The low-memory single-layer subtask represents the subject selecting a corresponding response based on a presented first emotional stimulus map area. The number of different types of emotion images included in the first emotional stimulus map area is a first numerical value. In the single-layer emotional cognitive stimulus subtask representation, participants selected corresponding response items based on a presented second emotional stimulus map area; the number of emotional image types included in the second emotional stimulus map area is a second value; the first value is less than the second value. In the low-memory double-layer emotional cognitive stimulus subtask representation, participants selected response items corresponding to a presented first non-emotional stimulus map area based on a presented third emotional stimulus map area; the number of emotional image types included in the third emotional stimulus map area is a third value. In the high-memory double-layer emotional cognitive stimulus subtask representation, participants selected response items corresponding to a presented second non-emotional stimulus map area based on a presented fourth emotional stimulus map area; the number of emotional image types included in the fourth emotional stimulus map area is a fourth value; the third value is less than the fourth value. This approach integrates logical cognitive factors and memory-based cognitive factors, further improving the effectiveness of emotional cognitive intervention.

[0023] In one possible implementation, the emotion regulation ability training includes: A pre-set emotional sample was presented to the target subject; Obtain the target subject's first subjective evaluation information on the emotional sample; the first subjective evaluation information includes emotional state, behavioral reason and situational background; The target participant is provided with guiding prompts; these prompts are used to enable the target participant to reinterpret the emotional sample. Obtain the target subject's second subjective evaluation information of the emotional sample; Based on the second subjective evaluation information and the first subjective evaluation information, system feedback information is generated; the system feedback information is used to guide the target subject to strengthen the training of cognitive reappraisal ability.

[0024] The auxiliary system for emotion cognition intervention provided in this embodiment includes emotion regulation ability training, comprising: presenting a preset emotion sample to the target subject; acquiring the target subject's first subjective evaluation information on the emotion sample; the first subjective evaluation information including emotional state, behavioral cause, and situational background; providing guidance prompts to the target subject; the guidance prompts being used to enable the target subject to reinterpret the emotion sample; acquiring the target subject's second subjective evaluation information on the emotion sample; generating system feedback information based on the second subjective evaluation information and the first subjective evaluation information; the system feedback information being used to guide the target subject to strengthen training in cognitive reappraisal ability, providing an implementation mechanism for emotion regulation ability training, which can improve the accuracy of emotion regulation ability training and further enhance the effectiveness of emotion cognition intervention.

[0025] Secondly, embodiments of this application provide an auxiliary method for emotion cognition intervention, the method comprising: Monitor rehabilitation training instructions; In response to the rehabilitation training instructions, a first stimulus scheme is presented to the target subject based on the emotional cognitive paradigm, and the target subject's EEG signals to be evaluated are collected when passively receiving the first stimulus scheme; PAC analysis is performed on the EEG signals to be evaluated to obtain the target subject's first PAC features; based on the first PAC features and the baseline PAC features, the target subject's feature lead pairs and feature frequency band pairs are obtained; the baseline PAC features are obtained by performing PAC analysis on the baseline EEG signals of the baseline subject when passively receiving the first stimulus scheme presented to the baseline subject based on the emotional cognitive paradigm. In response to the rehabilitation training instructions, the target subject's emotion regulation function was evaluated based on the emotion regulation quantification rules to obtain a cognitive reappraisal ability score. Based on the feature lead pairs, the feature frequency band pairs, and the cognitive reappraisal ability score, an individual intervention plan for the target subject is generated for implementing intervention training for the target subject.

[0026] In one possible implementation, the method further includes: Based on the target subject's emotional and cognitive rehabilitation type, the initial emotional and cognitive level information of the target subject is obtained; The method further includes: After completing one intervention training session, based on the target subject's emotional cognitive rehabilitation type, the target subject's stage emotional cognitive level information is obtained; if the previous intervention training is deemed effective based on the stage emotional cognitive level information and the initial emotional cognitive level information, then a new rehabilitation training instruction is generated for the target subject.

[0027] In one possible implementation, PAC features are obtained by: using the MI method based on KL divergence to calculate the PAC features corresponding to the EEG signal and each lead pair and preset frequency band pairs.

[0028] In one possible implementation, the frequency band pair includes a pre-value and a post-value; the pre-value is less than the post-value; the MI method based on KL divergence is used to calculate the PAC features corresponding to the EEG signal and each lead pair and the preset frequency band pair, including: Select the lead pairs one by one. For each selected lead pair, select the frequency band pairs one by one. For each selected frequency band pair, perform the following operations: The EEG signal is subjected to low-frequency filtering according to the previous value of the current frequency band pair to obtain a low-frequency signal, and the EEG signal is subjected to high-frequency filtering according to the subsequent value of the current frequency band pair to obtain a high-frequency signal. Information is extracted from the low-frequency signal and the high-frequency signal based on the Hilbert transform to obtain the phase time series information of the low-frequency signal and the amplitude time series information of the high-frequency signal. Based on the phase time series information, phase binning statistics are performed on the amplitude time series information to obtain amplitude distribution information; The amplitude distribution information is compared with the preset uniform distribution information to obtain the PAC features corresponding to the lead pair and frequency band pair.

[0029] In one possible implementation, the EEG signal includes the EEG signal to be evaluated and the reference EEG signal; obtaining the feature lead pair and feature frequency band pair of the target subject based on the first PAC feature and the reference PAC feature includes: Based on the first PAC feature and the reference PAC feature, the difference PAC feature corresponding to the target information is determined; the target information is an information combination consisting of a lead pair and a frequency band pair, constructed by selecting the lead pairs one by one and selecting the frequency band pairs for each selected lead pair one by one. The maximum value among the difference PAC features is selected as the target PAC feature; The lead pairs included in the target target points corresponding to the target PAC features are taken as the feature lead pairs, and the frequency band pairs included in the target target points corresponding to the target PAC features are taken as the feature frequency band pairs.

[0030] In one possible implementation, generating an individual intervention plan for the target subject based on the feature lead pairs, the feature frequency band pairs, and the cognitive reappraisal ability score includes: If the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the first type, then a single neuromodulation plan is generated as the individual intervention plan for the target subject; the first type represents that the subject has abnormal PAC characteristics and normal emotion regulation function. If the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the second type, then a single emotion regulation ability training program is generated as the individual intervention program for the target subject; the second type represents that the subject's PAC characteristics are normal, but the emotion regulation function is abnormal. If the characteristic lead pair, the characteristic frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the third type, then a simultaneous joint intervention plan for training neural regulation and emotion regulation ability is generated as the individual intervention plan for the target subject; the third type represents the subject's abnormal PAC characteristics and abnormal emotion regulation function.

[0031] In one possible implementation, the neuromodulation scheme includes employing the CFC-tACS method to determine the target stimulation frequency and target location based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, and outputting the stimulation signal in the form of nested PAC waveforms of low-frequency and high-frequency signals.

[0032] In one possible implementation, the stimulation signal is a signal formed by modulating the amplitude of the high-frequency signal with the phase of the low-frequency signal.

[0033] In one possible implementation, the emotion cognition paradigm includes multiple stimulus subtasks; the stimulus subtasks include single-layer emotion cognition stimulus subtasks and two-layer emotion cognition stimulus subtasks; the single-layer emotion cognition stimulus subtasks are used to detect weak logical emotion cognitions of the subject based on one region of interest; the two-layer emotion cognition stimulus subtasks are used to detect strong logical emotion cognitions of the subject based on two regions of interest; the single-layer emotion cognition stimulus subtasks include low-memory single-layer emotion cognition stimulus subtasks and high-memory single-layer emotion cognition stimulus subtasks; the two-layer emotion cognition stimulus subtasks include low-memory two-layer emotion cognition stimulus subtasks and high-memory two-layer emotion cognition stimulus subtasks; The low-memory single-layer emotional cognition stimulus subtask represents the subject selecting the corresponding response item based on the presented first emotional stimulus map area; the number of types of emotional images included in the first emotional stimulus map area is a first value. The high-memory single-layer emotional cognition stimulus subtask represents the subject selecting the corresponding response item based on the presented second emotional stimulus map area; the number of types of emotional images included in the second emotional stimulus map area is a second value; the first value is less than the second value; The low-memory two-layer emotional cognition stimulus subtask represents the participants selecting response items corresponding to the first non-emotional stimulus area based on the presented third emotional stimulus area; the number of types of emotional images included in the third emotional stimulus area is a third numerical value. The high-memory two-layer emotional cognition stimulus subtask represents the participants selecting response items corresponding to the presented second non-emotional stimulus area based on the presented fourth emotional stimulus area; the fourth emotional stimulus area includes the number of types of emotional images, which is a fourth value; the third value is less than the fourth value.

[0034] In one possible implementation, the emotion regulation ability training includes: A pre-set emotional sample was presented to the target subject; Obtain the target subject's first subjective evaluation information on the emotional sample; the first subjective evaluation information includes emotional state, behavioral reason and situational background; The target participant is provided with guiding prompts; these prompts are used to enable the target participant to reinterpret the emotional sample. Obtain the target subject's second subjective evaluation information of the emotional sample; Based on the second subjective evaluation information and the first subjective evaluation information, system feedback information is generated; the system feedback information is used to guide the target subject to strengthen the training of cognitive reappraisal ability.

[0035] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in any of the second aspects.

[0036] The technical effects of any of the implementation methods in the second or third aspect can be found in the technical effects of the implementation method in the first aspect, and will not be repeated here. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This application provides a structural block diagram of an auxiliary system for emotion cognition intervention. Figure 2 A structural block diagram of another auxiliary system for emotion cognition intervention provided in an embodiment of this application; Figure 3 A schematic diagram of a stimulus subtask of an auxiliary system for emotion cognition intervention provided in an embodiment of this application; Figure 4 A schematic diagram of the stimulation signal of an auxiliary system for emotion cognition intervention provided in an embodiment of this application; Figure 5 This is a flowchart illustrating an auxiliary method for emotion cognition intervention provided in an embodiment of this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] Emotional cognitive disorders are a common syndrome worldwide, characterized by high incidence and relapse rates, placing a heavy burden on public life and the socioeconomic system. Current interventions mainly include pharmacological, psychological, and neuromodulation approaches. These methods can alleviate symptoms to some extent, but their effectiveness is often influenced by individual differences.

[0041] Existing intervention models for emotional cognitive disorders are insufficient to meet the needs of diverse individuals, and their training effects are generally poor. Therefore, providing an auxiliary method for emotional cognitive intervention to improve its effectiveness is of significant practical importance.

[0042] Based on this, embodiments of this application provide an auxiliary system, method, and medium for emotion-cognitive intervention. The auxiliary system for emotion-cognitive intervention includes: a detection module for monitoring rehabilitation training instructions; a brain function assessment module for responding to rehabilitation training instructions, presenting a first stimulus scheme to the target subject based on an emotion-cognitive paradigm, and collecting the target subject's EEG signal to be assessed while passively receiving the first stimulus scheme; performing phase-amplitude coupled-phase (PAC) analysis on the EEG signal to be assessed to obtain the target subject's first PAC characteristics; and obtaining the target subject's characteristic lead pairs and characteristic frequency band pairs based on the first PAC characteristics and the baseline PAC characteristics; the baseline PAC characteristics are obtained by analyzing the target subject's passive reception of the first stimulus scheme presented to the target subject based on an emotion-cognitive paradigm. The system utilizes baseline EEG signals and performs PAC analysis to obtain PAC features. An emotion regulation assessment module, responding to rehabilitation training instructions, evaluates the target subject's emotion regulation function based on emotion regulation quantification rules, yielding a cognitive reappraisal ability score. An intervention strategy decision-making module generates individualized intervention plans for the target subject based on feature lead pairs, feature frequency band pairs, and cognitive reappraisal ability scores, enabling the execution of intervention training. By acquiring the subject's brain function and emotion regulation status, the system can more comprehensively detect and assess the subject's emotional and cognitive state, generating corresponding individualized intervention plans, thus improving the accuracy and effectiveness of emotional and cognitive intervention.

[0043] To make the inventive objectives, technical solutions, and advantages of the embodiments of this application clearer, the present application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] Figure 1 This paper illustrates a structural block diagram of an auxiliary system for emotion cognition intervention according to an embodiment of this application, which consists of, as shown in the diagram... Figure 1 As shown in the structural diagram, the auxiliary system 10 for emotion cognition intervention includes: Detection module 101 is used to monitor rehabilitation training instructions; The brain function assessment module 102 is used to respond to rehabilitation training instructions, present a first stimulus scheme to the target subject based on the emotional cognitive paradigm, and collect the EEG signals to be assessed by the target subject when passively receiving the first stimulus scheme; perform phase amplitude coupling PAC analysis on the EEG signals to be assessed to obtain the first PAC features of the target subject; based on the first PAC features and the baseline PAC features, obtain the characteristic lead pairs and characteristic frequency band pairs of the target subject; the baseline PAC features are obtained by performing PAC analysis on the baseline EEG signals of the baseline subject when passively receiving the first stimulus scheme presented to the baseline subject based on the emotional cognitive paradigm. The emotion regulation assessment module 103 is used to respond to rehabilitation training instructions, evaluate the emotion regulation function of the target subject based on the quantitative rules of emotion regulation, and obtain a cognitive re-evaluation ability score. The intervention strategy decision-making module 104 is used to generate individual intervention plans for target subjects based on feature lead pairs, feature frequency band pairs and cognitive reappraisal ability scores, so as to implement intervention training for target subjects.

[0045] The auxiliary system for emotion-cognitive intervention provided in this embodiment includes: a detection module for monitoring rehabilitation training instructions; a brain function assessment module for responding to rehabilitation training instructions, presenting a first stimulus scheme to the target subject based on an emotion-cognitive paradigm, and collecting the target subject's EEG signals to be assessed while passively receiving the first stimulus scheme; performing PAC analysis on the EEG signals to be assessed to obtain the target subject's first PAC characteristics; and obtaining the target subject's characteristic lead pairs and characteristic frequency band pairs based on the first PAC characteristics and the baseline PAC characteristics; the baseline PAC characteristics are obtained by analyzing the baseline PAC characteristics of the baseline subject when passively receiving the first stimulus scheme presented to the baseline subject based on an emotion-cognitive paradigm. The system utilizes EEG signals and performs PAC analysis to obtain PAC features. An emotion regulation assessment module, used in response to rehabilitation training instructions, evaluates the emotion regulation function of the target subject based on emotion regulation quantification rules, obtaining a cognitive reappraisal ability score. An intervention strategy decision-making module generates individualized intervention plans for the target subject based on feature lead pairs, feature frequency band pairs, and cognitive reappraisal ability scores, enabling the execution of intervention training. By acquiring the subject's brain function and emotion regulation state, the system can more comprehensively detect and assess the subject's emotional and cognitive state, generating corresponding individualized intervention plans, improving the accuracy of emotional and cognitive intervention, and ultimately enhancing its effectiveness.

[0046] In some embodiments of this application, emotional cognitive disorders include mood disorders. Mood disorders include depressive tendencies, anxiety disorders, and bipolar disorder.

[0047] In some implementations of the auxiliary system 10 for emotion and cognitive intervention, such as Figure 2As shown, the auxiliary system 10 for emotion cognition intervention also includes: The initial state assessment module 201 is used to obtain the initial emotional and cognitive level information of the target subject based on the target subject's emotional and cognitive rehabilitation type. The training status assessment module 202 is used to obtain the target subject's stage emotional and cognitive level information based on the target subject's emotional and cognitive rehabilitation type after completing an intervention training session; if the previous intervention training is deemed effective based on the stage emotional and cognitive level information and the initial emotional and cognitive level information, then a new rehabilitation training instruction is generated for the target subject.

[0048] In some embodiments of this application, the initial state assessment module 201 determines the core symptom quantification indicators of subjects with mood disorders.

[0049] In some embodiments of the auxiliary system 10 for emotion cognition intervention, the auxiliary system 10 for emotion cognition intervention further includes: The Emotional Cognition Intervention Implementation Module is used to conduct intervention training for target subjects based on individual intervention plans.

[0050] In some embodiments of this application, the emotional cognitive rehabilitation type is a disease type. Based on the target subject's emotional cognitive rehabilitation type, initial emotional cognitive level information of the target subject is obtained, including: selecting a scale according to the disease type; for example, subjects with depressive tendencies can be assessed using the HAMD scale, subjects with bipolar disorder can be assessed using the YMRS scale, and subjects with anxiety disorders can be assessed using the HAMA scale; subsequently, the scale scores are... The output consists of core symptom quantification indicators, which serve as an important reference for selecting subsequent intervention strategies.

[0051] In some embodiments of this application, the baseline subjects are healthy controls; the brain function assessment module 102 outputs individualized neuromodulation parameters based on the identification results of brain function abnormalities. Specifically, the brain function assessment module 102 adopts an emotion cognition paradigm to guide subjects to complete corresponding emotion cognition tasks and records EEG signals in real time during the tasks. Subsequently, PAC analysis is performed on the collected EEG signals across all frequency bands to obtain PAC features; PAC features are cross-frequency interaction features between different frequency bands. The PAC features of the subjects are compared with the average level of the healthy control group, and the PAC features with the largest numerical differences are selected, and their corresponding abnormal EEG lead pairs and frequency band pairs are located. Finally, the system outputs the abnormal lead pairs and frequency band pairs as personalized modulation targets and modulation parameters (including stimulation location and stimulation frequency), that is, the characteristic lead pairs and characteristic frequency band pairs of the target subjects, providing accurate parameter basis for subsequent intervention design and strategy optimization.

[0052] In some embodiments of this application, the emotion cognition paradigm includes multiple stimulus subtasks; the stimulus subtasks include single-layer emotion cognition stimulus subtasks and two-layer emotion cognition stimulus subtasks; the single-layer emotion cognition stimulus subtask is used to detect the subject's weak logical emotion cognition based on one region of interest; the two-layer emotion cognition stimulus subtask is used to detect the subject's strong logical emotion cognition based on two regions of interest; the single-layer emotion cognition stimulus subtask includes low-memory single-layer emotion cognition stimulus subtasks and high-memory single-layer emotion cognition stimulus subtasks; the two-layer emotion cognition stimulus subtask includes low-memory two-layer emotion cognition stimulus subtasks and high-memory two-layer emotion cognition stimulus subtasks; In the low-memory single-layer emotional cognition stimulus subtask representation, participants selected corresponding response items based on the presented first emotional stimulus map area; the number of types of emotional images included in the first emotional stimulus map area was a first value. In the high-memory single-layer emotional cognition stimulus subtask representation, participants selected corresponding response items based on the presented second emotional stimulus map area; the number of types of emotional images included in the second emotional stimulus map area was a second value; the first value was less than the second value; In the low-memory two-layer emotional cognition stimulus subtask representation, participants selected response items corresponding to the first non-emotional stimulus area based on the presented third emotional stimulus area; the number of types of emotional images included in the third emotional stimulus area was the third numerical value. In the high-memory two-layer emotional cognition stimulus subtask representation, participants selected response items corresponding to the presented second non-emotional stimulus area based on the presented fourth emotional stimulus area; the number of emotional image types included in the fourth emotional stimulus area was the fourth value; the third value was less than the fourth value.

[0053] For example, the emotion cognition paradigm can be an emotion-hierarchical cognitive control paradigm, such as... Figure 3 As shown, this paradigm contains four sub-tasks.

[0054] Subtask 1: Low-Memory Single-Layer Emotional Cognitive Stimulation Subtask, which is an emotion-single-layer-low-memory-capacity cognitive control task. Users are required to memorize two faces with different emotions and their corresponding buttons in advance (e.g., happy face corresponds to button 1, sad face corresponds to button 2). After the formal experiment begins, users need to judge the emotion of the face and respond with the button. Subtask 2: High-Memory Single-Layer Emotional Cognitive Stimulation Subtask, which is an emotion-single-layer high-memory-capacity cognitive control task. Users are required to memorize four faces of emotions and their corresponding buttons in advance (e.g., happy face corresponds to button 1, sad face corresponds to button 2, angry square corresponds to button 3, and fearful square corresponds to button 4). After the formal experiment begins, users need to judge the emotion of the face and respond with the button. Subtask 3: Low-Memory Two-Layer Emotional Cognitive Stimulation Subtask. This is a single-emotion, two-layer, low-memory-capacity cognitive control task. Users are required to memorize two faces representing different emotions and determine the parity / size of numbers below them based on the facial emotions, and then press the corresponding button. One face representing an emotion (e.g., happy) corresponds to determining the parity of the number below it (happy corresponds to button 1, sad corresponds to button 2), and the other face representing an emotion (e.g., sad) corresponds to determining the size of the number below it (less than 5 corresponds to button 3, greater than 5 corresponds to button 4). In the formal experiment, users need to first determine the facial emotion, then the number, and finally press the button. Subtask 4: High-Memory Two-Layer Emotional Cognitive Stimulation Subtask. This is a one-emotion-two-layer high-memory-capacity cognitive control task that requires users to memorize four emotional faces in advance and judge the parity / size of the numbers below the faces based on the facial emotions, and then press the corresponding keys. Two of the emotional faces (e.g., happy and sad) correspond to judging the parity of the numbers below the faces (happy corresponds to key 1, sad corresponds to key 2), and the other two emotional faces (e.g., angry and fear) correspond to judging the size of the numbers below the faces (less than 5 corresponds to key 3, greater than 5 corresponds to key 4). After the formal experiment begins, users need to first judge the facial emotions, then judge the numbers, and finally press the keys.

[0055] In some embodiments of this application, each subtask comprises 36 trials. Within a single trial, a rest period of 0.5-1.5 seconds is followed by a 3-second stimulus presentation. The 1.5-second rest period is used to ensure that the information from different trials does not overlap and to enhance the subject's attention. During the task stimulation, the user's body and head should remain as still as possible while maintaining wakefulness. Appropriate rest periods are allowed between each subtask. The emotional cognition paradigm is implemented using the Matlab toolbox Psychtoolbox, including settings for parameters such as the location and duration of the task stimuli. Visual stimuli are presented on a 27-inch monitor with a resolution of 1920. 1080 pixels. Synchronous event tags are sent to the EEG device simultaneously with the presentation of task stimuli.

[0056] In some embodiments of this application, during the EEG signal acquisition process, the Neuroscan SynAmps2 amplifier and a 64-lead electrode cap are used as EEG data acquisition devices to collect the cortical responses induced in subjects during the execution of emotional cognitive paradigms in healthy individuals. The left mastoid process (M1) is used as the reference electrode, the ground electrode is located at the 'AFZ' point on the forehead, the data sampling rate is 1000Hz, and the impedance of each lead electrode is below 10KΩ.

[0057] In some embodiments of this application, EEG signals are processed after acquisition.

[0058] In some embodiments of this application, EEG signal processing includes preprocessing. The baseline subject can be a randomly selected subject; the preprocessing can be: first, preprocessing the collected EEG data of the target subject and the baseline subject, including electrode localization and reference transformation, specifically changing the left mastoid reference 'M1' to the average reference of both mastoid processes. This reduces the impact of the reference electrode's offset position. Next, bandpass filtering of 1–150 Hz is applied, followed by notch filtering at 50 Hz, 100 Hz, and 150 Hz to remove 50 Hz power frequency interference and its harmonics. Then, downsampling and independent component analysis are used to remove artifacts. Further, the data is segmented based on the stimulus-evoked time stamps, and abnormal trials with amplitudes ≥100 μV are removed, ultimately including 60 leads in the data analysis.

[0059] In some embodiments of this application, the brain function assessment module 102 is specifically used for: The MI method based on KL divergence is used to calculate the PAC features corresponding to the EEG signal and each lead pair and preset frequency band pair.

[0060] In some embodiments of this application, the frequency band pair includes a preceding value and a following value; the preceding value is less than the following value; the brain function assessment module is specifically used for: Select lead pairs one by one. For each selected lead pair, select frequency band pairs one by one. For each selected frequency band pair, perform the following operations: Based on the previous value of the current frequency band pair, the EEG signal is subjected to low-frequency filtering to obtain a low-frequency signal, and based on the subsequent value of the current frequency band pair, the EEG signal is subjected to high-frequency filtering to obtain a high-frequency signal. Information is extracted from low-frequency and high-frequency signals based on Hilbert transform to obtain phase time series information of low-frequency signals and amplitude time series information of high-frequency signals. Phase binning statistics are performed on amplitude time series information based on phase time series information to obtain amplitude distribution information; The amplitude distribution information is compared with the preset uniform distribution information to obtain the PAC features corresponding to the lead pairs and frequency band pairs.

[0061] In some embodiments of this application, the electroencephalogram (EEG) signal includes the EEG signal to be evaluated and a reference EEG signal; the brain function assessment module is specifically used for: Based on the first PAC feature and the baseline PAC feature, the difference PAC feature corresponding to the target information is determined; the target information is constructed by selecting lead pairs one by one and frequency band pairs one by one for each selected lead pair. Select the maximum value among the difference PAC features as the target PAC feature; The lead pairs included in the target target points corresponding to the target PAC features are taken as feature lead pairs, and the frequency band pairs included in the target target points corresponding to the target PAC features are taken as feature frequency band pairs.

[0062] In some embodiments of this application, the PAC features are obtained through the following process: to assess cross-frequency coupling at the whole-brain level, the MI method based on KL divergence is used to calculate the PAC features of all lead pairs (60×60) in each trial on multiple typical frequency band pairs (δ-θ, δ-α, δ-β, δ-γ, θ-α, θ-β, θ-γ, α-β, α-γ, β-γ).

[0063] In practice, the calculation process for PAC features is as follows: First, the raw EEG signal is filtered for low frequency (e.g., delta band) and high frequency (e.g., beta band) respectively; then, Hilbert transform is applied to extract the phase time series P(t) of the low-frequency signal and the amplitude envelope A(t) of the high-frequency signal; subsequently, the low-frequency phase time series is divided into N intervals (phase bins), and the average intensity of the high-frequency amplitude within each bin is calculated, thereby constructing an amplitude distribution; finally, the MI value of the quantization coupling strength is obtained by calculating the KL divergence between this amplitude distribution and the uniform distribution, and its calculation formula is as follows: , Wherein, KL(U,X) represents the difference between two distributions measured using KL divergence; U represents a uniform distribution; X represents the amplitude distribution; N represents the number of phase boxes; The formula for calculating the KL divergence is as follows: , in, Shannon entropy is expressed by the following formula: , in, This means that the average amplitude of the high-frequency vibration is calculated for each phase box and then normalized.

[0064] The MI calculation result is between 0 and 1. The larger the MI value, the more obvious the coupling modulation relationship between the two frequency bands; conversely, the less obvious the coupling modulation relationship between the two.

[0065] In some embodiments of this application, the brain function assessment module 102 compares the target subject with the benchmark subject and determines the abnormality to achieve abnormality quantification and target localization, thereby obtaining the target subject's feature lead pairs and feature frequency band pairs.

[0066] In some embodiments of this application, the brain function assessment module 102 obtains the feature lead pairs and feature frequency band pairs of the target subject through the following steps: Step A01: Construct the PAC baseline for the baseline subject control group to obtain the baseline PAC characteristics.

[0067] The construction of the PAC baseline for the baseline subject control group includes: in the baseline subject control group, for a preset frequency band pair (f1,f2) (including δ-θ, δ-α, δ-β, δ-γ, θ-α, θ-β, θ-γ, α-β, α-γ, β-γ), calculating the average PAC value of each lead pair (i,j) under task conditions to form a baseline matrix. Each matrix element is a baseline PAC feature, characterizing the typical coupling strength of a healthy brain in specific frequency pairs and lead connections. For each frequency pair... The matrices are all 60×60 matrices.

[0068] Step A02: Extract the first PAC feature of the target subject.

[0069] Extract the first PAC feature of the target subject, that is, extract the corresponding PAC matrix of the target subject in each frequency band pair. Its dimensions are consistent with the baseline subject control group PAC baseline, and it is used to characterize the individual's current cross-frequency coupling pattern.

[0070] Step A03: Based on the first PAC feature and the baseline PAC feature, perform difference calculation and abnormal target point extraction to obtain the feature lead pairs and feature frequency band pairs of the target subject.

[0071] In practice, the deviation between the target subject and the baseline PAC characteristics is calculated on a frequency band-by-frequency band and lead-by-lead basis: Also a 60×60 matrix, its elements represent the degree to which the participant's PAC deviates from the baseline participant's for lead pair (i, j) in the frequency band pair (f1, f2). Subsequently, all... Units with a value > 0 (i.e., locations where the subject's coupling strength is lower than the baseline PAC characteristic); identify the units with the greatest difference from these: , The argmax operation is used to find parameters that maximize the function, and the corresponding lead pairs and frequency band pairs are the target parameters of the individual intervention plan.

[0072] Step A04: Personalized adjustment parameter output.

[0073] Personalized control parameter output, used to output core anomaly target parameters, including: anomaly frequency band pairs. Abnormal lead pairs: This output not only identifies the core neural pattern defects that distinguish the target subject from the baseline subject, but also provides a clear basis for the selection of stimulation targets and frequencies for subsequent CFC-tACS, enabling a direct conversion from "abnormality detection" to "intervention parameters".

[0074] In some embodiments of this application, the emotion regulation assessment module provides a basis for individualized emotion cognition intervention by identifying functional deficits in emotion regulation in subjects.

[0075] In some embodiments of this application, the emotion regulation assessment module uses the ERQ scale to quantitatively assess the subjects and obtain their scale scores on the cognitive reappraisal dimension. The average scale score of the cognitive reappraisal dimension was compared with that of the baseline control group. A poor performance will result in a cognitive reassessment score: , The output is a quantitative indicator of emotion regulation ability, which serves as an important reference for selecting subsequent individual intervention programs.

[0076] In some embodiments of this application, the intervention strategy decision module 104 is specifically used for: If the characteristic lead pair, characteristic frequency band pair, and cognitive reappraisal ability score indicate that the target subject is classified as Type I, then a single neuromodulation plan is generated as the individual intervention plan for the target subject; Type I indicates that the subject has abnormal PAC characteristics but normal emotion regulation function. If the characteristic lead pair, characteristic frequency band pair, and cognitive reappraisal ability score indicate that the target subject is classified as Type II, then a single emotion regulation ability training program is generated as the individual intervention program for the target subject; Type II subjects are characterized by normal PAC characteristics but abnormal emotion regulation function. If the characteristic lead pair, characteristic frequency band pair, and cognitive reappraisal ability score indicate that the target subject is classified as Type III, then a simultaneous joint intervention plan for training neural regulation and emotion regulation abilities is generated as the individual intervention plan for the target subject. Type III indicates that the subject has abnormal PAC characteristics and abnormal emotion regulation function.

[0077] For example, the intervention strategy decision-making module 104 integrates the PAC abnormality and cognitive reassessment score of the subject user to generate an individualized intervention plan. Specifically, when the system detects that the subject only has obvious PAC abnormality and his / her emotion regulation function is basically normal ( and ), prioritize single neuromodulation therapy; when only emotion regulation deficit is found and PAC is normal ( and If the subject exhibits both significant abnormalities in EEG PAC and emotion regulation deficits, then single-emotion regulation training should be prioritized; and In this case, a combined intervention model of neuromodulation and emotional training is adopted.

[0078] In some embodiments of this application, the neuromodulation scheme includes using the CFC-tACS method to determine the target stimulation frequency and target target location based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, and outputting the stimulation signal in the form of nested PAC waveforms of low-frequency signals and high-frequency signals.

[0079] In some embodiments of this application, the stimulation signal is a signal formed by modulating the amplitude of a high-frequency signal with the phase of a low-frequency signal.

[0080] In some embodiments, in terms of neural modulation, the CFC-tACS method is used to select individualized stimulation frequencies and target locations based on the maximum abnormal EEG characteristics of the subjects obtained from the brain function assessment module, and the stimulation signal is output in the form of nested PAC waveforms of low-frequency and high-frequency signals. Figure 4 A schematic diagram of the stimulus signal waveform of an auxiliary system for emotion cognition intervention provided in an embodiment of this application is shown. Specifically, the stimulus signal consists of a low-frequency signal. With high frequency signals Composition, through Phase modulation Amplitude is used to form the signal. Its parameters and generation rules are as follows: By using the target PAC characteristics of the subjects, i.e., the maximum PAC deficiency Determine the low-frequency signal With high frequency signals ,Right now: ; The formula for the stimulus signal is:

[0081] Where t represents the time variable, which is the entire composite waveform. An independent variable that changes over time is used to describe the value of the waveform at different times. This indicates the amplitude of a low-frequency signal (e.g., 0.6). Indicates the frequency of low-frequency signals (e.g., 3Hz); This indicates the amplitude of a high-frequency signal (e.g., 0.4). Indicates the frequency of a high-frequency signal (e.g., 20.5Hz); , is a gate function; , , , , Should It is a rectangular gate function. It is an integer index used to identify the first... A high-frequency wave component. From the formula... arrive It can be seen that it is used to distinguish different high-frequency waves, each Corresponding to a central time is High-frequency waves. It is a rectangular gate function The conditions for its value. When time With the The center time of a high-frequency wave The absolute value of the difference is less than the half-width time of the high-frequency wave. When, rectangular gate function When the value is 1, the corresponding high-frequency wave component will appear in the composite waveform; otherwise... A value of 0 means the high-frequency wave component does not appear, thus controlling the duration of the high-frequency wave within the low-frequency cycle. Therefore... The physical meaning is: at time t, count how many (From 1 to N) satisfies In other words, how many high-frequency wave components are "effective" at this moment ( The state is a summary of the number of multiple high-frequency wave components that exist at a specific moment.

[0082] This represents the angle that a high-frequency wave crosses within a low-frequency wave, for example, given... From 22.5° to 157.5°, spanning 135°, then, = .

[0083] Represents the initial phase of a high-frequency wave, according to The range of values or To determine whether to take 0 or The initial phase affects the phase state of the high-frequency wave at the beginning.

[0084] In some embodiments of this application, emotion regulation ability training includes: Present pre-set emotional samples to the target subjects; Obtain the target participants' first subjective evaluation information on the emotional sample; the first subjective evaluation information includes emotional state, behavioral reasons and situational context; Provide guiding prompts to the target participants; these prompts are used to help the target participants reinterpret the emotional samples. Obtain the target participants' second subjective evaluation information of the emotional sample; Based on the second and first subjective evaluation information, system feedback information is generated; the system feedback information is used to guide the target subjects to strengthen their cognitive reappraisal ability training.

[0085] In one embodiment of this application, cognitive reappraisal training is employed for psychological intervention. The training process includes four stages: First, standardized images of sadness are presented to the participants to evoke emotions; second, after observing the images, the participants are guided to make an initial subjective evaluation, including analyzing the emotional state of the person in the image, the reasons for their behavior, and the contextual background; subsequently, after the participants complete their initial evaluation, the training system or the experimenter provides guiding prompts to guide the participants to reinterpret the context in the image. For example, by asking questions such as "Are there other possible reasons that caused the person to appear sad?" and "Is this emotion temporary or adjustable?", the participants are guided to understand the context of the image from different perspectives; finally, under guidance, the participants revise their initial evaluation and receive system feedback to reinforce their mastery of the cognitive reappraisal strategy.

[0086] In some embodiments of this application, a complete cognitive reassessment training is a single cycle, performed once daily, to ensure the continuity and stability of cognitive correction.

[0087] In some embodiments of this application, the intervention effect is dynamically monitored and optimized through the training state evaluation module 202.

[0088] In one embodiment of this application, after each intervention cycle, the system first collects the scale scores again. The decline in emotional and cognitive levels before and after the intervention was calculated and defined as an indicator of intervention effectiveness. , in This indicates that the symptoms have lessened and the intervention has achieved positive results.

[0089] Subsequently, the system will call the brain function assessment module to retest the EEG PAC index, recalculate the cross-band PAC characteristics, and generate a new difference matrix. and neural modulation parameters: frequency pairs and lead pair Simultaneously, the emotion regulation assessment module is invoked to retest the emotion regulation ability index and recalculate the current deviation value of emotion regulation ability. Based on the retest results, the system automatically generates a personalized intervention plan for the next stage: updating the indicators. The system inputs intervention strategy decision-making modules to adaptively adjust the targets, frequencies, and intervention combinations for subsequent interventions. Through this closed-loop mechanism of "evaluation-optimization-re-intervention," the system can continuously iterate and correct intervention parameters, thereby ensuring the stability and long-term effectiveness of the intervention.

[0090] Based on the same inventive concept as the auxiliary system for emotion cognitive intervention provided in the above embodiments, this application also provides an auxiliary method for emotion cognitive intervention, applied to the auxiliary system 10 for emotion cognitive intervention, such as... Figure 5 As shown, this auxiliary method for emotional cognitive intervention includes the following steps: S501, Monitoring Rehabilitation Training Instructions.

[0091] S502, responding to rehabilitation training instructions, presents the first stimulus program to the target subject based on the emotional cognitive paradigm, and collects the target subject's EEG signal to be evaluated when passively receiving the first stimulus program; performs PAC analysis on the EEG signal to be evaluated to obtain the target subject's first PAC characteristics; based on the first PAC characteristics and the baseline PAC characteristics, obtains the target subject's characteristic lead pairs and characteristic frequency band pairs.

[0092] Among them, the baseline PAC features are obtained by performing PAC analysis on the baseline EEG signals of the baseline subjects when they passively receive the first stimulus scheme presented to them based on the emotion-cognitive paradigm.

[0093] S503, responding to rehabilitation training instructions, evaluates the emotional regulation function of the target subjects based on the quantitative rules of emotional regulation, and obtains a cognitive reappraisal ability score.

[0094] S504 generates an individual intervention plan for the target subject based on the characteristic lead pairs, characteristic frequency band pairs, and cognitive reappraisal ability scores, for use in implementing the intervention training for the target subject.

[0095] In one possible implementation, the method further includes: Based on the target participants' emotional and cognitive rehabilitation type, information on the target participants' initial emotional and cognitive level was obtained. The method also includes: After completing one intervention training session, the target subject's stage emotional and cognitive level information is obtained based on the emotional and cognitive rehabilitation type. If the previous intervention training is deemed effective based on the stage emotional and cognitive level information and the initial emotional and cognitive level information, new rehabilitation training instructions are generated for the target subject.

[0096] The auxiliary method for emotional cognitive intervention provided in this embodiment determines the initial emotional cognitive level information and the stage emotional cognitive level information based on the emotional cognitive rehabilitation type of the target subject, and generates new rehabilitation training instructions for the target subject when the previous intervention training is deemed effective, providing a dynamic management and control mechanism for rehabilitation training, and further improving the effect of emotional cognitive intervention.

[0097] In one possible implementation, PAC features are obtained by: using the MI method based on KL divergence to calculate the PAC features corresponding to the EEG signal and each lead pair and preset frequency band pairs.

[0098] The auxiliary method for emotion cognition intervention provided in this embodiment uses the modulation index (MI) method based on the Körbeck-Leibler divergence to calculate the PAC features corresponding to the EEG signals and each lead pair and preset frequency band pairs. The PAC features are determined by the MI method based on KL divergence, providing a cross-frequency coupling analysis mechanism at the whole brain level. This provides accurate parameter basis for determining the emotion cognition intervention plan and can further improve the effect of emotion cognition intervention.

[0099] In one possible implementation, the frequency band pair includes a preceding value and a following value; the preceding value is less than the following value; the MI method based on KL divergence is used to calculate the PAC features corresponding to the EEG signal and each lead pair and the preset frequency band pair, including: Select lead pairs one by one. For each selected lead pair, select frequency band pairs one by one. For each selected frequency band pair, perform the following operations: The EEG signal is subjected to low-frequency filtering based on the previous value of the current frequency band pair to obtain a low-frequency signal, and the EEG signal is subjected to high-frequency filtering based on the subsequent value of the current frequency band pair to obtain a high-frequency signal. Information is extracted from low-frequency and high-frequency signals based on Hilbert transform to obtain phase time series information of low-frequency signals and amplitude time series information of high-frequency signals. Phase binning statistics are performed on amplitude time series information based on phase time series information to obtain amplitude distribution information; The amplitude distribution information is compared with the preset uniform distribution information to obtain the PAC features corresponding to the lead pairs and frequency band pairs.

[0100] The auxiliary method for emotion cognition intervention provided in this embodiment includes frequency band pairs, which include a preceding value and a following value. The preceding value of the frequency band pair is less than the following value. Lead pairs are selected one by one. For each selected lead pair, frequency band pairs are selected sequentially. For each selected frequency band pair, the following operations are performed: low-frequency filtering of the EEG signal is performed according to the preceding value of the current frequency band pair to obtain a low-frequency signal, and high-frequency filtering is performed according to the following value of the current frequency band pair to obtain a high-frequency signal; information extraction is performed on the low-frequency and high-frequency signals based on Hilbert transform to obtain the phase time series information of the low-frequency signal and the amplitude time series information of the high-frequency signal; phase binning statistics are performed on the amplitude time series information based on the phase time series information to obtain amplitude distribution information; the amplitude distribution information is compared with preset uniform distribution information to obtain the PAC features corresponding to the lead pair and frequency band pair. This method can accurately and efficiently determine the PAC features of each lead pair on typical frequency band pairs, further improving the effectiveness of emotion cognition intervention.

[0101] In one possible implementation, the EEG signal includes the EEG signal to be evaluated and a reference EEG signal; based on the first PAC feature and the reference PAC feature, the target subject's feature lead pairs and feature frequency band pairs are obtained, including: Based on the first PAC feature and the baseline PAC feature, the difference PAC feature corresponding to the target information is determined; the target information is constructed by selecting lead pairs one by one and frequency band pairs one by one for each selected lead pair. Select the maximum value among the difference PAC features as the target PAC feature; The lead pairs included in the target target points corresponding to the target PAC features are taken as feature lead pairs, and the frequency band pairs included in the target target points corresponding to the target PAC features are taken as feature frequency band pairs.

[0102] The auxiliary method for emotion cognition intervention provided in this embodiment determines the difference PAC feature corresponding to the target information based on the first PAC feature and the benchmark PAC feature. The target information is constructed by selecting lead pairs one by one and frequency band pairs for each selected lead pair one by one. The maximum value in the difference PAC feature is selected as the target PAC feature. The lead pairs included in the target target point corresponding to the target PAC feature are used as feature lead pairs, and the frequency band pairs included in the target target point corresponding to the target PAC feature are used as feature frequency band pairs. This method can efficiently identify PAC features that are significantly different between the target subject and the benchmark subject, and further accurately locate the feature lead pairs and feature frequency band pairs, thereby further improving the effect of emotion cognition intervention.

[0103] In one possible implementation, an individualized intervention plan for the target subject is generated based on feature lead pairs, feature frequency band pairs, and cognitive reappraisal ability scores, including: If the characteristic lead pair, characteristic frequency band pair, and cognitive reappraisal ability score indicate that the target subject is classified as Type I, then a single neuromodulation plan is generated as the individual intervention plan for the target subject; Type I indicates that the subject has abnormal PAC characteristics but normal emotion regulation function. If the characteristic lead pair, characteristic frequency band pair, and cognitive reappraisal ability score indicate that the target subject is classified as Type II, then a single emotion regulation ability training program is generated as the individual intervention program for the target subject; Type II subjects are characterized by normal PAC characteristics but abnormal emotion regulation function. If the characteristic lead pair, characteristic frequency band pair, and cognitive reappraisal ability score indicate that the target subject is classified as Type III, then a simultaneous joint intervention plan for training neural regulation and emotion regulation abilities is generated as the individual intervention plan for the target subject. Type III indicates that the subject has abnormal PAC characteristics and abnormal emotion regulation function.

[0104] The auxiliary method for emotion cognition intervention provided in this embodiment can integrate neural regulation and emotion regulation ability training, and intervene in the subject's emotion cognition in a more targeted manner, thereby improving the accuracy of emotion cognition intervention and further enhancing the effect of emotion cognition intervention.

[0105] In one possible implementation, the neuromodulation scheme includes using the CFC-tACS method to determine the target stimulation frequency and target location based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, and outputting the stimulation signal in the form of nested PAC waveforms of low-frequency and high-frequency signals.

[0106] The auxiliary method for emotion-cognitive intervention provided in this embodiment includes a neuromodulation scheme employing the CFC-tACS method. Based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, the target stimulus frequency and target point location are determined, and the stimulus signal is output in the form of nested PAC waveforms of low-frequency and high-frequency signals. This system provides a neuromodulation implementation mechanism that can improve the accuracy of neuromodulation and further enhance the effectiveness of emotion-cognitive intervention.

[0107] In one possible implementation, the stimulus signal is a signal formed by phase modulation of a low-frequency signal onto the amplitude of a high-frequency signal.

[0108] The auxiliary method for emotion cognition intervention provided in this embodiment is based on phase modulation to form stimulus signals, thereby fusing low-frequency and high-frequency signals and further improving the effectiveness of emotion cognition intervention.

[0109] In one possible implementation, the emotion cognition paradigm includes multiple stimulus subtasks; the stimulus subtasks include single-layer emotion cognition stimulus subtasks and two-layer emotion cognition stimulus subtasks; the single-layer emotion cognition stimulus subtask is used to detect the subject's weak logical emotion cognition based on one region of interest; the two-layer emotion cognition stimulus subtask is used to detect the subject's strong logical emotion cognition based on two regions of interest; the single-layer emotion cognition stimulus subtask includes low-memory single-layer emotion cognition stimulus subtasks and high-memory single-layer emotion cognition stimulus subtasks; the two-layer emotion cognition stimulus subtask includes low-memory two-layer emotion cognition stimulus subtasks and high-memory two-layer emotion cognition stimulus subtasks; In the low-memory single-layer emotional cognition stimulus subtask representation, participants selected corresponding response items based on the presented first emotional stimulus map area; the number of types of emotional images included in the first emotional stimulus map area was a first value. In the high-memory single-layer emotional cognition stimulus subtask representation, participants selected corresponding response items based on the presented second emotional stimulus map area; the number of types of emotional images included in the second emotional stimulus map area was a second value; the first value was less than the second value; In the low-memory two-layer emotional cognition stimulus subtask representation, participants selected response items corresponding to the first non-emotional stimulus area based on the presented third emotional stimulus area; the number of types of emotional images included in the third emotional stimulus area was the third numerical value. In the high-memory two-layer emotional cognition stimulus subtask representation, participants selected response items corresponding to the presented second non-emotional stimulus area based on the presented fourth emotional stimulus area; the number of emotional image types included in the fourth emotional stimulus area was the fourth value; the third value was less than the fourth value.

[0110] The auxiliary method for emotion cognition intervention provided in this embodiment includes an emotion cognition paradigm comprising multiple stimulus sub-tasks. These sub-tasks include single-layer and double-layer emotion cognition stimulus sub-tasks. The single-layer sub-task is used to detect weak logical emotion cognition based on one region of interest. The double-layer sub-task is used to detect strong logical emotion cognition based on two regions of interest. The single-layer sub-task includes low-memory and high-memory sub-tasks. The double-layer sub-task includes low-memory and high-memory sub-tasks. The low-memory single-layer sub-task represents the subject selecting a corresponding response based on a presented first emotional stimulus map area. The number of different types of emotion images included in the first emotional stimulus map area is a first numerical value. In the single-layer emotional cognitive stimulus subtask representation, participants selected corresponding responses based on the presented second emotional stimulus map area; the number of emotional image types included in the second emotional stimulus map area was the second value; the first value was less than the second value. In the low-memory double-layer emotional cognitive stimulus subtask representation, participants selected responses based on the presented third emotional stimulus map area and the presented first non-emotional stimulus map area; the number of emotional image types included in the third emotional stimulus map area was the third value. In the high-memory double-layer emotional cognitive stimulus subtask representation, participants selected responses based on the presented fourth emotional stimulus map area and the presented second non-emotional stimulus map area; the number of emotional image types included in the fourth emotional stimulus map area was the fourth value; the third value was less than the fourth value. This approach can integrate logical cognitive factors and memory-based cognitive factors, further improving the effectiveness of emotional cognitive intervention.

[0111] In one possible implementation, emotion regulation training includes: Present pre-set emotional samples to the target subjects; Obtain the target participants' first subjective evaluation information on the emotional sample; the first subjective evaluation information includes emotional state, behavioral reasons and situational context; Provide guiding prompts to the target participants; these prompts are used to help the target participants reinterpret the emotional samples. Obtain the target participants' second subjective evaluation information of the emotional sample; Based on the second and first subjective evaluation information, system feedback information is generated; the system feedback information is used to guide the target subjects to strengthen their cognitive reappraisal ability training.

[0112] The auxiliary method for emotion cognition intervention provided in this embodiment, which trains emotion regulation ability, includes: presenting a preset emotion sample to the target subject; obtaining the target subject's first subjective evaluation information on the emotion sample; the first subjective evaluation information includes emotional state, behavioral reasons, and situational background; providing guidance prompts to the target subject; the guidance prompts are used to enable the target subject to reinterpret the emotion sample; obtaining the target subject's second subjective evaluation information on the emotion sample; generating system feedback information based on the second subjective evaluation information and the first subjective evaluation information; the system feedback information is used to guide the target subject to strengthen the training of cognitive reappraisal ability, providing an implementation mechanism for emotion regulation ability training, which can improve the accuracy of emotion regulation ability training and further improve the effect of emotion cognition intervention.

[0113] In one possible implementation, the method further includes: Intervention training is conducted on target subjects based on individual intervention plans.

[0114] The auxiliary method for emotion-cognitive intervention provided in the above embodiments includes: monitoring rehabilitation training instructions; responding to rehabilitation training instructions, presenting a first stimulus scheme to the target subject based on an emotion-cognitive paradigm, and collecting the target subject's EEG signal to be evaluated while passively receiving the first stimulus scheme; performing PAC analysis on the EEG signal to be evaluated to obtain the target subject's first PAC feature; and obtaining the target subject's feature lead pair and feature frequency band pair based on the first PAC feature and the baseline PAC feature; the baseline PAC feature is obtained by analyzing the target subject's passive reception of the first stimulus scheme presented to the target subject based on an emotion-cognitive paradigm. The baseline EEG signal is used to perform PAC analysis to obtain PAC features; in response to rehabilitation training instructions, based on emotion regulation quantification rules, the emotion regulation function of the target subject is evaluated to obtain a cognitive reappraisal ability score; based on feature lead pairs, feature frequency band pairs and cognitive reappraisal ability scores, an individual intervention plan for the target subject is generated for implementation of the intervention training. By obtaining the subject's brain function state and emotion regulation state, the subject's emotional cognitive state can be detected and evaluated more comprehensively, and an individual intervention plan can be generated accordingly, improving the accuracy of emotional cognitive intervention and thus improving the effectiveness of emotional cognitive intervention.

[0115] This application also provides a computer storage medium storing computer-executable instructions for implementing the auxiliary method for emotion cognition intervention in any embodiment of this application.

[0116] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the assistive method for emotion-cognitive intervention in any of the above embodiments. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0117] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An auxiliary system for emotional cognitive intervention, characterized in that, include: The detection module is used to monitor rehabilitation training instructions; The brain function assessment module is used to respond to the rehabilitation training instructions, present the first stimulus scheme to the target subject based on the emotional cognitive paradigm, and collect the electroencephalogram (EEG) signals to be assessed when the target subject passively receives the first stimulus scheme. Phase amplitude coupling (PAC) analysis is performed on the EEG signal to be evaluated to obtain the first PAC feature of the target subject; based on the first PAC feature and the baseline PAC feature, the characteristic lead pair and characteristic frequency band pair of the target subject are obtained; the baseline PAC feature is obtained by performing PAC analysis on the baseline EEG signal of the baseline subject when passively receiving the first stimulus scheme presented to the baseline subject based on the emotional cognitive paradigm; The emotion regulation assessment module is used to respond to the rehabilitation training instructions and evaluate the emotion regulation function of the target subject based on the emotion regulation quantification rules to obtain a cognitive reappraisal ability score. The intervention strategy decision-making module is used to generate an individual intervention plan for the target subject based on the feature lead pairs, the feature frequency band pairs, and the cognitive reappraisal ability score, so as to execute the intervention training for the target subject; The brain function assessment module is specifically used for: Based on the first PAC feature and the baseline PAC feature, the difference PAC feature corresponding to the target information is determined; the target information is an information combination consisting of a lead pair and a frequency band pair selected one by one for each selected lead pair. The maximum value among the difference PAC features is selected as the target PAC feature; The lead pairs included in the target target points corresponding to the target PAC features are taken as the feature lead pairs, and the frequency band pairs included in the target target points corresponding to the target PAC features are taken as the feature frequency band pairs.

2. The system according to claim 1, characterized in that, The system also includes: The initial state assessment module is used to obtain the initial emotional and cognitive level information of the target subject based on the target subject's emotional and cognitive rehabilitation type. The training status assessment module is used to obtain the target subject's stage emotional cognitive level information based on the target subject's emotional cognitive rehabilitation type after completing an intervention training session; if the previous intervention training is deemed effective based on the stage emotional cognitive level information and the initial emotional cognitive level information, then a new rehabilitation training instruction is generated for the target subject.

3. The system according to claim 1, characterized in that, The brain function assessment module is specifically used for: The modulation index (MI) method based on the Körbeck-Leibler KL divergence was used to calculate the PAC features corresponding to the EEG signal and each lead pair and the preset frequency band pair.

4. The system according to claim 3, characterized in that, The frequency band pair includes a preceding value and a following value; the preceding value is less than the following value; the brain function assessment module is specifically used for: Select the lead pairs one by one. For each selected lead pair, select the frequency band pairs one by one. For each selected frequency band pair, perform the following operations: The EEG signal is subjected to low-frequency filtering according to the previous value of the current frequency band pair to obtain a low-frequency signal, and the EEG signal is subjected to high-frequency filtering according to the subsequent value of the current frequency band pair to obtain a high-frequency signal. Information is extracted from the low-frequency signal and the high-frequency signal based on the Hilbert transform to obtain the phase time series information of the low-frequency signal and the amplitude time series information of the high-frequency signal. Based on the phase time series information, phase binning statistics are performed on the amplitude time series information to obtain amplitude distribution information; The amplitude distribution information is compared with the preset uniform distribution information to obtain the PAC features corresponding to the lead pair and frequency band pair.

5. The system according to claim 3, characterized in that, The EEG signal includes the EEG signal to be evaluated and the reference EEG signal.

6. The system according to claim 1, characterized in that, The intervention strategy decision-making module is specifically used for: If the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the first type, then a single neuromodulation plan is generated as the individual intervention plan for the target subject; the first type represents that the subject has abnormal PAC characteristics and normal emotion regulation function. If the feature lead pair, the feature frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the second type, then a single emotion regulation ability training program is generated as the individual intervention program for the target subject; the second type represents that the subject's PAC characteristics are normal, but the emotion regulation function is abnormal. If the characteristic lead pair, the characteristic frequency band pair, and the cognitive reappraisal ability score determine that the target subject belongs to the third type, then a simultaneous joint intervention plan for training neural regulation and emotion regulation ability is generated as the individual intervention plan for the target subject; the third type represents the subject's abnormal PAC characteristics and abnormal emotion regulation function.

7. The system according to claim 6, characterized in that, The neuromodulation scheme includes the use of cross-frequency coupled transcranial alternating current stimulation (CFC-tACS) method, which determines the target stimulation frequency and target location based on the characteristic lead pairs and characteristic frequency band pairs of the target subject, and outputs the stimulation signal in the form of nested PAC waveforms of low-frequency and high-frequency signals.

8. The system according to claim 7, characterized in that, The stimulation signal is a signal formed by modulating the amplitude of the high-frequency signal with the phase of the low-frequency signal.

9. The system according to claim 1, characterized in that, The emotion cognition paradigm includes multiple stimulus subtasks; the stimulus subtasks include single-layer emotion cognition stimulus subtasks and two-layer emotion cognition stimulus subtasks; the single-layer emotion cognition stimulus subtask is used to detect the subject's weak logical emotion cognition based on one region of interest; the two-layer emotion cognition stimulus subtask is used to detect the subject's strong logical emotion cognition based on two regions of interest; the single-layer emotion cognition stimulus subtask includes low-memory single-layer emotion cognition stimulus subtasks and high-memory single-layer emotion cognition stimulus subtasks; the two-layer emotion cognition stimulus subtask includes low-memory two-layer emotion cognition stimulus subtasks and high-memory two-layer emotion cognition stimulus subtasks; The low-memory single-layer emotional cognition stimulus subtask represents the subject selecting the corresponding response item based on the presented first emotional stimulus map area; the number of types of emotional images included in the first emotional stimulus map area is a first value; The high-memory single-layer emotional cognition stimulus subtask represents the subject selecting the corresponding response item based on the presented second emotional stimulus map area; the number of types of emotional images included in the second emotional stimulus map area is a second value; the first value is less than the second value; The low-memory two-layer emotional cognition stimulus subtask represents the participants selecting response items corresponding to the first non-emotional stimulus area based on the presented third emotional stimulus area; the number of types of emotional images included in the third emotional stimulus area is a third numerical value. The high-memory two-layer emotional cognition stimulus subtask represents the participants' selection of response items corresponding to the presented second non-emotional stimulus area based on the presented fourth emotional stimulus area; the fourth emotional stimulus area includes the number of types of emotional images as a fourth value; the third value is less than the fourth value.

10. The system according to claim 6, characterized in that, The emotion regulation skills training includes: A pre-set emotional sample was presented to the target subject; Obtain the target subject's first subjective evaluation information on the emotional sample; the first subjective evaluation information includes emotional state, behavioral reason and situational background; The target participant is provided with guiding prompts; these prompts are used to enable the target participant to reinterpret the emotional sample. Obtain the target subject's second subjective evaluation information of the emotional sample; Based on the second subjective evaluation information and the first subjective evaluation information, system feedback information is generated; the system feedback information is used to guide the target subject to strengthen the training of cognitive reappraisal ability.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements an auxiliary method for emotion cognition intervention, the method comprising: Monitor rehabilitation training instructions; In response to the rehabilitation training instructions, a first stimulus scheme is presented to the target subject based on the emotional cognitive paradigm, and the target subject's EEG signals to be evaluated are collected when passively receiving the first stimulus scheme; PAC analysis is performed on the EEG signals to be evaluated to obtain the target subject's first PAC features; based on the first PAC features and the baseline PAC features, the target subject's feature lead pairs and feature frequency band pairs are obtained; the baseline PAC features are obtained by performing PAC analysis on the baseline EEG signals of the baseline subject when passively receiving the first stimulus scheme presented to the baseline subject based on the emotional cognitive paradigm. In response to the rehabilitation training instructions, the target subject's emotion regulation function was evaluated based on the emotion regulation quantification rules to obtain a cognitive reappraisal ability score. Based on the feature lead pairs, the feature frequency band pairs, and the cognitive reappraisal ability score, an individual intervention plan for the target subject is generated for implementing intervention training for the target subject; The step of obtaining the feature lead pairs and feature frequency band pairs of the target subject based on the first PAC feature and the baseline PAC feature includes: Based on the first PAC feature and the baseline PAC feature, the difference PAC feature corresponding to the target information is determined; the target information is an information combination consisting of a lead pair and a frequency band pair selected one by one for each selected lead pair. The maximum value among the difference PAC features is selected as the target PAC feature; The lead pairs included in the target target points corresponding to the target PAC features are taken as the feature lead pairs, and the frequency band pairs included in the target target points corresponding to the target PAC features are taken as the feature frequency band pairs.

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