32 results about "Cognition.status" patented technology

The invention discloses a cross-subject EEG cognitive state recognition method based on a prototype clustering domain adaptation algorithm. According to the method, the concept of category domains isintroduced, on one hand, on the basis of multi-source domain alignment of labels, feature distribution differences between different categories are considered, structural fine-grained alignment underthe category conditions between different source domains in a feature space is researched, and the problem of category imbalance in the multi-source domains is converted into a mode of the category domains; and prototype theoretical clustering alignment between the source domain and the target domain is carried out, i.e., clustering between similar source domains is carried out on the target domain by taking a dynamic adjustment prototype center as a constraint, and similar features and sparse heterogeneous features between the domains are realized, wherein the former realizes intra-domain class conditional structure feature alignment, and the latter realizes global fine-grained structure feature alignment. According to the invention, the method can be compatible with category balance andimbalance, effectively solves the problem of individual difference of electroencephalogram signals in the field of brain cognitive calculation, has high generalization ability, and can be well suitable for clinical diagnosis and practical application.

The invention discloses a student Bloom mastery degree evaluation method and system and a storage medium. The method comprises the steps: constructing and training a deep knowledge tracking model; when the student generates a new learning behavior, predicting the question solving accuracy of the student on each exercise through the trained deep knowledge tracking model; and evaluating the Bloom grasp degree of the knowledge point of the student at the current moment based on the prediction result of the question solving accuracy of the student on each exercise. The Bloom mastery degree of theknowledge points can be modeled through the learning behavior sequence of the students and the deep knowledge tracking method, the ability information and the mastery degree of the students on the knowledge point granularity can be visually represented, and the method and system can be deployed to an online education and mixed education platform. Study ability changes and cognitive state changes of students are effectively represented, and the purpose of Bloom operation is achieved.

The invention provides a test question recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the cognitive state of a target user and each candidate test question special topic of the target user based on the historical answer record of the target user; on the basis of the cognitive state of the target user, determining similar users of the target user and model users whose cognitive states are superior to those of the target user; and on the basis of the mastering degree of the similar users for each candidate test question special topic and the mastering degree of the model user for each candidate test question special topic, determining a to-be-recommended special topic from each candidate test question special topic, and pushing the to-be-recommended special topic to the target user. The test questions are recommended by combining the mastering degrees of the similar users and the model users on the candidate test question themes, the test questions can be recommended according to the knowledge themes that the target user masters weak knowledge points, test question resources with high difficulty can be accurately selected and recommended to the target user, and personalized test question recommendation is achieved.

The present invention discloses an identification method of user operation intention and cognitive state in human-computer interaction. The method comprises the following steps: collecting eye movement characteristic data and electroencephalogram signal data of users in a human-computer interaction process; pre-processing the collected data and respectively dividing into a training set and a testset; and using the training set to train an SVM classifier and then using the optimized classifier to classify the test set to obtain corresponding operation intention and cognitive state. The identification method can identify operation requirements in the interaction process, provides a basis for adaptive adjustment of interfaces, at the same time, can judge the cognitive state of the interaction process and ensures reliability of completion of interaction tasks.

The invention provides a cross-subject EEG cognitive state detection method based on an efficient multi-source capsule network. The method comprises the following steps: aligning the feature distribution of a target domain and the feature distribution of a multi-source domain, so as to effectively migrate inter-domain features; constructing EEG into a multi-channel one-dimensional structure, so as to improve the training efficiency, and improve the model performance at the same time; secondly, introducing a self-expression module to capture potential relations between samples, so as to well adapt cross-subject EEG data analysis with significant individual differences under different tasks; and finally, providing a space attention algorithm based on a dynamic sub-capsule to further learn fine-grained feature information on the spatial level of the EEG data, and effectively describing the spatial relationship between parts and the partial-overall hierarchical relationship of the EEG data. According to the method, the individual difference problem of electroencephalogram signals in the field of brain cognitive calculation is effectively avoided, the method can be suitable for cognitive state recognition based on EEG under any task, the generalization ability is high, and the method can be well suitable for clinical diagnosis and practical application.

Provided are an Eye Movement analysis with Hidden Markov Model (EMHMM) with co-clustering, an Eye Movement analysis with Switching Hidden Markov Models (EMSHMM) to analyze eye movement data in cognitive tasks involving stimuli with different feature layouts and cognitive state changes, a switching hidden Markov model (SHMM) to capture a participant's cognitive state transitions during the task and an EMSHMM to assess preference decision-making tasks with two or more cognitive states. The EMSHMM provides quantitative measures of individual differences in cognitive behavior/style, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

The invention discloses a student score prediction method and device based on a fuzzy cloud cognitive diagnosis model. The method comprises the following steps: establishing a student cognitive cloud model; according to a solving result of the student cognitive cloud model, obtaining a mastery degree interval number of students on knowledge points; according to the mastering degree interval number of the knowledge points, obtaining the mastering degree interval number of the students on test questions; and obtaining a prediction score of the test questions according to a target model parameter obtained by iterative training and the mastering degree interval number of the students on the test questions. According to the fuzzy cloud cognitive diagnosis model, fuzzy interval numbers obtained through student cognitive cloud conversion are used for depicting fuzziness and uncertainty of knowledge point mastering degrees of students, and more comprehensive representation of student cognitive states is achieved; besides, the fuzzy cloud cognitive diagnosis model simplifies model parameters, shortens model execution time, and effectively improves the prediction accuracy and calculation efficiency of student scores in a large-scale online learning scene under the support of the model.

Provided are techniques for alert generation based on a cognitive state and a physical state. Wearables data, text communications, voice communications, and images of a caregiver providing care to a patient are obtained. The wearables data, the text communications, the voice communications, and the images are analyzed to identify a cognitive state and a physical state of the caregiver. The cognitive state and the physical state are compared to one or more care rules associated with tasks of the caregiver for the patient. In response to the comparison indicating any one of the cognitive state and the physical state prevent the caregiver from executing the tasks, an alert is generated. One or more recommendations for resolving the alert based on one or more recommendation rules are generated.

Disclosed are a universal cognitive state decoder based on a brain signal and a method and apparatus for predicting an ultra-high performance complex behavior using the same. The method of predicting a complex behavior may include configuring a high-level cognitive state decoder based on a brain signal for classifying a human's high-level core cognitive state, configuring a universal cognitive state decoder by including a calculated value of the high-level cognitive state decoder in another cognitive state decoder as an input value, and predicting a human's complex behavior using the universal cognitive state decoder.

The invention discloses an intelligent chronic disease screening system based on a cognitive state. The system comprises a data acquisition module which is used for acquiring a chronic disease clinical diagnosis guide data set and an expert consensus data set formed by experts in a chronic disease diagnosis and treatment process; a data processing module which is used for performing fuzzy clustering analysis on the same kind of chronic disease parameters in the expert consensus data set and the clinical diagnosis guide data set to obtain a chronic disease cognitive diagnosis parameter subset; a model generation module which is used for training a maximum entropy model based on the chronic disease cognitive diagnosis parameter subset and generating a chronic disease pre-classification model; and an intelligent screening module which is used for intelligently screening the chronic diseases according to a chronic disease cognitive diagnosis model. According to the invention, the consensus parameter subsets and the clinical diagnosis guide related data parameter subset set formed by experts in different fields in the chronic disease diagnosis and treatment process can be obtained, and fused chronic disease cognitive diagnosis is carried out.

The invention discloses a knowledge association path-fused cognitive tracking method, which comprises the following steps of: 1, constructing a question-knowledge point association matrix, 2, constructing a knowledge point association matrix, 3, constructing a knowledge point difficulty library, 4, calculating a skill mode, 5, aggregating, embedding and representing exercises and knowledge points, 6, embedding and representing exercises, 7, obtaining related historical exercises, and 7, obtaining a knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path-fused knowledge association path. 8, acquiring knowledge point mastering conditions of the students, 9, acquiring skill mode mastering conditions of the students, and 10, predicting future answering performance of the students. The method can start from the thinking process of the students in question making, considers the process that the students associate the knowledge points to solve the questions, integrates the knowledge association paths, and fully excavates the association relationship between the knowledge points, so that the cognitive state change of the students can be accurately and quickly tracked, and the future answering performance of the students can be predicted.