Cognitive Training Material Generation via Brain Semantic Mapping
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Solution Overview
Problem
Current cognitive training methods lack the ability to accurately stimulate functional cortical target points in the brain, as they do not establish a semantic map between cognitive training materials and brain responses, making individualized and effective stimulation impossible.
Innovation Solution
A cognitive training material generation method using deep learning that acquires multimedia features and magnetic resonance characterization, fitting them to create a semantic map, and training a deep learning model to generate targeted cognitive training materials that can accurately stimulate specific brain regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cognitive training materials are designed without establishing a semantic map between materials and brain responses, then the training can be applied broadly to all age groups, but the training cannot accurately stimulate functional cortical target points in the brain
Solution Approach 1:
The patent performs preliminary action by establishing a semantic map between cognitive training materials and brain MRI responses before conducting cognitive training. This pre-established mapping relationship allows for accurate selection of training materials that will stimulate specific cortical target points, resolving the contradiction by preparing the necessary information structure in advance.
Solution Approach 2:
The patent introduces a semantic map as an intermediary between cognitive training materials and brain responses. This semantic map serves as a mediator that connects external stimuli with internal brain reactions, enabling precise control over which cortical regions are stimulated while maintaining systematic organization of the training process.
2Reliability
If transcranial electrical stimulation is used to stimulate brain regions, then neural activity can be enhanced, but the stimulation size and effect vary from person to person and cannot stimulate deep brain regions
Solution Approach 1:
The patent employs self-service by using each individual's own brain MRI response data to create their personalized semantic map. The system utilizes the subject's inherent neural characteristics to generate customized training material selection criteria, eliminating the need for external calibration and ensuring consistent, individualized stimulation effects without requiring complex adjustment procedures.
3Ease of operation
If cognitive training is designed from application perspective without considering brain science, then training programs can be easily implemented, but there is no measure of whether training materials can accurately stimulate target brain regions
Solution Approach 1:
The patent implements feedback by using brain MRI responses as real-time feedback signals to evaluate whether cognitive training materials are effectively stimulating target cortical regions. This feedback mechanism allows for objective measurement of training effectiveness while maintaining ease of operation through automated analysis of the mapping relationship between materials and brain responses.
Data Source
AI summary
A cognitive training material generation method, a cognitive training method, a device, and a medium are provided. The cognitive training material generation method includes: acquiring a first feature and a second feature, the first feature including a multimedia material and semantic information corresponding to the multimedia material, the second feature including a magnetic resonance representation; fitting the first feature and the second feature, obtaining a semantic map according to a fitting result and a preset brain map, and acquiring target semantic information corresponding to a target point according to the semantic map; taking the first feature as input of a deep learning model and the second feature as a constraint of the deep learning model, training the deep learning model, and determining a weight parameter of the deep learning model; generating a cognitive training material according to the target semantic information and the weight parameter of the deep learning model.